From 62092d96c9b1c481ae16d19f23e441289403900d Mon Sep 17 00:00:00 2001 From: Zhe Sun Date: Tue, 20 Jul 2021 23:20:41 +0200 Subject: [PATCH] first release 0.1.0 --- .gitignore | 134 ++++++ CHANGELOG.md | 16 + CONTRIBUTING.md | 39 ++ LICENSE | 202 +++++++++ Makefile | 20 + README.md | 74 ++++ examples/figs/ind_prod_index.png | Bin 0 -> 18276 bytes examples/generate_stationary_process.ipynb | 212 ++++++++++ .../streamlit/GDP_per_capita_countries.csv | 267 ++++++++++++ examples/streamlit/app.py | 326 ++++++++++++++ examples/streamlit/requirements.txt | 2 + examples/use_external_factors.ipynb | 399 ++++++++++++++++++ figs/diagram.png | Bin 0 -> 411107 bytes figs/webui.png | Bin 0 -> 291325 bytes pyproject.toml | 6 + requirements.txt | 9 + setup.cfg | 2 + setup.py | 31 ++ tests/external_factors/__init__.py | 0 .../country_gdp_factor_test.py | 15 + .../eu_industry_product_factor_test.py | 13 + tests/generator_test.py | 50 +++ tests/holiday_factor_test.py | 23 + tests/sinusoidal_factor_test.py | 36 ++ timeseries_generator/__init__.py | 9 + timeseries_generator/base_factor.py | 132 ++++++ timeseries_generator/errors.py | 10 + .../external_factors/__init__.py | 3 + .../external_factors/country_gdp_factor.py | 91 ++++ .../eu_industry_product_factor.py | 57 +++ .../external_factors/external_factor.py | 59 +++ timeseries_generator/generator.py | 167 ++++++++ timeseries_generator/holiday_factor.py | 202 +++++++++ timeseries_generator/linear_trend.py | 163 +++++++ timeseries_generator/random_feature_factor.py | 73 ++++ .../public_data/GDP_per_capita_countries.csv | 267 ++++++++++++ .../resources/public_data/eu_prod_index.csv | 250 +++++++++++ timeseries_generator/sinusoidal_factor.py | 193 +++++++++ timeseries_generator/utils.py | 16 + timeseries_generator/weekday_factor.py | 75 ++++ timeseries_generator/white_noise.py | 126 ++++++ 41 files changed, 3769 insertions(+) create mode 100644 .gitignore create mode 100644 CHANGELOG.md create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 Makefile create mode 100644 README.md create mode 100644 examples/figs/ind_prod_index.png create mode 100644 examples/generate_stationary_process.ipynb create mode 100644 examples/streamlit/GDP_per_capita_countries.csv create mode 100644 examples/streamlit/app.py create mode 100644 examples/streamlit/requirements.txt create mode 100644 examples/use_external_factors.ipynb create mode 100644 figs/diagram.png create mode 100644 figs/webui.png create mode 100644 pyproject.toml create mode 100644 requirements.txt create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 tests/external_factors/__init__.py create mode 100644 tests/external_factors/country_gdp_factor_test.py create mode 100644 tests/external_factors/eu_industry_product_factor_test.py create mode 100644 tests/generator_test.py create mode 100644 tests/holiday_factor_test.py create mode 100644 tests/sinusoidal_factor_test.py create mode 100644 timeseries_generator/__init__.py create mode 100644 timeseries_generator/base_factor.py create mode 100644 timeseries_generator/errors.py create mode 100644 timeseries_generator/external_factors/__init__.py create mode 100644 timeseries_generator/external_factors/country_gdp_factor.py create mode 100644 timeseries_generator/external_factors/eu_industry_product_factor.py create mode 100644 timeseries_generator/external_factors/external_factor.py create mode 100644 timeseries_generator/generator.py create mode 100644 timeseries_generator/holiday_factor.py create mode 100644 timeseries_generator/linear_trend.py create mode 100644 timeseries_generator/random_feature_factor.py create mode 100644 timeseries_generator/resources/public_data/GDP_per_capita_countries.csv create mode 100644 timeseries_generator/resources/public_data/eu_prod_index.csv create mode 100644 timeseries_generator/sinusoidal_factor.py create mode 100644 timeseries_generator/utils.py create mode 100644 timeseries_generator/weekday_factor.py create mode 100644 timeseries_generator/white_noise.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..394dbd5 --- /dev/null +++ b/.gitignore @@ -0,0 +1,134 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Idea +.idea + +**/*.egg-info diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..f4d5ce0 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,16 @@ +# Changelog + +## v0.1 +- first release of time series generators, including: + - base factor + - linear trend factor + - sinusoidal factor + - white noise factor + - random factor + - holiday factor + - weekday factor + - country GDP factor + - EU industry index factor +- Examples + - notebooks which includes some simple examples + - streamlit dashboard \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..589a95c --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,39 @@ +# How to contribute + +There are a few guidelines that we need contributors to follow so that we are able to process requests as efficiently as possible. If you have any questions or concerns please feel free to contact us at [opensource@nike.com](mailto:opensource@nike.com). + +## Getting Started + +* Review our [Code of Conduct](https://github.com/Nike-Inc/nike-inc.github.io/blob/master/CONDUCT.md) +* Submit the [Individual Contributor License Agreement](https://www.clahub.com/agreements/Nike-Inc/nike-inc.github.io) +* Make sure you have a [GitHub account](https://github.com/signup/free) +* Submit a ticket for your issue, assuming one does not already exist. + * Clearly describe the issue including steps to reproduce when it is a bug. + * Make sure you fill in the earliest version that you know has the issue. +* Fork the repository on GitHub + +## Making Changes + +* Create a topic branch off of `master` before you start your work. + * Please avoid working directly on the `master` branch. +* Make commits of logical units. + * You may be asked to squash unnecessary commits down to logical units. +* Check for unnecessary whitespace with `git diff --check` before committing. +* Write meaningful, descriptive commit messages. +* Please follow existing code conventions when working on a file. + +## Submitting Changes + +* Push your changes to a topic branch in your fork of the repository. +* Submit a pull request to the repository in the Nike-Inc organization. +* After feedback has been given we expect responses within two weeks. After two weeks we may close the pull request if it isn't showing any activity. +* Bug fixes or features that lack appropriate tests may not be considered for merge. +* Changes that lower test coverage may not be considered for merge. + +# Additional Resources + +* [General GitHub documentation](https://help.github.com/) +* [GitHub pull request documentation](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/about-pull-requests) +* [Nike's Code of Conduct](https://github.com/Nike-Inc/nike-inc.github.io/blob/master/CONDUCT.md) +* [Nike's Individual Contributor License Agreement](https://www.clahub.com/agreements/Nike-Inc/nike-inc.github.io) +* [Nike OSS](https://nike-inc.github.io/) diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..c8b40d4 --- /dev/null +++ b/Makefile @@ -0,0 +1,20 @@ +.PHONY: clean build + +.EXPORT_ALL_VARIABLES: +CC_RED := $(shell echo '\033[0;31m') +CC_YELLOW := $(shell echo '\033[0;33m') +CC_GREEN := $(shell echo '\033[0;32m') +CC_NC := $(shell echo '\033[0;0m') + +clean: + @echo "$(CC_GREEN)cleaning build artifacts$(CC_NC)" + rm -rf timeseries_generator.egg-info + rm -rf dist + rm -rf build + +build: clean + @echo "$(CC_GREEN)building python package$(CC_NC)" + python -m build + +test: + pytest tests \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..06e362a --- /dev/null +++ b/README.md @@ -0,0 +1,74 @@ +# timeseries-generator +This repository consists of a python packages that generates synthetic time series dataset in a generic way (under `/timeseries_generator`) and demo notebooks on how to generate synthetic timeseries data (under `/examples`). The goal here is to have non-sensitive data available to demo solutions and test the effectiveness of those solutions and/or algorithms. In order to test your algorithm, you want to have time series available containing different kinds of trends. The python package should help create different kinds of time series while still being maintainable. + +## `timeseries_generator` package +For this package, it is assumed that a time series is composed of a base value multiplied by many factors. +``` +ts = base_value * factor1 * factor2 * ... * factorN + Noiser +``` +![Diagram](figs/diagram.png) + + +These factors can be anything, random noise, linear trends, to seasonality. The factors can affect different features. For example, some features in your time series may have a seasonal component, while others do not. + +Different factors are represented in different classes, which inherit from the `BaseFactor` class. Factor classes are input for the `Generator` class, which creates a dataframe containing the features, base value, all the different factors working on the base value and and the final factor and value. + +### Core concept +- **Generator**: a python class to generate the time series. A generator contains a list of factors and noiser. By overlaying the factors and noiser, generator can produce a customized time series +- **Factor**: a python class to generate the trend, seasonality, holiday factors, etc. Factors take effect by multiplying on the base value of the generator. +- **Noised**: a python class to generate time series noise data. Noiser take effect by summing on top of "factorized" time series. +This formula describes the concepts we talk above + +### Built-in Factors +- **LinearTrend**: give a linear trend based on the input slope and intercept +- **CountryYearlyTrend**: give a yearly-based market cap factor based on the GDP per - capita. +- **EUEcoTrendComponents**: give a monthly changed factor based on EU industry product public data +- **HolidayTrendComponents**: simulate the holiday sale peak. It adapts the holiday days - differently in different country +- **BlackFridaySaleComponents**: simulate the BlackFriday sale event +- **WeekendTrendComponents**: more sales in the weekend then weekend +- **FeatureRandFactorComponents**: set up different sale amount for different stores and different product +- **ProductSeasonTrendComponents**: simulate season-sensitive product sales. In this example code, we have 3 different types of product: + - winter jacket: inverse-proportional to the temperature, more sales in winter + - basketball top: proportional to the temperature, more sales in summer + - Yoga Mat: temperature insensitive + +## Installation +```sh +pip install timeseries-generator +``` + +## Usage +``` python +from timeseries_generator import LinearTrend, Generator, WhiteNoise, RandomFeatureFactor +import pandas as pd + +# setting up a linear tren +lt = LinearTrend(coef=2.0, offset=1., col_name="my_linear_trend") +g = Generator(factors={lt}, features=None, date_range=pd.date_range(start="01-01-2020", end="01-20-2020")) +g.generate() +g.plot() + +# update by adding some white noise to the generator +wn = WhiteNoise(stdev_factor=0.05) +g.update_factor(wn) +g.generate() +g.plot() +``` + +### Example Notebooks +We currently have 2 example notebooks available: +1. `generate_stationary_process`: Good for introducing the basics of the `timeseries_generator`. Shows how to apply +simple linear trends and how to introduce features and labels, as well as random noise. +1. `use_external_factors`: Goes more into detail and shows how to use the `external_factors` submodule. Shows how to +create seasonal trends. + +## Web based prototyping UI +We also use [Streamlit](https://streamlit.io/) to build a web-based UI to demonstrate how to use this package to generate synthesis time series data in an interactive web UI. +``` sh +streamlit run examples/streamlit/app.py +``` +![Web UI](figs/webui.png) + + +## License +This package is released under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0) diff --git a/examples/figs/ind_prod_index.png b/examples/figs/ind_prod_index.png new file mode 100644 index 0000000000000000000000000000000000000000..e08f4370adbe612fc57bfbddf8a6b33286897736 GIT binary patch literal 18276 zcmeHvWmr{T&@UX2ZV;62E(Ihc4$|Eq4N4>3APoW{T}mU}NOvjS-60*)eW<%J{^I-I zFZcW9c@Av$UNLKC&CG9R%|1c$vSO%6_()JtP^c2(!irE(Fk-;<0RkNG8>*$(5ef=g z$W%y3UP4HSRNmIg$n=dN6cla5Ydt+IaYouseSJN>&OQb@BwHuNz`zfRdY;|Qoy}jH z+fsW-KYi5FT6~1D2&3Ezm9EfIWey$#TdU(4sK%_ehR*Olw>{;Pa}qetbKuN!#c7U- 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zXyu-#STrKC1I7m;>K9b;B;}ag7{NZpYutas@PjvAZl2w{-MIHy zE8f5x`RIk=3s1|Z1?-c@Nj8&(0Q6RPijJvpJIF5m!V1fz!m8X=$4c>sP9cWa-0Oju ze}NMrre2>yD8BFK`-vqe&>RJTr-$NATnxdHg?|3)*Ia5d)%Ez-ZR zjt_8NyI^fszoB L6)t+A=ktF6vAm#_ literal 0 HcmV?d00001 diff --git a/examples/generate_stationary_process.ipynb b/examples/generate_stationary_process.ipynb new file mode 100644 index 0000000..dab1cd9 --- /dev/null +++ b/examples/generate_stationary_process.ipynb @@ -0,0 +1,212 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We want to be able to generate synthetic timeseries data to test and compare our algorithms.\n", + "In order to have some control over the trends in the timeseries, we use the simple package timeseries generator in this\n", + "repository." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%% python\n" + } + }, + "outputs": [], + "source": [ + "from timeseries_generator import LinearTrend, Generator, WhiteNoise, RandomFeatureFactor\n", + "import pandas as pd\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We can now start by setting up a linear trend.\n", + "Trends are collected in a generator, which takes any dependent variables as features and the trends you have added to\n", + "compose the time-series dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%% python\n" + } + }, + "outputs": [], + "source": [ + "lt = LinearTrend(coef=2.0, offset=1., col_name=\"my_linear_trend\")\n", + "\n", + "g: Generator = Generator(factors={lt}, features=None, date_range=pd.date_range(start=\"01-01-2020\", end=\"01-20-2020\"))\n", + "g.generate()\n", + "g.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We can update our package by adding some white noise to the generator. The white noise adds noise with a standard\n", + "deviation as a fraction of the total value to our timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%% python\n" + } + }, + "outputs": [], + "source": [ + "wn = WhiteNoise(stdev_factor=0.05)\n", + "g.update_factor(wn)\n", + "g.generate()\n", + "g.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We can add dependent feature columns to our timeseries. In this case, we introduce a random factor to these features.\n", + "This factor will be dependent on a feature that we introduce to the generator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%% python\n" + } + }, + "outputs": [], + "source": [ + "rff = RandomFeatureFactor(\n", + " feature=\"my_feature\",\n", + " feature_values=[\"feature1\", \"feature2\"],\n", + " min_factor_value=1,\n", + " max_factor_value=10\n", + ")\n", + "g.update_factor(rff)\n", + "g.features = {\"my_feature\": [\"feature1\", \"feature2\"]}\n", + "df: pd.DataFrame = g.generate()\n", + "print(df.head(5))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The resulting dataframe is a dataframe with all the features and factors influencing the features. We can plot the final value per product." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%% python\n" + } + }, + "outputs": [], + "source": [ + "df_plot=df.set_index('date')\n", + "df_plot[[\"my_feature\", \"value\"]].pivot(columns=[\"my_feature\"], values=\"value\").plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We can also add different linear trends for different feature values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%% python\n" + } + }, + "outputs": [], + "source": [ + "lt2 = LinearTrend(feature=\"my_feature\", feature_values={\n", + " \"feature1\": {\"coef\": 1., \"offset\": 1.},\n", + " \"feature2\": {\"coef\": 0.05, \"offset\": 1.}\n", + "})\n", + "\n", + "g.update_factor(lt2)\n", + "df = g.generate()\n", + "\n", + "df_plot=df.set_index('date')\n", + "df_plot[[\"my_feature\", \"value\"]].pivot(columns=[\"my_feature\"], values=\"value\").plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/streamlit/GDP_per_capita_countries.csv b/examples/streamlit/GDP_per_capita_countries.csv new file mode 100644 index 0000000..db337ca --- /dev/null +++ b/examples/streamlit/GDP_per_capita_countries.csv @@ -0,0 +1,267 @@ +Country Name,Country Code,Indicator Name,Indicator Code,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 +Aruba,ABW,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,6472.398709,7885.158927,9765.909207,11392.26915,12306.71768,13495.7943,14045.47457,14938.68157,16239.98785,16439.35636,16583.87586,17927.95944,19077.68743,19355.33354,20617.75047,20670.36701,20436.88713,20833.97636,22568.37499,23300.73836,24046.22638,25834.11178,27083.63486,24631.18205,23513.5277,24985.01392,24712.49326,26441.61994,26893.01151,28396.90842,28452.17061,29350.80502,30253.27936,, +Africa Eastern and Southern,AFE,GDP per capita (current US$),NY.GDP.PCAP.CD,127.6515181,130.3536425,134.6938383,142.1597688,150.8818969,161.0732943,169.4607857,181.2451065,190.5094228,208.7079878,220.512563,235.0109539,247.3974995,314.7063662,381.0676546,394.683534,385.8193222,419.8375464,447.5077152,525.2321531,666.0560803,673.7776422,623.9571203,639.6409006,577.2371279,490.3450062,541.6040033,641.2677958,657.8262219,655.7868495,696.1526459,703.6546783,682.5868506,665.2429261,659.5899024,722.1697159,701.7341974,718.8542776,660.4719664,635.1433677,672.2054736,591.0884442,589.2068235,757.4497587,916.6033667,1037.727057,1142.955902,1278.971341,1356.529948,1326.251549,1550.121225,1682.287263,1667.992618,1648.867514,1654.314865,1503.859334,1401.281053,1536.206783,1530.161917,1481.425292,1326.663658 +Afghanistan,AFG,GDP per capita (current 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US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,792.4549727,991.2280739,1153.9605,1245.624686,1276.370366,1346.858115,1372.667697,1477.915659,1678.073246,1872.468608,2162.530245,2408.714548,2757.88844,2886.998204,3117.889831,3192.231908,3153.818283,3272.553373,3411.96696,3632.678677,3861.397822,4370.618317,4705.790063,5056.977259,5041.012235,5213.795649,5682.705378,5741.653658,6645.985549,6658.223586,7212.986819,7832.349081,7290.834938,7258.166561,7291.74167,7444.089075,7789.632355,8370.040472,9096.538652,9628.172843,10152.83281,10486.37759,10815.91136,9680.175825 +Greenland,GRL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,1498.276437,1876.503239,2196.711711,2860.280576,3432.706033,4257.949712,4844.676329,5713.954919,7235.549741,8480.694827,9483.173076,8544.058329,7813.690667,7988.170959,7198.702247,7760.828404,11271.32143,14554.38754,16398.01109,16813.68395,18326.80512,18315.1963,18768.93014,16797.45886,18123.96303,21665.7019,21422.35754,19145.49604,20496.66137,20170.43842,19004.10729,19275.47334,20652.87833,27459.76278,32023.45221,32489.78191,35458.12312,39780.95144,44367.05565,44918.56488,43988.33249,47186.98147,45936.76595,47535.58995,50484.92757,44536.40131,48181.87419,50321.36875,54470.95639,, +Guatemala,GTM,GDP per capita (current US$),NY.GDP.PCAP.CD,252.7561712,253.22729,261.1791338,280.0966364,279.9304645,278.8281385,283.1927246,287.9680471,310.6617452,322.4375294,349.0249756,355.0903711,367.1188811,438.5448512,527.4176968,594.6518036,696.2220153,854.6366434,925.1257445,1027.083696,1143.440402,1217.26402,1200.249234,1212.716559,1234.702641,1233.080944,892.2929065,850.2185121,915.3980653,955.1134609,845.3069621,1011.754966,1093.962908,1164.374025,1293.470198,1424.682543,1487.607658,1649.001602,1755.849582,1619.510736,1664.298958,1550.360366,1682.994501,1737.879287,1859.098363,2068.500129,2251.087963,2490.749127,2802.461962,2651.817123,2852.547327,3228.045741,3355.036919,3522.773706,3779.642336,3994.636913,4173.301666,4454.04815,4478.424646,4638.634943,4603.339617 +Guam,GUM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21594.03969,22628.27869,24472.78202,26596.21479,26713.05839,27680.54996,29288.78186,30320.53859,31040.0843,31210.47029,32908.30677,33656.45357,34843.20557,35829.25036,36214.00692,36601.91988,36502.38282,37723.78134, +Guyana,GUY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,321.5452368,282.3040713,305.3663987,327.1157281,344.1680772,370.1596491,334.8422489,358.3219666,379.8958762,395.2029189,395.2923993,420.6656159,588.137017,663.3956652,602.6248473,590.0653705,658.4360002,683.3303919,773.1816708,729.6050922,616.9607897,628.1936565,564.5278745,588.6103534,660.1519,468.138331,551.2621746,509.3737497,533.5383588,468.1583109,499.0317093,602.1438729,712.7128542,816.5354249,927.1885834,988.3413168,951.9124771,926.7403362,954.4003181,955.6653806,974.9491933,997.2113729,1056.423886,1105.506825,3188.67316,3658.476971,4050.788213,4233.76442,4580.699136,4908.567014,5378.81197,5489.142758,5407.153711,5576.82607,5811.397924,6124.95367,6145.818743,6609.510431,6955.939217 +High income,HIC,GDP per capita (current US$),NY.GDP.PCAP.CD,1368.726712,1429.808847,1524.936605,1622.113844,1748.868971,1877.020232,2036.868226,2163.440037,2323.04123,2530.441687,2266.694395,2475.878108,2809.685705,3299.576286,3671.662638,3998.57429,4387.516988,4925.113578,5816.03816,6553.569937,7236.708016,7647.399435,7568.912821,7884.804759,8333.180899,8731.273531,10159.60132,11476.61138,12912.05148,13488.51103,14531.83196,15349.50997,16308.90927,16859.79891,18036.50766,19678.5057,19647.01317,19573.59436,19391.20709,25222.41315,25605.42719,25305.26827,26215.98543,29171.95831,32239.16068,33843.39367,35476.46499,38483.43325,40571.08377,37801.24782,39323.66889,42260.25461,42042.25086,42307.5711,42976.56675,40397.86011,41023.04565,42685.23497,45117.23759,45297.7165,43833.93023 +"Hong Kong SAR, China",HKG,GDP per capita (current US$),NY.GDP.PCAP.CD,429.4428744,436.7544115,487.8211341,565.7278104,629.5915258,676.8055071,685.9266141,723.2392256,714.4829696,825.5234491,960.0319615,1106.469717,1384.738437,1893.181242,2144.607713,2252.111881,2850.01461,3429.420276,3923.943731,4569.453707,5700.412634,5991.320261,6133.784079,5595.235139,6208.226159,6542.931537,7435.030698,9071.332602,10609.74564,12097.77516,13485.54489,15465.85886,17976.42938,20395.51737,22502.57974,23497.49231,24818.15455,27330.03335,25808.97095,25091.6666,25756.66378,25230.21633,24665.89,23977.01945,24928.10037,26649.7508,28224.21506,30594.01784,31515.66277,30697.34038,32549.99823,35142.48793,36730.8767,38403.77771,40315.28556,42431.88828,43731.10682,46165.85651,48542.68187,48354.47337,46323.86344 +Honduras,HND,GDP per capita (current US$),NY.GDP.PCAP.CD,164.6443995,169.9097551,179.8763063,185.0488471,200.4347285,216.8153947,227.7409563,240.5468429,252.5849145,253.2687207,266.1364061,261.2463703,278.616076,307.3448001,338.1553292,356.457397,414.6231405,497.9382908,895.6479864,993.7254974,1078.810346,1066.168742,1090.998237,1110.475663,1183.02593,1232.869615,1287.410739,1362.885102,1262.067151,1128.284091,993.4832532,911.7549816,942.6129447,913.3016313,836.6023818,936.6676543,887.6926123,948.9547702,1023.460734,1002.716922,1093.1081,1133.186372,1134.064445,1158.178083,1217.818021,1308.09197,1430.057614,1583.047451,1739.353377,1789.705553,1904.347152,2088.315547,2144.342363,2102.592449,2206.054397,2302.204411,2342.584904,2453.727115,2510.322943,2574.356751,2405.732848 +Heavily indebted poor countries (HIPC),HPC,GDP per capita (current US$),NY.GDP.PCAP.CD,92.72809263,95.89824206,98.73374807,103.5443265,110.3119814,120.6873745,127.2176501,128.8045264,130.6247393,136.9396523,142.3290905,147.1548541,155.5986033,180.912138,214.7258571,242.8914881,257.6847236,289.7486562,320.2809865,359.2552053,380.7936916,374.8511907,361.8828622,343.0941588,346.9175148,344.2520586,392.3437481,424.8268028,413.887314,386.9613632,392.759677,385.7454433,349.3600232,347.2148898,288.2249731,326.406005,329.8755973,334.0670616,344.0974623,336.9366323,359.3822582,334.1538451,348.8507849,384.1144683,428.4145105,475.339761,548.3366,636.997799,748.4684432,738.8307773,797.2101201,852.3707875,872.437832,945.0036949,970.9121683,927.1244575,909.5132254,944.4637175,979.8899885,984.8189428,973.2718546 +Croatia,HRV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4877.568417,5232.85176,5263.447002,5640.328953,5198.092739,4841.606375,5362.022897,6232.655595,8059.420173,9661.169574,10527.89268,11695.94466,13937.44839,16296.80592,14540.6416,13949.3265,14609.52435,13258.35985,13674.4178,13599.40978,11781.73479,12361.48383,13451.62495,15014.08502,14944.35622,13828.4695 +Haiti,HTI,GDP per capita (current US$),NY.GDP.PCAP.CD,70.66108456,68.76433933,70.13804588,71.96218417,77.86259725,82.94760837,84.9852522,83.41190396,81.59295367,85.29579643,70.82617925,76.26374897,76.90186371,94.90457191,112.996202,133.7502478,169.3107588,178.860637,180.273204,195.754238,245.2165669,256.2456189,249.4322126,268.2919292,293.1867733,317.0763459,357.7968297,309.30349,386.7772914,396.6861392,439.944188,483.8736354,308.3713974,251.7528877,285.1188538,363.2655445,368.6318638,415.7707421,455.5418961,499.3029687,811.5343577,739.348191,697.597063,561.8176913,663.7943295,766.6919755,792.8260107,981.1105146,1076.702447,1150.211089,1172.099015,1287.954058,1337.336475,1393.956085,1402.100289,1389.119779,1265.986932,1294.239628,1435.350852,1272.490699,1176.75582 +Hungary,HUN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3350.241139,3735.101486,3874.030564,4171.83487,4495.969153,4525.040091,4596.183443,4744.213002,4793.477848,4624.282203,5275.987149,6654.72998,8421.104565,10285.79194,11200.57695,11475.82271,13918.95766,15753.47023,13046.48316,13191.62126,14216.16561,12950.68652,13687.51406,14267.01215,12706.89121,13090.50673,14605.85435,16411.44111,16733.32217,15899.14828 +IBRD only,IBD,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,193.748097,198.4551912,215.7341215,229.7439097,244.0166708,273.8532197,344.4520645,422.070102,443.0372587,465.9084169,516.7541645,541.2423941,645.2605401,763.0220825,806.9413315,775.9853418,797.5094673,792.7125854,824.0429736,842.9159893,846.2312741,899.0348879,917.1558139,1032.027602,1042.200501,1027.332615,1088.44311,1175.263767,1305.225301,1406.799587,1472.319109,1413.816086,1359.787215,1463.142009,1464.545884,1470.198997,1633.929044,1926.883543,2280.109835,2676.307757,3293.45746,3915.625837,3760.768261,4521.982563,5288.461865,5567.74639,5824.770745,5965.710751,5579.990996,5572.724644,6094.71936,6415.154649,6510.727455,6187.685697 +IDA & IBRD total,IBT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,179.2283801,179.0554103,181.651997,185.8342515,201.977565,217.4737918,227.8943411,252.3932669,312.6997738,386.7295584,411.9684944,431.4370421,474.8378059,500.0907089,593.0142155,700.0485418,777.2030403,740.7118501,737.0621374,723.8646886,747.832259,759.3980163,763.643646,804.9910598,815.3686489,911.4248159,916.8701103,899.9583895,942.340569,1005.658166,1115.949528,1200.17599,1251.028197,1201.807272,1157.387832,1243.783083,1239.904722,1247.167976,1381.571438,1622.42913,1910.307684,2238.249424,2735.233638,3240.511404,3106.858574,3710.556573,4312.473561,4529.499118,4737.412327,4848.12374,4536.057792,4505.604602,4889.873864,5127.061541,5192.129777,4929.861141 +IDA total,IDA,GDP per capita (current US$),NY.GDP.PCAP.CD,92.33921225,96.0359644,100.0200261,103.5965093,108.8869779,117.733575,124.0321782,125.4240071,127.4493901,138.4474125,158.9173403,154.0566727,156.3362431,173.3517421,230.4385888,272.1715974,278.0082078,291.2549918,319.3755846,366.6203932,429.3522375,632.1404309,576.7061799,469.3980667,423.8848464,420.2531004,405.2496911,415.390824,413.0064596,391.964413,412.0615272,403.3378633,383.959863,355.9536897,331.1598726,372.7156204,398.6762214,402.3653086,399.1721644,401.3149855,434.8293822,421.7640704,446.1174048,486.8054734,556.7177913,632.2807323,744.4574291,855.97075,997.4222714,963.3877966,1083.105991,1190.085798,1249.630901,1347.17426,1408.98015,1366.150172,1305.681497,1321.432163,1364.109464,1396.320349,1364.157834 +IDA blend,IDB,GDP per capita (current US$),NY.GDP.PCAP.CD,100.2035983,104.7303915,109.3208392,112.8825782,120.1383233,127.1845955,134.8410453,132.0593733,135.7916839,151.3080583,202.5487828,183.1824824,200.2661752,207.9368328,291.533702,325.8805109,381.8306115,390.5742169,411.7886675,490.1496827,609.7480816,1138.369675,1009.300842,743.0963177,618.8118822,603.3273088,528.0290891,524.5188241,523.079968,489.9764779,525.3198064,513.4535829,502.2985486,434.3877902,428.6304212,489.2974093,526.739276,524.3002409,505.433417,514.0340363,572.634095,558.0325467,602.0875493,660.2706614,786.0745034,918.4400482,1123.446,1265.745034,1464.181449,1340.413854,1534.962515,1721.445534,1859.740421,1972.596895,2068.872068,1949.18058,1773.844239,1712.650913,1747.506594,1758.035927,1664.260131 +Indonesia,IDN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,53.53729568,65.05304384,74.60209639,79.7145354,79.17818799,90.87617148,131.0249053,202.4954054,233.1166609,278.1966702,333.6943767,365.915842,356.9213392,491.5795329,566.5777185,583.6698355,512.9591111,525.3339435,516.9609902,474.8591605,442.2154853,481.781485,530.0032545,585.0765629,631.7828578,681.9383856,827.9052662,912.2032939,1026.393436,1137.410101,1063.712376,463.9481582,671.0986097,780.1902047,748.2576087,900.1775881,1065.64852,1150.261367,1263.287332,1589.801489,1860.002811,2166.854231,2261.247309,3122.362673,3643.047176,3694.35934,3623.927241,3491.637491,3331.695115,3562.816334,3837.578024,3893.859578,4135.201531,3869.588427 +IDA only,IDX,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,347.0333915,333.2856155,319.2601257,320.3959059,322.9908409,341.9044036,359.6686633,356.5143742,341.771862,353.5814262,346.5269676,322.3800445,316.3130041,280.9106224,312.4308837,332.3352756,339.3564809,344.6528981,343.2438893,363.6049798,351.3676995,365.5459242,397.3159159,438.537396,484.9296404,549.3382878,644.9020519,757.2005819,771.2534696,851.2809904,915.6179926,932.2370758,1022.148077,1065.282163,1063.642258,1065.845678,1124.557574,1172.228221,1217.408147,1219.775623 +Isle of Man,IMN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12681.61415,14036.08285,15991.22782,18450.48294,20624.8028,20322.68202,21351.11694,24864.43629,29522.27779,35494.66734,37766.67954,42107.83723,54196.30173,70986.30133,65029.01975,69767.34336,77349.22524,79148.33225,83357.13268,92334.64631,85126.97047,82197.12677,83477.83429,89112.66772,, +India,IND,GDP per capita (current US$),NY.GDP.PCAP.CD,82.18860274,85.35430099,89.88175649,101.1264294,115.5374961,119.3189163,89.99730437,96.33913646,99.87596272,107.6223184,112.4344925,118.6032415,122.9818641,143.7786876,163.4781125,158.036171,161.0920922,186.2135042,205.6933833,224.0010188,266.5778508,270.4706009,274.1113337,291.2381101,276.6679583,296.43515,310.4659328,340.4168345,354.1492482,346.1128885,367.5566089,303.0556077,316.9539272,301.1590023,346.1029514,373.7664808,399.9500747,415.4937978,413.2989322,441.9987604,443.3141938,451.5729973,470.9867868,546.7266135,627.7742417,714.8610154,806.7532806,1028.334772,998.5223415,1101.960838,1357.563727,1458.104066,1443.882435,1449.610451,1573.885642,1605.605445,1732.554242,1980.66702,1996.915087,2100.751461,1900.70681 +Not classified,INX,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Ireland,IRL,GDP per capita (current US$),NY.GDP.PCAP.CD,685.6147124,739.2764064,797.0062884,852.1353017,965.1354227,1023.773726,1074.506506,1152.004952,1124.516636,1291.34992,1887.930321,2164.048248,2642.107003,3078.846458,3196.112279,3776.20278,3707.572862,4352.135905,5587.923752,6895.849301,8092.437902,7602.175919,7823.547029,7511.763109,7228.508762,7634.631721,10302.18386,12168.0295,13607.7996,14193.29327,17819.59523,17890.20612,19957.57653,18612.91349,20196.13556,24330.72213,26453.89235,28622.59902,30821.53025,26303.57435,26311.49712,28264.05202,32695.37099,41191.41422,47732.54759,50910.32995,54328.54428,61415.15484,61463.3481,52214.91098,48745.67899,51954.49662,48911.81974,51591.94256,55660.99748,62025.05779,63036.53657,70587.47618,79336.47135,80778.82961,83812.80297 +"Iran, Islamic Rep.",IRN,GDP per capita (current US$),NY.GDP.PCAP.CD,191.680825,196.9250906,203.4373696,208.132467,221.3212827,248.3411523,264.9796277,287.0829089,318.9919066,350.9135557,384.9440425,468.9569401,570.3506238,876.2681268,1453.734123,1581.930432,2017.411084,2315.893105,2168.869791,2427.465299,2441.440618,2499.988439,3008.145921,3583.329219,3568.655486,3805.581767,4244.690326,2617.70064,2318.464457,2197.956148,2214.327688,,,1067.307587,1185.686421,1569.255447,1932.805016,1804.336763,1723.835367,1756.896803,1670.009674,1909.412306,1911.678649,2253.935835,2756.198822,3246.051122,3774.358055,4904.666183,5717.314148,5709.948293,6599.660938,7781.405801,7927.844908,6018.3229,5585.525604,4904.327315,5253.42656,5520.314782,3598.483454,3114.622753,2282.551865 +Iraq,IRQ,GDP per capita (current US$),NY.GDP.PCAP.CD,,,254.7015332,250.7872054,288.1634817,,,,312.8221085,313.8171509,330.8853685,376.8917646,388.1112804,468.8540124,1018.095783,1151.818714,1471.149534,1591.882266,1847.668968,2852.111974,3850.264419,2693.156529,2936.59679,2747.965495,3090.004177,3112.995764,2970.786753,3492.276687,3773.375417,3873.619309,10356.90305,22.79534531,30.08638701,54.44157978,204.2723883,639.9231244,502.028676,968.5291421,932.3097306,1617.467894,2058.264401,1494.388802,1320.734706,854.8252808,1391.963489,1855.522348,2373.209448,3182.841351,4636.639325,3853.82862,4657.280269,6045.494567,6836.073995,7076.552265,6637.684375,4688.318017,4550.658638,5076.690795,5523.078375,5658.492267,4157.484495 +Iceland,ISL,GDP per capita (current 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US$),NY.GDP.PCAP.CD,241.5391631,243.8321218,246.1011784,253.9774464,268.7180512,276.2489041,299.2508569,353.3033311,314.6619539,348.1297634,363.2069165,441.1145778,517.7320528,546.1362039,711.0431909,753.6322943,681.8215291,1012.568184,1131.119908,1354.154947,1584.668393,1882.756998,2013.016222,2045.202676,2336.6914,2650.986469,3150.634904,3600.013356,4250.057652,4770.741897,5396.40485,5461.497752,5950.628813,6411.014695,7092.105802,7450.274145,7862.696469,8741.659658,8866.566371,9320.881307,9565.949909,10283.0328,10650.84515,10270.60416,10942.72823,11679.40971,13614.41512,14429.55024,15592.87088,15388.44344,15510.19915,16539.76901,16046.48694,16685.94759,18051.17903,18028.97343,18810.55938,19158.7448,19276.52127,19773.46237,17435.92743 +"Korea, Rep.",KOR,GDP per capita (current US$),NY.GDP.PCAP.CD,158.2493033,93.82864905,106.1485057,146.3143419,123.6034948,108.7221312,133.4748485,161.159931,198.431298,243.4224129,279.3049689,301.1765677,324.1962768,406.8988301,563.3559578,617.4560619,834.134208,1055.880041,1405.822374,1783.622313,1715.42946,1883.451279,1992.528225,2198.934472,2413.263925,2482.399956,2834.903848,3554.595206,4748.629608,5817.029181,6610.036508,7636.982429,8126.67039,8884.928319,10385.33617,12564.77813,13403.04959,12398.48003,8281.699982,10672.41793,12256.99357,11561.24837,13165.06574,14672.85747,16496.12009,19402.50263,21743.47745,24086.41044,21350.42798,19143.85161,23087.22564,25096.26388,25466.76052,27182.73431,29249.57522,28732.23108,29288.87044,31616.8434,33422.94421,31846.21823,31489.1227 +Kuwait,KWT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,4443.452338,4571.155742,4230.847778,4207.731536,4019.803916,3860.579007,4858.728915,5224.917155,5971.798916,13521.66733,11768.4683,12093.26837,12253.86654,12663.48935,19093.01649,20924.21201,17408.51822,14312.45856,13233.83966,13132.76511,12356.87866,9771.141271,11535.17454,10169.04084,11617.10646,8794.606046,5419.588163,,,,16932.08504,19358.37054,17748.84942,14166.16413,15435.13113,18440.37852,16587.24865,17846.37616,22148.37815,27011.654,35591.03713,42781.56475,45782.14857,55494.9301,37561.72593,38577.49828,48631.7834,51979.12069,49388.05338,44062.34091,29869.55275,27653.15762,29759.46744,33399.06048,32373.25111, +Latin America & Caribbean (excluding high income),LAC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,1895.762819,1902.18931,1827.860735,1861.542702,1839.722432,1895.54461,2099.824496,2249.228923,2596.773556,3161.359718,2894.980865,3289.77137,3700.704185,3850.674178,4104.235934,4424.814309,4387.231084,3866.596249,4229.831816,4071.347834,3575.739699,3590.960577,4076.412665,4888.421211,5666.300646,6640.291612,7682.708149,7122.83701,8773.191675,9878.568369,9843.211318,9963.788466,10096.42015,8515.465152,8202.393383,9054.988537,8705.66979,8526.199723,6993.817424 +Lao PDR,LAO,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,489.9577437,641.7401976,468.3042981,278.393228,148.9650659,172.523209,203.2560175,234.7643383,250.6044968,287.3948429,325.9416118,363.8800524,378.4286058,345.9218442,248.8390915,277.810213,325.1869371,326.9417867,320.0614597,362.8214135,417.9291685,475.6107962,590.6326063,710.3445761,900.3221829,948.6542409,1140.598657,1378.362016,1581.39614,1825.665672,1998.333108,2134.713062,2308.799808,2423.847598,2542.489769,2544.95255,2630.203774 +Lebanon,LBN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1234.239313,996.9046381,1012.648216,1605.371904,1899.65101,2446.527693,2820.486187,3321.297267,3791.607617,4305.641769,4669.585388,4640.384413,4491.641934,4422.389292,4579.459593,4576.387617,4630.790585,4575.107459,4626.853003,5207.792723,6111.327281,7354.953605,7761.641489,7675.30894,7952.151239,7933.233249,7687.930519,7644.545147,7626.300196,7792.59886,8012.539237,7583.699146,4891.00147 +Liberia,LBR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,306.8338642,306.7102516,306.4739396,243.0895775,286.0647253,294.8932201,336.1156743,396.6017613,478.3995401,470.9481214,513.4455667,596.8966353,657.9357791,721.9295456,721.1822986,710.3840166,714.6233922,698.7023587,677.3223191,621.8929536,583.2717273 +Libya,LBY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6514.318567,7040.776294,7284.917153,6447.143282,5892.622467,5161.685428,5536.866066,5998.160438,5243.596535,6819.039576,7142.771786,6266.489821,3703.042952,4673.145576,5800.588826,8163.00937,9336.353498,11300.19323,14382.59529,10275.26154,12064.77291,5554.180053,13025.27932,10363.80425,6466.908237,4337.919139,4035.196802,5756.699325,7877.12461,7685.948132,3699.228621 +St. Lucia,LCA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,1445.924317,1628.035705,1515.314318,1607.533895,2023.246778,2253.096688,2646.985551,2869.727936,3221.481282,3584.044619,4199.636497,4383.566572,4755.104291,4775.489985,4919.380367,5194.782925,5300.354676,5340.086973,5733.826989,5947.891784,5950.047485,5642.748904,5646.456535,6150.921369,6591.645501,6949.204173,7669.21347,7969.797993,8456.694632,8137.750991,8540.07098,8983.743733,9086.387955,9378.985047,9843.308534,10093.61802,10361.58388,11047.44499,11357.82657,11611.09784,9276.117829 +Latin America & Caribbean,LCN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,1969.874231,1950.746596,1881.383031,1909.09837,1890.816937,1959.798659,2168.946398,2323.408685,2669.251529,3224.380298,2994.325794,3383.399559,3801.379634,3986.603661,4243.831249,4578.767261,4549.050245,4042.987953,4400.795823,4244.81803,3757.260576,3787.273901,4309.235193,5145.814246,5957.981915,6935.858748,7966.56108,7402.54097,9075.903791,10204.24345,10198.38858,10339.94815,10433.17121,8888.231171,8600.757385,9461.211347,9158.440964,8951.811517,7417.252167 +Least developed countries: UN classification,LDC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,265.9114061,270.0867786,258.4152667,243.200337,244.7724722,256.1625403,274.0171611,297.8279088,302.7571884,300.1011127,307.6272449,302.3053113,272.9507146,270.179137,242.8060602,272.13466,291.6445436,299.892744,299.5137187,298.4607421,326.8437625,307.7869054,330.1872048,360.4763093,402.3233119,461.4052956,526.9725585,623.5391389,747.220291,742.1112231,826.4997561,906.4127026,932.5788117,997.6942912,1052.687815,1011.525345,986.9248236,1053.963522,1065.291106,1094.8944,1068.79814 +Low income,LIC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,349.6204688,341.253691,361.4351922,377.9224512,363.847592,345.7441456,353.8903207,345.1623411,302.292935,298.8722233,242.63157,265.8613386,273.1580552,283.6319296,276.7790254,270.9475165,324.8814922,295.0154087,309.6293132,332.5916608,373.998511,425.3783356,484.7757999,575.35693,679.5375025,680.7342243,745.925573,773.3160191,777.7348732,828.5573585,874.3229915,851.3552364,785.6338741,790.858529,795.9531592,820.8961823,821.8629308 +Liechtenstein,LIE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,4236.33302,4827.126337,5637.919093,7355.080307,8439.578876,10513.20529,11385.6967,12412.42797,17499.12595,19761.24848,20660.0176,19507.36553,19677.01849,19560.81037,18596.17269,19390.12664,28287.06328,37824.44216,41292.25772,39368.72709,49373.61027,50886.37531,55197.54701,55794.32733,64051.23221,78631.69911,79863.27908,72208.93467,76757.30022,81307.06837,74853.93873,74287.41337,79345.75247,89859.86537,100289.243,105399.2605,114374.2465,130655.637,143264.0594,126096.6104,141192.5347,158130.4575,149010.2249,173030.2083,178864.8519,167313.2663,165642.3863,171253.9643,180366.7152,, +Sri Lanka,LKA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,138.536654,117.0719883,120.714348,152.8526755,153.8987913,159.5059914,150.8966192,160.9131876,183.9275304,185.8597435,196.4228877,216.9950075,264.7463142,275.6277195,256.2868068,287.5616366,188.0584813,227.5097653,267.6685772,289.1308598,307.6382435,328.6506383,378.936857,369.581896,390.3507114,401.4770959,413.4550704,408.4508669,463.6187388,513.2584475,547.0545344,576.7798839,647.5791564,714.2332315,756.6569905,817.0644817,850.8116039,838.8834628,869.696285,832.8035723,867.4914868,982.195697,1065.784437,1248.698185,1435.816813,1630.388906,2037.322103,2090.401826,2799.648738,3200.861132,3350.521876,3610.289363,3819.25353,3843.780672,3886.291502,4076.747003,4058.803511,3851.56099,3682.038425 +Lower middle income,LMC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,92.62777351,100.715395,110.8407685,117.0583635,105.1026016,109.7017345,115.3523417,125.3263208,135.7499736,139.710851,148.5941175,178.623907,232.6217376,252.3149711,274.1715376,308.0470692,331.7267615,372.2535913,441.1301839,517.8179213,519.986302,501.5089058,483.2794585,502.5527656,521.7720249,498.9352899,496.8112774,485.3870992,510.8370464,470.3429626,488.2955413,482.1794685,509.5381634,566.972738,618.7428038,620.8641357,558.8140589,592.9282015,612.6646325,615.6781649,648.5027178,732.6005767,835.0621169,955.2579703,1115.995981,1350.95694,1503.931528,1504.962194,1801.825852,2020.207967,2087.322877,2093.378612,2145.495731,2074.977957,2134.936109,2266.935303,2264.39294,2352.783694,2200.401758 +Low & middle income,LMY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,137.5538048,147.7659714,162.7513355,175.591062,174.9096176,177.765462,181.9862577,197.4256522,212.625433,222.3143134,246.9916017,304.3681907,378.42762,408.3041682,426.5961479,467.695865,492.9046412,581.8610409,683.1956392,757.0141517,725.2579026,725.4725458,713.3778031,737.4902588,749.3647015,752.2378126,792.4192688,801.5499947,895.2064244,893.4852463,871.8289297,913.4027639,973.120945,1073.813077,1152.707025,1202.738651,1150.901197,1110.294944,1197.567458,1192.064316,1199.578557,1329.528342,1559.466988,1834.838489,2152.706926,2632.85285,3118.882541,3007.070668,3599.195315,4190.19908,4413.675302,4617.698319,4730.574358,4435.358223,4406.185159,4779.975791,5006.095615,5075.833988,4819.438606 +Lesotho,LSO,GDP per capita (current US$),NY.GDP.PCAP.CD,41.30036454,41.85674859,48.16203156,53.08783393,57.47005138,59.50177373,60.20278873,61.58381798,62.46399613,65.57904294,66.80592968,72.65886604,75.12364839,109.876874,133.4173662,128.8431755,123.7134401,157.3488151,210.6837023,222.7291009,321.9987319,315.1295639,246.4011487,266.1885605,223.6435731,176.0181377,204.1784867,252.3027525,288.4066208,297.2612441,350.0587845,404.1982497,466.2747021,458.5533069,471.8420449,527.6998379,489.1310607,507.0978889,464.804531,451.9269342,436.4881373,405.6057121,382.1896086,573.6517781,753.3759044,842.812631,904.6061298,846.5423208,889.1342855,874.7298605,1119.843641,1287.269536,1229.636232,1166.911756,1194.575627,1146.064688,1018.931093,1102.94441,1192.48432,1113.37214,861.0145481 +Late-demographic dividend,LTE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,747.9431773,784.2727903,794.5361535,841.8239022,886.2742949,921.7886422,1000.650151,1044.789691,1100.933369,1197.614868,1108.979683,1138.557794,1282.416075,1550.143799,1693.519543,1757.505209,1676.089409,1522.870373,1662.119598,1705.163602,1793.104822,2026.338046,2420.140713,2905.71507,3487.678209,4368.649054,5401.953424,5191.681054,6189.740881,7508.409149,7983.938847,8553.671946,8819.293767,8219.917354,8190.668154,9034.761906,9849.980323,9989.873379,9758.448234 +Lithuania,LTU,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,534.6318456,674.0833204,819.7192863,917.1457945,901.6544853,953.8017326,1021.157981,1199.239521,1592.765818,1940.271748,2276.275739,2672.728861,3557.521008,4332.452812,3423.410731,3472.736045,4168.366038,4162.726055,4554.745954,4797.543023,14263.96458,15008.31324,16885.40739,19176.17652,19555.21107,19997.59253 +Luxembourg,LUX,GDP per capita (current US$),NY.GDP.PCAP.CD,2242.015817,2222.366366,2311.798849,2441.038555,2755.633117,2780.092719,2900.433218,2909.660629,3175.367369,3658.900683,110.3041238,113.3967858,140.807669,191.1187428,230.1189526,223.2969562,243.5620726,269.1071383,334.5174014,390.1965414,424.2516223,355.100935,323.1131484,317.5517919,311.2102646,320.3178046,465.7885327,575.9774474,647.2351043,683.1046717,858.8218395,917.3763184,1015.558475,1028.20935,1127.514331,1309.669518,1303.197285,1166.134084,1179.562101,51671.85031,48857.30234,48221.14409,53182.9425,65590.39658,75855.58783,80319.6435,89814.89762,106153.2694,114760.7061,103607.7888,105067.7183,115913.1928,106742.9078,113657.436,118981.9066,101417.2743,104349.227,107627.151,116654.2611,114685.1684,115873.6026 +Latvia,LVA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3094.398641,3456.252198,3814.352366,4226.979018,4479.209969,4777.571466,5084.096071,5878.971649,7306.628397,9054.965449,10778.25433,13801.64711,20050.86031,23365.17759,17486.1854,16197.29315,19771.03512,19815.40701,21514.9343,15734.46544,13780.14598,14325.51757,15682.22145,17858.27998,17794.47862,17619.95239 +"Macao SAR, China",MAC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,4549.016837,4342.342034,4796.93299,4804.682041,5183.06018,6352.522656,7135.115518,8130.402595,9442.486665,10647.57154,13564.12522,15304.28709,16704.89883,18277.0425,18244.35896,18107.65985,16595.29338,15649.65887,15835.99101,15665.29797,16424.93211,17921.10457,22568.31017,25183.13268,30121.56043,36550.50516,40810.82804,41010.06826,52473.19099,66891.28197,76572.15179,89260.7571,93022.87514,74839.33204,73569.43369,81044.88998,87554.71386,86117.65543, +St. Martin (French part),MAF,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Morocco,MAR,GDP per capita (current US$),NY.GDP.PCAP.CD,164.8008324,158.9235801,181.1826409,196.5312779,201.261983,206.449018,196.3144515,202.8572717,212.7508556,232.1259133,246.0156622,265.2076642,302.5644904,364.6361067,439.0123592,502.756524,524.1791237,590.2322509,690.1708003,809.627607,1078.83991,861.7979584,836.4350626,750.0216074,668.385423,660.9467358,840.0116864,920.5913726,1066.330652,1071.177518,1206.011886,1266.848894,1299.421278,1199.319104,1326.941452,1432.309836,1561.129611,1396.769667,1472.385983,1447.969193,1334.943464,1339.294077,1416.488396,1725.457466,1952.902534,2018.025547,2196.011276,2499.259938,2890.360715,2866.92411,2839.92612,3046.947854,2912.658275,3121.680081,3171.699192,2875.257985,2896.722196,3035.454433,3226.982794,3230.409722,3009.24946 +Monaco,MCO,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,12478.13122,13812.71818,16735.01323,21417.58295,22712.94336,28247.54967,28810.87301,31430.43382,38349.39576,45845.10983,51515.06413,44369.56135,41371.87681,38893.30323,36382.97346,37552.05564,52158.67774,63077.77457,68441.25161,68583.60405,84303.87843,83721.99142,91670.60401,85397.32656,89378.03979,101866.6041,101212.0026,90833.29746,92999.25345,91283.25659,82365.49236,83724.46591,90506.85951,108656.8026,123523.4932,124197.2754,134048.6219,169966.3069,185785.7259,154775.3808,150737.8925,169016.1961,157520.2194,177673.7454,189432.37,165989.5051,170028.6557,167517.0597,185978.6093,190512.7374, +Moldova,MDA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,593.7661383,575.319576,657.4911903,578.9097663,399.6207703,440.6720307,507.5576584,570.9726454,682.5255088,897.4499588,1034.707058,1183.379211,1531.685476,2111.201495,1899.010117,2437.529978,2942.255949,3045.739874,3322.038436,3328.801449,2732.457113,2880.439281,3509.69345,4230.363037,4494.018403,4551.130676 +Madagascar,MDG,GDP per capita (current US$),NY.GDP.PCAP.CD,131.9931654,133.8477609,138.120004,138.4071657,142.6593399,144.4847301,152.1078972,157.4763093,165.4783754,165.0115988,169.0705601,177.4983312,193.1285826,231.4359587,261.0355901,302.1492277,280.6789305,294.9342463,324.3846534,408.9371863,596.7749793,530.504306,518.1834912,493.0889593,399.3447291,377.8566642,420.0097235,301.7444677,291.2108877,281.8117066,338.9477127,272.5249191,301.9969866,320.5639901,269.5607119,284.8227287,354.7413311,297.1150598,297.2536718,279.9212866,293.6071574,334.4416177,319.2163788,368.7972369,284.4877263,319.5374751,338.7511983,438.6555158,536.351379,467.5398004,471.959197,531.2654985,518.1529543,541.0660586,530.8611206,467.2355537,475.9556084,515.2933093,518.401123,526.2245722,495.4904009 +Maldives,MDV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,268.2966315,273.3560224,282.226235,328.1029601,598.8774616,670.8606604,723.0238044,695.8240429,803.7222438,875.4440986,963.8374667,1064.09463,1205.621336,1328.656047,1431.657896,1569.932617,1737.733635,1926.286035,2011.944337,2154.268226,2234.595861,3039.302776,3049.207981,3476.006273,3941.431136,3640.012132,4809.93374,5574.401981,6614.23618,6636.543871,7076.739821,7291.465967,7265.723399,7928.476443,8499.307148,9033.459724,9209.399665,9577.702376,10276.77339,10626.43335,7455.855728 +Middle East & North Africa,MEA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,346.2813134,374.7659435,410.7817631,473.6500598,558.8629791,734.4361445,1331.520355,1433.459392,1758.249737,1981.42014,2054.302291,2606.121723,3269.840158,3269.640026,3158.480146,3093.28563,3049.021257,3012.393828,2960.076591,2592.289437,2454.471565,2465.278426,3146.160136,2110.136272,2246.920533,2212.128839,2236.375319,2453.31778,2706.482802,2785.733791,2657.629738,2840.441077,3132.384939,3043.963252,2960.895125,3229.65736,3739.504748,4411.117865,5057.124554,5869.280745,7181.743845,6278.622933,7172.938023,8326.334924,8897.740335,8656.287655,8527.81667,7357.406221,7257.019433,7403.30469,7546.701353,7511.823088,6669.90343 +Mexico,MEX,GDP per capita (current US$),NY.GDP.PCAP.CD,,,378.1534954,409.0455701,469.4761369,494.9702613,534.2736315,565.6334089,606.2111327,650.3117272,689.7949287,738.5584763,826.4773502,981.4589828,1242.090043,1476.313217,1453.670672,1301.324622,1589.27364,2034.988669,3027.375274,3803.030926,2597.982903,2147.719559,2478.210836,2569.239714,1733.912993,1862.886204,2247.981386,2687.914845,3112.268595,3661.947965,4170.623383,5650.026286,5854.418117,3928.223711,4412.116298,5289.167653,5481.181954,6157.193044,7157.8145,7544.568723,7593.137345,7075.369643,7484.486399,8277.671564,9068.294218,9642.680517,10016.57121,8002.972178,9271.398396,10203.4213,10241.72783,10725.18359,10928.91601,9616.645558,8744.515559,9287.849587,9686.514244,9946.033829,8346.702379 +Marshall Islands,MHL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,972.9322837,1047.644764,1196.898025,1234.65704,1144.739245,1392.345568,1493.620755,1603.193323,1588.539507,1660.340633,1704.584427,1849.295317,1996.086538,2152.208547,2382.962699,2194.208578,2194.232256,2229.712447,2267.569122,2272.678016,2389.060707,2515.624045,2457.65454,2431.166018,2471.352046,2537.717206,2646.873996,2704.463554,2662.334015,2846.065542,3046.290071,3181.6247,3246.63025,3185.261354,3199.886846,3490.998389,3672.576783,3793.550983,4073.10983, +Middle income,MIC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,141.40517,151.9944666,167.7402873,180.9744276,180.0352419,182.7154673,187.2810699,203.2700686,219.1622863,228.9722178,254.7631229,314.7924479,391.5967139,421.2455228,439.8042917,482.2484683,508.3285214,601.5674396,708.4451573,786.1735259,752.4687446,753.1564947,740.5016906,767.0495887,778.5690679,780.6625011,824.8911918,835.954116,935.8588359,935.0610123,915.240922,960.7233832,1029.28817,1136.792707,1222.053899,1276.139793,1221.69955,1179.277941,1270.770459,1268.153003,1276.480729,1417.024735,1665.126496,1962.464345,2306.190677,2825.277068,3350.992145,3232.697918,3878.920426,4528.294188,4777.978851,5003.164883,5129.100928,4811.877098,4790.88586,5208.923451,5465.316026,5548.164668,5272.149232 +North Macedonia,MKD,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2354.275256,2477.683038,1225.373149,1352.028452,1794.70084,2359.792009,2338.070661,1964.89272,1866.854729,1908.593544,1854.536271,1815.840915,1949.682449,2409.789359,2762.593686,3037.35452,3326.438511,4037.122452,4794.334682,4546.152171,4545.392131,5063.885906,4697.196689,5210.153044,5468.126225,4843.492387,5132.997146,5432.414531,6087.468311,6022.24099,5888.004015 +Mali,MLI,GDP per capita (current 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US$),NY.GDP.PCAP.CD,,,,,,101.2591791,105.5566874,162.3238308,277.909232,342.4750102,354.0821008,401.9004645,472.5203841,598.5661842,1954.883148,2377.10755,2760.541488,2803.394083,2653.281709,3419.265724,5181.826219,5947.280838,5855.822404,5828.101618,6164.192659,6677.398407,4691.209375,4822.101942,4997.39045,5382.732526,6448.13836,5988.866834,6278.652491,6029.183803,6013.200263,6261.764469,6830.639424,7039.621237,6215.715369,6915.370949,8601.271932,8475.968623,8626.965289,9066.312383,10126.13779,12377.08003,14420.51202,15838.46957,22139.74068,16823.79495,18712.57015,20876.58504,21872.61054,20865.78787,20035.21731,16033.46383,14609.98176,15127.88819,16521.20623,15343.0435, +Other small states,OSS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,267.2766439,310.9220454,368.6869919,523.3623053,945.2863494,1033.49937,1258.464939,1387.89811,1499.376443,1904.965914,2586.082743,2540.246779,2346.073777,2163.609792,2126.450908,1970.080033,1943.44326,2245.873328,2457.929762,2509.94504,2935.081323,2957.328728,3165.051458,2961.92544,3044.76155,3451.430247,3571.401584,3634.884235,3465.919428,3675.362236,4115.280906,3996.873535,4211.043478,5120.159415,6238.576086,7423.965193,8498.140227,10064.87015,12112.16407,10175.39973,11677.37384,14905.0585,15218.63283,15558.88036,15759.54728,12814.44191,12411.00993,13243.40882,14403.23731,13821.33996,11821.7459 +Pakistan,PAK,GDP per capita (current US$),NY.GDP.PCAP.CD,83.33794593,89.40903403,91.31918488,95.70337782,104.8802176,116.4467247,125.546992,139.1184751,145.9456727,153.4169694,172.4656661,178.5551136,153.3840396,101.1646559,137.108934,168.0803848,191.3010842,213.1687025,243.3358381,260.5623336,303.0509594,348.2950607,368.2773993,332.5210468,349.1820502,337.8285325,335.0201588,339.3322552,379.4545167,384.3643055,371.6785676,411.859439,429.1469022,442.4922573,434.4654042,489.8818298,497.2161314,476.3812276,461.2167361,454.2761123,576.1955804,544.4942514,534.3039007,599.3763305,687.8364127,748.9225893,836.8605262,908.0950852,990.8466112,957.9956753,987.409712,1164.978823,1198.117637,1208.91771,1251.175719,1356.667831,1368.426961,1464.926195,1482.213067,1284.702047,1193.733397 +Panama,PAN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,542.3281858,583.4115618,608.1004849,648.4360821,686.0694008,742.1140689,775.6393081,827.1733263,889.2377077,974.9369999,1040.810314,1157.624086,1287.954959,1395.476185,1444.748181,1489.964055,1721.77463,1918.162773,2332.126385,2577.745173,2782.289796,2791.771146,2849.053877,2947.691556,2996.620763,2945.169327,2492.144328,2446.230921,2603.847676,2803.944925,3122.087316,3339.914374,3489.151106,3494.51729,3529.852222,3741.28733,3974.664305,4082.614583,4060.317793,4046.429148,4126.232386,4267.292687,4592.152614,4916.907612,5348.884253,6166.187862,7154.274496,7576.140467,8082.019584,9358.261574,10722.25235,11889.07056,12796.05753,13630.32287,14343.98139,15146.40237,15544.73352,15727.97449,12269.049 +Peru,PER,GDP per capita (current US$),NY.GDP.PCAP.CD,253.2649213,277.5687634,305.7616169,325.5163392,382.7439648,441.182112,507.5309532,500.8734862,450.4014675,490.4136037,552.1797687,599.1156553,646.1428162,752.2233843,922.8464967,1094.161608,1006.890862,899.2555182,748.9924969,932.7466182,1033.418632,1203.706341,1182.730928,919.1688561,910.9674658,836.9460873,753.5315298,1000.532272,729.8761993,1040.962255,1196.586858,1524.770536,1566.011279,1488.032995,1881.737355,2194.017247,2232.075816,2306.438527,2163.119357,1924.486419,1955.588006,1941.475342,2021.240038,2145.643889,2417.034363,2729.499172,3154.331349,3606.070689,4220.616378,4196.311627,5082.353706,5869.323882,6528.971775,6756.752996,6672.877373,6229.100674,6204.996457,6710.507602,6957.793411,7027.612207,6126.87454 +Philippines,PHL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,156.7038396,168.0409486,175.9557977,187.136834,199.9568263,207.4439574,224.6355216,241.7108897,186.7746963,201.0412611,211.4121806,258.3847442,343.2905078,360.7533479,402.801155,450.3408107,506.3988106,596.8372794,685.2214898,732.3678574,742.4533753,646.0393861,594.5532575,566.2618527,535.7035912,579.7015416,644.3650151,705.5790855,715.913608,715.7468314,814.7684389,816.4123244,939.9188171,1062.13308,1160.309779,1127.526626,966.9892338,1087.378156,1072.809037,990.5640487,1036.158331,1048.008205,1121.490006,1244.348741,1452.438089,1744.639979,1991.229749,1905.895236,2217.472153,2450.735694,2694.305109,2871.429701,2959.645435,3001.043182,3073.654686,3123.245642,3252.110274,3485.340844,3298.829851 +Palau,PLW,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7657.951214,8092.207323,8307.957438,7771.209368,8317.113942,9626.612414,9843.049373,10381.42387,10598.88283,10252.75014,10356.63362,11094.83322,12044.10547,12561.33614,13711.7617,15876.46193,16653.42589,16065.29283,15657.17715,14907.77735, +Papua New Guinea,PNG,GDP per capita (current US$),NY.GDP.PCAP.CD,102.1766587,106.5853255,111.6004653,115.6641376,125.4250001,138.4706328,153.9456827,170.0953128,182.6222427,202.7498539,231.9462843,251.8724651,294.2979516,434.5994936,479.047218,432.0431119,469.5461972,496.8041299,574.8479325,659.4481356,712.9190725,681.0247781,628.5237849,661.8879359,641.9915399,593.8152473,632.5174724,732.3921728,831.0329102,786.8789271,697.5389685,801.4729647,905.249168,1005.151953,1086.291169,893.7731191,970.0904769,906.3609204,678.8966674,608.2815307,602.1864903,515.6847804,491.8343081,568.2463486,618.0367032,749.1865116,1256.979648,1401.949447,1672.951024,1626.312855,1949.351185,2406.910967,2790.676303,2729.888751,2920.782986,2679.346579,2509.629637,2695.249009,2801.371393,2829.167085,2636.800249 +Poland,POL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1731.209531,2235.541089,2459.020685,2497.195241,2874.825311,3686.8193,4147.465501,4123.135618,4518.108226,4398.081043,4501.454067,4991.244358,5207.171421,5701.602212,6681.386472,8021.505743,9035.410467,11254.51739,13996.02515,11526.05589,12613.011,13879.56099,13097.27085,13696.4663,14271.30585,12578.49547,12447.43959,13864.68176,15468.40941,15694.84082,15656.18273 +Pre-demographic dividend,PRE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,104.0707735,107.7746352,114.8402206,121.2546542,129.387916,124.6051331,126.0557118,136.1261366,168.5163716,156.5310694,178.8981235,212.6422202,300.2379818,336.4832326,392.958994,415.2200255,442.3782891,558.5297741,678.9276888,951.3635401,863.5124775,670.5644952,603.1871707,599.3953935,570.0794504,612.1639661,606.9267242,577.5701423,892.0662562,390.3747782,362.4275093,313.1099177,287.4599736,359.0559995,375.2561177,403.1584621,404.9008388,432.657074,491.7505878,451.9851008,503.8971065,531.2650962,644.8492173,781.6757901,960.6799242,1129.299685,1383.589208,1220.972895,1401.231512,1569.825577,1672.779198,1780.21985,1822.918991,1560.519966,1393.575355,1415.718939,1449.099681,1481.042628,1342.482391 +Puerto Rico,PRI,GDP per capita (current US$),NY.GDP.PCAP.CD,717.5148431,777.2150274,854.744805,931.751666,1006.434446,1110.832691,1207.811824,1335.274112,1480.693176,1661.866885,1852.354673,2044.319906,2246.476714,2432.414219,2614.500941,2738.243153,2946.461953,3208.771356,3567.752629,4024.50942,4502.838428,4920.722937,5115.008514,5217.722729,5730.117009,6008.054486,6455.184461,6980.490143,7595.444656,8033.089441,8652.507492,9064.018517,9659.3389,10212.27676,10876.41882,11579.185,12173.16369,12817.64496,14304.40499,15220.99134,16192.12697,18123.1987,18731.45939,19557.12025,20988.99233,21959.3227,22935.94116,23664.88235,24898.33459,25768.72589,26435.74879,27278.88305,27944.73389,28513.16574,28981.45733,29763.4883,30627.1634,31108.75275,31604.70151,32850.54861,32290.92114 +"Korea, Dem. People's Rep.",PRK,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Portugal,PRT,GDP per capita (current US$),NY.GDP.PCAP.CD,360.4992985,382.729947,407.8528436,432.5117296,468.7810816,520.9106594,575.0076807,646.8227201,719.0804919,795.7593714,4.659039341,5.311395693,6.493661103,8.720277638,9.975256974,10.61622652,10.84158635,11.3081404,12.25934794,13.74335166,16.80074542,16.19127675,15.36228515,13.64537495,12.58355088,13.49304868,19.26239942,23.96095116,28.04935454,30.20916122,39.33051583,44.68889363,53.92557278,47.56056611,49.7685565,58.7643121,60.78181251,57.74137124,60.85227317,12475.29177,11526.37207,11734.76497,12936.69282,15797.78213,18064.15809,18780.12751,19839.45405,22811.05648,24949.04136,23151.21541,22520.64231,23217.2955,20563.7136,21653.19598,22103.70097,19250.10654,19991.97249,21490.42986,23562.55452,23284.52723,22439.87687 +Paraguay,PRY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,204.2008304,208.8501353,215.0668973,220.096548,230.5024334,240.2705038,262.0676904,296.1091884,374.3419598,489.5614817,541.4879412,593.6543256,712.6585154,849.5248677,1103.913114,1398.054298,1718.499822,1608.724557,1635.633487,1260.743798,892.8904517,984.3830553,1020.381636,1063.474754,1156.924625,1376.165325,1611.399861,1610.212247,1591.374158,1686.845278,1897.098347,2002.68346,1993.804376,1812.993667,1694.11121,1663.604939,1565.054289,1300.852367,1365.416418,1679.49512,1843.634108,2271.088885,2976.824014,4047.909791,3626.818637,4359.428415,5329.387437,5185.141549,5936.975959,6118.31811,5413.776021,5324.609068,5678.869972,5782.78455,5380.963322,4949.7497 +West Bank and Gaza,PSE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1201.581542,1326.562857,1317.466751,1389.164971,1465.046057,1499.492177,1476.17185,1335.553195,1156.217473,1257.69857,1422.190886,1543.701414,1570.1044,1664.245717,2035.202341,2191.781787,2557.075624,2880.798437,3067.438727,3315.297539,3352.112595,3272.154324,3527.613824,3620.360487,3562.330943,3656.858271,3239.731108 +Pacific island small states,PSS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,888.8178288,873.1707938,892.6431754,1014.193732,1216.585462,1381.523641,1391.092712,1326.694915,1232.975077,1271.756692,1182.025003,1269.253083,1217.411368,1231.003485,1256.767268,1387.658757,1452.739102,1566.580402,1631.79258,1889.866732,2018.182155,2129.48169,2104.973294,1780.27116,1935.280799,1755.41807,1711.101284,1776.616245,2060.327203,2349.736091,2568.750645,2672.03361,2909.128514,3063.47838,2706.025385,2965.36361,3440.720461,3608.92391,3702.149345,3970.784808,3832.438806,3962.439848,4220.736436,4336.985308,4268.838313,3626.290589 +Post-demographic dividend,PST,GDP per capita (current US$),NY.GDP.PCAP.CD,1348.510729,1408.629883,1504.359084,1601.253631,1726.878267,1855.523564,2014.477773,2141.815529,2301.901848,2508.090598,2236.874944,2440.48091,2771.06936,3246.590333,3570.445414,3912.303207,4276.900423,4796.00449,5690.08932,6366.38835,6956.149891,7346.41868,7333.062576,7708.242326,8186.07697,8622.27591,10111.49345,11434.01444,12885.73766,13445.47954,14464.67221,15262.255,16156.17575,16728.57451,17902.27443,19551.70815,19448.0526,19358.63582,19222.01714,25331.87919,25655.73602,25414.39322,26391.3133,29433.24746,32528.54665,34054.37706,35656.0601,38688.88049,40668.88388,38155.0067,39575.55831,42376.96301,42139.50088,42379.92468,43071.90983,40618.13069,41350.74271,42961.82847,45367.46377,45650.14027,44217.89723 +French Polynesia,PYF,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,1889.242414,2229.752737,2210.064698,2512.267382,2274.456315,2299.176389,2593.115329,2751.171538,3516.394216,4375.530738,5260.420752,5401.97363,5669.999124,6969.934043,8169.159542,8885.644455,8103.453324,7907.982239,7977.589853,8005.802085,8514.273652,12659.10948,13630.02523,14051.4108,13470.02737,15913.51087,16038.83647,17169.70396,17539.54729,16444.15545,18262.41308,17788.06904,15724.04503,16303.0238,16074.27098,14324.11839,,,,,,,,,,,,,,,,,,,, +Qatar,QAT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,2755.556485,3246.715887,3909.888056,5583.61784,15631.69314,15292.57418,18906.15333,19842.03606,21109.99605,27455.0149,35010.39528,34926.704,27402.26749,20910.45334,19645.31466,16590.80272,12704.36631,12901.52096,13611.44569,14052.58982,15454.17967,14124.26385,15434.21009,14270.97377,14544.00559,15849.56574,17337.42187,21104.76372,18593.41407,21723.8142,29976.16763,28517.22084,30214.67042,34517.60321,42124.94084,51455.95094,59530.561,65421.37692,80234.19083,59094.65918,67403.08768,82409.94164,85075.98654,85050.68435,83858.34046,63039.11263,57162.96801,59124.86727,65907.94843,62087.97413,50805.46357 +Romania,ROU,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,1673.838364,1769.889931,1817.902101,1680.705623,1260.749045,1102.103785,1158.132516,1323.104325,1650.276481,1633.010644,1577.323357,1852.467309,1599.889533,1659.90764,1825.179805,2119.882673,2679.41191,3494.944517,4617.929016,5757.496429,8360.166321,10435.04398,8548.118672,8214.076893,9099.217539,8507.104775,9547.852151,10043.67745,8969.148921,9548.587403,10807.00917,12398.98198,12889.80961,12896.08862 +Russian Federation,RUS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3777.235373,3428.762153,3492.710314,3490.452505,3098.802639,2930.670201,2662.104034,2665.779867,2643.929196,2737.572024,1834.861843,1330.757236,1771.594059,2100.352512,2377.529577,2975.125353,4102.364833,5323.463083,6920.189125,9101.255049,11635.27293,8562.813277,10674.99578,14311.08432,15420.87454,15974.64463,14095.64874,9313.013625,8704.898413,10720.33266,11287.35528,11497.64925,10126.72179 +Rwanda,RWA,GDP per capita (current US$),NY.GDP.PCAP.CD,40.53721128,40.68926811,40.94418195,41.21829693,41.08387938,45.98928276,37.48883932,46.64274253,48.78409978,51.7946093,58.52527645,57.58923534,61.81190225,70.80298517,72.89440258,131.019799,141.5310109,160.3842428,188.1847569,222.8134383,243.4870745,264.0263841,255.6370123,260.0054447,268.9232767,279.1049643,301.7981245,318.2065002,337.9554619,330.4971213,349.8733096,269.8504233,302.7386376,314.7513041,126.954894,221.6289261,229.8867674,288.4093021,285.7102739,287.3738192,260.6011858,238.7812778,233.1512636,249.7433131,273.6336903,331.6901134,366.8943924,438.6641845,543.5453565,579.726615,609.724917,668.4946722,725.1697703,722.8943285,743.559038,751.0773926,744.7607488,772.3169891,783.6371628,820.1492886,797.8555543 +South Asia,SAS,GDP per capita (current US$),NY.GDP.PCAP.CD,82.30257917,85.66701664,89.91132804,98.97857963,110.8746439,116.4844985,95.79954822,103.0626366,105.6144063,113.5083829,120.1287917,124.9074412,122.74614,137.1577257,162.3755547,169.8135591,161.9053028,183.114197,203.4850498,222.5682114,262.0772529,271.3147007,274.134306,283.4323535,275.5886055,292.8093575,303.0843367,328.9861461,345.6574502,341.1423114,359.2317094,312.6577036,324.9073248,314.99846,349.8634695,380.353701,407.7515518,419.3197881,416.9828766,438.310721,453.2376041,455.8581249,469.712186,538.711136,613.9477974,691.6926172,774.8637986,959.3051264,959.4611995,1041.994401,1257.499445,1368.944608,1366.115421,1383.560005,1496.00791,1543.489948,1652.196,1867.505482,1894.169093,1959.342256,1804.917623 +Saudi Arabia,SAU,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,777.2672726,801.0447392,921.3350532,1177.724612,1511.670381,2227.006073,6437.418089,6304.116447,8202.77002,9038.879187,9282.661166,12241.25715,16977.98593,17871.992,13944.95128,11039.2398,9632.538746,7919.651547,6304.259838,5932.29551,5856.382339,6085.956465,7246.01592,7883.245268,7932.093233,7485.541756,7421.289194,7690.576284,8335.804742,8551.676059,7419.161144,8007.958194,9171.331476,8684.645762,8695.396483,9609.968686,11185.13704,13791.45476,15384.73836,16516.63178,20078.26007,16113.14371,19262.5476,23745.88058,25243.602,24845.12466,24464.21256,20627.92693,19878.76384,20802.46631,23337.01755,23139.79798,20110.31619 +Sudan,SDN,GDP per capita (current US$),NY.GDP.PCAP.CD,125.859839,133.0822461,140.7251235,139.309841,139.223411,141.0819293,140.6857807,147.9976737,150.0225368,160.3841481,176.9540281,187.0769418,196.9108097,236.6172204,294.9852318,348.0100792,419.9310196,506.6597172,431.812084,491.5774251,392.4148478,509.130652,453.7961819,390.7812971,445.9484969,552.974546,683.0674027,849.6123318,632.3353761,611.9483003,483.9526109,432.47895,260.5103596,320.4853315,449.710583,473.4134709,300.5649303,378.9547617,355.1525542,328.068653,366.172742,383.0160766,418.2517586,484.8449083,573.3069414,689.2096107,905.3096991,1128.410266,1304.533051,1163.797223,1489.874087,1437.779831,1332.914187,1381.489462,1625.463728,1656.905841,1325.156019,1103.000026,825.8689411,753.2821741,595.467833 +Senegal,SEN,GDP per capita (current US$),NY.GDP.PCAP.CD,312.9929628,321.3612396,320.5015389,322.3007333,332.0811554,328.5631692,329.0533134,319.6393517,326.1957965,301.2671658,304.7340906,305.3080465,358.3679264,399.8086239,437.6157344,574.1332295,567.444141,567.0256629,618.1550892,751.0253442,807.8059295,713.5152286,679.2100742,586.1429002,555.1906931,590.1329701,808.3292307,943.5124978,905.7090934,871.6409618,982.0178763,935.4919598,972.4318336,895.6132848,595.0300068,727.9896576,735.9819092,661.6537598,695.9523781,693.1804693,617.1556841,651.6098743,684.3230203,835.7905935,937.4023096,998.1215541,1031.379928,1198.392811,1403.949385,1308.937808,1271.583281,1366.774501,1317.779802,1372.665779,1396.657339,1219.249415,1269.903294,1361.701972,1458.069093,1430.148259,1487.757309 +Singapore,SGP,GDP per capita (current US$),NY.GDP.PCAP.CD,428.05898,449.1510713,472.0858245,511.2055753,485.5338582,516.5353033,566.8076776,626.0335277,708.6060665,812.6827969,925.8039129,1071.41154,1264.375107,1685.459796,2341.705514,2489.911575,2758.94038,2846.335988,3193.905657,3900.533553,4928.139118,5596.585977,6077.634241,6633.236674,7228.317584,7001.766748,6799.930363,7539.029302,8914.441226,10394.539,11861.75616,14502.37999,16135.91365,18290.02824,21553.0309,24914.41126,26233.6289,26375.97195,21829.29987,21796.08444,23852.32703,21700.02005,22159.68886,23730.15245,27608.53737,29961.26328,33769.15416,39432.93835,40007.46926,38927.20688,47236.96023,53890.42873,55546.48854,56967.42579,57562.53079,55646.61875,56848.17539,61176.45636,66679.04649,65640.70795,59797.75218 +Solomon Islands,SLB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,173.4358483,187.3695034,184.760133,,301.2516795,235.3330168,308.2557686,453.7879423,385.8154636,414.2486511,448.0700351,515.6646801,678.825745,794.5203603,814.5532436,781.352611,711.3676714,690.3981792,611.0009251,529.2537155,541.1625701,599.0680065,569.4045556,688.5145764,709.6463897,815.4946362,886.1354418,1153.447534,1306.73753,1381.8009,1385.570531,1171.427864,1215.167354,1017.613757,965.7656751,795.6015338,788.6131193,867.1071722,1014.85528,1119.693297,1260.289404,1388.963877,1428.505673,1604.20583,1938.891187,2141.821522,2248.614935,2274.887866,2167.119928,2225.486842,2332.844216,2411.862926,2344.231634,2258.400365 +Sierra Leone,SLE,GDP per capita (current US$),NY.GDP.PCAP.CD,138.9386399,139.3635697,143.4726483,143.6203068,150.7489709,143.2804924,147.155821,134.3174567,124.7574582,151.7407964,158.2614873,149.9072346,163.0132868,197.4465226,218.0792685,223.6748635,191.7413237,218.1918006,296.4441449,334.8038972,324.8303951,321.8227789,365.9044852,274.9017793,293.3157246,225.1831913,125.1521265,173.670346,253.83121,219.2946023,150.3889975,179.3612103,156.4031039,177.5972628,211.5922322,202.3160428,218.3668902,196.1153817,153.458492,150.0064246,138.6987225,229.3756595,252.396007,266.4469054,266.5693713,292.3490663,323.3890184,360.3716605,408.4810085,391.200943,401.8348761,448.3376621,566.3782351,716.8358269,714.6997957,588.2288628,501.4151655,499.3809746,533.9915333,527.5341745,484.5212675 +El Salvador,SLV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,274.1752595,282.1897331,288.1869903,290.025603,293.3879515,308.4398701,314.6156825,326.7697702,363.8296353,410.2895015,453.4924515,548.1520223,678.0392587,706.6155011,767.799244,778.448009,736.5911467,717.5438904,729.7387104,751.7250214,769.8035753,753.9799282,781.0600073,816.2199828,840.7123359,914.1317947,983.1815042,1073.310253,1216.700841,1380.708375,1585.108896,1684.784505,1778.836337,1886.359966,1930.627457,2001.540049,2072.301591,2124.10182,2209.497245,2278.43033,2428.569111,2631.822147,2786.157922,2933.39424,2858.483344,2983.228806,3266.012588,3428.409332,3509.528132,3589.041296,3705.579704,3805.995686,3910.255969,4052.624775,4167.730939,3798.636521 +San Marino,SMR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40864.32547,36604.49338,38730.80304,41464.74643,51270.12923,59558.77272,60900.54329,64261.03273,72697.70599,78643.55065,66703.21823,60254.7763,57297.51331,49988.46363,51570.98395,50817.33082,42662.90265,43828.23246,45399.02906,48996.59225,47731.20854, +Somalia,SOM,GDP per capita (current US$),NY.GDP.PCAP.CD,65.47971611,68.10639697,70.81304897,73.60720426,76.48039847,79.42881316,81.87111424,84.19896458,86.55032162,90.45797623,93.6546805,95.3538885,119.8182066,144.3445525,128.70334,183.1953649,188.6610689,103.8186092,105.1133335,100.1940609,96.0960668,107.372127,117.1935354,110.884784,119.1748173,131.8173641,138.0960504,147.151477,148.216672,153.1407104,126.9249732,,,,,,,,,,,,,,,,,,,,,,,297.9553578,295.2666099,293.4551719,295.9673926,309.0543803,314.542678,320.0381483,309.4154546 +Serbia,SRB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2207.450598,2864.084727,3380.041416,2571.17271,2571.30404,914.7857199,1727.281196,2283.846685,3005.426352,3502.802924,3720.479155,4382.617279,5848.476405,7101.040141,6169.114195,5735.422857,6809.159804,6015.945228,6755.073675,6600.056809,5588.980728,5765.200762,6292.543629,7252.401858,7411.558649,7666.244476 +Sub-Saharan Africa (excluding high income),SSA,GDP per capita (current US$),NY.GDP.PCAP.CD,117.2216912,121.0200739,125.9572217,132.0231121,140.5012724,148.9689103,156.0788586,155.181898,160.6062165,176.7634433,206.7648568,202.3962522,223.1789432,278.0236271,355.1074191,382.6147913,408.0134042,430.9232692,458.186123,545.743906,682.0023783,944.7571134,839.5021767,712.9801047,609.7255011,555.8048825,559.2123435,617.7586889,618.2371781,596.6211144,654.5186918,643.8902738,626.9752518,571.8670265,543.2400084,613.9925568,616.5945163,622.9121224,587.1635867,578.7131548,601.3588981,559.3968319,594.0852881,724.9286237,877.9607012,1015.557303,1173.629973,1325.883599,1480.814611,1373.0657,1591.640542,1739.987881,1775.533706,1839.496037,1860.252141,1657.286312,1507.096028,1562.913964,1595.496495,1597.727503,1482.909594 +South Sudan,SSD,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1669.493752,1337.882231,1535.706892,1516.404378,1179.739711,1779.470365,1322.820364,1119.651437,,,,, +Sub-Saharan Africa,SSF,GDP per capita (current US$),NY.GDP.PCAP.CD,117.2503419,121.0440707,125.983524,132.0522985,140.5332889,148.9978053,156.1077551,155.2100887,160.6289071,176.7816474,206.7810836,202.4254842,223.2326925,278.0827746,355.164923,382.6771388,408.0699995,431.0189148,458.3364045,545.992855,682.2676089,944.9746136,839.7150303,713.2088338,609.9784023,556.0997859,559.5854212,618.1978651,618.7363862,597.1514109,655.1510917,644.5168298,627.7000451,572.6539436,544.0309423,614.7892269,617.3598969,623.7544766,588.0604026,579.609234,602.2155476,560.2475317,595.0160938,725.8310442,879.0041985,1016.662747,1174.811264,1327.036394,1481.833402,1373.92591,1592.59208,1741.010781,1776.51779,1840.729073,1861.462891,1658.514663,1508.352794,1564.22416,1596.825251,1599.015966,1483.769566 +Small states,SST,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,440.8517464,487.0918099,563.5716831,689.791793,1052.413521,1176.522156,1319.039038,1471.11485,1553.196048,1891.537672,2468.330067,2499.619581,2436.511268,2308.455561,2240.087088,2112.955979,2031.06816,2272.370592,2452.292537,2501.749453,2843.287569,2851.491816,2983.99408,2897.890863,3002.708098,3364.438137,3523.180835,3709.576479,3630.645495,3842.719669,4211.601183,4149.502394,4354.808518,5098.494774,6031.65404,7072.012236,8059.11871,9396.490545,11151.23499,9382.64461,10650.17845,13214.7843,13525.31686,13822.18876,14016.28428,11759.92876,11368.10556,12066.37189,13040.90638,12622.52891,10864.0078 +Sao Tome and Principe,STP,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,524.6698721,577.628404,678.7379639,745.3202999,866.5049956,883.0970413,896.8677913,1098.755219,1067.908053,1090.260763,1254.541598,1330.620114,1564.768548,1770.467688,1584.775657,1700.097997,1813.807434,1953.513257,1987.579702,2157.840446 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US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16597.20846,16767.14081,18385.62908,17480.89552,18265.20702,18699.21595,16317.54936,16519.88465,17547.55462,19374.08099,19273.25395,19156.88961 +Slovenia,SVN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23817.88673,27595.59997,24792.12798,23532.48085,25128.01504,22641.80512,23503.28249,24247.17332,20890.16643,21678.35947,23512.81727,26115.91337,25940.72803,25179.66717 +Sweden,SWE,GDP per capita (current US$),NY.GDP.PCAP.CD,2114.002973,2288.921701,2468.694589,2657.022505,2941.048899,3206.09917,3454.429789,3720.926845,3926.409001,4234.161348,4736.207995,5132.706353,6027.107408,7301.299018,8089.869029,10117.32699,10868.29656,11448.51295,12620.58844,14877.22213,17097.93372,15586.30182,13738.95408,12608.21545,13099.02084,13666.81803,17981.06148,21792.41569,24534.89403,25662.31679,30593.72413,31822.97925,32800.79495,24425.20125,26083.7158,30283.09835,32999.1855,30312.51076,30596.64972,30940.97326,29624.76987,27247.76321,29899.1236,37321.77867,42821.75084,43437.13093,46593.29168,53700.24369,56152.55944,46946.84271,52869.04429,60755.75955,58037.82132,61126.9432,60020.36046,51545.48361,51965.15715,53791.50873,54589.06039,51686.85135,51925.71376 +Eswatini,SWZ,GDP per capita (current US$),NY.GDP.PCAP.CD,104.2140558,125.3114919,131.1620895,151.5017044,177.9669161,187.9483705,200.2461123,189.3771021,196.3021737,251.750832,260.0289791,307.3688719,321.0694305,471.5690788,545.2576171,576.8838728,528.5842538,571.2468283,619.5424325,725.1126728,922.0016494,938.1748182,853.5499178,851.5962684,732.2962204,515.0725603,620.7120357,780.2685423,894.3395802,872.7829383,1355.389001,1367.783196,1481.861181,1528.892444,1563.749507,1833.099316,1695.134775,1781.888523,1609.214093,1557.063331,1728.710498,1521.769121,1405.448752,2148.63254,2699.130772,3083.838202,3176.69114,3327.445066,3137.387151,3385.858935,4168.489518,4496.612962,4527.681573,4230.328486,4038.55354,3679.819798,3425.528759,3914.421839,4105.897212,3894.66963,3415.459446 +Sint Maarten (Dutch part),SXM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27997.28983,28460.33262,27942.88054,33043.68838,32274.89055,31616.6817,29369.05683,29160.10381,, +Seychelles,SYC,GDP per capita (current US$),NY.GDP.PCAP.CD,288.057842,270.2793628,287.0447884,308.1952644,332.3050031,328.484901,337.6462466,333.2338125,314.3510648,314.1798528,343.8811821,401.6080395,546.9510613,648.5319241,744.5069419,806.2326445,814.474738,1044.353068,1376.546579,2030.135903,2329.353358,2419.034419,2296.307729,2280.451551,2338.075652,2588.552804,3165.945038,3638.988011,4128.118232,4407.20094,5302.843727,5314.663128,6128.445569,6559.129994,6555.504407,6748.931109,6583.201018,7280.989621,7715.918147,7747.612159,7578.851053,7663.13708,8331.261997,8524.961236,10176.65871,11092.51074,12014.39987,12154.82994,11122.86207,9706.95606,10804.68447,12189.09516,12006.68296,14765.67398,14700.33434,14745.34146,15068.62035,15906.08391,16390.82491,16198.51748,11425.09293 +Syrian Arab Republic,SYR,GDP per capita (current 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US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2299.199385,2214.372133,2339.884276,2318.472306,2175.822279,2155.895128,1933.850607,2130.326596,2190.796902,2257.62472,2181.338378,1891.061635,2057.391413,2096.229447,2357.59087,2897.757267,3749.13695,4689.900992,5663.705176,7270.742821,8975.490206,7093.373319,8289.886009,10017.72511,10406.48496,10973.16488,10319.43686,8008.230261,7665.383797,8553.532336,8868.02881,9002.297601,8368.476616 +Togo,TGO,GDP per capita (current 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US$),NY.GDP.PCAP.CD,100.767475,107.498001,113.7828498,118.143907,125.9386942,137.9186467,161.0055216,166.9230634,174.7847538,186.8791675,192.127686,194.266671,209.3747807,269.8879592,332.1752699,351.6193358,391.5693898,445.4208525,528.6267551,589.88506,682.9299728,721.0593448,742.8512326,798.0804499,818.0389335,747.7034373,813.4515045,936.7804583,1123.437643,1295.464722,1508.94247,1716.41542,1927.882603,2209.36303,2491.427836,2846.586834,3043.980314,2468.18473,1845.82887,2033.258009,2007.735271,1893.264239,2096.187802,2359.116831,2660.126835,2894.062656,3369.543198,3973.017172,4379.658787,4213.006654,5076.339872,5492.1215,5860.581471,6168.261356,5951.883487,5840.053067,5993.305516,6593.818352,7296.879608,7817.011522,7189.041835 +Tajikistan,TJK,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,497.6324574,250.3408858,391.9437685,293.9804022,267.4329629,213.5441443,178.3612515,155.1885177,219.0402456,177.5596287,138.4291467,171.0480646,190.0026776,237.7573352,311.6272313,340.5831832,408.8384135,526.6432883,715.8657063,676.1235111,749.5524123,847.3824321,969.2961039,1048.225602,1104.172358,978.4049812,807.1025861,848.6723799,853.2188734,890.5444024,859.1379611 +Turkmenistan,TKM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,684.2226917,861.7846271,838.5665772,865.7868319,,410.2729264,832.7538547,623.2419057,590.2812211,554.8339157,562.6417026,590.3871315,548.6879133,643.1909754,774.482152,967.8874353,1283.908782,1453.917784,1704.41538,2136.472101,2600.368758,3904.465301,4036.456805,4439.202128,5649.962106,6675.178055,7304.285771,7962.244925,6432.685325,6387.682575,6587.092604,6967.375043,7612.03518, +Latin America & the Caribbean (IDA & IBRD countries),TLA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,1925.026961,1901.116809,1819.897698,1849.243744,1823.649975,1888.059842,2093.812935,2250.671306,2599.224462,3179.06989,2943.052979,3338.857326,3750.15312,3930.879355,4201.935036,4534.745475,4490.563563,3956.800214,4312.574979,4136.456542,3627.949226,3649.529348,4171.391187,5014.231943,5823.437678,6812.366805,7861.741278,7278.974799,8991.087026,10139.62059,10123.72578,10261.55107,10350.15899,8745.674436,8440.432404,9316.218215,9001.310135,8776.748232,7244.555569 +Timor-Leste,TLS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.0859486,529.7937005,508.7911659,517.7860016,453.4806958,464.524032,446.4575768,523.7375385,614.435755,676.6183307,806.4135263,947.5107667,1013.042872,1210.03999,1232.453997,1332.791772,1353.746815,1286.406785,1230.22528,1560.508615,1381.17566 +Middle East & North Africa (IDA & IBRD countries),TMN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1128.509329,1142.355491,1308.722841,1471.416543,1520.230433,1512.178651,1626.583647,1693.147654,1670.262428,1580.613676,1701.553507,1987.316634,2301.481869,2630.113457,3196.620819,3891.740846,3687.834841,4180.083724,4644.969214,5036.826624,4624.312535,4466.514805,3932.172284,3923.436,3802.414802,3500.262154,3504.714745,3063.189564 +Tonga,TON,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,368.0522381,336.6254647,378.2380471,455.1050152,484.0849456,572.8984514,666.2957055,662.8664303,649.3125796,685.0015944,639.6296255,724.8632095,865.870072,1127.550611,1121.213468,1194.541034,1388.040501,1436.783691,1449.306003,2047.138431,2176.381296,2307.130962,2225.100675,1973.544795,2043.751046,2091.113009,1839.086867,1845.669127,2030.775469,2301.654115,2594.418739,2873.076637,2910.145122,3331.546555,3006.606344,3527.952342,4002.813771,4581.783245,4428.146521,4354.24436,4336.239603,4157.877249,4513.432531,4740.700272,4903.012138, 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US$),NY.GDP.PCAP.CD,117.2503419,121.0440707,125.983524,132.0522985,140.5332889,148.9978053,156.1077551,155.2100887,160.6289071,176.7816474,206.7810836,202.4254842,223.2326925,278.0827746,355.164923,382.6771388,408.0699995,431.0189148,458.3364045,545.992855,682.2676089,944.9746136,839.7150303,713.2088338,609.9784023,556.0997859,559.5854212,618.1978651,618.7363862,597.1514109,655.1510917,644.5168298,627.7000451,572.6539436,544.0309423,614.7892269,617.3598969,623.7544766,588.0604026,579.609234,602.2155476,560.2475317,595.0160938,725.8310442,879.0041985,1016.662747,1174.811264,1327.036394,1481.833402,1373.92591,1592.59208,1741.010781,1776.51779,1840.729073,1861.462891,1658.514663,1508.352794,1564.22416,1596.825251,1599.015966,1483.769566 +Trinidad and Tobago,TTO,GDP per capita (current US$),NY.GDP.PCAP.CD,631.7065847,676.4011784,704.2231582,760.3978853,788.696647,807.8698381,787.3384067,823.5563085,815.3271085,831.426913,869.354117,938.269947,1119.038073,1332.78153,2049.247301,2416.248314,2439.177473,3020.242941,3381.234537,4306.781889,5748.673495,6345.001087,7268.105868,6821.850306,6715.606873,6302.609227,4051.476568,4016.449771,3734.461714,3564.356935,4150.284861,4318.502428,4398.495123,3754.800372,3959.262873,4249.094373,4579.978105,4554.356458,4790.123738,5387.164386,6435.134212,6939.828471,7053.087371,8807.866068,10293.86925,12327.28071,14102.59329,16539.89075,21204.00818,14514.24061,16683.39306,19034.12071,19157.45977,20143.54541,20270.93377,18214.46062,16250.62869,16173.74005,17037.86236,16637.16294,15384.03946 +Tunisia,TUN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,217.9112744,224.2950878,229.0282314,250.6938109,260.4090585,284.220679,325.5646532,422.8566931,504.8284053,641.1042608,765.1896458,779.0448678,863.0676112,984.8001008,1157.377923,1371.835501,1286.553975,1206.642967,1203.658234,1156.914545,1147.42922,1199.562537,1259.136146,1281.201295,1253.326397,1491.119777,1551.377998,1799.111582,1660.875002,1743.154533,1975.899862,2113.587432,2208.206243,2292.711741,2386.589492,2211.835015,2253.034852,2344.396907,2760.405122,3111.374381,3193.204358,3369.924443,3776.332046,4307.580023,4128.463959,4141.975964,4264.678122,4152.684394,4222.70514,4305.482357,3861.6924,3697.938729,3481.241106,3438.805067,3351.573443,3319.819024 +Turkey,TUR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,309.4466243,350.6629852,369.5834686,386.3580606,444.5494828,481.6936799,526.2134746,571.6177702,489.9303677,455.1048582,558.420923,686.4900665,927.7992157,1136.375596,1275.956574,1427.371795,1549.644358,2079.220269,1564.247166,1579.073778,1402.406435,1310.255652,1246.824468,1368.401652,1510.676311,1705.894384,1745.364927,2021.85953,2794.350494,2735.707615,2842.369978,3180.187577,2270.337325,2897.86664,3053.947231,3144.385703,4499.737508,4116.17056,4337.478003,3142.920998,3687.956093,4760.104019,6101.632117,7456.296101,8101.856924,9791.88245,10941.17215,9103.474051,10742.77498,11420.55546,11795.63346,12614.78161,12157.99043,11006.27952,10894.60338,10589.66772,9453.196172,9126.594392,8538.16906 +Tuvalu,TUV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,990.3981751,1038.957834,1069.478537,1047.505216,1175.810083,1185.840519,1323.907506,1361.589349,1367.084534,1464.805341,1463.166211,1392.334348,1610.653001,1874.853819,2181.636269,2184.565259,2263.575948,2645.109505,2936.521547,2602.619444,3024.761774,3643.121608,3506.308144,3457.376266,3398.39675,3197.772252,3253.899536,3574.065288,3701.709255,4055.89561,4143.109753 +Tanzania,TZA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,221.5881441,186.2186779,173.7906179,195.661453,175.5580799,157.0608125,161.176878,182.361928,219.5502111,253.4759847,395.5326208,400.2636718,410.9523655,406.5387537,411.9723218,431.2797298,459.2592952,492.6313397,485.4974495,552.8395016,687.3904455,695.2168433,743.4036639,781.4338957,867.8574989,970.3996014,1030.077648,947.9333517,966.5030166,1004.906588,1042.838759,1085.884861,1076.470385 +Uganda,UGA,GDP per capita (current US$),NY.GDP.PCAP.CD,62.50962537,63.22096734,62.22631459,69.17354301,76.31148622,110.802289,112.0321537,113.1692177,117.3845508,128.074736,133.9715946,146.5889396,150.2165273,167.13582,200.9123531,219.7510474,221.6160047,258.2961789,206.6384294,177.2015733,100.0302757,104.2722215,164.6877539,164.2626181,256.7408119,241.7460015,260.3284059,401.5634663,402.2793423,314.7687216,248.0293243,185.0181172,153.9440238,167.9412492,201.6026565,281.9661333,287.3882907,289.5044945,295.4052653,261.3479065,261.8691416,239.473127,245.5000403,254.3012043,296.0142675,333.7315763,349.2171001,403.6637446,474.5179496,796.5309244,819.0074652,828.9941128,786.738711,806.6119363,879.7349486,843.6295453,733.3971317,746.8307512,770.2570601,794.4514454,817.0357569 +Ukraine,UKR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,1249.445248,1449.966166,1597.535384,1569.757138,,1378.636186,1257.350096,1011.979433,935.9761546,872.6987229,991.2204337,835.2509828,635.7644092,635.7044182,780.3211789,878.6187489,1047.50279,1366.016476,1826.931391,2300.769709,3065.611294,3887.242337,2542.995424,2965.139715,3569.758121,3855.417729,4029.711252,3104.643206,2124.662319,2187.73051,2640.675677,3096.817402,3662.563087,3726.927149 +Upper middle income,UMC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,256.3722294,278.0366216,298.7354655,315.4419327,361.1954014,453.8669819,549.3812104,588.3174134,598.6009487,645.7061192,670.4358032,825.4312655,968.9188198,1032.516522,950.6506174,977.7980221,975.8744286,1009.64278,1009.804584,1047.525499,1152.691509,1190.300249,1371.866268,1410.003108,1359.252288,1460.581233,1576.646869,1741.873227,1868.073705,1983.439391,1943.011472,1823.09592,1999.770467,1997.475954,1984.911243,2196.228838,2618.793237,3130.137606,3698.577609,4565.964786,5551.370357,5308.889324,6396.662114,7593.699493,8092.060926,8614.228386,8859.135678,8257.060889,8156.475382,8965.562497,9588.189552,9696.753839,9295.764144 +Uruguay,URY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,656.7132268,584.2916335,741.244998,701.7047182,664.4398411,580.9702588,574.3468825,717.140948,760.5870648,996.0880666,775.9881598,1404.726557,1448.325811,1250.200808,1290.630911,1440.226655,1707.485587,2479.962509,3485.991103,3764.821504,3107.400832,1716.178567,1620.934562,1571.306,1940.214425,2415.903719,2676.533381,2732.287535,2990.367133,3578.288157,4082.538363,4721.007648,5458.867361,5985.116994,6317.561877,7329.866939,7713.618768,7250.27477,6875.025469,6284.45968,4090.891888,3624.205685,4120.544205,5226.9441,5887.845162,7026.503055,9091.079039,9451.932449,11992.02377,14236.715,15171.58017,16973.68924,16831.94819,15613.74597,16715.63347,18690.89384,18703.86029,17688.01501,15438.41167 +United States,USA,GDP per capita (current US$),NY.GDP.PCAP.CD,3007.123445,3066.562869,3243.843078,3374.515171,3573.941185,3827.52711,4146.316646,4336.426587,4695.92339,5032.144743,5234.296666,5609.3826,6094.01799,6726.358956,7225.69136,7801.456664,8592.253537,9452.576519,10564.94822,11674.18631,12574.79151,13976.10975,14433.78773,15543.89372,17121.22548,18236.82773,19071.22719,20038.9411,21417.01193,22857.15443,23888.60001,24342.2589,25418.99078,26387.29373,27694.85342,28690.8757,29967.71272,31459.13898,32853.67695,34513.5615,36334.90878,37133.24281,38023.16111,39496.48588,41712.80107,44114.74778,46298.73144,47975.9677,48382.55845,47099.98047,48466.6576,49882.55813,51602.93105,53106.53677,55049.98833,56863.3715,58021.4005,60109.65573,63064.41841,65279.52903,63543.57779 +Uzbekistan,UZB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,651.4192061,652.8074753,603.3520153,596.9835856,576.4471105,585.9323642,600.5981578,623.0026524,623.2161328,702.4807441,558.2268024,456.7062895,383.343068,396.377979,465.1198869,546.7768502,654.2838373,830.4076942,1082.286025,1213.265328,1634.312095,1926.292997,2137.025122,2281.410973,2492.336643,2615.025134,2567.799207,1826.566919,1529.082864,1719.05721,1685.764947 +St. Vincent and the Grenadines,VCT,GDP per capita (current US$),NY.GDP.PCAP.CD,161.3752968,170.3812109,174.4960771,162.8054525,173.3873415,175.6584477,185.2539592,180.3982494,173.1274602,185.8985095,203.8651507,219.1676488,298.1860427,322.4208641,347.9698985,347.4328617,339.136668,505.1087102,616.536676,713.4678685,818.7691628,1006.383534,1112.505045,1186.541948,1301.119602,1393.660519,1528.713579,1657.907062,1884.174036,2005.631798,2236.197812,2364.525383,2575.069302,2650.111143,2678.509305,2924.829997,3069.824777,3223.229718,3465.623925,3625.720503,3676.352092,3985.807613,4272.956059,4447.918312,4809.934335,5070.381521,5625.399437,6307.313617,6415.343305,6232.371642,6292.498854,6242.252962,6390.310632,6639.484897,6684.377547,6921.702479,7074.548765,7213.025857,7361.400962,7457.239776,7297.91235 +"Venezuela, RB",VEN,GDP per capita (current US$),NY.GDP.PCAP.CD,955.4464181,970.3563984,1023.355237,1077.403051,864.4712211,869.5350225,876.1539264,892.8794648,937.5986712,930.9262123,1014.453263,1105.622678,1155.404076,1367.912274,2036.437849,2082.310039,2314.990328,2593.076488,2737.001671,3270.600986,3893.697346,4252.337521,4228.902648,4108.481006,3555.806233,3577.783432,3398.132171,2634.22025,3220.861914,2270.957799,2475.380473,2661.033851,2938.160197,2857.93866,2720.367063,3529.589128,3151.269278,3758.844934,3921.718474,4127.108981,4842.036601,4986.675788,3700.87974,3272.615024,4325.696756,5504.97724,6833.378912,8454.469536,11432.74592,11765.09976,13825.35712,10955.53801,12986.22269,12457.7686,16055.64532,,,,,, +British Virgin Islands,VGB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Virgin Islands (U.S.),VIR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30126.53328,31823.41828,35024.8004,40930.17252,41561.70122,44333.88408,39207.72715,38771.63204,40043.55971,39144.88872,37850.77827,34820.11459,33573.71943,34796.49436,36022.54548,36287.87949,37233.29689,, +Vietnam,VNM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,231.4522858,422.7802977,575.4626827,390.4122234,94.56473524,95.18825998,138.4474504,139.2001228,182.3084018,221.1291752,276.8126628,324.1472239,348.0174071,348.3243173,362.9195028,390.0933309,404.8078612,430.0528724,480.5798343,546.909619,687.4797274,784.3723759,906.2842299,1149.424495,1217.268583,1317.890646,1525.119025,1735.152116,1886.69017,2030.278447,2085.101349,2192.174482,2365.521615,2566.447487,2715.27598,2785.724157 +Vanuatu,VUT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,1059.673506,1048.33559,959.811346,943.1943897,945.0665727,1137.138278,1014.36599,951.2056376,1024.906658,1136.609999,1078.516272,1152.169246,1335.833604,1347.430174,1255.090071,1423.896602,1482.705562,1522.062208,1559.43224,1473.666114,1478.58574,1470.63587,1363.185058,1354.100243,1580.575634,1788.122206,1887.226576,2049.532809,2352.973255,2629.053124,2573.889015,2839.40634,3173.822022,2997.293432,2954.774511,2926.67996,2695.666186,2805.66532,3082.475083,3125.404999,3102.34679,2782.98434 +World,WLD,GDP per capita (current US$),NY.GDP.PCAP.CD,445.4313125,459.5891009,484.8193992,513.2638658,551.8156103,588.7757088,625.102789,654.0681104,691.3743612,746.2258503,684.2203443,736.2362911,825.9668905,971.2057271,1096.749433,1183.987582,1277.296922,1417.342999,1626.412359,1836.190212,2038.428473,2160.558402,2107.883724,2159.532729,2229.607397,2311.340364,2595.274077,2844.12331,3136.042504,3230.489211,3481.900382,3614.009899,3750.871211,3864.764255,4106.690721,4468.334605,4502.473871,4505.74828,4405.798387,5378.225205,5487.890203,5403.527448,5542.501678,6134.098725,6824.094238,7298.108184,7813.439616,8696.505826,9427.843407,8840.426854,9555.570991,10493.16985,10606.35762,10783.68124,10951.69061,10251.02048,10294.18971,10834.34434,11372.72733,11417.17406,10925.74607 +Samoa,WSM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,770.3215557,706.4499894,685.7502956,597.3447482,628.7548498,694.0391033,824.2975133,758.8761523,772.5343204,765.8366184,799.4624531,796.5850105,1310.68479,1322.35067,1460.047151,1659.114822,1559.147665,1490.926388,1542.066736,1556.999425,1633.007661,1909.154441,2353.455973,2590.489858,2796.601169,3133.671327,3378.953029,3168.228207,3566.458277,3933.459357,4022.177848,4037.615168,3937.779197,4073.729164,4109.059523,4259.634172,4188.571058,4324.101774,4067.47263 +Kosovo,XKX,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1087.762401,1490.361033,1413.532587,1638.104739,2086.537325,2147.464271,2237.126598,2685.986222,3254.820269,3209.694109,3286.557443,3741.875052,3596.804655,3891.266363,4080.330717,3603.025501,3780.003689,4045.614209,4419.914327,4445.890702,4287.200903 +"Yemen, Rep.",YEM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,482.2481211,482.0605713,498.9633818,393.7390588,291.472071,285.5695931,374.0114314,428.0605956,384.4999404,451.5709253,554.4486653,550.3603645,579.8531406,620.382726,709.9653788,832.8441987,921.4183549,1017.292048,1229.246675,1116.084396,1334.784902,1374.621401,1446.536472,1607.152173,1674.002572,1601.830063,1138.603723,960.528848,824.117718,, +South Africa,ZAF,GDP per capita (current US$),NY.GDP.PCAP.CD,443.0099197,454.9620132,473.0114048,511.4973636,548.9960578,584.7041627,621.2205434,675.1353451,711.2257424,780.6259187,834.5525029,897.1300938,917.3558548,1225.089468,1499.141068,1512.770965,1416.708821,1535.118781,1722.22663,2071.550931,2905.807641,2913.242551,2601.057861,2820.433717,2429.0275,1807.976468,2015.813567,2582.494672,2711.5153,2756.212148,3139.966225,3285.972326,3479.083205,3388.772084,3445.228281,3751.838541,3494.423859,3549.550699,3154.011816,3081.561385,3032.439182,2666.475027,2502.277005,3751.282251,4833.627517,5383.657217,5602.010466,6095.62157,5760.805172,5862.797108,7328.6152,8007.476439,7501.661096,6832.725546,6433.400402,5734.633422,5272.543648,6131.47945,6372.605655,6001.401121,5090.715025 +Zambia,ZMB,GDP per capita (current US$),NY.GDP.PCAP.CD,232.1885645,220.0420671,212.5784491,213.8967589,242.3844725,303.2817405,343.37367,360.2012387,409.7753493,485.872618,436.7692354,390.579238,427.819426,491.1116401,653.278691,529.7428421,537.2206872,469.594201,514.4919068,587.944616,654.4120135,639.5434833,637.5456904,496.1977536,408.7779201,329.5116708,232.5390873,307.8727161,488.744735,511.3217299,408.7693313,409.7272755,376.498819,378.1255592,412.2607521,418.5150247,385.1521522,448.3701601,358.5559589,335.7122914,345.6896207,382.9410352,382.2419633,435.4581994,538.591553,702.7411184,1047.918843,1124.290586,1394.0005,1159.907499,1489.459087,1672.907535,1763.069442,1878.346811,1762.427817,1338.290927,1280.806543,1535.196574,1516.3711,1305.002214,1050.919283 +Zimbabwe,ZWE,GDP per capita (current US$),NY.GDP.PCAP.CD,278.8138468,280.8286629,276.6882328,277.4797151,281.558896,293.308788,277.2345317,292.2705329,299.3989358,341.985387,356.2289954,397.7953353,471.9369028,563.0330783,654.4141916,694.5324939,664.1027557,650.1557988,627.9671176,723.1067339,901.4984147,1043.748057,1073.064116,940.557953,741.8750005,635.0060586,675.8067749,707.57554,793.4492583,816.0767657,841.9739583,809.0511401,619.3720826,591.7196824,611.8652764,623.2095851,741.0958552,731.947626,544.9838381,580.0705709,563.0575041,568.3862905,530.5304463,478.0076044,482.9984515,476.5554025,447.8547399,431.7872587,356.6932344,771.5990323,948.331481,1093.653409,1304.968011,1429.998461,1434.896277,1445.069702,1464.588957,1335.665064,1352.162653,1156.154864,1128.210711 \ No newline at end of file diff --git a/examples/streamlit/app.py b/examples/streamlit/app.py new file mode 100644 index 0000000..9ba1ba6 --- /dev/null +++ b/examples/streamlit/app.py @@ -0,0 +1,326 @@ +import sys +from contextlib import contextmanager +from io import StringIO + +import numpy as np +import pandas as pd +import datetime + +import streamlit as st +from streamlit.report_thread import REPORT_CONTEXT_ATTR_NAME +from threading import current_thread +import altair as alt +import functools +import base64 + +from timeseries_generator import ( + Generator, + HolidayFactor, + LinearTrend, + RandomFeatureFactor, + WeekdayFactor, + WhiteNoise, +) +from timeseries_generator.external_factors import ( + CountryGdpFactor, + EUIndustryProductFactor, +) + +sys.path.append("../..") + +np.random.seed(42) + +st.set_page_config( + page_title="Awesome TS Generator", layout="wide", initial_sidebar_state="auto" +) + + +@contextmanager +def st_redirect(src, dst): + placeholder = st.empty() + output_func = getattr(placeholder, dst) + + with StringIO() as buffer: + old_write = src.write + + def new_write(b): + if getattr(current_thread(), REPORT_CONTEXT_ATTR_NAME, None): + buffer.write(b) + output_func(buffer.getvalue()) + else: + old_write(b) + + try: + src.write = new_write + yield + finally: + src.write = old_write + + +@contextmanager +def st_stdout(dst): + with st_redirect(sys.stdout, dst): + yield + + +@contextmanager +def st_stderr(dst): + with st_redirect(sys.stderr, dst): + yield + + +# ----------------------------------- +# Streamlit APP start from here + +st.title("Awesome Time Series Syntheic Data Generator") + + +@st.cache +def get_country_gdppc_df(): + df = pd.read_csv( + "./examples/streamlit/GDP_per_capita_countries.csv", encoding="utf-8-sig" + ) + return df + + +@st.cache() +def get_country_list(): + df = get_country_gdppc_df() + return df["Country Name"].unique() + + +st.sidebar.subheader("Input a base amount") +base_amount = st.sidebar.number_input("", value=1000, format="%d") + +feature_dict = {} + +st.sidebar.subheader("Input features") +country_factor_flag = st.sidebar.checkbox("Country") +if country_factor_flag: + countries = st.sidebar.multiselect( + "Choose countries", list(get_country_list()), ["Netherlands", "Italy"] + ) + feature_dict["country"] = countries + +feature_flag = st.sidebar.checkbox("Add more feature(s)") +if feature_flag: + feature_raw_str = st.sidebar.text_input( + "Input feature list (must separate by comma)", "product" + ) + feature_list = feature_raw_str.split(",") + + for feat in feature_list: + default_val_l = [f"{feat}_{i}" for i in range(3)] + feat_val_l = st.sidebar.text_input( + f"Input values of feature [{feat}] (must separate by comma)", + ",".join(default_val_l), + ) + feature_dict[feat] = feat_val_l.split(",") + +factor_list = [] + + +# ------------------------- +# add feature related factors + +st.sidebar.subheader("Select factor for each feature") + +feat_factor_dict = { + "random_factor": RandomFeatureFactor, + "country_factor": CountryGdpFactor, +} + +factor_switch_dict = {} +for feat in feature_dict.keys(): + factor_switch = st.sidebar.checkbox(f"{feat}", key=f"factor_switch_{feat}") + if factor_switch: + feat_factor_options = st.sidebar.multiselect( + f"select factor for [{feat}]", + ("random_factor", "country_factor", "linear_factor"), + ) + if len(feat_factor_options) > 0: + for factor in feat_factor_options: + if factor == "random_factor": + factor_list.append( + RandomFeatureFactor( + feature=feat, + feature_values=feature_dict[feat], + col_name=f"random_feature_factor_{feat}", + ) + ) + if factor == "country_factor": + if feat == "country": + factor_list.append( + CountryGdpFactor(country_list=feature_dict[feat]) + ) + if factor == "linear_factor": + feat_val_linear_trend_dict = {} + for feat_val in feature_dict[feat]: + coef = st.sidebar.number_input( + f"Linear slope of {feat_val}", + value=1.0, + format="%f", + key="linear_trend_{feat}_{feat_val}", + ) + feat_val_linear_trend_dict[feat_val] = { + "coef": coef, + "offset": 0, + } + factor_list.append( + LinearTrend( + feature=feat, + feature_values=feat_val_linear_trend_dict, + col_name=f"lin_trend_{feat}", + ) + ) + + +# add global factors + +st.sidebar.subheader("Add other factor") + +if country_factor_flag: + is_holiday = st.sidebar.checkbox("Add holiday factor") + holiday_scale = st.sidebar.slider( + "Holiday factor scale", min_value=1, max_value=10, value=2, step=1 + ) + if is_holiday: + if "country" in feature_dict: + holiday_factor = HolidayFactor( + country_list=feature_dict["country"], holiday_factor=holiday_scale + ) + else: + holiday_factor = HolidayFactor( + country_list=["Netherlands"], holiday_factor=holiday_scale + ) + factor_list.append(holiday_factor) + + +is_weekend = st.sidebar.checkbox("Add weekend factor") +weekend_scale = st.sidebar.slider( + "weekend factor scale", min_value=1, max_value=10, value=1, step=1 +) +if is_weekend: + factor_list.append(WeekdayFactor(intensity_scale=weekend_scale)) + +is_eu_economics = st.sidebar.checkbox("Add EU economics factor") +eu_eco_scale = st.sidebar.slider( + "EU eco factor scale", min_value=1, max_value=20, value=5, step=1 +) +if is_eu_economics: + factor_list.append(EUIndustryProductFactor(intensive_scale=eu_eco_scale)) + +is_noise = st.sidebar.checkbox("Add random noise") +if is_noise: + factor_list.append(WhiteNoise()) + + +# --------------------------- +# select time period + +st.subheader("Input start date and end date") +col1, col2 = st.beta_columns(2) +with col1: + start_date = st.date_input( + "Start data", datetime.date(2019, 1, 1), min_value=datetime.date(2012, 1, 1) + ) +with col2: + end_date = st.date_input( + "End date", datetime.date(2020, 12, 31), min_value=start_date + ) + +# generate time series +g: Generator = Generator( + factors=set(factor_list), + features=feature_dict, + date_range=pd.date_range(start_date, end_date), + base_value=base_amount, +) +df_sale = g.generate() + + +# ------------------------------------------------ +# visualization + +st.subheader("Generated time series data") + +# get all features in feature_dict +all_features = list(feature_dict.keys()) + +vis_feat_l = st.multiselect("Choose features to aggregate", all_features, all_features) +if len(vis_feat_l) > 0: + group_feat_l = vis_feat_l.copy() + group_feat_l.insert(0, "date") + df_vis = df_sale.groupby(group_feat_l)["value"].sum().reset_index() +else: + df_vis = df_sale.copy() + + +df_plot = df_vis[["date", "value"]] + +if len(vis_feat_l) > 0: + color_col = "-".join(vis_feat_l) + df_plot[color_col] = functools.reduce( + lambda x, y: x + "-" + y, (df_vis[feat] for feat in vis_feat_l) + ) + + base = ( + alt.Chart(df_plot) + .mark_line() + .encode(x="date:T", y="value:Q", color=f"{color_col}:N") + ) + + selection = alt.selection_multi(fields=[color_col], bind="legend") + + chart = ( + base.mark_line() + .encode(opacity=alt.condition(selection, alt.value(1), alt.value(0.2))) + .add_selection(selection) + .interactive() + ) + + st.altair_chart(chart, use_container_width=True) +else: + + base = ( + alt.Chart(df_plot) + .mark_line() + .encode( + x="date:T", + y="value:Q", + ) + .interactive() + ) + + st.altair_chart(base, use_container_width=True) + + +# -------------- +# download dataframe +def get_table_download_link(df): + """Generates a link allowing the data in a given panda dataframe to be downloaded + in: dataframe + out: href string + """ + csv = df.to_csv(index=False) + b64 = base64.b64encode( + csv.encode() + ).decode() # some strings <-> bytes conversions necessary here + href = f'Download csv file' + return href + + +st.markdown(get_table_download_link(df_vis), unsafe_allow_html=True) + + +# ------------- +# show dataframe + +col1, col2 = st.beta_columns(2) +with col1: + show_base_df = st.checkbox("Show dataframe") +with col2: + topn = st.number_input("Top N rows", value=50, format="%d") +if show_base_df: + show_col = ["date"] + vis_feat_l + ["value"] + st.dataframe(df_vis[show_col].head(topn)) diff --git a/examples/streamlit/requirements.txt b/examples/streamlit/requirements.txt new file mode 100644 index 0000000..4c394b4 --- /dev/null +++ b/examples/streamlit/requirements.txt @@ -0,0 +1,2 @@ +streamlit==0.75.0 +altair==4.1.0 \ No newline at end of file diff --git a/examples/use_external_factors.ipynb b/examples/use_external_factors.ipynb new file mode 100644 index 0000000..e6a9eae --- /dev/null +++ b/examples/use_external_factors.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# External Factors\n", + "The timeseries generator also supports using a few external factors that are publicly available, to give your dataset a more \"real world\" look.\n", + "In this notebook, we show how these external factors can be used" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from pandas._libs.tslibs.timestamps import Timestamp\n", + "from timeseries_generator.external_factors import CountryGdpFactor, EUIndustryProductFactor\n", + "from timeseries_generator import Generator, HolidayFactor, RandomFeatureFactor, SinusoidalFactor, WeekdayFactor, WhiteNoise\n", + "\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "The first component that we have is the Country GDP factor. This gives a factor based on the GDP of the countries\n", + "involved. The following snippet shows an example of the resulting dataframe. By default it uses the countries Italy, Netherlands and Romania." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "start_date = Timestamp(\"01-01-2018\")\n", + "end_date = Timestamp(\"01-01-2020\")\n", + "\n", + "c_gdp_factor = CountryGdpFactor()\n", + "c_gdp_factor.plot(start_date=start_date, end_date=end_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Another built-in factor is the `EUIndustryProductFactor`. This factor creates a factor based on the industry product factor based on public data of the ECB (European Central Bank) This data is unique per year over the whole of Europe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eu_industry_product_factor = EUIndustryProductFactor()\n", + "eu_industry_product_factor.plot(start_date=start_date, end_date=end_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Holidays play a large role in sales of consumer products. We therefore included a holiday factor which makes use of the excellent `workalendar` package. You can enter a holiday factor, and include special holidays. In this case, Christmas gets a custom factor. We smooth the factors here, as we know that not all presents get bought on the same day." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "holiday_factor = HolidayFactor(\n", + " holiday_factor=2.,\n", + " special_holiday_factors={\n", + " \"Christmas Day\": 10.\n", + " }\n", + ")\n", + "holiday_factor.plot(start_date=start_date, end_date=end_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sales are also expected to rise during the weekends. For these kinds of usecases, we have included the `WeekdayFactor`. This factor also works with the `workalendar` package. You can give different days of the week different factors:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "weekday_factor = WeekdayFactor(\n", + " col_name=\"weekend_boost_factor\",\n", + " factor_values={4: 1.15, 5: 1.3, 6: 1.3} # Here we assign a factor of 1.15 to Friday, and 1.3 to Sat/Sun \n", + ")\n", + "weekday_factor.plot(start_date=start_date, end_date=\"01-31-2018\") # Using a smaller timerange here to keep the plot viewable" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Different products have different sales numbers. The same holds for different stores. Here, this is simulated by using a random factor for both our different stores and our different products." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "feature_store_random_components = RandomFeatureFactor(\n", + " feature_values=[\"store1\", \"store2\", \"store3\"],\n", + " feature=\"store\",\n", + " col_name=\"random_store_factor\"\n", + ")\n", + "feature_store_random_components.plot(start_date=start_date, end_date=end_date)\n", + "\n", + "feature_product_random_components = RandomFeatureFactor(\n", + " feature_values=[\"winter jacket\", \"Yoga Mat\", \"basketball top\"],\n", + " feature=\"product\",\n", + " col_name=\"random_product_factor\"\n", + ")\n", + "feature_product_random_components.plot(start_date=start_date, end_date=end_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Not all products sell the same amount all year long. We simulate this by introducing a sinusoidal factor working on both the winter jacket and basketball top products. Ny using a wavelength of a year and different phases (unit here is in days) we simulate summer and winter sales." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "product_seasonal_components = SinusoidalFactor(\n", + " feature=\"product\",\n", + " col_name=\"product_seasonal_trend_factor\",\n", + " feature_values={\n", + " \"winter jacket\": {\n", + " \"wavelength\": 365.,\n", + " \"amplitude\": 0.2,\n", + " \"phase\": 365/4,\n", + " \"mean\": 1.\n", + " },\n", + " \"basketball top\": {\n", + " \"wavelength\": 365.,\n", + " \"amplitude\": 0.2,\n", + " \"phase\": 0.,\n", + " \"mean\": 1.\n", + " }\n", + " }\n", + ")\n", + "product_seasonal_components.plot(start_date=start_date, end_date=end_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "We add some noise to the dataset. In this case, we add Gaussian White Noise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "white_noise = WhiteNoise()\n", + "white_noise.plot(start_date=start_date, end_date=end_date)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Now that we have viewed each of the different components, we can add them together in the generator to obtain the synthetic timeseries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "features_dict = {\n", + " \"country\": [\"Netherlands\", \"Italy\", \"Romania\"],\n", + " \"store\": [\"store1\", \"store2\", \"store3\"],\n", + " \"product\": [\"winter jacket\", \"Yoga Mat\", \"basketball top\"]\n", + " }\n", + "g: Generator = Generator(\n", + " factors={\n", + " c_gdp_factor,\n", + " eu_industry_product_factor,\n", + " holiday_factor,\n", + " weekday_factor,\n", + " feature_store_random_components,\n", + " feature_product_random_components,\n", + " product_seasonal_components,\n", + " white_noise\n", + " },\n", + " features=features_dict,\n", + " date_range=pd.date_range(start=start_date, end=end_date),\n", + " base_value=10000\n", + ")\n", + " \n", + "df = g.generate()\n", + " \n", + "plot_df = df.set_index('date')\n", + "plot_df[['country', 'store', 'value', 'product']].pivot(columns=['country', 'store', 'product'], values='value').plot(figsize=(24,8))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Export the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "df_export = df.drop(axis=1, columns=['base_amount'])\n", + "df_export.to_csv('synthetic_data.csv', index=False, header=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "from ipywidgets import interact, interactive, fixed, interact_manual\n", + "import ipywidgets as widgets\n", + "\n", + "from collections import defaultdict\n", + "\n", + "checkbox_feat_dict = features_dict.copy()\n", + "\n", + "# create checkbox widgets\n", + "checkbox_dict = {}\n", + "vbox_l = [] \n", + "for feat, feat_vals in checkbox_feat_dict.items():\n", + " hbox_l = []\n", + " for val in feat_vals:\n", + " desc = f'{feat}|{val}'\n", + " cb_widget = widgets.Checkbox(\n", + " value=False,\n", + " description=desc,\n", + " disabled=False\n", + " )\n", + " hbox_l.append(cb_widget)\n", + " checkbox_dict[desc] = cb_widget\n", + " vbox_l.append(widgets.HBox(hbox_l))\n", + "\n", + "ui = widgets.VBox(vbox_l) \n", + "\n", + "all_feat_l = list(checkbox_feat_dict.keys())\n", + "\n", + "selected_feat = []\n", + "def plot_select_feat(**kwargs):\n", + " selected_feat.clear()\n", + "\n", + " for key in kwargs:\n", + " if kwargs[key] is True:\n", + " selected_feat.append(key)\n", + " \n", + " if len(selected_feat) > 0:\n", + " # filter dataframe by selected checkbox\n", + " feat_sel_l = [(s.split('|')[0], s.split('|')[1]) for s in selected_feat]\n", + "\n", + " #------------------------\n", + " # get the conditions\n", + " feat_dict = defaultdict(list)\n", + " for col, cond in feat_sel_l:\n", + " feat_dict[col].append(cond)\n", + " \n", + " # get `or` conditions for checkbox in the same group, e.g., store=store1 or store2\n", + " cond_str_l = []\n", + " for col, val_l in feat_dict.items():\n", + " or_cond = \" | \".join([f\"(df['{col}'] == {repr(val)})\" for val in val_l])\n", + " cond_str_l.append(or_cond)\n", + " \n", + " # get `and` condidtion for checkbox in differetn group\n", + " conditions = \" & \".join([f'({cond})' for cond in cond_str_l])\n", + " #--------------------------\n", + "\n", + " df_sel = df[eval(conditions)]\n", + " \n", + " # get pivot table\n", + " df_sel = df_sel.set_index('date')\n", + " df_plot = df_sel[all_feat_l + ['value']].pivot(columns=all_feat_l, values='value')\n", + " \n", + " df_plot.plot(figsize=(24,8))\n", + " \n", + " \n", + "\n", + "out = widgets.interactive_output(plot_select_feat, checkbox_dict)\n", + "display(ui, out)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.7" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file diff --git a/figs/diagram.png b/figs/diagram.png new file mode 100644 index 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file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6fd010d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,9 @@ +build==0.3.0 +jupyter==1.0.0 +jupyterlab==3.0.4 +matplotlib==3.3.3 +pandas==1.2.0 +pytest==6.2.2 +scipy==1.6.0 +twine==3.3.0 +workalendar==15.0.1 \ No newline at end of file diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..7c2b287 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,2 @@ +[bdist_wheel] +universal = 1 \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..e9b5572 --- /dev/null +++ b/setup.py @@ -0,0 +1,31 @@ +from setuptools import setup, find_packages + +with open("README.md", "r", encoding="utf-8") as fh: + long_description = fh.read() + +setup( + name="timeseries_generator", + description="Library for generating time series data", + long_description=long_description, + long_description_content_type="text/markdown", + packages=find_packages(), + # TODO: Once we use apis to import public data, the `package_data` is no longer required. + package_data={ + "timeseries_generator": ["resources/public_data/*.csv"] + }, + version="0.1.0", + url='https://github.com/Nike-Inc/ts-generator', + author='Zhe Sun, Jaap Langemeijer', + author_email='zhe.sun@nike.com', + install_requires=[ + "pandas==1.2.0", + "workalendar==15.0.1", + "matplotlib==3.3.3" + ], + classifiers=[ + "Programming Language :: Python :: 3", + "Operating System :: OS Independent" + ], + license='Apache License, v2.0', + python_requires='>=3.6', +) diff --git a/tests/external_factors/__init__.py b/tests/external_factors/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/external_factors/country_gdp_factor_test.py b/tests/external_factors/country_gdp_factor_test.py new file mode 100644 index 0000000..5c42a9c --- /dev/null +++ b/tests/external_factors/country_gdp_factor_test.py @@ -0,0 +1,15 @@ +import unittest +from typing import List + +from pandas import DataFrame + +from timeseries_generator.external_factors import CountryGdpFactor + + +class TestCountryGdpFactor(unittest.TestCase): + + def test_loading(self): + country_list: List[str] = ["Netherlands", "Belgium"] + cgf: CountryGdpFactor = CountryGdpFactor(country_list=country_list) + df: DataFrame = cgf.generate("01-01-2018", "01-01-2020") + self.assertEqual(set(country_list), set(df["country"].unique()).intersection(set(country_list))) diff --git a/tests/external_factors/eu_industry_product_factor_test.py b/tests/external_factors/eu_industry_product_factor_test.py new file mode 100644 index 0000000..e1c0845 --- /dev/null +++ b/tests/external_factors/eu_industry_product_factor_test.py @@ -0,0 +1,13 @@ +import unittest + +from pandas import DataFrame + +from timeseries_generator.external_factors import EUIndustryProductFactor + + +class TestEUIndustryProductFactor(unittest.TestCase): + + def test_loading(self): + euipf: EUIndustryProductFactor = EUIndustryProductFactor() + df: DataFrame = euipf.generate("01-01-2018", "01-01-2020") + self.assertAlmostEqual(1.059, df.head(1)[euipf.col_name].values[0]) diff --git a/tests/generator_test.py b/tests/generator_test.py new file mode 100644 index 0000000..207b393 --- /dev/null +++ b/tests/generator_test.py @@ -0,0 +1,50 @@ +import unittest +from typing import List, Dict + +from pandas import date_range +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator import SinusoidalFactor, Generator + + +class TestGenerator(unittest.TestCase): + def setUp(self) -> None: + self.start_date: Timestamp = Timestamp("01-01-2018") + self.end_date: Timestamp = Timestamp("01-01-2020") + self.features_dict: Dict[str, List[str]] = { + "country": ["Netherlands", "Italy", "Romania"], + "store": ["store1", "store2", "store3"], + "product": ["winter jacket", "Yoga Mat", "basketball top"] + } + self.product_seasonal_components: SinusoidalFactor = SinusoidalFactor( + feature="product", + col_name="product_seasonal_trend_factor", + feature_values={ + "winter jacket": { + "wavelength": 365., + "amplitude": 0.2, + "phase": 365 / 4, + "mean": 1. + }, + "basketball top": { + "wavelength": 365., + "amplitude": 0.2, + "phase": 0., + "mean": 1. + } + } + ) + + def testGeneratorForUnrepresentedFeatures(self): + """ + test whether values unrepresented in a certain feature still show up with factor 1 + """ + g: Generator = Generator( + factors={ + self.product_seasonal_components + }, + features=self.features_dict, + date_range=date_range(start=self.start_date, end=self.end_date), + base_value=1 + ) + self.assertEqual(len(self.features_dict), len(g.generate()["product"].unique())) diff --git a/tests/holiday_factor_test.py b/tests/holiday_factor_test.py new file mode 100644 index 0000000..8659a0f --- /dev/null +++ b/tests/holiday_factor_test.py @@ -0,0 +1,23 @@ +import unittest + +from pandas import DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator import HolidayFactor + + +class TestHolidayFactor(unittest.TestCase): + + def setUp(self) -> None: + self.start_date = Timestamp("01-01-2018") + self.end_date = Timestamp("01-01-2020") + + def testGenerate(self): + holiday_factor = HolidayFactor( + holiday_factor=2., + special_holiday_factors={ + "Thanksgiving Day": 10. + } + ) + df: DataFrame = holiday_factor.generate(start_date=self.start_date, end_date=self.end_date) + self.assertAlmostEqual(2., df[holiday_factor.col_name].head(1).values[0]) diff --git a/tests/sinusoidal_factor_test.py b/tests/sinusoidal_factor_test.py new file mode 100644 index 0000000..434dd54 --- /dev/null +++ b/tests/sinusoidal_factor_test.py @@ -0,0 +1,36 @@ +import unittest + +from pandas import DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator import SinusoidalFactor + + +class TestSinusoidalFactor(unittest.TestCase): + def setUp(self) -> None: + self.start_date = Timestamp("01-01-2018") + self.end_date = Timestamp("01-01-2020") + + def testGenerateOnAll(self): + sf: SinusoidalFactor = SinusoidalFactor(wavelength=365., amplitude=1., phase=0., mean=1.) + df: DataFrame = sf.generate(start_date=self.start_date, end_date=self.end_date) + self.assertAlmostEqual(1., df[sf.col_name].values[0]) + + def testGenerateOnFeature(self): + sf: SinusoidalFactor = SinusoidalFactor(feature="my_feature", feature_values={ + "foo": { + "wavelength": 365, + "amplitude": 1., + "phase": 0., + "mean": 1. + }, + "bar": { + "wavelength": 365., + "amplitude": 1., + "phase": 365/4, + "mean": 1. + } + }) + df: DataFrame = sf.generate(start_date=self.start_date, end_date=self.end_date) + self.assertAlmostEqual(1., df[sf.col_name].head(1).values[0]) + self.assertAlmostEqual(2., df[sf.col_name].head(2).values[1]) diff --git a/timeseries_generator/__init__.py b/timeseries_generator/__init__.py new file mode 100644 index 0000000..61442d5 --- /dev/null +++ b/timeseries_generator/__init__.py @@ -0,0 +1,9 @@ +from .base_factor import BaseFactor +from .errors import * +from .generator import Generator +from .holiday_factor import HolidayFactor +from .linear_trend import LinearTrend +from .random_feature_factor import RandomFeatureFactor +from .sinusoidal_factor import SinusoidalFactor +from .weekday_factor import WeekdayFactor +from .white_noise import WhiteNoise diff --git a/timeseries_generator/base_factor.py b/timeseries_generator/base_factor.py new file mode 100644 index 0000000..fa76588 --- /dev/null +++ b/timeseries_generator/base_factor.py @@ -0,0 +1,132 @@ +from abc import ABC, abstractmethod +from typing import List, Dict, Optional, Union, Tuple + +from matplotlib.figure import Figure +from matplotlib.axes._subplots import SubplotBase +from matplotlib.pyplot import subplots +from pandas import DataFrame, date_range, DatetimeIndex +from pandas._libs.tslibs.timestamps import Timestamp + + +class BaseFactor(ABC): + def __init__( + self, + col_name: str, + features: Optional[Dict[str, List[str]]] = None, + date_col_name: str = "date", + apply_to_all: bool = False, + ): + """ + BaseFactor which has to be implemented by all factors. + + Args: + col_name: Column name of the factor. + features: Features that this factor applies to. + date_col_name: Name of the date_column of the generated date. + apply_to_all: Whether this factor applies to all features in the generator. Use this if you want to access + all features in the generator + """ + if features is None: + features = {} + if apply_to_all and features: + raise AttributeError( + "Factor cannot apply to all features, while specifying a feature." + ) + self._features = features + self._col_name = col_name + self._date_col_name = date_col_name + self._apply_to_all = apply_to_all + + @property + def col_name(self): + return self._col_name + + @col_name.setter + def col_name(self, name: str): + self._col_name = name + + @property + def features(self): + return self._features + + @features.setter + def features(self, keys: Dict[str, List[str]]): + self._features = keys + + @property + def date_col_name(self): + return self._date_col_name + + @date_col_name.setter + def date_col_name(self, name: str): + self._date_col_name = name + + @property + def apply_to_all(self): + return self._apply_to_all + + @staticmethod + def get_datetime_index( + start_date: Union[Timestamp, str, int, float], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> DatetimeIndex: + """ + Utility function to return datetime function from start_date and optional end_date. Takes in multiple types. + Based on the end date, may take a default number of periods. + + Args: + start_date: start date of the DateTimeIndex. + end_date: optional end date. + + Returns: + :obj:`DateTimeIndex` compatable with this module. + """ + periods: Optional[int] = None + if not isinstance(start_date, Timestamp): + start_date = Timestamp(start_date) + if end_date and not isinstance(end_date, Timestamp): + end_date = Timestamp(end_date) + elif end_date is None: + periods = 50 + return date_range(start=start_date, end=end_date, periods=periods) + + @abstractmethod + def generate( + self, + start_date: Union[Timestamp, str, int, float], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> DataFrame: + """ + Generates a DataFrame compatible with the `Generator` class. Must be implemented for the factor to be used and + collected in the `Generator` class. + Args: + start_date: start date of the DateTimeIndex. + end_date: optional end date. + Returns: + :obj:`DataFrame` containing the factor values, the corresponding date and optionally some feature labels. + """ + ... + + def plot( + self, + start_date: Union[Timestamp, str, int, float], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> Tuple[Figure, SubplotBase]: + """ + Plots the factor on a 2D line plot. Convenience method to show what the factor looks like. + Args: + start_date: start date of the DateTimeIndex. + end_date: optional end date. + + Returns: + a tuple containing the figure and axes handle + """ + df: DataFrame = self.generate(start_date=start_date, end_date=end_date) + fig, ax = subplots() + if self._features: + for label, grp in df.groupby(list(self.features.keys())): + grp.plot(x=self.date_col_name, y=self.col_name, ax=ax, label=label) + else: + df.plot(x=self.date_col_name, y=self.col_name, ax=ax) + + return fig, ax diff --git a/timeseries_generator/errors.py b/timeseries_generator/errors.py new file mode 100644 index 0000000..efb1e3a --- /dev/null +++ b/timeseries_generator/errors.py @@ -0,0 +1,10 @@ +class FactorAlreadyExistsError(Exception): + def __init__(self, expression, message): + self.expression = expression + self.message = message + + +class DuplicateNameError(Exception): + def __init__(self, expression, message): + self.expression = expression + self.message = message diff --git a/timeseries_generator/external_factors/__init__.py b/timeseries_generator/external_factors/__init__.py new file mode 100644 index 0000000..b1c906b --- /dev/null +++ b/timeseries_generator/external_factors/__init__.py @@ -0,0 +1,3 @@ +from .country_gdp_factor import CountryGdpFactor +from .eu_industry_product_factor import EUIndustryProductFactor +from .external_factor import ExternalFactor diff --git a/timeseries_generator/external_factors/country_gdp_factor.py b/timeseries_generator/external_factors/country_gdp_factor.py new file mode 100644 index 0000000..6c6cfb3 --- /dev/null +++ b/timeseries_generator/external_factors/country_gdp_factor.py @@ -0,0 +1,91 @@ +from pathlib import Path +from typing import Optional, List + +from pandas import DataFrame, to_datetime, read_csv +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator.external_factors.external_factor import ExternalFactor + + +MIN_DATE = Timestamp("01-01-1960") +MAX_DATE = Timestamp("12-31-2020") + + +class CountryGdpFactor(ExternalFactor): + def __init__( + self, + col_name: str = "country_gdp_factor", + country_feature_name: Optional[str] = None, + country_list: Optional[List[str]] = None, + ): + """ + This factor uses GDP per capita to generate yearly trend. + + Raw GDP per captia data is downloaded from GDP per capita (GDPPC): + https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.CD?downloadformat=excel + + An example dataframe as Country trend: + date country factor + 2020-01-01 Netherlands 0.76 + 2020-02-01 Netherlands 0.76 + 2020-01-01 Italy 0.34 + 2020-02-01 Italy 0.34 + ... + + Args: + col_name: column name of the feature. + country_feature_name: customized name of the feature. Defaults to "country". + country_list: List of countries included in the feature. + + """ + + if country_feature_name is None: + country_feature_name = "country" + + if country_list is None: + country_list = ["Netherlands", "Italy", "Romania"] + + super().__init__( + features={country_feature_name: country_list}, + col_name=col_name, + min_date=MIN_DATE, + max_date=MAX_DATE, + ) + + def load_data(self) -> DataFrame: + """ + Load GDPPC data, and prepare for 10 year history + """ + df = read_csv( + Path(__file__).parent.parent + / "resources" + / "public_data" + / "GDP_per_capita_countries.csv", + encoding="utf-8-sig", + ) + + # use GDP per capita of NL in 2015 as the base amount to normalize GDPPC data + base_gdppc_amount = df.loc[df["Country Name"] == "Netherlands"]["2015"].values[ + 0 + ] + + # pick up the countries + df = df[df["Country Name"].isin(self.features[iter(self.features).__next__()])] + df = df.set_index("Country Name") + + # transpose the dataframe, so that each row is a year. Remove headers + df = df.T[3:] + + # set year as datetime type + df.index = to_datetime(df.index, format="%Y") + + # get daily sample and forward fill + df = df.resample("D").ffill() + + df = df.stack().reset_index() + df.columns = [self._date_col_name, "country", self._col_name] + + # normalize the country GDPPC by NL 2015 GDP + df[self._col_name] = df[self._col_name] / base_gdppc_amount + + return df diff --git a/timeseries_generator/external_factors/eu_industry_product_factor.py b/timeseries_generator/external_factors/eu_industry_product_factor.py new file mode 100644 index 0000000..80714ab --- /dev/null +++ b/timeseries_generator/external_factors/eu_industry_product_factor.py @@ -0,0 +1,57 @@ +from pathlib import Path + +from pandas import read_csv, to_datetime, DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator.external_factors.external_factor import ExternalFactor + + +MIN_DATE = Timestamp("01-01-2000") +MAX_DATE = Timestamp("10-31-2020") + + +class EUIndustryProductFactor(ExternalFactor): + def __init__(self, col_name="eu_industry_product_factor", intensive_scale=1): + """ + This component use EU industry product index to generate a monthly trend + The raw data is downloaded from https://sdw.ecb.europa.eu/quickview.do;jsessionid=8AE7EC2C574223EB1E7713897E7B7190?SERIES_KEY=132.STS.M.I8.Y.PROD.NS0020.4.000&start=&end=31-12-2020&submitOptions.x=0&submitOptions.y=0&trans=N + + An example dataframe as GlobalEcoTrendComponents trend: + date country factor + 2020-01-01 Netherlands 0.76 + 2020-02-01 Netherlands 0.76 + 2020-01-01 Italy 0.34 + 2020-02-01 Italy 0.34 + ... + + Args: + col_name: column name of the factor + + """ + super().__init__(col_name=col_name, min_date=MIN_DATE, max_date=MAX_DATE) + self._intensive_sale = intensive_scale + + def load_data(self) -> DataFrame: + df = read_csv( + Path(__file__).parent.parent + / "resources" + / "public_data" + / "eu_prod_index.csv", + names=[self._date_col_name, "value", "is_estimated"], + ) + + df[self._date_col_name] = to_datetime(df[self._date_col_name], format="%Y-%m") + df = df.set_index(self._date_col_name) + + # get daily sample and forward fill + df = df.resample("D").ffill() + + # reset index and rename + df = df.reset_index() + df = df.drop(axis=1, columns="is_estimated") + df.columns = [self._date_col_name, self._col_name] + + # normalize the industry product index + df[self._col_name] = df[self._col_name] / 100 * self._intensive_sale + + return df diff --git a/timeseries_generator/external_factors/external_factor.py b/timeseries_generator/external_factors/external_factor.py new file mode 100644 index 0000000..2bb07d6 --- /dev/null +++ b/timeseries_generator/external_factors/external_factor.py @@ -0,0 +1,59 @@ +from abc import ABC, abstractmethod +from typing import Optional, Dict, List, Union + +from pandas import DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator.base_factor import BaseFactor + + +class ExternalFactor(BaseFactor, ABC): + def __init__( + self, + col_name: str, + features: Optional[Dict[str, List[str]]] = None, + date_col_name: str = "date", + apply_to_all: bool = False, + min_date: Optional[Union[Timestamp, str, int, float]] = None, + max_date: Optional[Union[Timestamp, str, int, float]] = None, + ): + super().__init__( + col_name=col_name, + features=features, + date_col_name=date_col_name, + apply_to_all=apply_to_all, + ) + self._min_date = min_date + self._max_date = max_date + + @property + def min_date(self) -> Optional[Union[Timestamp, str, int, float]]: + return self._min_date + + @min_date.setter + def min_date(self, date: Optional[Union[Timestamp, str, int, float]]): + self._min_date = date + + @property + def max_date(self) -> Optional[Union[Timestamp, str, int, float]]: + return self._max_date + + @max_date.setter + def max_date(self, date: Optional[Union[Timestamp, str, int, float]]): + self._max_date = date + + @abstractmethod + def load_data(self) -> DataFrame: + ... + + def generate( + self, + start_date: Union[Timestamp, str, int, float], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> DataFrame: + data: DataFrame = self.load_data() + if end_date is None: + df_sel = data[(data["date"] >= start_date)] + else: + df_sel = data[(data["date"] >= start_date) & (data["date"] < end_date)] + return df_sel diff --git a/timeseries_generator/generator.py b/timeseries_generator/generator.py new file mode 100644 index 0000000..51629a1 --- /dev/null +++ b/timeseries_generator/generator.py @@ -0,0 +1,167 @@ +from itertools import product +from typing import List, Dict, Set, Optional + +import pandas as pd + +from timeseries_generator.base_factor import BaseFactor +from timeseries_generator.errors import FactorAlreadyExistsError, DuplicateNameError + + +class Generator: + def __init__( + self, + factors: Set[BaseFactor], + features: Optional[Dict[str, List[str]]] = None, + date_range: pd.DatetimeIndex = None, + base_value: float = 1.0, + ): + """ + Collects relevant features and creates a resulting DataFrame based on the selected features. + + Args: + factors: factors that will be applied to the timeseries. + features: features that will be taken into account in the timeseries generation. + date_range: daterange of the resulting dataframe. + base_value: base value of the resulting value of the time series. Mainly useful to give a correct order of + magnitude to your resulting data. + """ + if features is None: + features = {} + if date_range is None: + pd.date_range(pd.datetime(1970, 1, 1), periods=50), + self._factors = factors + self._features = features + self._base_value = base_value + self._date_range = date_range + self._ts = None + + @property + def factors(self): + return self._factors + + @factors.setter + def factors(self, factors: Set[BaseFactor]): + self._factors = factors + + @property + def features(self): + return self._features + + @features.setter + def features(self, features: Dict[str, List[str]]): + self._features = features + + @property + def base_value(self): + return self._base_value + + @base_value.setter + def base_value(self, value: float): + self._base_value = value + + @property + def ts(self): + return self._ts + + @ts.setter + def ts(self, ts: pd.DataFrame): + self._ts = ts + + def generate(self) -> pd.DataFrame: + """ + generates synthetic time series data based on the input factors. Uses the generate method in the factors to + obtain mergeable dataframes. + + Returns: + DataFrame containing the feature labels and values. + + Raises: + DuplicateNameError: when factors have overlapping names. + """ + # generate a combination of date and features data + ts: pd.DataFrame = pd.DataFrame( + product(list(self._date_range), *self._features.values()), + columns=["date"] + list(self._features.keys()), + ) + + # Add base amount + ts["base_amount"] = self._base_value + + # Merge the factors on the base_df + for f in self._factors: + if f.apply_to_all: + f.features = self._features # apply all features to the factor + df: pd.DataFrame = f.generate( + start_date=self._date_range[0], end_date=self._date_range[-1] + ) + if f.date_col_name != "date": + df.rename( + columns={f.date_col_name: "date"} + ) # rename date column to standard "date" name + + ts = ts.merge( + df, + how="left", + on=list(f.features.keys()) + ["date"], # Add date to merge columns + ).fillna( + 1 + ) # Factor 1 means no effect + + factor_names = list(map(lambda factor: factor.col_name, self._factors)) + if len(factor_names) != len(set(factor_names)): + raise DuplicateNameError( + f'duplicate factor names in factor names: "{factor_names}"' + ) + + ts["total_factor"] = ts[factor_names].prod(axis=1) + ts["value"] = ts["total_factor"] * ts["base_amount"] + self._ts = ts + + return ts + + def plot(self): + """ + plots the generated timeseries data + """ + self._ts.plot(x="date", y="value", figsize=(24, 8)) + + def add_factor(self, factor: BaseFactor): + """ + Add factor to time series. + + Args: + factor: factor to add to the generator. + + Raises: + FactorAlreadyExistsError: when the factor already exists in the generator. + """ + if factor.col_name in map(lambda f: f.col_name, self._factors): + raise FactorAlreadyExistsError( + f'factor "{factor}" already exists in generator ' + f"{self.__class__.__name__}." + ) + self._factors.add(factor) + + def update_factor(self, factor: BaseFactor): + """ + add or update factor to the time series. + + Args: + factor: factor to add to the generator, or factor to update the definition of. + """ + factors: Set[BaseFactor] = self._factors + if factor.col_name in map(lambda f: f.col_name, self._factors): + factors = set( + filter(lambda f: f.col_name != factor.col_name, self._factors) + ) + factors.add(factor) + self._factors = factors + return factors + + def remove_factor(self, factor: BaseFactor): + """ + remove factor from time series. + Args: + factor: factor to remove from the generator. + """ + self._factors.remove(factor) diff --git a/timeseries_generator/holiday_factor.py b/timeseries_generator/holiday_factor.py new file mode 100644 index 0000000..b0c67a5 --- /dev/null +++ b/timeseries_generator/holiday_factor.py @@ -0,0 +1,202 @@ +import pkgutil +from datetime import date, datetime +from typing import Optional, List, Dict, Tuple, Union + +import workalendar +from pandas import DataFrame, Series, date_range, concat, isnull +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator.external_factors.external_factor import BaseFactor + +WORKALENDAR_CONTINENTS = ["africa", "america", "asia", "europe", "oceania", "usa"] + + +class HolidayFactor(BaseFactor): + def __init__( + self, + col_name="holiday_trend_factor", + holiday_factor: float = 3.0, + special_holiday_factors: Optional[Dict[str, float]] = None, + country_feature_name: Optional[str] = None, + country_list: Optional[List[str]] = None, + ): + + """ + This component uses public holiday information to generate factor + + We use python workalendar (https://github.com/peopledoc/workalendar) to retrieve holiday information + + N.B. we use `pip install workalendar` to install the package + + An example dataframe as holiday trend: + date country factor + 2020-01-01 Netherlands 10 + 2020-02-01 Netherlands 1 + 2020-01-01 Italy 12 + 2020-02-01 Italy 1 + ... + + Args: + col_name: column name of the factor + holiday_factor: factor of the found holiday + special_holiday_factors: a dictionary countaining the holidays (keys) and altered factors from the + `holiday_factor` (values). + country_feature_name: name of the country feature introduced here. + country_list: list of countries included in the feature. + """ + if special_holiday_factors is None: + special_holiday_factors = {} + + if country_feature_name is None: + country_feature_name = "country" + + if country_list is None: + country_list = ["Netherlands", "Italy", "Romania"] + + self._holiday_factor = holiday_factor + self._special_holiday_factors = special_holiday_factors + + super().__init__( + features={country_feature_name: country_list}, col_name=col_name + ) + + @property + def holiday_factor(self): + return self._holiday_factor + + @holiday_factor.setter + def holiday_factor(self, factor: float): + self._holiday_factor = factor + + @property + def special_holiday_factors(self): + return self._special_holiday_factors + + @special_holiday_factors.setter + def special_holiday_factors(self, factors: Dict[str, float]): + self._special_holiday_factors = factors + + def generate( + self, + start_date: Union[Timestamp, str, int, float], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> DataFrame: + def clean_holiday_tuples(input_tuples: Tuple[datetime, str]): + """ + The return value of holidays may have overlapping date, for example: + 2016-05-05 Ascension Thursday + 2016-05-05 Liberation Day + + Then, we only keep the first holiday + """ + visited = set() + output = [] + + for a, b in input_tuples: + if not a in visited: + visited.add(a) + output.append((a, b)) + + return output + + def get_holiday_factor(holiday: Optional[str]): + """ + get factor from holidays. + """ + if isnull(holiday): + return 1 + + if holiday in self._special_holiday_factors: + return self._special_holiday_factors[holiday] + + return self._holiday_factor + + def get_country_holiday_df(country_name: str): + """ + Get all holiday days by give country in MAX_HISTORY_YEARS + """ + # get all workalendar modules + workalendar_country_modules: List[str] = [ + modname + for importer, modname, ispkg in pkgutil.walk_packages( + workalendar.__path__, prefix=f"{workalendar.__name__}." + ) + if not ispkg and modname.count(".") == 2 + ] + workalendar_country_module = list( + filter( + lambda work_cal: country_name.lower() + in work_cal, # module names are lowercase + workalendar_country_modules, + ) + ) + if len(workalendar_country_module) != 1: + raise ValueError( + f'country_name: "{country_name}" not recognized in workalendar modules:' + f"{workalendar_country_modules}" + ) + # Dynamically import the right class + module_parts = workalendar_country_module[0].split(".") + cal = getattr( + getattr(getattr(workalendar, module_parts[1]), module_parts[2]), + country_name, + )() + + df = DataFrame() + for year in range( + start_date.year, end_date.year + 1 + ): # plus one to include the end_date year itself + holidays_tuples = clean_holiday_tuples(cal.holidays(int(year))) + + # TODO: make this more efficient, loop over tuple once + datetime_l = [elem[0] for elem in holidays_tuples] + holiday_l = [elem[1] for elem in holidays_tuples] + + # create series and fill NA for non-holiday date + holiday_series = Series(holiday_l, index=datetime_l) + + # set full year index datetime + ix = date_range( + start=date(int(year), 1, 1), end=date(int(year), 12, 31), freq="D" + ) + holiday_series = holiday_series.reindex(ix) + + country_holiday_df = holiday_series.to_frame() + + # add country column + country_holiday_df["country"] = country_name + + country_holiday_df = country_holiday_df.reset_index() + country_holiday_df.columns = [self._date_col_name, "holiday", "country"] + + # get factor + country_holiday_df[self._col_name] = country_holiday_df[ + "holiday" + ].apply(get_holiday_factor) + + df = df.append(country_holiday_df) + + # Apply smoothing to the curve using a gaussian moving window + df[self._col_name] = ( + df[self._col_name] + .rolling(10, win_type="gaussian", min_periods=1) + .mean(std=2) + ) + + return df + + holiday_df = concat( + map(get_country_holiday_df, self._features[iter(self._features).__next__()]) + ) + holiday_df = holiday_df.drop(axis=1, columns="holiday") + + if end_date is None: + df_sel = holiday_df[(holiday_df[self._date_col_name] >= start_date)] + else: + df_sel = holiday_df[ + (holiday_df[self._date_col_name] >= start_date) + & (holiday_df[self._date_col_name] < end_date) + ] + + # reindex to rangelist + return df_sel.reset_index().drop(axis=1, columns="index") diff --git a/timeseries_generator/linear_trend.py b/timeseries_generator/linear_trend.py new file mode 100644 index 0000000..4c29ea7 --- /dev/null +++ b/timeseries_generator/linear_trend.py @@ -0,0 +1,163 @@ +from typing import Optional, Dict, Union + +from numpy.ma import arange +from pandas import DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator.base_factor import BaseFactor +from timeseries_generator.utils import get_cartesian_product + + +class LinearTrend(BaseFactor): + def __init__( + self, + coef: Optional[float] = None, + offset: Optional[float] = None, + feature: Optional[str] = None, + feature_values: Optional[Dict[str, Dict[str, float]]] = None, + col_name: str = "lin_trend", + ): + """ + Linear Trend Factor. Can either just be a linear trend, or a trend that acts on certain features. + + Args: + coef: coefficient of the linear trend. Optional when specifying a feature that the trend depends on. + offset: offset of the linear trend. Optional when specifying a feature that the trend depends on. + feature: feature that the linear trend depends on. + feature_values: coefficient and offset values per feature value. + col_name: column name of the feature in the resulting DataFrames. + + Examples: + Linear trend over all features: + >>> LinearTrend(coef=0.05, offset=1.) + + different trends for different features: + >>> LinearTrend(feature="my_feature", feature_values={ + ... "foo_feat": {"coef": 0.05, "offset": 1.}, + ... "bar_feat": {"coef": 0.1, "offset": 0.5} + ... } + ... }) + """ + if not ( + (coef and offset and not feature and not feature_values) + or (not coef and not offset and feature and feature_values) + ): + raise AttributeError( + "Either set `coef` and `offset` or `features` and `feature_values`" + ) + if feature: + if any( + map( + lambda value: value.get("coef") is None + or value.get("offset") is None, + feature_values.values(), + ) + ): + raise AttributeError( + f"Each feature value should have a `coef` and an `offset`, you have entered: {feature_values}" + ) + features = {feature: list(feature_values.keys())} + else: + features = None + super().__init__(col_name=col_name, features=features) + self._coef = coef + self._offset = offset + self._feature = feature + self._feature_values = feature_values + + @property + def coef(self): + return self._coef + + @coef.setter + def coef(self, value: float): + if self._feature_values: + raise ValueError("Cannot set coef when feature_values is set.") + self._coef = value + + @property + def offset(self): + return self._offset + + @offset.setter + def offset(self, value: float): + if self._feature_values: + raise ValueError("Cannot set offset when feature_values is set.") + self._offset = value + + @property + def feature(self): + return self._feature + + @feature.setter + def feature(self, feature: str): + self._feature = feature + + @property + def feature_values(self): + return self._feature_values + + @feature_values.setter + def feature_values(self, feature_values): + if self._coef or self._offset: + raise ValueError("Cannot set feature_values when offset and coef are set.") + elif any( + map( + lambda value: value.get("coef") is None or value.get("offset") is None, + feature_values.values(), + ) + ): + raise AttributeError( + f"Each feature value should have a `coef` and an `offset`, you have entered: {feature_values}" + ) + self._feature_values = feature_values + + def generate( + self, + start_date: Optional[Union[Timestamp, str, int, float]], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> DataFrame: + + dr: DataFrame = self.get_datetime_index( + start_date=start_date, end_date=end_date + ).to_frame(index=False, name=self._date_col_name) + + start_date_ts: Timestamp = dr.iloc[0][self._date_col_name] + + if self._feature_values: + + def get_factor_col(row) -> float: + days: float = float( + getattr(row[self._date_col_name] - start_date_ts, "days") + ) # Only working in days + # y = ax + b + # the coef is the total slope across the whole time period + # in order to calculate the daily delta, we need to divide the length of the date + return row["coef"] / len(dr) * days + 1 + row["offset"] + + df: DataFrame = DataFrame( + { + self._feature: self._feature_values.keys(), + "coef": map( + lambda feat: feat["coef"], self._feature_values.values() + ), + "offset": map( + lambda feat: feat["offset"], self._feature_values.values() + ), + } + ) + + factor_df: DataFrame = get_cartesian_product(dr, df) + factor_df[self._col_name] = factor_df.apply(get_factor_col, axis=1) + factor_df = factor_df.drop(["coef", "offset"], axis=1) + + else: + # y = ax + b + df: DataFrame = DataFrame( + (self._coef / len(dr) * arange(start=0, stop=len(dr)) + 1) + + self._offset, + columns=[self._col_name], + ) + factor_df: DataFrame = dr.join(df) + + return factor_df diff --git a/timeseries_generator/random_feature_factor.py b/timeseries_generator/random_feature_factor.py new file mode 100644 index 0000000..9e1be84 --- /dev/null +++ b/timeseries_generator/random_feature_factor.py @@ -0,0 +1,73 @@ +from typing import List, Any, Optional, Union + +import numpy as np +from pandas import DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator import BaseFactor +from timeseries_generator.utils import get_cartesian_product + + +class RandomFeatureFactor(BaseFactor): + def __init__( + self, + feature: str, + feature_values: List[Any], + min_factor_value: float = 1.0, + max_factor_value: float = 10.0, + col_name: str = "random_feature_factor", + ): + """ + Creates a random factor for every feature value. + + Args: + feature: feature name. + feature_values: values (labels) of the feature. + min_factor_value: minimum factor value. + max_factor_value: maximum factor value. + col_name: + + Examples: + Create a factor for every store in our store list: "store_1", "store_2" + >>> rff = RandomFeatureFactor( + ... feature="store", + ... feature_values=["store_1", "store_2"], + ... min_factor_value=1, + ... max_factor_value=10 + ... ) + """ + super().__init__(col_name=col_name, features={feature: feature_values}) + + self._feature = feature + self._feature_values = feature_values + if min_factor_value > max_factor_value: + raise ValueError( + f'min_factor_value: "{min_factor_value}" > max_factor_value: "{max_factor_value}"' + ) + self._min_factor_value = min_factor_value + self._max_factor_value = max_factor_value + + def generate( + self, + start_date: Union[Timestamp, str, int, float], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> DataFrame: + + dr: DataFrame = self.get_datetime_index( + start_date=start_date, end_date=end_date + ).to_frame(index=False, name=self._date_col_name) + + # randomly generate factor + # rand_value = min + ((max - min) * value) + feat_factor = self._min_factor_value + ( + (self._max_factor_value - self._min_factor_value) + * np.random.random(len(self._feature_values)) + ) + + # generate factor df + factor_df = DataFrame( + {self._feature: self._feature_values, self._col_name: feat_factor} + ) + + # cartesian product of factor df and datetime df + return get_cartesian_product(dr, factor_df) diff --git a/timeseries_generator/resources/public_data/GDP_per_capita_countries.csv b/timeseries_generator/resources/public_data/GDP_per_capita_countries.csv new file mode 100644 index 0000000..db337ca --- /dev/null +++ b/timeseries_generator/resources/public_data/GDP_per_capita_countries.csv @@ -0,0 +1,267 @@ +Country Name,Country Code,Indicator Name,Indicator Code,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020 +Aruba,ABW,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,6472.398709,7885.158927,9765.909207,11392.26915,12306.71768,13495.7943,14045.47457,14938.68157,16239.98785,16439.35636,16583.87586,17927.95944,19077.68743,19355.33354,20617.75047,20670.36701,20436.88713,20833.97636,22568.37499,23300.73836,24046.22638,25834.11178,27083.63486,24631.18205,23513.5277,24985.01392,24712.49326,26441.61994,26893.01151,28396.90842,28452.17061,29350.80502,30253.27936,, +Africa Eastern and Southern,AFE,GDP per capita (current US$),NY.GDP.PCAP.CD,127.6515181,130.3536425,134.6938383,142.1597688,150.8818969,161.0732943,169.4607857,181.2451065,190.5094228,208.7079878,220.512563,235.0109539,247.3974995,314.7063662,381.0676546,394.683534,385.8193222,419.8375464,447.5077152,525.2321531,666.0560803,673.7776422,623.9571203,639.6409006,577.2371279,490.3450062,541.6040033,641.2677958,657.8262219,655.7868495,696.1526459,703.6546783,682.5868506,665.2429261,659.5899024,722.1697159,701.7341974,718.8542776,660.4719664,635.1433677,672.2054736,591.0884442,589.2068235,757.4497587,916.6033667,1037.727057,1142.955902,1278.971341,1356.529948,1326.251549,1550.121225,1682.287263,1667.992618,1648.867514,1654.314865,1503.859334,1401.281053,1536.206783,1530.161917,1481.425292,1326.663658 +Afghanistan,AFG,GDP per capita (current US$),NY.GDP.PCAP.CD,,,58.4580087,78.70642878,82.09530653,101.1083252,137.594298,160.8984342,129.108311,129.3297604,156.5187713,159.567509,135.317228,143.1446495,173.6536247,186.5108383,197.4455076,224.2250016,247.3540878,275.7381154,272.6555102,264.1111971,,,,,,,,,,,,,,,,,,,,,179.4265792,190.6838143,211.3820742,242.0313132,263.7336019,359.6931579,364.660679,438.0761424,543.3029674,591.1627983,641.871438,637.165464,613.8565052,578.4663529,509.2201005,519.8889126,493.7565814,507.1033919,508.8084095 +Africa Western and Central,AFW,GDP per capita (current US$),NY.GDP.PCAP.CD,107.9637786,113.114697,118.8658374,123.4789666,131.892939,138.5668194,144.3683946,128.620051,129.6789964,143.7576353,195.4906842,169.1685289,200.2269103,241.7820753,333.2898755,377.898463,444.5042983,454.9230878,482.6184998,584.5447854,718.901922,1317.316549,1136.794683,816.1392859,656.9047755,651.7004575,585.0782923,584.2900543,561.5255977,510.5683463,594.8995996,558.5990402,547.8230707,438.2623107,376.3549208,459.3649032,495.7790364,486.5230163,484.0907416,500.4361797,502.031662,516.9279338,605.0610071,681.8823592,826.8165382,988.8393936,1224.057612,1400.086007,1666.899847,1444.201899,1655.130138,1827.518347,1936.390962,2123.392433,2166.743309,1886.248158,1666.422406,1606.978332,1695.959215,1772.339155,1714.4268 +Angola,AGO,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,711.4095786,642.7681968,620.3331234,623.8153624,638.0990068,758.7450632,685.7281277,756.7679262,792.8330172,891.1492803,948.338178,,,,251.0783484,398.7909982,522.6796243,514.3851744,423.619049,387.7851241,556.8386317,527.3335285,872.4944418,982.9610078,1255.564513,1902.42215,2599.565948,3121.996085,4080.941034,3122.781599,3587.883645,4615.468219,5100.097027,5254.881126,5408.4117,4166.979833,3506.073128,4095.810057,3289.643995,2809.626088,1895.770869 +Albania,ALB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,639.4847358,639.8659094,693.8734746,674.7933831,652.7743214,697.9955966,617.2304355,336.5869945,200.8522198,367.2792251,586.4163396,750.6044492,1009.977668,717.3805674,813.7902636,1033.241693,1126.683318,1281.659393,1425.124849,1846.118813,2373.579844,2673.787283,2972.743265,3595.037163,4370.540127,4114.140114,4094.350334,4437.142885,4247.629984,4413.060861,4578.631994,3952.801215,4124.055726,4531.020806,5284.380184,5355.847795,5215.276752 +Andorra,AND,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,3238.690261,3496.532023,4218.271599,5342.16856,6320.809632,7169.101006,7151.249909,7751.843307,9127.81228,11818.47334,12378.78451,10372.78605,9610.020616,8025.207641,7728.906695,7775.614217,10361.14777,12615.12618,14304.6406,15165.28126,18878.85231,19534.26363,20549.80558,16515.6584,16235.06804,18458.17399,19016.28819,18355.62786,18895.11066,19262.30899,21854.2468,22970.51221,25067.24006,32272.84589,37966.18725,40064.22493,42674.75897,47804.84993,48719.65877,43504.21565,40850.24814,43334.81141,38686.46126,39540.72481,41303.92937,35770.7767,37475.63506,38964.90448,41791.96984,40897.33087, +Arab World,ARB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,307.5869784,328.3193064,359.5469852,404.8275044,469.9740234,578.6952638,1063.545509,1143.761235,1377.560668,1536.229212,1632.798269,2138.824494,2802.494494,2801.160549,2549.407597,2324.846079,2288.086807,2198.46706,2038.489679,2112.07154,2019.827199,2060.161514,2750.960304,1857.821247,1952.888247,1925.113215,1930.002535,2044.925966,2191.201682,2309.370533,2182.007896,2377.023018,2678.314514,2536.786256,2484.845185,2696.046923,3134.872479,3765.927294,4360.06425,4963.699983,6149.711791,5180.55547,5948.674349,6870.098616,7455.041881,7505.174192,7436.580078,6346.032347,6093.331009,6095.66343,6568.984052,6570.174197,5802.104129 +United Arab Emirates,ARE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,26848.08818,30117.94893,33823.18166,28457.07803,33512.63342,42764.54056,44987.53799,40025.82262,34843.15963,32309.83271,29720.89772,23467.87932,23726.30705,22295.11871,24028.2625,27729.39059,26612.25311,26420.85988,25596.73939,25848.01625,27221.93441,28975.07996,29512.67479,26899.60177,28470.88586,33291.36625,31280.75586,31567.54631,33499.10278,36333.18733,39365.45964,41907.42147,41809.46216,44498.94051,32024.19778,33893.26387,39194.67224,40976.49075,42412.6395,43751.80565,38663.4005,38141.86713,40644.79119,43839.32449,43103.33629, +Argentina,ARG,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,686.1870959,1127.092299,1887.320558,2560.376996,1875.286927,1948.224631,2129.708348,2146.36498,2520.920764,2758.83475,2776.322006,2927.897278,3553.377527,2659.708175,2926.126411,3613.621764,3562.87603,3985.1925,2383.867473,4333.483372,5735.359981,6823.538837,6969.119729,7483.140334,7408.708664,7721.354105,8213.125127,8289.507568,7774.736203,7708.099115,7208.373114,2593.404563,3349.8063,4277.721573,5109.852245,5919.012338,7245.446857,9020.873323,8225.137583,10385.96443,12848.8642,13082.66433,13080.25473,12334.79825,13789.06042,12790.24247,14613.04182,11633.49801,9912.281809,8441.919439 +Armenia,ARM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,637.8559212,590.5058756,369.7072323,357.2028486,400.5155938,456.3749333,504.0598426,523.2844107,609.1716537,597.4328988,622.7409229,694.4234554,783.241215,930.1254727,1191.921006,1643.756889,2158.148074,3139.28071,4010.861381,2994.340471,3218.378299,3525.807198,3681.844691,3838.17388,3986.231624,3607.289299,3591.828052,3914.527854,4220.540321,4622.738182,4267.452119 +American Samoa,ASM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,8666.655382,8807.462812,8528.248777,8395.31877,8340.52344,8874.877928,9740.824491,11910.01323,10216.81763,10223.29836,11496.5241,11450.7242,11525.15639,12059.63516,12038.2497,11003.82976,11557.67116,11534.56754, +Antigua and Barbuda,ATG,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,1246.69016,1416.540531,1760.831324,2124.618526,2392.686958,2660.558091,2948.317275,3372.60207,3899.39184,4703.179401,5463.595383,6454.835451,7076.536335,7347.646369,7602.345334,7745.7218,8136.188547,8771.143727,8406.358945,9030.574978,9491.660766,9940.852228,10260.59941,10872.29295,10367.31961,10401.05088,10797.95106,11446.96914,12557.54785,13995.80442,15622.69287,16044.1058,14160.562,13048.96058,12746.63347,13272.73494,12910.59063,13501.58092,14285.32978,15198.74297,15383.5764,16673.4369,17113.34977,14449.99652 +Australia,AUS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,1851.841851,1964.15047,2128.068355,2277.558398,2340.438685,2576.284509,2720.082609,2986.949507,3299.843207,3489.952294,3943.787639,4763.62839,6473.165153,6994.308218,7476.748748,7765.070544,8241.997276,9281.523356,10194.39454,11833.74321,12766.52225,11518.66852,12431.94585,11437.71314,11364.23946,11624.62096,14254.51356,17798.55637,18211.50006,18821.79526,18570.12046,17634.53475,18046.13648,20319.63063,21861.32551,23468.59683,21318.96417,20533.03506,21679.24784,19490.86111,20082.48327,23447.031,30430.67644,33999.24286,36044.92281,40960.05449,49601.65671,42772.35917,52022.1256,62517.83375,68012.1479,68150.10704,62510.79117,56755.72171,49971.13146,54027.96682,57354.96405,55057.2047,51812.15404 +Austria,AUT,GDP per capita (current US$),NY.GDP.PCAP.CD,935.4604269,1031.815004,1087.834243,1167.000532,1269.412583,1374.53214,1486.968606,1569.667183,1677.673528,1825.386126,149.6170815,173.035007,212.5043858,282.7430506,336.5210955,384.1135775,412.6560891,494.9649761,596.2973542,711.7743061,789.8861968,682.0242945,683.8523085,693.0858219,653.3986318,666.5779593,950.7789578,1191.35271,1277.43121,1269.504563,1575.660891,1628.622332,1808.053179,1749.982468,1863.916351,2204.01321,2166.436414,1940.725591,1988.416964,27183.47593,24625.60072,24558.76368,26527.59309,32294.04886,36889.23351,38417.45779,40669.32696,46915.3374,51919.98358,48153.32402,46903.76159,51442.27625,48564.91734,50731.12725,51786.37717,44195.81759,45307.58786,47426.51196,51478.28526,50121.55421,48105.36462 +Azerbaijan,AZE,GDP per capita (current 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US$),NY.GDP.PCAP.CD,1397.594803,1472.385714,1525.775853,1613.456884,1748.288118,1873.567774,1986.747159,2058.781882,1951.758596,2100.667869,2347.730246,2649.671014,3030.504708,3426.520112,3666.236108,4299.354057,4138.094675,4681.668593,5976.885651,7805.073396,10032.17868,9598.508938,9145.329109,8691.194954,8179.121916,8651.705793,10611.15369,13118.01842,15988.01649,16240.00625,19096.2412,19900.18849,20488.09967,18390.21782,19708.43564,23124.85792,24334.81898,26717.96323,28237.89163,28667.70904,28154.95388,27719.42137,29981.57019,34385.4918,40210.32844,41933.00443,44473.66528,50447.79001,47269.94483,38735.73974,39536.77268,42047.61425,42449.10767,43401.31053,47452.19914,45039.23595,41048.34966,40306.06743,42995.67729,42354.41465,40284.63846 +Georgia,GEO,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1614.648226,,,547.3348931,519.8575812,578.3446099,689.0594414,807.0322858,851.5259582,673.5434387,749.908535,801.9904139,853.5164537,1010.00798,1305.047486,1642.760938,1996.057129,2635.353882,3324.735879,2822.66743,3233.295943,4021.743306,4421.818242,4623.745725,4739.188338,4014.185944,4062.169888,4357.000936,4722.787783,4697.983652,4278.860771 +Ghana,GHA,GDP per capita (current US$),NY.GDP.PCAP.CD,183.4495898,190.218883,195.4923977,211.0645678,230.0887795,265.3236888,267.748225,214.8321482,200.307094,230.287227,253.5666219,269.3682352,228.8597328,259.7018437,296.8900239,281.4061265,271.1255783,306.809965,345.8344229,372.0601208,402.0607078,372.0774435,345.6413332,337.1633482,355.6667037,352.3527144,435.0681877,374.4703415,372.6839796,365.8896827,398.6370811,433.7732648,409.7463898,370.4194549,328.7445269,379.9879849,397.135755,384.7963283,407.523237,410.3340116,258.4710399,269.0149848,304.564636,367.8213621,417.5080667,492.5440562,913.3938493,1081.166318,1217.064435,1077.662058,1299.345212,1549.462719,1587.560932,2361.090324,1899.668336,1705.575082,1913.473536,2020.68179,2194.232477,2210.355923,2328.534642 +Gibraltar,GIB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Guinea,GIN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,354.421365,351.9090455,398.5461528,394.3311424,419.8098708,461.3753125,489.0716041,475.426317,477.7624592,508.1015161,518.3656626,493.8221614,456.4914841,429.7196923,363.4822793,336.1540207,343.6026312,393.7396899,407.3009759,322.41554,453.4037133,659.992931,715.0965267,674.0854826,672.4249304,651.1361134,717.0505359,769.0031667,787.2385644,769.2554754,732.2915119,855.5752706,955.1112811,1058.143368,1194.037865 +"Gambia, The",GMB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,106.4132245,109.4537158,93.84769049,100.1017727,112.610651,116.5611998,120.1435044,148.1746571,183.0817649,213.3068921,201.1248785,239.43311,288.0557773,335.8048909,378.312989,332.6806063,318.6078621,304.8374354,244.3733422,298.6604123,234.6562868,265.4916532,305.3136177,310.2993373,331.8177403,695.4109885,695.1553715,711.726181,682.2141569,696.4495843,728.6690719,669.401096,678.6761783,637.9390629,594.1493885,505.4216361,411.7718939,335.906217,642.7562183,665.7202157,662.3628772,780.3810509,924.5098746,833.2813604,860.6364339,762.7631177,742.7776286,700.5160422,607.4299047,660.7235712,690.7804932,679.7550717,732.7207273,777.8152452,787.0064509 +Guinea-Bissau,GNB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,111.6888386,109.3325999,119.7282088,119.7760284,130.3675607,142.2818203,145.8778567,148.978152,158.8667021,153.006192,141.5593514,194.9246536,204.2487981,197.0310455,162.6721541,164.9419353,145.9054808,190.4523799,176.2593916,223.4714185,250.149442,257.7891756,221.7991653,227.0232453,220.9604242,233.2432587,243.4382959,237.1300773,178.8483647,190.6723062,308.9103184,319.9574494,333.0585793,372.0561949,405.0751184,436.4752202,430.0039403,493.7219932,599.9952372,559.4145709,558.1746681,703.6605673,616.375664,634.6620391,623.3130914,603.3993815,661.4578387,738.5499449,802.7673851,749.453747,727.5201717 +Equatorial Guinea,GNQ,GDP per capita (current US$),NY.GDP.PCAP.CD,,,34.79058136,40.75147132,46.9760524,234.3440851,243.7690913,247.8441287,225.4865662,221.137218,218.2055377,217.321466,226.0013479,292.4598313,354.3014528,407.71064,418.0080975,429.4165844,,,202.6274483,138.9394517,155.6174781,144.1963121,151.772907,176.4150588,207.052649,243.737477,254.5355336,217.0723884,267.4680729,256.2263358,301.177109,294.0705049,210.4095434,285.5525482,450.6460023,824.5510831,663.7247513,1067.643955,1725.557584,2313.165937,2744.191483,3618.544476,6152.131238,10963.40638,12857.36882,15908.41119,22942.6101,16668.1217,17288.8426,21641.6939,21711.15159,20390.73727,19394.08066,11283.39805,9250.316493,9667.857801,10005.61679,8419.933336,7143.238705 +Greece,GRC,GDP per capita (current US$),NY.GDP.PCAP.CD,520.3227443,590.7800548,617.0577577,695.2286244,783.705881,899.2812389,997.4304675,1068.114556,1154.438531,1324.058989,4.383538084,4.846822799,5.572326576,7.34153416,8.297579463,9.257635573,9.953189348,11.40431963,13.77264344,16.74576703,17.30070891,15.78878386,16.36943335,14.72943532,14.24128265,14.12547133,16.60003589,19.26378722,22.29696821,23.02552311,28.17422133,29.89996235,32.79808754,30.52762903,32.55112278,38.03393133,40.34860487,39.40455129,39.53717164,40.67421212,35.77305267,12549.03689,14177.57216,18518.37884,21995.47794,22560.14729,24821.93675,28863.97329,32127.98319,29828.75602,26716.64883,25483.88256,21913.93783,21755.33568,21630.54726,18057.3581,17897.54231,18608.19253,19775.65344,19150.7862,17676.19248 +Grenada,GRD,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,792.4549727,991.2280739,1153.9605,1245.624686,1276.370366,1346.858115,1372.667697,1477.915659,1678.073246,1872.468608,2162.530245,2408.714548,2757.88844,2886.998204,3117.889831,3192.231908,3153.818283,3272.553373,3411.96696,3632.678677,3861.397822,4370.618317,4705.790063,5056.977259,5041.012235,5213.795649,5682.705378,5741.653658,6645.985549,6658.223586,7212.986819,7832.349081,7290.834938,7258.166561,7291.74167,7444.089075,7789.632355,8370.040472,9096.538652,9628.172843,10152.83281,10486.37759,10815.91136,9680.175825 +Greenland,GRL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,1498.276437,1876.503239,2196.711711,2860.280576,3432.706033,4257.949712,4844.676329,5713.954919,7235.549741,8480.694827,9483.173076,8544.058329,7813.690667,7988.170959,7198.702247,7760.828404,11271.32143,14554.38754,16398.01109,16813.68395,18326.80512,18315.1963,18768.93014,16797.45886,18123.96303,21665.7019,21422.35754,19145.49604,20496.66137,20170.43842,19004.10729,19275.47334,20652.87833,27459.76278,32023.45221,32489.78191,35458.12312,39780.95144,44367.05565,44918.56488,43988.33249,47186.98147,45936.76595,47535.58995,50484.92757,44536.40131,48181.87419,50321.36875,54470.95639,, +Guatemala,GTM,GDP per capita (current US$),NY.GDP.PCAP.CD,252.7561712,253.22729,261.1791338,280.0966364,279.9304645,278.8281385,283.1927246,287.9680471,310.6617452,322.4375294,349.0249756,355.0903711,367.1188811,438.5448512,527.4176968,594.6518036,696.2220153,854.6366434,925.1257445,1027.083696,1143.440402,1217.26402,1200.249234,1212.716559,1234.702641,1233.080944,892.2929065,850.2185121,915.3980653,955.1134609,845.3069621,1011.754966,1093.962908,1164.374025,1293.470198,1424.682543,1487.607658,1649.001602,1755.849582,1619.510736,1664.298958,1550.360366,1682.994501,1737.879287,1859.098363,2068.500129,2251.087963,2490.749127,2802.461962,2651.817123,2852.547327,3228.045741,3355.036919,3522.773706,3779.642336,3994.636913,4173.301666,4454.04815,4478.424646,4638.634943,4603.339617 +Guam,GUM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21594.03969,22628.27869,24472.78202,26596.21479,26713.05839,27680.54996,29288.78186,30320.53859,31040.0843,31210.47029,32908.30677,33656.45357,34843.20557,35829.25036,36214.00692,36601.91988,36502.38282,37723.78134, +Guyana,GUY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,321.5452368,282.3040713,305.3663987,327.1157281,344.1680772,370.1596491,334.8422489,358.3219666,379.8958762,395.2029189,395.2923993,420.6656159,588.137017,663.3956652,602.6248473,590.0653705,658.4360002,683.3303919,773.1816708,729.6050922,616.9607897,628.1936565,564.5278745,588.6103534,660.1519,468.138331,551.2621746,509.3737497,533.5383588,468.1583109,499.0317093,602.1438729,712.7128542,816.5354249,927.1885834,988.3413168,951.9124771,926.7403362,954.4003181,955.6653806,974.9491933,997.2113729,1056.423886,1105.506825,3188.67316,3658.476971,4050.788213,4233.76442,4580.699136,4908.567014,5378.81197,5489.142758,5407.153711,5576.82607,5811.397924,6124.95367,6145.818743,6609.510431,6955.939217 +High income,HIC,GDP per capita (current US$),NY.GDP.PCAP.CD,1368.726712,1429.808847,1524.936605,1622.113844,1748.868971,1877.020232,2036.868226,2163.440037,2323.04123,2530.441687,2266.694395,2475.878108,2809.685705,3299.576286,3671.662638,3998.57429,4387.516988,4925.113578,5816.03816,6553.569937,7236.708016,7647.399435,7568.912821,7884.804759,8333.180899,8731.273531,10159.60132,11476.61138,12912.05148,13488.51103,14531.83196,15349.50997,16308.90927,16859.79891,18036.50766,19678.5057,19647.01317,19573.59436,19391.20709,25222.41315,25605.42719,25305.26827,26215.98543,29171.95831,32239.16068,33843.39367,35476.46499,38483.43325,40571.08377,37801.24782,39323.66889,42260.25461,42042.25086,42307.5711,42976.56675,40397.86011,41023.04565,42685.23497,45117.23759,45297.7165,43833.93023 +"Hong Kong SAR, China",HKG,GDP per capita (current US$),NY.GDP.PCAP.CD,429.4428744,436.7544115,487.8211341,565.7278104,629.5915258,676.8055071,685.9266141,723.2392256,714.4829696,825.5234491,960.0319615,1106.469717,1384.738437,1893.181242,2144.607713,2252.111881,2850.01461,3429.420276,3923.943731,4569.453707,5700.412634,5991.320261,6133.784079,5595.235139,6208.226159,6542.931537,7435.030698,9071.332602,10609.74564,12097.77516,13485.54489,15465.85886,17976.42938,20395.51737,22502.57974,23497.49231,24818.15455,27330.03335,25808.97095,25091.6666,25756.66378,25230.21633,24665.89,23977.01945,24928.10037,26649.7508,28224.21506,30594.01784,31515.66277,30697.34038,32549.99823,35142.48793,36730.8767,38403.77771,40315.28556,42431.88828,43731.10682,46165.85651,48542.68187,48354.47337,46323.86344 +Honduras,HND,GDP per capita (current US$),NY.GDP.PCAP.CD,164.6443995,169.9097551,179.8763063,185.0488471,200.4347285,216.8153947,227.7409563,240.5468429,252.5849145,253.2687207,266.1364061,261.2463703,278.616076,307.3448001,338.1553292,356.457397,414.6231405,497.9382908,895.6479864,993.7254974,1078.810346,1066.168742,1090.998237,1110.475663,1183.02593,1232.869615,1287.410739,1362.885102,1262.067151,1128.284091,993.4832532,911.7549816,942.6129447,913.3016313,836.6023818,936.6676543,887.6926123,948.9547702,1023.460734,1002.716922,1093.1081,1133.186372,1134.064445,1158.178083,1217.818021,1308.09197,1430.057614,1583.047451,1739.353377,1789.705553,1904.347152,2088.315547,2144.342363,2102.592449,2206.054397,2302.204411,2342.584904,2453.727115,2510.322943,2574.356751,2405.732848 +Heavily indebted poor countries (HIPC),HPC,GDP per capita (current US$),NY.GDP.PCAP.CD,92.72809263,95.89824206,98.73374807,103.5443265,110.3119814,120.6873745,127.2176501,128.8045264,130.6247393,136.9396523,142.3290905,147.1548541,155.5986033,180.912138,214.7258571,242.8914881,257.6847236,289.7486562,320.2809865,359.2552053,380.7936916,374.8511907,361.8828622,343.0941588,346.9175148,344.2520586,392.3437481,424.8268028,413.887314,386.9613632,392.759677,385.7454433,349.3600232,347.2148898,288.2249731,326.406005,329.8755973,334.0670616,344.0974623,336.9366323,359.3822582,334.1538451,348.8507849,384.1144683,428.4145105,475.339761,548.3366,636.997799,748.4684432,738.8307773,797.2101201,852.3707875,872.437832,945.0036949,970.9121683,927.1244575,909.5132254,944.4637175,979.8899885,984.8189428,973.2718546 +Croatia,HRV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4877.568417,5232.85176,5263.447002,5640.328953,5198.092739,4841.606375,5362.022897,6232.655595,8059.420173,9661.169574,10527.89268,11695.94466,13937.44839,16296.80592,14540.6416,13949.3265,14609.52435,13258.35985,13674.4178,13599.40978,11781.73479,12361.48383,13451.62495,15014.08502,14944.35622,13828.4695 +Haiti,HTI,GDP per capita (current US$),NY.GDP.PCAP.CD,70.66108456,68.76433933,70.13804588,71.96218417,77.86259725,82.94760837,84.9852522,83.41190396,81.59295367,85.29579643,70.82617925,76.26374897,76.90186371,94.90457191,112.996202,133.7502478,169.3107588,178.860637,180.273204,195.754238,245.2165669,256.2456189,249.4322126,268.2919292,293.1867733,317.0763459,357.7968297,309.30349,386.7772914,396.6861392,439.944188,483.8736354,308.3713974,251.7528877,285.1188538,363.2655445,368.6318638,415.7707421,455.5418961,499.3029687,811.5343577,739.348191,697.597063,561.8176913,663.7943295,766.6919755,792.8260107,981.1105146,1076.702447,1150.211089,1172.099015,1287.954058,1337.336475,1393.956085,1402.100289,1389.119779,1265.986932,1294.239628,1435.350852,1272.490699,1176.75582 +Hungary,HUN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3350.241139,3735.101486,3874.030564,4171.83487,4495.969153,4525.040091,4596.183443,4744.213002,4793.477848,4624.282203,5275.987149,6654.72998,8421.104565,10285.79194,11200.57695,11475.82271,13918.95766,15753.47023,13046.48316,13191.62126,14216.16561,12950.68652,13687.51406,14267.01215,12706.89121,13090.50673,14605.85435,16411.44111,16733.32217,15899.14828 +IBRD only,IBD,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,193.748097,198.4551912,215.7341215,229.7439097,244.0166708,273.8532197,344.4520645,422.070102,443.0372587,465.9084169,516.7541645,541.2423941,645.2605401,763.0220825,806.9413315,775.9853418,797.5094673,792.7125854,824.0429736,842.9159893,846.2312741,899.0348879,917.1558139,1032.027602,1042.200501,1027.332615,1088.44311,1175.263767,1305.225301,1406.799587,1472.319109,1413.816086,1359.787215,1463.142009,1464.545884,1470.198997,1633.929044,1926.883543,2280.109835,2676.307757,3293.45746,3915.625837,3760.768261,4521.982563,5288.461865,5567.74639,5824.770745,5965.710751,5579.990996,5572.724644,6094.71936,6415.154649,6510.727455,6187.685697 +IDA & IBRD total,IBT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,179.2283801,179.0554103,181.651997,185.8342515,201.977565,217.4737918,227.8943411,252.3932669,312.6997738,386.7295584,411.9684944,431.4370421,474.8378059,500.0907089,593.0142155,700.0485418,777.2030403,740.7118501,737.0621374,723.8646886,747.832259,759.3980163,763.643646,804.9910598,815.3686489,911.4248159,916.8701103,899.9583895,942.340569,1005.658166,1115.949528,1200.17599,1251.028197,1201.807272,1157.387832,1243.783083,1239.904722,1247.167976,1381.571438,1622.42913,1910.307684,2238.249424,2735.233638,3240.511404,3106.858574,3710.556573,4312.473561,4529.499118,4737.412327,4848.12374,4536.057792,4505.604602,4889.873864,5127.061541,5192.129777,4929.861141 +IDA total,IDA,GDP per capita (current US$),NY.GDP.PCAP.CD,92.33921225,96.0359644,100.0200261,103.5965093,108.8869779,117.733575,124.0321782,125.4240071,127.4493901,138.4474125,158.9173403,154.0566727,156.3362431,173.3517421,230.4385888,272.1715974,278.0082078,291.2549918,319.3755846,366.6203932,429.3522375,632.1404309,576.7061799,469.3980667,423.8848464,420.2531004,405.2496911,415.390824,413.0064596,391.964413,412.0615272,403.3378633,383.959863,355.9536897,331.1598726,372.7156204,398.6762214,402.3653086,399.1721644,401.3149855,434.8293822,421.7640704,446.1174048,486.8054734,556.7177913,632.2807323,744.4574291,855.97075,997.4222714,963.3877966,1083.105991,1190.085798,1249.630901,1347.17426,1408.98015,1366.150172,1305.681497,1321.432163,1364.109464,1396.320349,1364.157834 +IDA blend,IDB,GDP per capita (current US$),NY.GDP.PCAP.CD,100.2035983,104.7303915,109.3208392,112.8825782,120.1383233,127.1845955,134.8410453,132.0593733,135.7916839,151.3080583,202.5487828,183.1824824,200.2661752,207.9368328,291.533702,325.8805109,381.8306115,390.5742169,411.7886675,490.1496827,609.7480816,1138.369675,1009.300842,743.0963177,618.8118822,603.3273088,528.0290891,524.5188241,523.079968,489.9764779,525.3198064,513.4535829,502.2985486,434.3877902,428.6304212,489.2974093,526.739276,524.3002409,505.433417,514.0340363,572.634095,558.0325467,602.0875493,660.2706614,786.0745034,918.4400482,1123.446,1265.745034,1464.181449,1340.413854,1534.962515,1721.445534,1859.740421,1972.596895,2068.872068,1949.18058,1773.844239,1712.650913,1747.506594,1758.035927,1664.260131 +Indonesia,IDN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,53.53729568,65.05304384,74.60209639,79.7145354,79.17818799,90.87617148,131.0249053,202.4954054,233.1166609,278.1966702,333.6943767,365.915842,356.9213392,491.5795329,566.5777185,583.6698355,512.9591111,525.3339435,516.9609902,474.8591605,442.2154853,481.781485,530.0032545,585.0765629,631.7828578,681.9383856,827.9052662,912.2032939,1026.393436,1137.410101,1063.712376,463.9481582,671.0986097,780.1902047,748.2576087,900.1775881,1065.64852,1150.261367,1263.287332,1589.801489,1860.002811,2166.854231,2261.247309,3122.362673,3643.047176,3694.35934,3623.927241,3491.637491,3331.695115,3562.816334,3837.578024,3893.859578,4135.201531,3869.588427 +IDA only,IDX,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,347.0333915,333.2856155,319.2601257,320.3959059,322.9908409,341.9044036,359.6686633,356.5143742,341.771862,353.5814262,346.5269676,322.3800445,316.3130041,280.9106224,312.4308837,332.3352756,339.3564809,344.6528981,343.2438893,363.6049798,351.3676995,365.5459242,397.3159159,438.537396,484.9296404,549.3382878,644.9020519,757.2005819,771.2534696,851.2809904,915.6179926,932.2370758,1022.148077,1065.282163,1063.642258,1065.845678,1124.557574,1172.228221,1217.408147,1219.775623 +Isle of Man,IMN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,12681.61415,14036.08285,15991.22782,18450.48294,20624.8028,20322.68202,21351.11694,24864.43629,29522.27779,35494.66734,37766.67954,42107.83723,54196.30173,70986.30133,65029.01975,69767.34336,77349.22524,79148.33225,83357.13268,92334.64631,85126.97047,82197.12677,83477.83429,89112.66772,, +India,IND,GDP per capita (current US$),NY.GDP.PCAP.CD,82.18860274,85.35430099,89.88175649,101.1264294,115.5374961,119.3189163,89.99730437,96.33913646,99.87596272,107.6223184,112.4344925,118.6032415,122.9818641,143.7786876,163.4781125,158.036171,161.0920922,186.2135042,205.6933833,224.0010188,266.5778508,270.4706009,274.1113337,291.2381101,276.6679583,296.43515,310.4659328,340.4168345,354.1492482,346.1128885,367.5566089,303.0556077,316.9539272,301.1590023,346.1029514,373.7664808,399.9500747,415.4937978,413.2989322,441.9987604,443.3141938,451.5729973,470.9867868,546.7266135,627.7742417,714.8610154,806.7532806,1028.334772,998.5223415,1101.960838,1357.563727,1458.104066,1443.882435,1449.610451,1573.885642,1605.605445,1732.554242,1980.66702,1996.915087,2100.751461,1900.70681 +Not classified,INX,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Ireland,IRL,GDP per capita (current US$),NY.GDP.PCAP.CD,685.6147124,739.2764064,797.0062884,852.1353017,965.1354227,1023.773726,1074.506506,1152.004952,1124.516636,1291.34992,1887.930321,2164.048248,2642.107003,3078.846458,3196.112279,3776.20278,3707.572862,4352.135905,5587.923752,6895.849301,8092.437902,7602.175919,7823.547029,7511.763109,7228.508762,7634.631721,10302.18386,12168.0295,13607.7996,14193.29327,17819.59523,17890.20612,19957.57653,18612.91349,20196.13556,24330.72213,26453.89235,28622.59902,30821.53025,26303.57435,26311.49712,28264.05202,32695.37099,41191.41422,47732.54759,50910.32995,54328.54428,61415.15484,61463.3481,52214.91098,48745.67899,51954.49662,48911.81974,51591.94256,55660.99748,62025.05779,63036.53657,70587.47618,79336.47135,80778.82961,83812.80297 +"Iran, Islamic Rep.",IRN,GDP per capita (current US$),NY.GDP.PCAP.CD,191.680825,196.9250906,203.4373696,208.132467,221.3212827,248.3411523,264.9796277,287.0829089,318.9919066,350.9135557,384.9440425,468.9569401,570.3506238,876.2681268,1453.734123,1581.930432,2017.411084,2315.893105,2168.869791,2427.465299,2441.440618,2499.988439,3008.145921,3583.329219,3568.655486,3805.581767,4244.690326,2617.70064,2318.464457,2197.956148,2214.327688,,,1067.307587,1185.686421,1569.255447,1932.805016,1804.336763,1723.835367,1756.896803,1670.009674,1909.412306,1911.678649,2253.935835,2756.198822,3246.051122,3774.358055,4904.666183,5717.314148,5709.948293,6599.660938,7781.405801,7927.844908,6018.3229,5585.525604,4904.327315,5253.42656,5520.314782,3598.483454,3114.622753,2282.551865 +Iraq,IRQ,GDP per capita (current US$),NY.GDP.PCAP.CD,,,254.7015332,250.7872054,288.1634817,,,,312.8221085,313.8171509,330.8853685,376.8917646,388.1112804,468.8540124,1018.095783,1151.818714,1471.149534,1591.882266,1847.668968,2852.111974,3850.264419,2693.156529,2936.59679,2747.965495,3090.004177,3112.995764,2970.786753,3492.276687,3773.375417,3873.619309,10356.90305,22.79534531,30.08638701,54.44157978,204.2723883,639.9231244,502.028676,968.5291421,932.3097306,1617.467894,2058.264401,1494.388802,1320.734706,854.8252808,1391.963489,1855.522348,2373.209448,3182.841351,4636.639325,3853.82862,4657.280269,6045.494567,6836.073995,7076.552265,6637.684375,4688.318017,4550.658638,5076.690795,5523.078375,5658.492267,4157.484495 +Iceland,ISL,GDP per capita (current US$),NY.GDP.PCAP.CD,1414.982269,1418.12587,1562.23073,1831.705656,2297.920643,2723.52095,3215.694178,3125.649492,2354.480027,2039.196295,2576.353444,3252.099178,4014.8266,5437.091172,7040.436646,6454.38712,7583.205921,9957.254436,11236.7012,12640.64081,14821.68546,15137.28877,13711.69892,11671.82472,11959.52416,12361.19686,16406.06448,22453.19769,24451.98261,22434.34984,25384.92564,26802.97039,27124.25856,23579.80153,24018.63862,26633.60487,27614.87334,27919.16715,31030.03902,32381.62822,32096.39404,28897.44295,32409.22793,39476.70685,47334.95313,56794.88022,57492.93425,69495.72674,56943.35966,41301.27943,43237.07295,47714.59223,45995.54788,49804.983,54576.74481,52951.68151,61987.92636,72010.14903,74348.36228,68883.14877,59260.88714 +Israel,ISR,GDP per capita (current US$),NY.GDP.PCAP.CD,1229.174748,1436.384439,1094.635848,1257.811405,1375.892256,1429.314605,1513.883606,1468.123862,1647.877274,1852.392539,,,,,,,,,,,,,,,,,,,,,,,,,,18166.84989,19400.98229,19701.60151,19462.19453,19162.95576,21101.00759,20353.86397,18479.60387,19033.61684,19933.71659,20585.63748,21854.83701,24939.56531,29599.60817,27719.5449,30694.16768,33610.69403,32510.3453,36316.47837,37738.41607,35813.71341,37282.53056,40474.66813,41704.82857,43588.71315,43610.52033 +Italy,ITA,GDP per capita (current US$),NY.GDP.PCAP.CD,804.4926233,887.3367446,990.2601522,1126.019337,1222.54454,1304.453817,1402.442354,1533.692877,1651.939377,1813.388126,1.088153097,1.190789558,1.379496601,1.655417302,1.870308834,2.121283756,2.083090838,2.377479738,2.897593451,3.61014566,4.367454237,3.936931069,3.902545357,4.045086003,3.997253391,4.126665128,5.843420815,7.352015979,8.131021587,8.462681482,10.75617841,11.33918865,12.0045452,9.677399594,9.987552555,10.67268337,11.92116849,11.27422197,11.52666088,22005.05454,20137.59122,20500.9544,22376.2979,27526.32246,31317.20079,32055.09208,33529.7266,37870.74751,40944.91242,37226.75719,36035.645,38649.63948,35051.52127,35560.08141,35565.72138,30242.38614,30960.73151,32406.72032,34625.58421,33566.7873,31676.2021 +Jamaica,JAM,GDP per capita (current US$),NY.GDP.PCAP.CD,429.2541461,453.0578344,463.8905134,485.2438081,518.8138716,553.4506858,615.9643437,636.6162749,593.6247238,644.0583348,748.9594581,808.6335546,968.9729309,968.9572294,1188.550667,1410.446,1443.053458,1561.735828,1255.731737,1137.027471,1238.839069,1356.016631,1474.235401,1593.184998,1029.136715,899.2567575,1168.097303,1383.761704,1602.195824,1832.758909,1897.690884,1683.331431,1436.567481,2189.528292,2173.165848,2596.010121,2889.781119,3250.895306,3368.194716,3375.674975,3392.123878,3437.655517,3609.203331,3480.511665,3733.798671,4103.601637,4331.292053,4623.747897,4928.126409,4335.178411,4704.04776,5111.465987,5209.859264,4989.734089,4834.284009,4907.927415,4843.7499,5070.099503,5359.993789,5369.498371,4664.52915 +Jordan,JOR,GDP per capita (current 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US$),NY.GDP.PCAP.CD,475.3190756,568.9077427,639.6407854,724.6937622,843.6168785,928.5188486,1068.55844,1239.318377,1451.337703,1684.659403,2056.122044,2272.077844,2967.041996,3974.746092,4353.824355,4674.445481,5197.622337,6335.286871,8820.693343,9103.564756,9463.352464,10360.1767,9575.608995,10421.2139,10978.91981,11576.69049,17113.25894,20748.99092,25059.01395,24822.77257,25371.46417,28915.01536,31414.97637,35681.96849,39200.49134,43428.95237,38436.94162,35021.72995,31902.76693,36026.55449,38532.04147,33846.45241,32289.3456,34808.40168,37688.87242,37217.64479,35433.98541,35275.21969,39339.29987,40855.1804,44507.67639,48167.99727,48603.47665,40454.44746,38109.41211,34524.46986,38761.81815,38386.51115,39159.42356,40113.06097, +Kazakhstan,KAZ,GDP per capita (current 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US$),NY.GDP.PCAP.CD,97.44550102,94.65129268,100.3944951,103.778721,108.2936884,104.7116739,118.2079104,120.9875906,128.397262,133.6653459,141.8804934,151.8299586,173.4947318,199.1142353,227.1059675,240.0831927,246.4868614,307.016432,348.8064444,394.6373811,442.5428212,401.6961835,362.6218506,324.3965017,323.3733809,308.6487027,351.0294899,372.7798687,377.1553527,361.1546923,361.3282651,332.4188107,324.1373782,220.0697184,265.2323057,325.7789291,421.3391971,445.8772551,465.909801,414.6793098,397.4826588,395.3294716,389.5426302,429.787819,451.6687666,511.6163943,685.9544229,825.666555,902.0699578,905.1316534,951.68778,971.6330365,1136.869893,1210.385435,1315.802225,1336.883265,1410.532025,1572.345734,1708.001428,1816.547124,1838.209968 +Kyrgyz Republic,KGZ,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,609.1728912,575.6439745,513.0359437,449.0657902,372.30781,364.2264974,394.8601215,376.4296133,345.1381316,258.0492288,279.6195693,308.4096116,321.7270332,380.5064324,433.2349766,476.55213,543.1107024,721.7686908,966.3936272,871.2243893,880.0377751,1123.883168,1177.974735,1282.437162,1279.769783,1121.082835,1120.666513,1242.769643,1308.139779,1374.031136,1173.611356 +Cambodia,KHM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,109.4811702,117.8234502,123.6459254,134.3042796,138.8429755,144.0117785,157.1901393,142.2652037,102.6789615,135.8487853,69.23329893,94.3585945,78.11203511,,,,,,,,,,,,,,,,,,,254.1166348,270.5429401,322.9315754,319.2863136,304.7647688,268.9903722,295.9031784,300.613679,321.1502236,338.9874773,362.3354822,408.5136388,474.1111921,539.7503289,631.5252576,745.609127,738.0547312,785.5026671,882.275614,950.880346,1013.420536,1093.495976,1162.904995,1269.591499,1385.260066,1512.126989,1643.121389,1512.727753 +Kiribati,KIR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,279.5213239,293.9385613,358.8162377,592.0806924,1576.124975,999.4160647,735.1242355,682.6769224,784.7191836,728.9361104,652.9201724,688.3379652,666.3502247,612.897464,657.3617116,502.0417322,489.8259877,499.802782,621.9999015,580.3853498,549.9010785,644.7808349,638.4896458,619.7200492,715.1950992,724.9218715,842.9911892,842.2392327,801.0843947,832.0247088,796.8032036,735.0691064,826.9168653,1015.667006,1131.152504,1214.635603,1168.473302,1374.395199,1428.134977,1312.025001,1516.76766,1734.902397,1788.690394,1715.813041,1642.822669,1542.616968,1584.737028,1640.57196,1698.329587,1655.050693,1670.824691 +St. Kitts and Nevis,KNA,GDP per capita (current US$),NY.GDP.PCAP.CD,241.5391631,243.8321218,246.1011784,253.9774464,268.7180512,276.2489041,299.2508569,353.3033311,314.6619539,348.1297634,363.2069165,441.1145778,517.7320528,546.1362039,711.0431909,753.6322943,681.8215291,1012.568184,1131.119908,1354.154947,1584.668393,1882.756998,2013.016222,2045.202676,2336.6914,2650.986469,3150.634904,3600.013356,4250.057652,4770.741897,5396.40485,5461.497752,5950.628813,6411.014695,7092.105802,7450.274145,7862.696469,8741.659658,8866.566371,9320.881307,9565.949909,10283.0328,10650.84515,10270.60416,10942.72823,11679.40971,13614.41512,14429.55024,15592.87088,15388.44344,15510.19915,16539.76901,16046.48694,16685.94759,18051.17903,18028.97343,18810.55938,19158.7448,19276.52127,19773.46237,17435.92743 +"Korea, Rep.",KOR,GDP per capita (current US$),NY.GDP.PCAP.CD,158.2493033,93.82864905,106.1485057,146.3143419,123.6034948,108.7221312,133.4748485,161.159931,198.431298,243.4224129,279.3049689,301.1765677,324.1962768,406.8988301,563.3559578,617.4560619,834.134208,1055.880041,1405.822374,1783.622313,1715.42946,1883.451279,1992.528225,2198.934472,2413.263925,2482.399956,2834.903848,3554.595206,4748.629608,5817.029181,6610.036508,7636.982429,8126.67039,8884.928319,10385.33617,12564.77813,13403.04959,12398.48003,8281.699982,10672.41793,12256.99357,11561.24837,13165.06574,14672.85747,16496.12009,19402.50263,21743.47745,24086.41044,21350.42798,19143.85161,23087.22564,25096.26388,25466.76052,27182.73431,29249.57522,28732.23108,29288.87044,31616.8434,33422.94421,31846.21823,31489.1227 +Kuwait,KWT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,4443.452338,4571.155742,4230.847778,4207.731536,4019.803916,3860.579007,4858.728915,5224.917155,5971.798916,13521.66733,11768.4683,12093.26837,12253.86654,12663.48935,19093.01649,20924.21201,17408.51822,14312.45856,13233.83966,13132.76511,12356.87866,9771.141271,11535.17454,10169.04084,11617.10646,8794.606046,5419.588163,,,,16932.08504,19358.37054,17748.84942,14166.16413,15435.13113,18440.37852,16587.24865,17846.37616,22148.37815,27011.654,35591.03713,42781.56475,45782.14857,55494.9301,37561.72593,38577.49828,48631.7834,51979.12069,49388.05338,44062.34091,29869.55275,27653.15762,29759.46744,33399.06048,32373.25111, +Latin America & Caribbean (excluding high income),LAC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,1895.762819,1902.18931,1827.860735,1861.542702,1839.722432,1895.54461,2099.824496,2249.228923,2596.773556,3161.359718,2894.980865,3289.77137,3700.704185,3850.674178,4104.235934,4424.814309,4387.231084,3866.596249,4229.831816,4071.347834,3575.739699,3590.960577,4076.412665,4888.421211,5666.300646,6640.291612,7682.708149,7122.83701,8773.191675,9878.568369,9843.211318,9963.788466,10096.42015,8515.465152,8202.393383,9054.988537,8705.66979,8526.199723,6993.817424 +Lao PDR,LAO,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,489.9577437,641.7401976,468.3042981,278.393228,148.9650659,172.523209,203.2560175,234.7643383,250.6044968,287.3948429,325.9416118,363.8800524,378.4286058,345.9218442,248.8390915,277.810213,325.1869371,326.9417867,320.0614597,362.8214135,417.9291685,475.6107962,590.6326063,710.3445761,900.3221829,948.6542409,1140.598657,1378.362016,1581.39614,1825.665672,1998.333108,2134.713062,2308.799808,2423.847598,2542.489769,2544.95255,2630.203774 +Lebanon,LBN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1234.239313,996.9046381,1012.648216,1605.371904,1899.65101,2446.527693,2820.486187,3321.297267,3791.607617,4305.641769,4669.585388,4640.384413,4491.641934,4422.389292,4579.459593,4576.387617,4630.790585,4575.107459,4626.853003,5207.792723,6111.327281,7354.953605,7761.641489,7675.30894,7952.151239,7933.233249,7687.930519,7644.545147,7626.300196,7792.59886,8012.539237,7583.699146,4891.00147 +Liberia,LBR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,306.8338642,306.7102516,306.4739396,243.0895775,286.0647253,294.8932201,336.1156743,396.6017613,478.3995401,470.9481214,513.4455667,596.8966353,657.9357791,721.9295456,721.1822986,710.3840166,714.6233922,698.7023587,677.3223191,621.8929536,583.2717273 +Libya,LBY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,6514.318567,7040.776294,7284.917153,6447.143282,5892.622467,5161.685428,5536.866066,5998.160438,5243.596535,6819.039576,7142.771786,6266.489821,3703.042952,4673.145576,5800.588826,8163.00937,9336.353498,11300.19323,14382.59529,10275.26154,12064.77291,5554.180053,13025.27932,10363.80425,6466.908237,4337.919139,4035.196802,5756.699325,7877.12461,7685.948132,3699.228621 +St. Lucia,LCA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,1445.924317,1628.035705,1515.314318,1607.533895,2023.246778,2253.096688,2646.985551,2869.727936,3221.481282,3584.044619,4199.636497,4383.566572,4755.104291,4775.489985,4919.380367,5194.782925,5300.354676,5340.086973,5733.826989,5947.891784,5950.047485,5642.748904,5646.456535,6150.921369,6591.645501,6949.204173,7669.21347,7969.797993,8456.694632,8137.750991,8540.07098,8983.743733,9086.387955,9378.985047,9843.308534,10093.61802,10361.58388,11047.44499,11357.82657,11611.09784,9276.117829 +Latin America & Caribbean,LCN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,1969.874231,1950.746596,1881.383031,1909.09837,1890.816937,1959.798659,2168.946398,2323.408685,2669.251529,3224.380298,2994.325794,3383.399559,3801.379634,3986.603661,4243.831249,4578.767261,4549.050245,4042.987953,4400.795823,4244.81803,3757.260576,3787.273901,4309.235193,5145.814246,5957.981915,6935.858748,7966.56108,7402.54097,9075.903791,10204.24345,10198.38858,10339.94815,10433.17121,8888.231171,8600.757385,9461.211347,9158.440964,8951.811517,7417.252167 +Least developed countries: UN classification,LDC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,265.9114061,270.0867786,258.4152667,243.200337,244.7724722,256.1625403,274.0171611,297.8279088,302.7571884,300.1011127,307.6272449,302.3053113,272.9507146,270.179137,242.8060602,272.13466,291.6445436,299.892744,299.5137187,298.4607421,326.8437625,307.7869054,330.1872048,360.4763093,402.3233119,461.4052956,526.9725585,623.5391389,747.220291,742.1112231,826.4997561,906.4127026,932.5788117,997.6942912,1052.687815,1011.525345,986.9248236,1053.963522,1065.291106,1094.8944,1068.79814 +Low income,LIC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,349.6204688,341.253691,361.4351922,377.9224512,363.847592,345.7441456,353.8903207,345.1623411,302.292935,298.8722233,242.63157,265.8613386,273.1580552,283.6319296,276.7790254,270.9475165,324.8814922,295.0154087,309.6293132,332.5916608,373.998511,425.3783356,484.7757999,575.35693,679.5375025,680.7342243,745.925573,773.3160191,777.7348732,828.5573585,874.3229915,851.3552364,785.6338741,790.858529,795.9531592,820.8961823,821.8629308 +Liechtenstein,LIE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,4236.33302,4827.126337,5637.919093,7355.080307,8439.578876,10513.20529,11385.6967,12412.42797,17499.12595,19761.24848,20660.0176,19507.36553,19677.01849,19560.81037,18596.17269,19390.12664,28287.06328,37824.44216,41292.25772,39368.72709,49373.61027,50886.37531,55197.54701,55794.32733,64051.23221,78631.69911,79863.27908,72208.93467,76757.30022,81307.06837,74853.93873,74287.41337,79345.75247,89859.86537,100289.243,105399.2605,114374.2465,130655.637,143264.0594,126096.6104,141192.5347,158130.4575,149010.2249,173030.2083,178864.8519,167313.2663,165642.3863,171253.9643,180366.7152,, +Sri Lanka,LKA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,138.536654,117.0719883,120.714348,152.8526755,153.8987913,159.5059914,150.8966192,160.9131876,183.9275304,185.8597435,196.4228877,216.9950075,264.7463142,275.6277195,256.2868068,287.5616366,188.0584813,227.5097653,267.6685772,289.1308598,307.6382435,328.6506383,378.936857,369.581896,390.3507114,401.4770959,413.4550704,408.4508669,463.6187388,513.2584475,547.0545344,576.7798839,647.5791564,714.2332315,756.6569905,817.0644817,850.8116039,838.8834628,869.696285,832.8035723,867.4914868,982.195697,1065.784437,1248.698185,1435.816813,1630.388906,2037.322103,2090.401826,2799.648738,3200.861132,3350.521876,3610.289363,3819.25353,3843.780672,3886.291502,4076.747003,4058.803511,3851.56099,3682.038425 +Lower middle income,LMC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,92.62777351,100.715395,110.8407685,117.0583635,105.1026016,109.7017345,115.3523417,125.3263208,135.7499736,139.710851,148.5941175,178.623907,232.6217376,252.3149711,274.1715376,308.0470692,331.7267615,372.2535913,441.1301839,517.8179213,519.986302,501.5089058,483.2794585,502.5527656,521.7720249,498.9352899,496.8112774,485.3870992,510.8370464,470.3429626,488.2955413,482.1794685,509.5381634,566.972738,618.7428038,620.8641357,558.8140589,592.9282015,612.6646325,615.6781649,648.5027178,732.6005767,835.0621169,955.2579703,1115.995981,1350.95694,1503.931528,1504.962194,1801.825852,2020.207967,2087.322877,2093.378612,2145.495731,2074.977957,2134.936109,2266.935303,2264.39294,2352.783694,2200.401758 +Low & middle income,LMY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,137.5538048,147.7659714,162.7513355,175.591062,174.9096176,177.765462,181.9862577,197.4256522,212.625433,222.3143134,246.9916017,304.3681907,378.42762,408.3041682,426.5961479,467.695865,492.9046412,581.8610409,683.1956392,757.0141517,725.2579026,725.4725458,713.3778031,737.4902588,749.3647015,752.2378126,792.4192688,801.5499947,895.2064244,893.4852463,871.8289297,913.4027639,973.120945,1073.813077,1152.707025,1202.738651,1150.901197,1110.294944,1197.567458,1192.064316,1199.578557,1329.528342,1559.466988,1834.838489,2152.706926,2632.85285,3118.882541,3007.070668,3599.195315,4190.19908,4413.675302,4617.698319,4730.574358,4435.358223,4406.185159,4779.975791,5006.095615,5075.833988,4819.438606 +Lesotho,LSO,GDP per capita (current US$),NY.GDP.PCAP.CD,41.30036454,41.85674859,48.16203156,53.08783393,57.47005138,59.50177373,60.20278873,61.58381798,62.46399613,65.57904294,66.80592968,72.65886604,75.12364839,109.876874,133.4173662,128.8431755,123.7134401,157.3488151,210.6837023,222.7291009,321.9987319,315.1295639,246.4011487,266.1885605,223.6435731,176.0181377,204.1784867,252.3027525,288.4066208,297.2612441,350.0587845,404.1982497,466.2747021,458.5533069,471.8420449,527.6998379,489.1310607,507.0978889,464.804531,451.9269342,436.4881373,405.6057121,382.1896086,573.6517781,753.3759044,842.812631,904.6061298,846.5423208,889.1342855,874.7298605,1119.843641,1287.269536,1229.636232,1166.911756,1194.575627,1146.064688,1018.931093,1102.94441,1192.48432,1113.37214,861.0145481 +Late-demographic dividend,LTE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,747.9431773,784.2727903,794.5361535,841.8239022,886.2742949,921.7886422,1000.650151,1044.789691,1100.933369,1197.614868,1108.979683,1138.557794,1282.416075,1550.143799,1693.519543,1757.505209,1676.089409,1522.870373,1662.119598,1705.163602,1793.104822,2026.338046,2420.140713,2905.71507,3487.678209,4368.649054,5401.953424,5191.681054,6189.740881,7508.409149,7983.938847,8553.671946,8819.293767,8219.917354,8190.668154,9034.761906,9849.980323,9989.873379,9758.448234 +Lithuania,LTU,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,534.6318456,674.0833204,819.7192863,917.1457945,901.6544853,953.8017326,1021.157981,1199.239521,1592.765818,1940.271748,2276.275739,2672.728861,3557.521008,4332.452812,3423.410731,3472.736045,4168.366038,4162.726055,4554.745954,4797.543023,14263.96458,15008.31324,16885.40739,19176.17652,19555.21107,19997.59253 +Luxembourg,LUX,GDP per capita (current US$),NY.GDP.PCAP.CD,2242.015817,2222.366366,2311.798849,2441.038555,2755.633117,2780.092719,2900.433218,2909.660629,3175.367369,3658.900683,110.3041238,113.3967858,140.807669,191.1187428,230.1189526,223.2969562,243.5620726,269.1071383,334.5174014,390.1965414,424.2516223,355.100935,323.1131484,317.5517919,311.2102646,320.3178046,465.7885327,575.9774474,647.2351043,683.1046717,858.8218395,917.3763184,1015.558475,1028.20935,1127.514331,1309.669518,1303.197285,1166.134084,1179.562101,51671.85031,48857.30234,48221.14409,53182.9425,65590.39658,75855.58783,80319.6435,89814.89762,106153.2694,114760.7061,103607.7888,105067.7183,115913.1928,106742.9078,113657.436,118981.9066,101417.2743,104349.227,107627.151,116654.2611,114685.1684,115873.6026 +Latvia,LVA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3094.398641,3456.252198,3814.352366,4226.979018,4479.209969,4777.571466,5084.096071,5878.971649,7306.628397,9054.965449,10778.25433,13801.64711,20050.86031,23365.17759,17486.1854,16197.29315,19771.03512,19815.40701,21514.9343,15734.46544,13780.14598,14325.51757,15682.22145,17858.27998,17794.47862,17619.95239 +"Macao SAR, China",MAC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,4549.016837,4342.342034,4796.93299,4804.682041,5183.06018,6352.522656,7135.115518,8130.402595,9442.486665,10647.57154,13564.12522,15304.28709,16704.89883,18277.0425,18244.35896,18107.65985,16595.29338,15649.65887,15835.99101,15665.29797,16424.93211,17921.10457,22568.31017,25183.13268,30121.56043,36550.50516,40810.82804,41010.06826,52473.19099,66891.28197,76572.15179,89260.7571,93022.87514,74839.33204,73569.43369,81044.88998,87554.71386,86117.65543, +St. Martin (French part),MAF,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Morocco,MAR,GDP per capita (current US$),NY.GDP.PCAP.CD,164.8008324,158.9235801,181.1826409,196.5312779,201.261983,206.449018,196.3144515,202.8572717,212.7508556,232.1259133,246.0156622,265.2076642,302.5644904,364.6361067,439.0123592,502.756524,524.1791237,590.2322509,690.1708003,809.627607,1078.83991,861.7979584,836.4350626,750.0216074,668.385423,660.9467358,840.0116864,920.5913726,1066.330652,1071.177518,1206.011886,1266.848894,1299.421278,1199.319104,1326.941452,1432.309836,1561.129611,1396.769667,1472.385983,1447.969193,1334.943464,1339.294077,1416.488396,1725.457466,1952.902534,2018.025547,2196.011276,2499.259938,2890.360715,2866.92411,2839.92612,3046.947854,2912.658275,3121.680081,3171.699192,2875.257985,2896.722196,3035.454433,3226.982794,3230.409722,3009.24946 +Monaco,MCO,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,12478.13122,13812.71818,16735.01323,21417.58295,22712.94336,28247.54967,28810.87301,31430.43382,38349.39576,45845.10983,51515.06413,44369.56135,41371.87681,38893.30323,36382.97346,37552.05564,52158.67774,63077.77457,68441.25161,68583.60405,84303.87843,83721.99142,91670.60401,85397.32656,89378.03979,101866.6041,101212.0026,90833.29746,92999.25345,91283.25659,82365.49236,83724.46591,90506.85951,108656.8026,123523.4932,124197.2754,134048.6219,169966.3069,185785.7259,154775.3808,150737.8925,169016.1961,157520.2194,177673.7454,189432.37,165989.5051,170028.6557,167517.0597,185978.6093,190512.7374, +Moldova,MDA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,593.7661383,575.319576,657.4911903,578.9097663,399.6207703,440.6720307,507.5576584,570.9726454,682.5255088,897.4499588,1034.707058,1183.379211,1531.685476,2111.201495,1899.010117,2437.529978,2942.255949,3045.739874,3322.038436,3328.801449,2732.457113,2880.439281,3509.69345,4230.363037,4494.018403,4551.130676 +Madagascar,MDG,GDP per capita (current US$),NY.GDP.PCAP.CD,131.9931654,133.8477609,138.120004,138.4071657,142.6593399,144.4847301,152.1078972,157.4763093,165.4783754,165.0115988,169.0705601,177.4983312,193.1285826,231.4359587,261.0355901,302.1492277,280.6789305,294.9342463,324.3846534,408.9371863,596.7749793,530.504306,518.1834912,493.0889593,399.3447291,377.8566642,420.0097235,301.7444677,291.2108877,281.8117066,338.9477127,272.5249191,301.9969866,320.5639901,269.5607119,284.8227287,354.7413311,297.1150598,297.2536718,279.9212866,293.6071574,334.4416177,319.2163788,368.7972369,284.4877263,319.5374751,338.7511983,438.6555158,536.351379,467.5398004,471.959197,531.2654985,518.1529543,541.0660586,530.8611206,467.2355537,475.9556084,515.2933093,518.401123,526.2245722,495.4904009 +Maldives,MDV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,268.2966315,273.3560224,282.226235,328.1029601,598.8774616,670.8606604,723.0238044,695.8240429,803.7222438,875.4440986,963.8374667,1064.09463,1205.621336,1328.656047,1431.657896,1569.932617,1737.733635,1926.286035,2011.944337,2154.268226,2234.595861,3039.302776,3049.207981,3476.006273,3941.431136,3640.012132,4809.93374,5574.401981,6614.23618,6636.543871,7076.739821,7291.465967,7265.723399,7928.476443,8499.307148,9033.459724,9209.399665,9577.702376,10276.77339,10626.43335,7455.855728 +Middle East & North Africa,MEA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,346.2813134,374.7659435,410.7817631,473.6500598,558.8629791,734.4361445,1331.520355,1433.459392,1758.249737,1981.42014,2054.302291,2606.121723,3269.840158,3269.640026,3158.480146,3093.28563,3049.021257,3012.393828,2960.076591,2592.289437,2454.471565,2465.278426,3146.160136,2110.136272,2246.920533,2212.128839,2236.375319,2453.31778,2706.482802,2785.733791,2657.629738,2840.441077,3132.384939,3043.963252,2960.895125,3229.65736,3739.504748,4411.117865,5057.124554,5869.280745,7181.743845,6278.622933,7172.938023,8326.334924,8897.740335,8656.287655,8527.81667,7357.406221,7257.019433,7403.30469,7546.701353,7511.823088,6669.90343 +Mexico,MEX,GDP per capita (current US$),NY.GDP.PCAP.CD,,,378.1534954,409.0455701,469.4761369,494.9702613,534.2736315,565.6334089,606.2111327,650.3117272,689.7949287,738.5584763,826.4773502,981.4589828,1242.090043,1476.313217,1453.670672,1301.324622,1589.27364,2034.988669,3027.375274,3803.030926,2597.982903,2147.719559,2478.210836,2569.239714,1733.912993,1862.886204,2247.981386,2687.914845,3112.268595,3661.947965,4170.623383,5650.026286,5854.418117,3928.223711,4412.116298,5289.167653,5481.181954,6157.193044,7157.8145,7544.568723,7593.137345,7075.369643,7484.486399,8277.671564,9068.294218,9642.680517,10016.57121,8002.972178,9271.398396,10203.4213,10241.72783,10725.18359,10928.91601,9616.645558,8744.515559,9287.849587,9686.514244,9946.033829,8346.702379 +Marshall Islands,MHL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,972.9322837,1047.644764,1196.898025,1234.65704,1144.739245,1392.345568,1493.620755,1603.193323,1588.539507,1660.340633,1704.584427,1849.295317,1996.086538,2152.208547,2382.962699,2194.208578,2194.232256,2229.712447,2267.569122,2272.678016,2389.060707,2515.624045,2457.65454,2431.166018,2471.352046,2537.717206,2646.873996,2704.463554,2662.334015,2846.065542,3046.290071,3181.6247,3246.63025,3185.261354,3199.886846,3490.998389,3672.576783,3793.550983,4073.10983, +Middle income,MIC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,141.40517,151.9944666,167.7402873,180.9744276,180.0352419,182.7154673,187.2810699,203.2700686,219.1622863,228.9722178,254.7631229,314.7924479,391.5967139,421.2455228,439.8042917,482.2484683,508.3285214,601.5674396,708.4451573,786.1735259,752.4687446,753.1564947,740.5016906,767.0495887,778.5690679,780.6625011,824.8911918,835.954116,935.8588359,935.0610123,915.240922,960.7233832,1029.28817,1136.792707,1222.053899,1276.139793,1221.69955,1179.277941,1270.770459,1268.153003,1276.480729,1417.024735,1665.126496,1962.464345,2306.190677,2825.277068,3350.992145,3232.697918,3878.920426,4528.294188,4777.978851,5003.164883,5129.100928,4811.877098,4790.88586,5208.923451,5465.316026,5548.164668,5272.149232 +North Macedonia,MKD,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2354.275256,2477.683038,1225.373149,1352.028452,1794.70084,2359.792009,2338.070661,1964.89272,1866.854729,1908.593544,1854.536271,1815.840915,1949.682449,2409.789359,2762.593686,3037.35452,3326.438511,4037.122452,4794.334682,4546.152171,4545.392131,5063.885906,4697.196689,5210.153044,5468.126225,4843.492387,5132.997146,5432.414531,6087.468311,6022.24099,5888.004015 +Mali,MLI,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,48.27992471,59.46751727,57.99175065,60.4756805,65.38456414,79.15741066,90.10013606,84.59767549,128.1509529,142.3780078,156.4059494,178.9908135,229.3458141,248.1890037,212.7202013,180.5134123,171.9996571,160.2280599,177.759939,232.892667,259.3120456,265.4389164,262.9677236,317.3892318,315.4562807,319.8380297,310.1383539,223.0177124,282.3410489,282.6328859,267.1892911,281.8725493,323.28717,270.5429979,307.7058258,336.4171873,393.406773,440.9584495,489.0228209,523.0430083,597.4798388,697.0878685,701.7120403,710.2742494,837.605821,778.6252695,805.0339796,848.279043,751.4728866,780.7235695,830.0215153,894.8047765,879.1227752,858.9158055 +Malta,MLT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,828.4216803,874.0663356,975.7588009,1143.620203,1245.361225,1560.112154,1726.559448,2037.897337,2558.740255,3195.552586,3948.403126,3898.243037,3788.050632,3527.04,3332.885357,3322.421283,4194.653939,5083.669139,5814.364771,6040.609862,7191.923603,7558.277383,8220.245518,7296.310144,8000.518005,9857.480771,10062.73358,9909.894481,10409.30845,10633.60338,10432.32812,10402.23337,11289.88984,13669.49712,15197.05671,15888.17232,16723.88418,19485.87119,22205.35681,21083.27715,21799.17426,23155.55479,22527.63676,24771.07658,26754.26845,24921.60368,25741.44606,28250.69777,30672.29224,30186.19596,27884.64283 +Myanmar,MMR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,165.4532771,99.74491959,90.53242894,159.9572784,186.5400678,182.3844099,216.0026033,295.6383586,512.5800861,637.5217394,840.0744393,1018.13173,1061.923411,1035.80424,1121.942049,1140.76652,1144.474341,1158.434566,1257.803576,1279.089756,1477.355368,1400.218242 +Middle East & North Africa (excluding high income),MNA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1129.275416,1142.886215,1308.858912,1469.834289,1518.843268,1511.650133,1625.215986,1690.824934,1666.677149,1576.046051,1696.736935,1981.139393,2293.141407,2618.367619,3179.519799,3870.830995,3670.95687,4161.671846,4624.841887,5014.187299,4609.466436,4453.932158,3924.979736,3919.466963,3801.05245,3501.870767,3507.507575,3066.225355 +Montenegro,MNE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1627.072633,1909.598702,2106.635068,2789.159065,3380.165121,3674.617924,4425.678873,5976.394145,7367.751909,6727.107767,6688.4824,7328.932252,6586.721279,7189.295425,7388.147794,6517.163752,7033.604899,7803.411137,8850.094631,8910.651554,7686.092949 +Mongolia,MNG,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,1332.640563,1435.767097,1495.017389,1121.664763,1137.685618,1465.445436,1485.533781,1533.448708,1670.695211,1172.446287,1072.635835,587.3032317,339.5205869,405.9755499,631.920915,580.9100918,505.5923093,477.3341262,444.9945833,474.2170943,524.0540084,571.594979,646.192471,797.9777263,998.829388,1334.212724,1632.727582,2136.562402,1714.361827,2643.287083,3757.565415,4351.888461,4366.089537,4158.521471,3918.587015,3660.151944,3669.406934,4134.979372,4339.844625,4007.308885 +Northern Mariana Islands,MNP,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,21981.78456,21379.02474,21139.06359,18763.15278,17714.63336,16988.13728,17191.1902,14669.52061,14804.24673,13496.75078,13736.97197,14091.19118,15044.9359,16314.38355,21891.18479,27584.74351,22886.67405,20659.64029, +Mozambique,MOZ,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,272.6139075,191.2178344,189.9352925,187.0846016,187.2938682,241.6473879,283.5142141,313.0661607,346.5754492,319.3596208,296.2684069,302.5452337,326.0751909,383.2702219,416.8098242,435.3340633,482.1985636,563.6496343,520.4026316,471.9043433,594.5861471,657.6446765,664.0775104,673.969212,589.85905,428.9266729,461.4150941,503.3179252,503.5706566,448.6074536 +Mauritania,MRT,GDP per capita (current US$),NY.GDP.PCAP.CD,,181.8360961,182.1818927,181.0904764,234.6333581,258.9904796,262.3122678,269.839802,288.4418017,265.1650445,269.8044919,283.9813252,321.7148148,393.28245,474.5192062,528.6200399,565.5770128,566.2303052,553.7607512,636.223424,680.1861099,696.81573,678.7087912,692.7873417,620.7608293,567.2497793,648.5451421,715.3403265,732.97351,731.9468971,740.7361713,1021.923446,1010.103031,840.2632166,862.2742228,904.0907235,898.5131619,851.4255276,814.1703353,775.2720141,676.5690224,646.1151155,639.6675829,717.8998676,803.7779156,970.8423607,1259.541505,1357.124285,1579.511548,1389.338533,1610.921603,1879.772472,1815.218939,1892.094,1677.109019,1524.071753,1536.854888,1577.86437,1601.291932,1679.442605,1672.923535 +Mauritius,MUS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,776.6443341,893.9149524,1087.698161,1275.035826,1171.576087,1165.15842,1086.535025,1088.436401,1027.993975,1054.474835,1422.556552,1815.351405,2046.047998,2075.538168,2506.179312,2669.3277,2973.206977,2973.797821,3197.331024,3599.555202,3899.435192,3646.630626,3593.234081,3695.934909,3929.075495,3856.625227,4018.948897,4793.718179,5388.065784,5282.906022,5695.969327,6574.654338,8030.063005,7318.12641,8000.376432,9197.042991,9291.236379,9637.015729,10153.93269,9260.447303,9681.623107,10484.90583,11208.3273,11097.58693,8622.675515 +Malawi,MWI,GDP per capita (current US$),NY.GDP.PCAP.CD,44.52394299,46.58158517,47.69838391,48.50542835,48.29328535,55.49822441,61.4076384,62.01992808,54.91629873,58.00942919,61.76552389,75.66369202,81.89135592,87.22748953,104.7971848,113.8799734,120.8726248,141.0103755,160.9387662,174.186763,198.0111239,193.0150259,179.7299592,181.5376428,173.4495492,155.6556983,154.3953318,145.3229066,159.7747397,175.2194003,199.9863636,229.5273955,185.7858674,213.2405252,121.2640654,141.9543474,227.5849064,259.4504941,165.8952835,163.6142015,156.3858168,150.1489426,298.4334104,267.3990119,282.5671076,289.5552148,308.1636182,332.2590946,387.6057212,438.2118568,478.6686885,534.9510502,391.5617052,348.4298737,371.2695217,380.5969877,315.7779871,497.3104097,535.3513457,583.1102036,625.2941292 +Malaysia,MYS,GDP per capita (current US$),NY.GDP.PCAP.CD,234.9388974,225.9335935,230.2605937,279.7175405,289.0092278,310.3279258,321.0941605,317.4242105,323.4024867,347.3730942,357.6567994,383.6714717,445.3501578,661.022691,799.9319789,764.5663786,886.2300432,1027.292911,1246.558309,1576.048806,1774.740297,1769.099402,1852.256484,2047.770288,2234.257204,2000.148286,1728.684805,1947.808238,2072.077686,2216.250446,2441.741991,2653.526169,3113.645677,3433.162787,3728.110481,4329.707998,4798.61177,4637.865661,3263.334883,3492.670125,4043.662923,3913.429386,4165.726104,4461.847325,4952.213958,5587.02478,6209.126167,7243.456801,8474.587762,7292.494973,9040.568495,10399.37021,10817.43171,10970.1043,11319.06194,9955.242722,9817.78709,10259.30481,11377.6542,11414.20269,10401.79401 +North America,NAC,GDP per capita (current US$),NY.GDP.PCAP.CD,2939.448335,2991.592897,3155.097538,3283.141453,3480.426945,3729.969704,4044.247215,4231.867351,4579.98226,4912.487595,5129.345096,5504.811305,5997.110711,6640.81151,7206.804654,7773.113798,8613.004439,9400.513452,10424.56082,11515.43965,12437.75151,13816.08018,14242.81092,15336.61762,16803.72597,17833.06526,18619.69282,19672.48052,21172.75211,22644.7051,23645.66272,24086.16747,24967.6669,25765.56034,26925.3551,27891.31983,29105.28751,30518.0427,31691.91037,33319.26645,35155.32439,35830.97616,36673.14553,38400.99029,40776.74466,43361.61296,45738.2797,47660.77086,48228.7513,46501.25253,48387.21093,50123.85358,51717.78701,53068.03255,54648.01934,55542.96607,56451.5175,58605.34429,61381.63415,63341.78364,61453.13406 +Namibia,NAM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,2289.915294,2081.391107,1917.234383,2030.180243,1678.791909,1342.253184,1459.021134,1787.467087,1866.798071,1829.07175,1947.062911,2029.861558,2260.810348,2090.69179,2303.330555,2443.995849,2398.257444,2446.928577,2236.671499,2193.223615,2185.604112,1950.648536,1808.884901,2621.700392,3464.418222,3739.531718,4059.101349,4405.410397,4212.367195,4295.387144,5394.996711,5806.748245,5942.29274,5392.09494,5469.901401,4896.61526,4546.986687,5367.114673,5587.985613,5037.342629,4211.050929 +New Caledonia,NCL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,1763.475067,1756.219653,1865.665999,2165.901146,2529.89264,3203.711446,3446.952794,4031.016039,4220.1935,4865.650375,6163.380119,5957.541117,6158.946739,6152.782529,7561.192276,8443.107052,6817.832529,6208.782634,5540.234973,5249.0536,5534.631413,7634.3344,9271.735404,12665.66323,13092.26473,14800.02869,15133.16224,16261.29693,16640.80235,16037.02524,18721.05644,18257.21505,16341.58735,15387.86958,14611.8328,12579.59511,,,,,,,,,,,,,,,,,,,, +Niger,NER,GDP per capita (current US$),NY.GDP.PCAP.CD,132.651771,139.3403234,148.1891599,158.7160645,153.259717,172.039679,174.354913,160.5918309,150.3812123,142.6991849,144.0850939,149.5420414,155.7769905,193.0550554,203.5664959,202.257101,199.5315864,235.1886384,313.8994464,362.4959638,418.8552657,352.2055202,318.0582306,276.1827999,217.469236,208.2971058,267.4712431,304.6835764,302.0968122,280.1412999,437.589995,396.4169918,395.2751628,344.5340063,211.3864925,242.6203967,244.813779,225.0220766,250.6574617,232.1873864,197.8326834,208.3770599,228.2359086,268.349893,286.4900658,321.7236612,336.2819503,390.2844802,478.502559,464.057999,476.8695322,512.595335,529.7444581,552.569139,564.5967488,484.2386326,498.1053379,517.9770442,572.4301385,553.8949553,565.0613331 +Nigeria,NGA,GDP per capita (current US$),NY.GDP.PCAP.CD,92.9604656,96.97902998,104.3870285,107.5405726,113.1687166,117.1886348,124.3077925,99.40607876,97.20174269,121.245373,224.1044847,160.2487361,209.2260448,252.2326939,402.8499123,438.3313022,556.7021704,536.2161789,527.3112806,662.2639609,874.402071,2180.197621,1843.909353,1222.629304,902.2158499,882.520001,639.0131233,598.2648621,549.237433,474.2320409,567.528649,502.9141389,477.1776238,270.2239667,321.3206741,408.1810448,461.5196196,479.9837641,469.4305487,497.8415739,567.9307221,590.381815,741.7474939,795.3862287,1007.874342,1268.383462,1656.424794,1883.461388,2242.8719,1891.335297,2280.437337,2487.598017,2723.822191,2961.549422,3098.985791,2687.480056,2176.002772,1968.565398,2027.778549,2229.858652,2097.092473 +Nicaragua,NIC,GDP per capita (current US$),NY.GDP.PCAP.CD,126.248168,131.4506526,140.5573456,150.5036589,170.434841,273.906703,284.55869,299.0809355,307.2917569,320.4285036,322.7002946,333.114264,344.2533977,414.4358419,558.8632852,566.6221647,638.3048137,750.2502313,695.9796442,481.7590681,670.4437172,728.6269776,713.479925,772.835232,852.466606,718.6849973,754.8025687,985.0477809,658.4620614,248.149686,241.8764121,348.8646053,410.7673844,393.6035457,847.3736884,890.0054491,908.6338479,909.3364921,943.9228567,972.8867935,1007.499839,1034.551387,1000.936715,1005.733044,1080.269119,1162.289685,1226.633987,1327.957086,1499.266728,1444.36951,1503.872231,1655.818055,1760.467407,1811.636803,1934.062922,2049.851666,2107.57406,2159.161926,2014.570287,1926.699694,1905.256756 +Netherlands,NLD,GDP per capita (current US$),NY.GDP.PCAP.CD,1068.784587,1159.392357,1240.677894,1328.036649,1541.947365,1708.096356,1835.801424,1991.360686,2185.248659,2642.956118,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,28272.64325,26214.49855,26896.54811,29343.245,35750.97466,40436.61823,41994.71353,44900.93814,51799.20855,57879.94376,52722.21306,50999.74512,54230.3129,50070.1416,52198.89756,52900.53742,45193.40322,46039.10593,48675.22234,53044.53244,52295.03905,52304.05739 +Norway,NOR,GDP per capita (current US$),NY.GDP.PCAP.CD,1441.75566,1560.324931,1667.24743,1775.582655,1937.884614,2164.468823,2317.194884,2514.043772,2662.117492,2875.235844,3306.237988,3736.345841,4413.54275,5689.555915,6811.517091,8204.386111,8927.174361,10266.12067,11462.75092,13046.6231,15772.16108,15512.48418,15224.81528,14927.58567,14989.56229,15753.49169,18883.1594,22506.06473,24207.34338,24281.09614,28242.75575,28596.74553,30523.98096,27963.55025,29315.90768,34875.94289,37321.92598,36629.02659,34788.37138,36371.18308,38131.2801,38542.91156,43084.58669,50134.77276,57603.55117,66810.47852,74147.93469,85140.44461,96944.09561,79977.27313,87693.79007,100600.5624,101524.1419,102913.4508,97019.18275,74355.51586,70460.56053,75496.75406,82267.80932,75826.08188,67294.47698 +Nepal,NPL,GDP per capita (current US$),NY.GDP.PCAP.CD,50.30493772,51.81124873,55.02568891,46.86145326,46.00340388,67.00747097,81.16442237,73.96790076,66.54894009,66.63834579,71.71859114,71.6298781,81.386573,75.63696074,92.75222233,117.4177468,105.8928233,98.54200742,111.8066138,126.1333096,129.5860224,148.0802633,152.2896698,151.9925535,156.6505397,155.4078184,165.3618292,167.7999446,193.499648,191.1208533,191.8788839,202.0805891,170.5866512,178.6259878,193.2795509,203.9807806,204.6857552,217.7879819,210.6115609,214.1067032,229.4903926,246.7255581,244.7208435,252.4023518,286.1576726,315.80563,346.9453313,391.3800572,470.455459,478.1732528,592.4012071,799.5833769,804.1416073,823.3598746,844.8531248,901.7496077,899.523581,1048.453755,1178.525932,1194.956876,1155.142854 +Nauru,NRU,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,4752.175082,6560.27487,9562.667866,9648.495655,10171.48073,8365.07289,9555.79043,10360.77731,11614.66508,10983.22465, +New Zealand,NZL,GDP per capita (current US$),NY.GDP.PCAP.CD,2312.949992,2343.292213,2448.628633,2622.220271,2813.546976,2151.294927,2191.312542,2188.399139,1885.156152,2077.895543,,2772.717137,3294.604756,4322.664839,4610.491768,4171.532249,4373.394375,4950.350085,5936.644009,6667.832543,7466.591584,7813.936341,7655.964597,7598.124787,6713.638295,7600.409737,9427.163963,12330.75145,13759.03746,13311.68968,13662.58615,12229.93009,11793.01384,13094.18251,15279.93963,17399.92902,18793.53422,17473.65462,14738.27103,15321.67598,13641.01786,13882.86651,16873.56988,21912.7269,25420.33729,27240.2972,26944.32595,33538.66175,28205.87404,30442.17791,34215.7937,39168.28478,40170.14298,42994.23164,43366.50805,37463.77328,40818.08621,43225.80257,42621.39877,41999.36829,41791.79309 +OECD members,OED,GDP per capita (current US$),NY.GDP.PCAP.CD,1293.092798,1349.302739,1437.229277,1528.040297,1647.633905,1765.211938,1913.819878,2030.39748,2177.47941,2368.278979,2120.137843,2305.806013,2613.300406,3059.494738,3372.125874,3689.77788,4017.545791,4481.197818,5303.255102,5961.713439,6531.660044,6924.257961,6796.632611,7075.419616,7494.934553,7873.574451,9118.619963,10275.91867,11562.72111,12068.3485,13023.59793,13746.44002,14552.72785,15148.63564,16126.04898,17435.72025,17386.3499,17349.62515,17317.55468,22508.71311,22852.59174,22576.82527,23391.72228,25960.46203,28680.95235,30106.99103,31535.88103,34252.70479,36027.59889,33517.97455,34900.42906,37358.34999,37092.25632,37356.99165,37892.19914,35551.4431,35998.8356,37352.37241,39271.11682,39412.09959,37975.91377 +Oman,OMN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,101.2591791,105.5566874,162.3238308,277.909232,342.4750102,354.0821008,401.9004645,472.5203841,598.5661842,1954.883148,2377.10755,2760.541488,2803.394083,2653.281709,3419.265724,5181.826219,5947.280838,5855.822404,5828.101618,6164.192659,6677.398407,4691.209375,4822.101942,4997.39045,5382.732526,6448.13836,5988.866834,6278.652491,6029.183803,6013.200263,6261.764469,6830.639424,7039.621237,6215.715369,6915.370949,8601.271932,8475.968623,8626.965289,9066.312383,10126.13779,12377.08003,14420.51202,15838.46957,22139.74068,16823.79495,18712.57015,20876.58504,21872.61054,20865.78787,20035.21731,16033.46383,14609.98176,15127.88819,16521.20623,15343.0435, +Other small states,OSS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,267.2766439,310.9220454,368.6869919,523.3623053,945.2863494,1033.49937,1258.464939,1387.89811,1499.376443,1904.965914,2586.082743,2540.246779,2346.073777,2163.609792,2126.450908,1970.080033,1943.44326,2245.873328,2457.929762,2509.94504,2935.081323,2957.328728,3165.051458,2961.92544,3044.76155,3451.430247,3571.401584,3634.884235,3465.919428,3675.362236,4115.280906,3996.873535,4211.043478,5120.159415,6238.576086,7423.965193,8498.140227,10064.87015,12112.16407,10175.39973,11677.37384,14905.0585,15218.63283,15558.88036,15759.54728,12814.44191,12411.00993,13243.40882,14403.23731,13821.33996,11821.7459 +Pakistan,PAK,GDP per capita (current US$),NY.GDP.PCAP.CD,83.33794593,89.40903403,91.31918488,95.70337782,104.8802176,116.4467247,125.546992,139.1184751,145.9456727,153.4169694,172.4656661,178.5551136,153.3840396,101.1646559,137.108934,168.0803848,191.3010842,213.1687025,243.3358381,260.5623336,303.0509594,348.2950607,368.2773993,332.5210468,349.1820502,337.8285325,335.0201588,339.3322552,379.4545167,384.3643055,371.6785676,411.859439,429.1469022,442.4922573,434.4654042,489.8818298,497.2161314,476.3812276,461.2167361,454.2761123,576.1955804,544.4942514,534.3039007,599.3763305,687.8364127,748.9225893,836.8605262,908.0950852,990.8466112,957.9956753,987.409712,1164.978823,1198.117637,1208.91771,1251.175719,1356.667831,1368.426961,1464.926195,1482.213067,1284.702047,1193.733397 +Panama,PAN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,542.3281858,583.4115618,608.1004849,648.4360821,686.0694008,742.1140689,775.6393081,827.1733263,889.2377077,974.9369999,1040.810314,1157.624086,1287.954959,1395.476185,1444.748181,1489.964055,1721.77463,1918.162773,2332.126385,2577.745173,2782.289796,2791.771146,2849.053877,2947.691556,2996.620763,2945.169327,2492.144328,2446.230921,2603.847676,2803.944925,3122.087316,3339.914374,3489.151106,3494.51729,3529.852222,3741.28733,3974.664305,4082.614583,4060.317793,4046.429148,4126.232386,4267.292687,4592.152614,4916.907612,5348.884253,6166.187862,7154.274496,7576.140467,8082.019584,9358.261574,10722.25235,11889.07056,12796.05753,13630.32287,14343.98139,15146.40237,15544.73352,15727.97449,12269.049 +Peru,PER,GDP per capita (current US$),NY.GDP.PCAP.CD,253.2649213,277.5687634,305.7616169,325.5163392,382.7439648,441.182112,507.5309532,500.8734862,450.4014675,490.4136037,552.1797687,599.1156553,646.1428162,752.2233843,922.8464967,1094.161608,1006.890862,899.2555182,748.9924969,932.7466182,1033.418632,1203.706341,1182.730928,919.1688561,910.9674658,836.9460873,753.5315298,1000.532272,729.8761993,1040.962255,1196.586858,1524.770536,1566.011279,1488.032995,1881.737355,2194.017247,2232.075816,2306.438527,2163.119357,1924.486419,1955.588006,1941.475342,2021.240038,2145.643889,2417.034363,2729.499172,3154.331349,3606.070689,4220.616378,4196.311627,5082.353706,5869.323882,6528.971775,6756.752996,6672.877373,6229.100674,6204.996457,6710.507602,6957.793411,7027.612207,6126.87454 +Philippines,PHL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,156.7038396,168.0409486,175.9557977,187.136834,199.9568263,207.4439574,224.6355216,241.7108897,186.7746963,201.0412611,211.4121806,258.3847442,343.2905078,360.7533479,402.801155,450.3408107,506.3988106,596.8372794,685.2214898,732.3678574,742.4533753,646.0393861,594.5532575,566.2618527,535.7035912,579.7015416,644.3650151,705.5790855,715.913608,715.7468314,814.7684389,816.4123244,939.9188171,1062.13308,1160.309779,1127.526626,966.9892338,1087.378156,1072.809037,990.5640487,1036.158331,1048.008205,1121.490006,1244.348741,1452.438089,1744.639979,1991.229749,1905.895236,2217.472153,2450.735694,2694.305109,2871.429701,2959.645435,3001.043182,3073.654686,3123.245642,3252.110274,3485.340844,3298.829851 +Palau,PLW,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,7657.951214,8092.207323,8307.957438,7771.209368,8317.113942,9626.612414,9843.049373,10381.42387,10598.88283,10252.75014,10356.63362,11094.83322,12044.10547,12561.33614,13711.7617,15876.46193,16653.42589,16065.29283,15657.17715,14907.77735, +Papua New Guinea,PNG,GDP per capita (current US$),NY.GDP.PCAP.CD,102.1766587,106.5853255,111.6004653,115.6641376,125.4250001,138.4706328,153.9456827,170.0953128,182.6222427,202.7498539,231.9462843,251.8724651,294.2979516,434.5994936,479.047218,432.0431119,469.5461972,496.8041299,574.8479325,659.4481356,712.9190725,681.0247781,628.5237849,661.8879359,641.9915399,593.8152473,632.5174724,732.3921728,831.0329102,786.8789271,697.5389685,801.4729647,905.249168,1005.151953,1086.291169,893.7731191,970.0904769,906.3609204,678.8966674,608.2815307,602.1864903,515.6847804,491.8343081,568.2463486,618.0367032,749.1865116,1256.979648,1401.949447,1672.951024,1626.312855,1949.351185,2406.910967,2790.676303,2729.888751,2920.782986,2679.346579,2509.629637,2695.249009,2801.371393,2829.167085,2636.800249 +Poland,POL,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1731.209531,2235.541089,2459.020685,2497.195241,2874.825311,3686.8193,4147.465501,4123.135618,4518.108226,4398.081043,4501.454067,4991.244358,5207.171421,5701.602212,6681.386472,8021.505743,9035.410467,11254.51739,13996.02515,11526.05589,12613.011,13879.56099,13097.27085,13696.4663,14271.30585,12578.49547,12447.43959,13864.68176,15468.40941,15694.84082,15656.18273 +Pre-demographic dividend,PRE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,104.0707735,107.7746352,114.8402206,121.2546542,129.387916,124.6051331,126.0557118,136.1261366,168.5163716,156.5310694,178.8981235,212.6422202,300.2379818,336.4832326,392.958994,415.2200255,442.3782891,558.5297741,678.9276888,951.3635401,863.5124775,670.5644952,603.1871707,599.3953935,570.0794504,612.1639661,606.9267242,577.5701423,892.0662562,390.3747782,362.4275093,313.1099177,287.4599736,359.0559995,375.2561177,403.1584621,404.9008388,432.657074,491.7505878,451.9851008,503.8971065,531.2650962,644.8492173,781.6757901,960.6799242,1129.299685,1383.589208,1220.972895,1401.231512,1569.825577,1672.779198,1780.21985,1822.918991,1560.519966,1393.575355,1415.718939,1449.099681,1481.042628,1342.482391 +Puerto Rico,PRI,GDP per capita (current US$),NY.GDP.PCAP.CD,717.5148431,777.2150274,854.744805,931.751666,1006.434446,1110.832691,1207.811824,1335.274112,1480.693176,1661.866885,1852.354673,2044.319906,2246.476714,2432.414219,2614.500941,2738.243153,2946.461953,3208.771356,3567.752629,4024.50942,4502.838428,4920.722937,5115.008514,5217.722729,5730.117009,6008.054486,6455.184461,6980.490143,7595.444656,8033.089441,8652.507492,9064.018517,9659.3389,10212.27676,10876.41882,11579.185,12173.16369,12817.64496,14304.40499,15220.99134,16192.12697,18123.1987,18731.45939,19557.12025,20988.99233,21959.3227,22935.94116,23664.88235,24898.33459,25768.72589,26435.74879,27278.88305,27944.73389,28513.16574,28981.45733,29763.4883,30627.1634,31108.75275,31604.70151,32850.54861,32290.92114 +"Korea, Dem. People's Rep.",PRK,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Portugal,PRT,GDP per capita (current US$),NY.GDP.PCAP.CD,360.4992985,382.729947,407.8528436,432.5117296,468.7810816,520.9106594,575.0076807,646.8227201,719.0804919,795.7593714,4.659039341,5.311395693,6.493661103,8.720277638,9.975256974,10.61622652,10.84158635,11.3081404,12.25934794,13.74335166,16.80074542,16.19127675,15.36228515,13.64537495,12.58355088,13.49304868,19.26239942,23.96095116,28.04935454,30.20916122,39.33051583,44.68889363,53.92557278,47.56056611,49.7685565,58.7643121,60.78181251,57.74137124,60.85227317,12475.29177,11526.37207,11734.76497,12936.69282,15797.78213,18064.15809,18780.12751,19839.45405,22811.05648,24949.04136,23151.21541,22520.64231,23217.2955,20563.7136,21653.19598,22103.70097,19250.10654,19991.97249,21490.42986,23562.55452,23284.52723,22439.87687 +Paraguay,PRY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,204.2008304,208.8501353,215.0668973,220.096548,230.5024334,240.2705038,262.0676904,296.1091884,374.3419598,489.5614817,541.4879412,593.6543256,712.6585154,849.5248677,1103.913114,1398.054298,1718.499822,1608.724557,1635.633487,1260.743798,892.8904517,984.3830553,1020.381636,1063.474754,1156.924625,1376.165325,1611.399861,1610.212247,1591.374158,1686.845278,1897.098347,2002.68346,1993.804376,1812.993667,1694.11121,1663.604939,1565.054289,1300.852367,1365.416418,1679.49512,1843.634108,2271.088885,2976.824014,4047.909791,3626.818637,4359.428415,5329.387437,5185.141549,5936.975959,6118.31811,5413.776021,5324.609068,5678.869972,5782.78455,5380.963322,4949.7497 +West Bank and Gaza,PSE,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1201.581542,1326.562857,1317.466751,1389.164971,1465.046057,1499.492177,1476.17185,1335.553195,1156.217473,1257.69857,1422.190886,1543.701414,1570.1044,1664.245717,2035.202341,2191.781787,2557.075624,2880.798437,3067.438727,3315.297539,3352.112595,3272.154324,3527.613824,3620.360487,3562.330943,3656.858271,3239.731108 +Pacific island small states,PSS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,888.8178288,873.1707938,892.6431754,1014.193732,1216.585462,1381.523641,1391.092712,1326.694915,1232.975077,1271.756692,1182.025003,1269.253083,1217.411368,1231.003485,1256.767268,1387.658757,1452.739102,1566.580402,1631.79258,1889.866732,2018.182155,2129.48169,2104.973294,1780.27116,1935.280799,1755.41807,1711.101284,1776.616245,2060.327203,2349.736091,2568.750645,2672.03361,2909.128514,3063.47838,2706.025385,2965.36361,3440.720461,3608.92391,3702.149345,3970.784808,3832.438806,3962.439848,4220.736436,4336.985308,4268.838313,3626.290589 +Post-demographic dividend,PST,GDP per capita (current US$),NY.GDP.PCAP.CD,1348.510729,1408.629883,1504.359084,1601.253631,1726.878267,1855.523564,2014.477773,2141.815529,2301.901848,2508.090598,2236.874944,2440.48091,2771.06936,3246.590333,3570.445414,3912.303207,4276.900423,4796.00449,5690.08932,6366.38835,6956.149891,7346.41868,7333.062576,7708.242326,8186.07697,8622.27591,10111.49345,11434.01444,12885.73766,13445.47954,14464.67221,15262.255,16156.17575,16728.57451,17902.27443,19551.70815,19448.0526,19358.63582,19222.01714,25331.87919,25655.73602,25414.39322,26391.3133,29433.24746,32528.54665,34054.37706,35656.0601,38688.88049,40668.88388,38155.0067,39575.55831,42376.96301,42139.50088,42379.92468,43071.90983,40618.13069,41350.74271,42961.82847,45367.46377,45650.14027,44217.89723 +French Polynesia,PYF,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,1889.242414,2229.752737,2210.064698,2512.267382,2274.456315,2299.176389,2593.115329,2751.171538,3516.394216,4375.530738,5260.420752,5401.97363,5669.999124,6969.934043,8169.159542,8885.644455,8103.453324,7907.982239,7977.589853,8005.802085,8514.273652,12659.10948,13630.02523,14051.4108,13470.02737,15913.51087,16038.83647,17169.70396,17539.54729,16444.15545,18262.41308,17788.06904,15724.04503,16303.0238,16074.27098,14324.11839,,,,,,,,,,,,,,,,,,,, +Qatar,QAT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,2755.556485,3246.715887,3909.888056,5583.61784,15631.69314,15292.57418,18906.15333,19842.03606,21109.99605,27455.0149,35010.39528,34926.704,27402.26749,20910.45334,19645.31466,16590.80272,12704.36631,12901.52096,13611.44569,14052.58982,15454.17967,14124.26385,15434.21009,14270.97377,14544.00559,15849.56574,17337.42187,21104.76372,18593.41407,21723.8142,29976.16763,28517.22084,30214.67042,34517.60321,42124.94084,51455.95094,59530.561,65421.37692,80234.19083,59094.65918,67403.08768,82409.94164,85075.98654,85050.68435,83858.34046,63039.11263,57162.96801,59124.86727,65907.94843,62087.97413,50805.46357 +Romania,ROU,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,1673.838364,1769.889931,1817.902101,1680.705623,1260.749045,1102.103785,1158.132516,1323.104325,1650.276481,1633.010644,1577.323357,1852.467309,1599.889533,1659.90764,1825.179805,2119.882673,2679.41191,3494.944517,4617.929016,5757.496429,8360.166321,10435.04398,8548.118672,8214.076893,9099.217539,8507.104775,9547.852151,10043.67745,8969.148921,9548.587403,10807.00917,12398.98198,12889.80961,12896.08862 +Russian Federation,RUS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,3777.235373,3428.762153,3492.710314,3490.452505,3098.802639,2930.670201,2662.104034,2665.779867,2643.929196,2737.572024,1834.861843,1330.757236,1771.594059,2100.352512,2377.529577,2975.125353,4102.364833,5323.463083,6920.189125,9101.255049,11635.27293,8562.813277,10674.99578,14311.08432,15420.87454,15974.64463,14095.64874,9313.013625,8704.898413,10720.33266,11287.35528,11497.64925,10126.72179 +Rwanda,RWA,GDP per capita (current US$),NY.GDP.PCAP.CD,40.53721128,40.68926811,40.94418195,41.21829693,41.08387938,45.98928276,37.48883932,46.64274253,48.78409978,51.7946093,58.52527645,57.58923534,61.81190225,70.80298517,72.89440258,131.019799,141.5310109,160.3842428,188.1847569,222.8134383,243.4870745,264.0263841,255.6370123,260.0054447,268.9232767,279.1049643,301.7981245,318.2065002,337.9554619,330.4971213,349.8733096,269.8504233,302.7386376,314.7513041,126.954894,221.6289261,229.8867674,288.4093021,285.7102739,287.3738192,260.6011858,238.7812778,233.1512636,249.7433131,273.6336903,331.6901134,366.8943924,438.6641845,543.5453565,579.726615,609.724917,668.4946722,725.1697703,722.8943285,743.559038,751.0773926,744.7607488,772.3169891,783.6371628,820.1492886,797.8555543 +South Asia,SAS,GDP per capita (current 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US$),NY.GDP.PCAP.CD,,,,,,,,,777.2672726,801.0447392,921.3350532,1177.724612,1511.670381,2227.006073,6437.418089,6304.116447,8202.77002,9038.879187,9282.661166,12241.25715,16977.98593,17871.992,13944.95128,11039.2398,9632.538746,7919.651547,6304.259838,5932.29551,5856.382339,6085.956465,7246.01592,7883.245268,7932.093233,7485.541756,7421.289194,7690.576284,8335.804742,8551.676059,7419.161144,8007.958194,9171.331476,8684.645762,8695.396483,9609.968686,11185.13704,13791.45476,15384.73836,16516.63178,20078.26007,16113.14371,19262.5476,23745.88058,25243.602,24845.12466,24464.21256,20627.92693,19878.76384,20802.46631,23337.01755,23139.79798,20110.31619 +Sudan,SDN,GDP per capita (current US$),NY.GDP.PCAP.CD,125.859839,133.0822461,140.7251235,139.309841,139.223411,141.0819293,140.6857807,147.9976737,150.0225368,160.3841481,176.9540281,187.0769418,196.9108097,236.6172204,294.9852318,348.0100792,419.9310196,506.6597172,431.812084,491.5774251,392.4148478,509.130652,453.7961819,390.7812971,445.9484969,552.974546,683.0674027,849.6123318,632.3353761,611.9483003,483.9526109,432.47895,260.5103596,320.4853315,449.710583,473.4134709,300.5649303,378.9547617,355.1525542,328.068653,366.172742,383.0160766,418.2517586,484.8449083,573.3069414,689.2096107,905.3096991,1128.410266,1304.533051,1163.797223,1489.874087,1437.779831,1332.914187,1381.489462,1625.463728,1656.905841,1325.156019,1103.000026,825.8689411,753.2821741,595.467833 +Senegal,SEN,GDP per capita (current US$),NY.GDP.PCAP.CD,312.9929628,321.3612396,320.5015389,322.3007333,332.0811554,328.5631692,329.0533134,319.6393517,326.1957965,301.2671658,304.7340906,305.3080465,358.3679264,399.8086239,437.6157344,574.1332295,567.444141,567.0256629,618.1550892,751.0253442,807.8059295,713.5152286,679.2100742,586.1429002,555.1906931,590.1329701,808.3292307,943.5124978,905.7090934,871.6409618,982.0178763,935.4919598,972.4318336,895.6132848,595.0300068,727.9896576,735.9819092,661.6537598,695.9523781,693.1804693,617.1556841,651.6098743,684.3230203,835.7905935,937.4023096,998.1215541,1031.379928,1198.392811,1403.949385,1308.937808,1271.583281,1366.774501,1317.779802,1372.665779,1396.657339,1219.249415,1269.903294,1361.701972,1458.069093,1430.148259,1487.757309 +Singapore,SGP,GDP per capita (current 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US$),NY.GDP.PCAP.CD,,,,,,,,173.4358483,187.3695034,184.760133,,301.2516795,235.3330168,308.2557686,453.7879423,385.8154636,414.2486511,448.0700351,515.6646801,678.825745,794.5203603,814.5532436,781.352611,711.3676714,690.3981792,611.0009251,529.2537155,541.1625701,599.0680065,569.4045556,688.5145764,709.6463897,815.4946362,886.1354418,1153.447534,1306.73753,1381.8009,1385.570531,1171.427864,1215.167354,1017.613757,965.7656751,795.6015338,788.6131193,867.1071722,1014.85528,1119.693297,1260.289404,1388.963877,1428.505673,1604.20583,1938.891187,2141.821522,2248.614935,2274.887866,2167.119928,2225.486842,2332.844216,2411.862926,2344.231634,2258.400365 +Sierra Leone,SLE,GDP per capita (current US$),NY.GDP.PCAP.CD,138.9386399,139.3635697,143.4726483,143.6203068,150.7489709,143.2804924,147.155821,134.3174567,124.7574582,151.7407964,158.2614873,149.9072346,163.0132868,197.4465226,218.0792685,223.6748635,191.7413237,218.1918006,296.4441449,334.8038972,324.8303951,321.8227789,365.9044852,274.9017793,293.3157246,225.1831913,125.1521265,173.670346,253.83121,219.2946023,150.3889975,179.3612103,156.4031039,177.5972628,211.5922322,202.3160428,218.3668902,196.1153817,153.458492,150.0064246,138.6987225,229.3756595,252.396007,266.4469054,266.5693713,292.3490663,323.3890184,360.3716605,408.4810085,391.200943,401.8348761,448.3376621,566.3782351,716.8358269,714.6997957,588.2288628,501.4151655,499.3809746,533.9915333,527.5341745,484.5212675 +El Salvador,SLV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,274.1752595,282.1897331,288.1869903,290.025603,293.3879515,308.4398701,314.6156825,326.7697702,363.8296353,410.2895015,453.4924515,548.1520223,678.0392587,706.6155011,767.799244,778.448009,736.5911467,717.5438904,729.7387104,751.7250214,769.8035753,753.9799282,781.0600073,816.2199828,840.7123359,914.1317947,983.1815042,1073.310253,1216.700841,1380.708375,1585.108896,1684.784505,1778.836337,1886.359966,1930.627457,2001.540049,2072.301591,2124.10182,2209.497245,2278.43033,2428.569111,2631.822147,2786.157922,2933.39424,2858.483344,2983.228806,3266.012588,3428.409332,3509.528132,3589.041296,3705.579704,3805.995686,3910.255969,4052.624775,4167.730939,3798.636521 +San Marino,SMR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,40864.32547,36604.49338,38730.80304,41464.74643,51270.12923,59558.77272,60900.54329,64261.03273,72697.70599,78643.55065,66703.21823,60254.7763,57297.51331,49988.46363,51570.98395,50817.33082,42662.90265,43828.23246,45399.02906,48996.59225,47731.20854, +Somalia,SOM,GDP per capita (current US$),NY.GDP.PCAP.CD,65.47971611,68.10639697,70.81304897,73.60720426,76.48039847,79.42881316,81.87111424,84.19896458,86.55032162,90.45797623,93.6546805,95.3538885,119.8182066,144.3445525,128.70334,183.1953649,188.6610689,103.8186092,105.1133335,100.1940609,96.0960668,107.372127,117.1935354,110.884784,119.1748173,131.8173641,138.0960504,147.151477,148.216672,153.1407104,126.9249732,,,,,,,,,,,,,,,,,,,,,,,297.9553578,295.2666099,293.4551719,295.9673926,309.0543803,314.542678,320.0381483,309.4154546 +Serbia,SRB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2207.450598,2864.084727,3380.041416,2571.17271,2571.30404,914.7857199,1727.281196,2283.846685,3005.426352,3502.802924,3720.479155,4382.617279,5848.476405,7101.040141,6169.114195,5735.422857,6809.159804,6015.945228,6755.073675,6600.056809,5588.980728,5765.200762,6292.543629,7252.401858,7411.558649,7666.244476 +Sub-Saharan Africa (excluding high income),SSA,GDP per capita (current US$),NY.GDP.PCAP.CD,117.2216912,121.0200739,125.9572217,132.0231121,140.5012724,148.9689103,156.0788586,155.181898,160.6062165,176.7634433,206.7648568,202.3962522,223.1789432,278.0236271,355.1074191,382.6147913,408.0134042,430.9232692,458.186123,545.743906,682.0023783,944.7571134,839.5021767,712.9801047,609.7255011,555.8048825,559.2123435,617.7586889,618.2371781,596.6211144,654.5186918,643.8902738,626.9752518,571.8670265,543.2400084,613.9925568,616.5945163,622.9121224,587.1635867,578.7131548,601.3588981,559.3968319,594.0852881,724.9286237,877.9607012,1015.557303,1173.629973,1325.883599,1480.814611,1373.0657,1591.640542,1739.987881,1775.533706,1839.496037,1860.252141,1657.286312,1507.096028,1562.913964,1595.496495,1597.727503,1482.909594 +South Sudan,SSD,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1669.493752,1337.882231,1535.706892,1516.404378,1179.739711,1779.470365,1322.820364,1119.651437,,,,, +Sub-Saharan Africa,SSF,GDP per capita (current US$),NY.GDP.PCAP.CD,117.2503419,121.0440707,125.983524,132.0522985,140.5332889,148.9978053,156.1077551,155.2100887,160.6289071,176.7816474,206.7810836,202.4254842,223.2326925,278.0827746,355.164923,382.6771388,408.0699995,431.0189148,458.3364045,545.992855,682.2676089,944.9746136,839.7150303,713.2088338,609.9784023,556.0997859,559.5854212,618.1978651,618.7363862,597.1514109,655.1510917,644.5168298,627.7000451,572.6539436,544.0309423,614.7892269,617.3598969,623.7544766,588.0604026,579.609234,602.2155476,560.2475317,595.0160938,725.8310442,879.0041985,1016.662747,1174.811264,1327.036394,1481.833402,1373.92591,1592.59208,1741.010781,1776.51779,1840.729073,1861.462891,1658.514663,1508.352794,1564.22416,1596.825251,1599.015966,1483.769566 +Small states,SST,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,440.8517464,487.0918099,563.5716831,689.791793,1052.413521,1176.522156,1319.039038,1471.11485,1553.196048,1891.537672,2468.330067,2499.619581,2436.511268,2308.455561,2240.087088,2112.955979,2031.06816,2272.370592,2452.292537,2501.749453,2843.287569,2851.491816,2983.99408,2897.890863,3002.708098,3364.438137,3523.180835,3709.576479,3630.645495,3842.719669,4211.601183,4149.502394,4354.808518,5098.494774,6031.65404,7072.012236,8059.11871,9396.490545,11151.23499,9382.64461,10650.17845,13214.7843,13525.31686,13822.18876,14016.28428,11759.92876,11368.10556,12066.37189,13040.90638,12622.52891,10864.0078 +Sao Tome and Principe,STP,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,524.6698721,577.628404,678.7379639,745.3202999,866.5049956,883.0970413,896.8677913,1098.755219,1067.908053,1090.260763,1254.541598,1330.620114,1564.768548,1770.467688,1584.775657,1700.097997,1813.807434,1953.513257,1987.579702,2157.840446 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US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16597.20846,16767.14081,18385.62908,17480.89552,18265.20702,18699.21595,16317.54936,16519.88465,17547.55462,19374.08099,19273.25395,19156.88961 +Slovenia,SVN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,23817.88673,27595.59997,24792.12798,23532.48085,25128.01504,22641.80512,23503.28249,24247.17332,20890.16643,21678.35947,23512.81727,26115.91337,25940.72803,25179.66717 +Sweden,SWE,GDP per capita (current US$),NY.GDP.PCAP.CD,2114.002973,2288.921701,2468.694589,2657.022505,2941.048899,3206.09917,3454.429789,3720.926845,3926.409001,4234.161348,4736.207995,5132.706353,6027.107408,7301.299018,8089.869029,10117.32699,10868.29656,11448.51295,12620.58844,14877.22213,17097.93372,15586.30182,13738.95408,12608.21545,13099.02084,13666.81803,17981.06148,21792.41569,24534.89403,25662.31679,30593.72413,31822.97925,32800.79495,24425.20125,26083.7158,30283.09835,32999.1855,30312.51076,30596.64972,30940.97326,29624.76987,27247.76321,29899.1236,37321.77867,42821.75084,43437.13093,46593.29168,53700.24369,56152.55944,46946.84271,52869.04429,60755.75955,58037.82132,61126.9432,60020.36046,51545.48361,51965.15715,53791.50873,54589.06039,51686.85135,51925.71376 +Eswatini,SWZ,GDP per capita (current US$),NY.GDP.PCAP.CD,104.2140558,125.3114919,131.1620895,151.5017044,177.9669161,187.9483705,200.2461123,189.3771021,196.3021737,251.750832,260.0289791,307.3688719,321.0694305,471.5690788,545.2576171,576.8838728,528.5842538,571.2468283,619.5424325,725.1126728,922.0016494,938.1748182,853.5499178,851.5962684,732.2962204,515.0725603,620.7120357,780.2685423,894.3395802,872.7829383,1355.389001,1367.783196,1481.861181,1528.892444,1563.749507,1833.099316,1695.134775,1781.888523,1609.214093,1557.063331,1728.710498,1521.769121,1405.448752,2148.63254,2699.130772,3083.838202,3176.69114,3327.445066,3137.387151,3385.858935,4168.489518,4496.612962,4527.681573,4230.328486,4038.55354,3679.819798,3425.528759,3914.421839,4105.897212,3894.66963,3415.459446 +Sint Maarten (Dutch part),SXM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,27997.28983,28460.33262,27942.88054,33043.68838,32274.89055,31616.6817,29369.05683,29160.10381,, +Seychelles,SYC,GDP per capita (current US$),NY.GDP.PCAP.CD,288.057842,270.2793628,287.0447884,308.1952644,332.3050031,328.484901,337.6462466,333.2338125,314.3510648,314.1798528,343.8811821,401.6080395,546.9510613,648.5319241,744.5069419,806.2326445,814.474738,1044.353068,1376.546579,2030.135903,2329.353358,2419.034419,2296.307729,2280.451551,2338.075652,2588.552804,3165.945038,3638.988011,4128.118232,4407.20094,5302.843727,5314.663128,6128.445569,6559.129994,6555.504407,6748.931109,6583.201018,7280.989621,7715.918147,7747.612159,7578.851053,7663.13708,8331.261997,8524.961236,10176.65871,11092.51074,12014.39987,12154.82994,11122.86207,9706.95606,10804.68447,12189.09516,12006.68296,14765.67398,14700.33434,14745.34146,15068.62035,15906.08391,16390.82491,16198.51748,11425.09293 +Syrian Arab Republic,SYR,GDP per capita (current US$),NY.GDP.PCAP.CD,187.5372923,200.1834841,227.7883369,238.4376376,257.5781647,273.9622218,241.7122013,275.2676929,295.4499136,365.6934068,337.0394246,394.1420282,449.9434662,460.2944794,708.4208365,905.9499685,979.3282682,954.7628852,1112.612128,1151.175005,1462.630014,1677.125701,1699.532983,1769.740062,1700.476184,1540.436647,1208.004315,999.5922587,902.5500974,815.648854,988.9489105,1012.986979,1004.996857,1009.648898,725.5675537,794.4452084,934.6226484,955.8444526,974.4389492,991.2503996,1177.629339,1258.447772,1263.255418,1253.395146,1407.178266,1571.738236,1748.905772,2032.623183,,,,,,,,,,,,, +Turks and Caicos Islands,TCA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,16625.48892,15785.96212,16384.90083,18181.09252,20533.18903,24559.14472,25457.9765,27650.11538,22020.34886,21029.69563,21839.00992,21345.02598,21715.31397,23778.51913,26183.88504,28241.47929,27545.12879,29553.1367,31350.86663,23879.92665 +Chad,TCD,GDP per capita (current US$),NY.GDP.PCAP.CD,104.4717189,109.1292505,114.5818066,116.7765439,120.8206547,125.9754788,128.342694,130.9607828,129.7319508,132.16998,128.7917736,134.6863325,153.4439645,165.613494,163.1365433,211.468197,207.5288221,219.8136948,256.8238713,227.0800033,228.8224844,190.107209,176.8423264,172.2602177,185.3943371,202.7455568,203.5012509,214.9381823,265.2409713,248.3033757,291.5533573,304.8744725,296.0396966,222.9231832,173.9902724,206.2606526,221.6730401,205.8626958,224.5537395,190.5590719,166.1757089,197.1460821,221.4165369,292.6008802,454.3363652,658.566973,710.3963987,799.603742,929.3827519,803.6863884,892.5688697,984.7361305,967.3529488,979.8117426,1020.287979,776.0197523,693.4497674,665.9488849,726.1510071,709.5400432,614.4651899 +East Asia & Pacific (IDA & IBRD countries),TEA,GDP per capita (current US$),NY.GDP.PCAP.CD,89.20475456,76.33215659,71.99619395,76.00537993,85.9983571,97.90417413,104.2447489,98.40959529,96.7480682,105.7215644,114.6767543,120.0211289,132.8064193,163.3212277,180.2906069,198.798529,198.2719603,225.4763228,214.8711176,244.9996355,278.4313229,292.5314654,301.8363892,311.4401295,332.745109,358.8675322,351.889412,341.4965175,372.1930551,395.5857976,417.2833447,446.07782,492.9589816,534.0844353,634.2325215,773.0550686,877.1774826,897.8262859,813.6041707,885.5810289,964.5359749,1017.955942,1118.592545,1254.144049,1442.499348,1657.903631,1982.333142,2490.388367,3124.303639,3366.269985,4049.422511,4907.543722,5432.816401,5953.112445,6370.399755,6588.031586,6679.251594,7248.229616,8051.812548,8280.234696,8378.63094 +Europe & Central Asia (IDA & IBRD countries),TEC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,2299.199385,2214.372133,2339.884276,2318.472306,2175.822279,2155.895128,1933.850607,2130.326596,2190.796902,2257.62472,2181.338378,1891.061635,2057.391413,2096.229447,2357.59087,2897.757267,3749.13695,4689.900992,5663.705176,7270.742821,8975.490206,7093.373319,8289.886009,10017.72511,10406.48496,10973.16488,10319.43686,8008.230261,7665.383797,8553.532336,8868.02881,9002.297601,8368.476616 +Togo,TGO,GDP per capita (current US$),NY.GDP.PCAP.CD,76.63869662,79.12028165,81.99444408,87.79229795,99.93788939,109.6201206,121.8341754,124.8788571,124.349764,131.5701918,120.0538809,131.0984359,149.3508287,176.4738164,237.8142181,256.1021441,251.3265307,308.6953426,319.9418085,337.4496798,417.6691399,342.1906961,281.8105522,252.973786,228.6486926,234.35611,315.4621221,359.7907244,385.4254188,367.7925841,431.4503884,414.7814374,429.042361,306.1512474,238.4654535,309.8182936,336.9770039,334.1438424,342.6579042,329.8164697,302.958592,292.8231035,328.3981073,396.9205798,413.3298662,406.562366,408.0571173,449.7382505,546.3503702,540.6087126,534.0447826,587.0974928,571.8067239,621.3988536,640.9342196,570.9099672,803.1518934,830.7452767,901.5230169,893.3524541,914.9505826 +Thailand,THA,GDP per capita (current US$),NY.GDP.PCAP.CD,100.767475,107.498001,113.7828498,118.143907,125.9386942,137.9186467,161.0055216,166.9230634,174.7847538,186.8791675,192.127686,194.266671,209.3747807,269.8879592,332.1752699,351.6193358,391.5693898,445.4208525,528.6267551,589.88506,682.9299728,721.0593448,742.8512326,798.0804499,818.0389335,747.7034373,813.4515045,936.7804583,1123.437643,1295.464722,1508.94247,1716.41542,1927.882603,2209.36303,2491.427836,2846.586834,3043.980314,2468.18473,1845.82887,2033.258009,2007.735271,1893.264239,2096.187802,2359.116831,2660.126835,2894.062656,3369.543198,3973.017172,4379.658787,4213.006654,5076.339872,5492.1215,5860.581471,6168.261356,5951.883487,5840.053067,5993.305516,6593.818352,7296.879608,7817.011522,7189.041835 +Tajikistan,TJK,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,497.6324574,250.3408858,391.9437685,293.9804022,267.4329629,213.5441443,178.3612515,155.1885177,219.0402456,177.5596287,138.4291467,171.0480646,190.0026776,237.7573352,311.6272313,340.5831832,408.8384135,526.6432883,715.8657063,676.1235111,749.5524123,847.3824321,969.2961039,1048.225602,1104.172358,978.4049812,807.1025861,848.6723799,853.2188734,890.5444024,859.1379611 +Turkmenistan,TKM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,684.2226917,861.7846271,838.5665772,865.7868319,,410.2729264,832.7538547,623.2419057,590.2812211,554.8339157,562.6417026,590.3871315,548.6879133,643.1909754,774.482152,967.8874353,1283.908782,1453.917784,1704.41538,2136.472101,2600.368758,3904.465301,4036.456805,4439.202128,5649.962106,6675.178055,7304.285771,7962.244925,6432.685325,6387.682575,6587.092604,6967.375043,7612.03518, +Latin America & the Caribbean (IDA & IBRD countries),TLA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,1925.026961,1901.116809,1819.897698,1849.243744,1823.649975,1888.059842,2093.812935,2250.671306,2599.224462,3179.06989,2943.052979,3338.857326,3750.15312,3930.879355,4201.935036,4534.745475,4490.563563,3956.800214,4312.574979,4136.456542,3627.949226,3649.529348,4171.391187,5014.231943,5823.437678,6812.366805,7861.741278,7278.974799,8991.087026,10139.62059,10123.72578,10261.55107,10350.15899,8745.674436,8440.432404,9316.218215,9001.310135,8776.748232,7244.555569 +Timor-Leste,TLS,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,415.0859486,529.7937005,508.7911659,517.7860016,453.4806958,464.524032,446.4575768,523.7375385,614.435755,676.6183307,806.4135263,947.5107667,1013.042872,1210.03999,1232.453997,1332.791772,1353.746815,1286.406785,1230.22528,1560.508615,1381.17566 +Middle East & North Africa (IDA & IBRD countries),TMN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1128.509329,1142.355491,1308.722841,1471.416543,1520.230433,1512.178651,1626.583647,1693.147654,1670.262428,1580.613676,1701.553507,1987.316634,2301.481869,2630.113457,3196.620819,3891.740846,3687.834841,4180.083724,4644.969214,5036.826624,4624.312535,4466.514805,3932.172284,3923.436,3802.414802,3500.262154,3504.714745,3063.189564 +Tonga,TON,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,368.0522381,336.6254647,378.2380471,455.1050152,484.0849456,572.8984514,666.2957055,662.8664303,649.3125796,685.0015944,639.6296255,724.8632095,865.870072,1127.550611,1121.213468,1194.541034,1388.040501,1436.783691,1449.306003,2047.138431,2176.381296,2307.130962,2225.100675,1973.544795,2043.751046,2091.113009,1839.086867,1845.669127,2030.775469,2301.654115,2594.418739,2873.076637,2910.145122,3331.546555,3006.606344,3527.952342,4002.813771,4581.783245,4428.146521,4354.24436,4336.239603,4157.877249,4513.432531,4740.700272,4903.012138, 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US$),NY.GDP.PCAP.CD,117.2503419,121.0440707,125.983524,132.0522985,140.5332889,148.9978053,156.1077551,155.2100887,160.6289071,176.7816474,206.7810836,202.4254842,223.2326925,278.0827746,355.164923,382.6771388,408.0699995,431.0189148,458.3364045,545.992855,682.2676089,944.9746136,839.7150303,713.2088338,609.9784023,556.0997859,559.5854212,618.1978651,618.7363862,597.1514109,655.1510917,644.5168298,627.7000451,572.6539436,544.0309423,614.7892269,617.3598969,623.7544766,588.0604026,579.609234,602.2155476,560.2475317,595.0160938,725.8310442,879.0041985,1016.662747,1174.811264,1327.036394,1481.833402,1373.92591,1592.59208,1741.010781,1776.51779,1840.729073,1861.462891,1658.514663,1508.352794,1564.22416,1596.825251,1599.015966,1483.769566 +Trinidad and Tobago,TTO,GDP per capita (current US$),NY.GDP.PCAP.CD,631.7065847,676.4011784,704.2231582,760.3978853,788.696647,807.8698381,787.3384067,823.5563085,815.3271085,831.426913,869.354117,938.269947,1119.038073,1332.78153,2049.247301,2416.248314,2439.177473,3020.242941,3381.234537,4306.781889,5748.673495,6345.001087,7268.105868,6821.850306,6715.606873,6302.609227,4051.476568,4016.449771,3734.461714,3564.356935,4150.284861,4318.502428,4398.495123,3754.800372,3959.262873,4249.094373,4579.978105,4554.356458,4790.123738,5387.164386,6435.134212,6939.828471,7053.087371,8807.866068,10293.86925,12327.28071,14102.59329,16539.89075,21204.00818,14514.24061,16683.39306,19034.12071,19157.45977,20143.54541,20270.93377,18214.46062,16250.62869,16173.74005,17037.86236,16637.16294,15384.03946 +Tunisia,TUN,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,217.9112744,224.2950878,229.0282314,250.6938109,260.4090585,284.220679,325.5646532,422.8566931,504.8284053,641.1042608,765.1896458,779.0448678,863.0676112,984.8001008,1157.377923,1371.835501,1286.553975,1206.642967,1203.658234,1156.914545,1147.42922,1199.562537,1259.136146,1281.201295,1253.326397,1491.119777,1551.377998,1799.111582,1660.875002,1743.154533,1975.899862,2113.587432,2208.206243,2292.711741,2386.589492,2211.835015,2253.034852,2344.396907,2760.405122,3111.374381,3193.204358,3369.924443,3776.332046,4307.580023,4128.463959,4141.975964,4264.678122,4152.684394,4222.70514,4305.482357,3861.6924,3697.938729,3481.241106,3438.805067,3351.573443,3319.819024 +Turkey,TUR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,309.4466243,350.6629852,369.5834686,386.3580606,444.5494828,481.6936799,526.2134746,571.6177702,489.9303677,455.1048582,558.420923,686.4900665,927.7992157,1136.375596,1275.956574,1427.371795,1549.644358,2079.220269,1564.247166,1579.073778,1402.406435,1310.255652,1246.824468,1368.401652,1510.676311,1705.894384,1745.364927,2021.85953,2794.350494,2735.707615,2842.369978,3180.187577,2270.337325,2897.86664,3053.947231,3144.385703,4499.737508,4116.17056,4337.478003,3142.920998,3687.956093,4760.104019,6101.632117,7456.296101,8101.856924,9791.88245,10941.17215,9103.474051,10742.77498,11420.55546,11795.63346,12614.78161,12157.99043,11006.27952,10894.60338,10589.66772,9453.196172,9126.594392,8538.16906 +Tuvalu,TUV,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,990.3981751,1038.957834,1069.478537,1047.505216,1175.810083,1185.840519,1323.907506,1361.589349,1367.084534,1464.805341,1463.166211,1392.334348,1610.653001,1874.853819,2181.636269,2184.565259,2263.575948,2645.109505,2936.521547,2602.619444,3024.761774,3643.121608,3506.308144,3457.376266,3398.39675,3197.772252,3253.899536,3574.065288,3701.709255,4055.89561,4143.109753 +Tanzania,TZA,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,221.5881441,186.2186779,173.7906179,195.661453,175.5580799,157.0608125,161.176878,182.361928,219.5502111,253.4759847,395.5326208,400.2636718,410.9523655,406.5387537,411.9723218,431.2797298,459.2592952,492.6313397,485.4974495,552.8395016,687.3904455,695.2168433,743.4036639,781.4338957,867.8574989,970.3996014,1030.077648,947.9333517,966.5030166,1004.906588,1042.838759,1085.884861,1076.470385 +Uganda,UGA,GDP per capita (current US$),NY.GDP.PCAP.CD,62.50962537,63.22096734,62.22631459,69.17354301,76.31148622,110.802289,112.0321537,113.1692177,117.3845508,128.074736,133.9715946,146.5889396,150.2165273,167.13582,200.9123531,219.7510474,221.6160047,258.2961789,206.6384294,177.2015733,100.0302757,104.2722215,164.6877539,164.2626181,256.7408119,241.7460015,260.3284059,401.5634663,402.2793423,314.7687216,248.0293243,185.0181172,153.9440238,167.9412492,201.6026565,281.9661333,287.3882907,289.5044945,295.4052653,261.3479065,261.8691416,239.473127,245.5000403,254.3012043,296.0142675,333.7315763,349.2171001,403.6637446,474.5179496,796.5309244,819.0074652,828.9941128,786.738711,806.6119363,879.7349486,843.6295453,733.3971317,746.8307512,770.2570601,794.4514454,817.0357569 +Ukraine,UKR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,1249.445248,1449.966166,1597.535384,1569.757138,,1378.636186,1257.350096,1011.979433,935.9761546,872.6987229,991.2204337,835.2509828,635.7644092,635.7044182,780.3211789,878.6187489,1047.50279,1366.016476,1826.931391,2300.769709,3065.611294,3887.242337,2542.995424,2965.139715,3569.758121,3855.417729,4029.711252,3104.643206,2124.662319,2187.73051,2640.675677,3096.817402,3662.563087,3726.927149 +Upper middle income,UMC,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,256.3722294,278.0366216,298.7354655,315.4419327,361.1954014,453.8669819,549.3812104,588.3174134,598.6009487,645.7061192,670.4358032,825.4312655,968.9188198,1032.516522,950.6506174,977.7980221,975.8744286,1009.64278,1009.804584,1047.525499,1152.691509,1190.300249,1371.866268,1410.003108,1359.252288,1460.581233,1576.646869,1741.873227,1868.073705,1983.439391,1943.011472,1823.09592,1999.770467,1997.475954,1984.911243,2196.228838,2618.793237,3130.137606,3698.577609,4565.964786,5551.370357,5308.889324,6396.662114,7593.699493,8092.060926,8614.228386,8859.135678,8257.060889,8156.475382,8965.562497,9588.189552,9696.753839,9295.764144 +Uruguay,URY,GDP per capita (current US$),NY.GDP.PCAP.CD,,,656.7132268,584.2916335,741.244998,701.7047182,664.4398411,580.9702588,574.3468825,717.140948,760.5870648,996.0880666,775.9881598,1404.726557,1448.325811,1250.200808,1290.630911,1440.226655,1707.485587,2479.962509,3485.991103,3764.821504,3107.400832,1716.178567,1620.934562,1571.306,1940.214425,2415.903719,2676.533381,2732.287535,2990.367133,3578.288157,4082.538363,4721.007648,5458.867361,5985.116994,6317.561877,7329.866939,7713.618768,7250.27477,6875.025469,6284.45968,4090.891888,3624.205685,4120.544205,5226.9441,5887.845162,7026.503055,9091.079039,9451.932449,11992.02377,14236.715,15171.58017,16973.68924,16831.94819,15613.74597,16715.63347,18690.89384,18703.86029,17688.01501,15438.41167 +United States,USA,GDP per capita (current US$),NY.GDP.PCAP.CD,3007.123445,3066.562869,3243.843078,3374.515171,3573.941185,3827.52711,4146.316646,4336.426587,4695.92339,5032.144743,5234.296666,5609.3826,6094.01799,6726.358956,7225.69136,7801.456664,8592.253537,9452.576519,10564.94822,11674.18631,12574.79151,13976.10975,14433.78773,15543.89372,17121.22548,18236.82773,19071.22719,20038.9411,21417.01193,22857.15443,23888.60001,24342.2589,25418.99078,26387.29373,27694.85342,28690.8757,29967.71272,31459.13898,32853.67695,34513.5615,36334.90878,37133.24281,38023.16111,39496.48588,41712.80107,44114.74778,46298.73144,47975.9677,48382.55845,47099.98047,48466.6576,49882.55813,51602.93105,53106.53677,55049.98833,56863.3715,58021.4005,60109.65573,63064.41841,65279.52903,63543.57779 +Uzbekistan,UZB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,651.4192061,652.8074753,603.3520153,596.9835856,576.4471105,585.9323642,600.5981578,623.0026524,623.2161328,702.4807441,558.2268024,456.7062895,383.343068,396.377979,465.1198869,546.7768502,654.2838373,830.4076942,1082.286025,1213.265328,1634.312095,1926.292997,2137.025122,2281.410973,2492.336643,2615.025134,2567.799207,1826.566919,1529.082864,1719.05721,1685.764947 +St. Vincent and the Grenadines,VCT,GDP per capita (current US$),NY.GDP.PCAP.CD,161.3752968,170.3812109,174.4960771,162.8054525,173.3873415,175.6584477,185.2539592,180.3982494,173.1274602,185.8985095,203.8651507,219.1676488,298.1860427,322.4208641,347.9698985,347.4328617,339.136668,505.1087102,616.536676,713.4678685,818.7691628,1006.383534,1112.505045,1186.541948,1301.119602,1393.660519,1528.713579,1657.907062,1884.174036,2005.631798,2236.197812,2364.525383,2575.069302,2650.111143,2678.509305,2924.829997,3069.824777,3223.229718,3465.623925,3625.720503,3676.352092,3985.807613,4272.956059,4447.918312,4809.934335,5070.381521,5625.399437,6307.313617,6415.343305,6232.371642,6292.498854,6242.252962,6390.310632,6639.484897,6684.377547,6921.702479,7074.548765,7213.025857,7361.400962,7457.239776,7297.91235 +"Venezuela, RB",VEN,GDP per capita (current US$),NY.GDP.PCAP.CD,955.4464181,970.3563984,1023.355237,1077.403051,864.4712211,869.5350225,876.1539264,892.8794648,937.5986712,930.9262123,1014.453263,1105.622678,1155.404076,1367.912274,2036.437849,2082.310039,2314.990328,2593.076488,2737.001671,3270.600986,3893.697346,4252.337521,4228.902648,4108.481006,3555.806233,3577.783432,3398.132171,2634.22025,3220.861914,2270.957799,2475.380473,2661.033851,2938.160197,2857.93866,2720.367063,3529.589128,3151.269278,3758.844934,3921.718474,4127.108981,4842.036601,4986.675788,3700.87974,3272.615024,4325.696756,5504.97724,6833.378912,8454.469536,11432.74592,11765.09976,13825.35712,10955.53801,12986.22269,12457.7686,16055.64532,,,,,, +British Virgin Islands,VGB,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, +Virgin Islands (U.S.),VIR,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,30126.53328,31823.41828,35024.8004,40930.17252,41561.70122,44333.88408,39207.72715,38771.63204,40043.55971,39144.88872,37850.77827,34820.11459,33573.71943,34796.49436,36022.54548,36287.87949,37233.29689,, +Vietnam,VNM,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,,,,,,,231.4522858,422.7802977,575.4626827,390.4122234,94.56473524,95.18825998,138.4474504,139.2001228,182.3084018,221.1291752,276.8126628,324.1472239,348.0174071,348.3243173,362.9195028,390.0933309,404.8078612,430.0528724,480.5798343,546.909619,687.4797274,784.3723759,906.2842299,1149.424495,1217.268583,1317.890646,1525.119025,1735.152116,1886.69017,2030.278447,2085.101349,2192.174482,2365.521615,2566.447487,2715.27598,2785.724157 +Vanuatu,VUT,GDP per capita (current US$),NY.GDP.PCAP.CD,,,,,,,,,,,,,,,,,,,,1059.673506,1048.33559,959.811346,943.1943897,945.0665727,1137.138278,1014.36599,951.2056376,1024.906658,1136.609999,1078.516272,1152.169246,1335.833604,1347.430174,1255.090071,1423.896602,1482.705562,1522.062208,1559.43224,1473.666114,1478.58574,1470.63587,1363.185058,1354.100243,1580.575634,1788.122206,1887.226576,2049.532809,2352.973255,2629.053124,2573.889015,2839.40634,3173.822022,2997.293432,2954.774511,2926.67996,2695.666186,2805.66532,3082.475083,3125.404999,3102.34679,2782.98434 +World,WLD,GDP per capita (current 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1, + col_name: str = "sinusoidal_factor", + date_col_name: str = "date", + feature: Optional[str] = None, + feature_values: Optional[Dict[str, Dict[str, float]]] = None, + ): + """ + Introduces a sinusoidal factor, useful for introducing seasonal patterns. + Either supply wavelength, amplitude, phase and mean to apply the factor to the entire time series, or specify + features with their characteristics to apply different factors to different features. + + Args: + wavelength: wavelength in days + amplitude: amplitude of the factor + phase: phase in days + mean: absolute mean of the factor. + col_name: name of the factor column. + date_col_name: name of the resulting date column + feature: feature with sinusoidal factor + feature_values: feature labels affected by the sinusoidal factor + + Examples: + Create a standard sinusoidal factor for all features: + >>> sf = SinusoidalFactor(wavelength=5., amplitude=1., phase=2., mean=1.5) + ... sf.generate(start_date="01-01-2020", end_date="01-14-2020") + + Create a sinusoidal factor over the timespan of a year, to increase the sales of winter jackets in the + winter 200%, decreasing the sales of the jacket in peak summer times to 0%: + >>> sf = SinusoidalFactor(feature="product_type", feature_values={ + ... "winter_jacket": { + ... "wavelength": 365., "amplitude": 1., "phase": 365/4, "mean": 1 + ... } + ... }) + """ + if (feature is None) ^ (feature_values is None): + raise AttributeError( + "Either set `feature` and `feature_values` or set neither." + ) + elif feature: + # Check for all keys in dict + if any( + map( + lambda item: len( + {"wavelength", "amplitude", "phase", "mean"} + & set(item[1].keys()) + ) + != 4, + feature_values.items(), + ) + ): + raise AttributeError( + f"Please set {VARIABLES} for every label. You have entered: {feature_values}" + ) + features = {feature: list(feature_values.keys())} + else: + features = None + + self._wavelength = wavelength + self._amplitude = amplitude + self._phase = phase + self._mean = mean + self._feature = feature + self._feature_values = feature_values + super().__init__( + col_name=col_name, date_col_name=date_col_name, features=features + ) + + @property + def wavelength(self) -> float: + return self._wavelength + + @wavelength.setter + def wavelength(self, wavelength: Optional[float]): + self._wavelength = wavelength + + @property + def amplitude(self) -> float: + return self._amplitude + + @amplitude.setter + def amplitude(self, amplitude: float): + self._amplitude = amplitude + + @property + def phase(self) -> float: + return self._phase + + @phase.setter + def phase(self, phase: float): + self._phase = phase + + @property + def mean(self) -> float: + return self._mean + + @mean.setter + def mean(self, mean: float): + self._mean = mean + + @property + def feature(self) -> str: + return self._feature + + @feature.setter + def feature(self, feature: str): + self._feature = feature + + @property + def feature_values(self) -> Optional[Dict[str, Dict[str, float]]]: + return self._feature_values + + @feature_values.setter + def feature_values(self, values: Optional[Dict[str, Dict[str, float]]]): + if any(map(lambda label: len(set(VARIABLES) & set(label.keys())) != 4, values)): + raise AttributeError( + f"Please set {VARIABLES} for every label. You have entered: {values}" + ) + self._feature_values = values + + def generate( + self, + start_date: Union[Timestamp, str, int, float], + end_date: Optional[Union[Timestamp, str, int, float]] = None, + ) -> DataFrame: + dr: DataFrame = self.get_datetime_index( + start_date=start_date, end_date=end_date + ).to_frame(index=False, name=self._date_col_name) + + start_date_ts: Timestamp = dr.iloc[0][self._date_col_name] + + if self._feature_values: + + def get_factor_col(row) -> float: + t: float = float( + getattr(row[self._date_col_name] - start_date_ts, "days") + ) # Only working in days + # y(t) A * sin(2 * pi * freq * t + phase) + mean + return ( + row["amplitude"] + * sin(2 * pi * (t + row["phase"]) / row["wavelength"]) + + row["mean"] + ) + + df: DataFrame = DataFrame( + dict( + {self._feature: list(self._feature_values.keys())}, + **{ + var: list( + map(lambda feat: feat[1][var], self._feature_values.items()) + ) + for var in VARIABLES + }, + ) + ) + + factor_df: DataFrame = get_cartesian_product(dr, df) + factor_df[self._col_name] = factor_df.apply(get_factor_col, axis=1) + factor_df = factor_df.drop(VARIABLES, axis=1) + else: + # y(t) A * sin(2 * pi * freq * t + phase) + mean + df: DataFrame = DataFrame( + self._amplitude + * sin( + 2 + * pi + * (arange(start=0, stop=len(dr)) + self._phase) + / self._wavelength + ) + + self._mean, + columns=[self._col_name], + ) + factor_df: DataFrame = dr.join(df) + + return factor_df diff --git a/timeseries_generator/utils.py b/timeseries_generator/utils.py new file mode 100644 index 0000000..6a94756 --- /dev/null +++ b/timeseries_generator/utils.py @@ -0,0 +1,16 @@ +from pandas import DataFrame + + +def get_cartesian_product(df1: DataFrame, df2: DataFrame) -> DataFrame: + """ + Utility function that gets cartesian product of two dataframes. + Args: + df1: first dataframe. + df2: second dataframe. + + Returns: + DataFrame containing the cartesian product of both dataframes + + """ + df = df1.assign(key=1).merge(df2.assign(key=1), on="key").drop("key", axis=1) + return df diff --git a/timeseries_generator/weekday_factor.py b/timeseries_generator/weekday_factor.py new file mode 100644 index 0000000..665a889 --- /dev/null +++ b/timeseries_generator/weekday_factor.py @@ -0,0 +1,75 @@ +from typing import Optional, Dict, Union + +from pandas import DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator.base_factor import BaseFactor + + +class WeekdayFactor(BaseFactor): + """ + Some time series may behave differently depending on the day of the week. With this factor you can emulate this + behaviour. Default behavior is to show an increased factor in the weekends, and a slightly increased factor on + friday. + + The output dataframe will be two columns: date and factor + + + Examples: + Generate the default behaviour. + >>> wf = WeekdayFactor() + ... wf.generate(start_date="01-01-2020", end_date="01-14-2020") + + Generate custom weekday behaviour with a 1.5 increase on mondays and tuesdays. + >>> wf = WeekdayFactor( + ... col_name="early_week_boost_factor", + ... factor_values={1: 1.5, 2: 1.5} + ... ) + """ + + def __init__( + self, + factor_values: Optional[Dict[int, float]] = None, + col_name: str = "weekend_trend_factor", + intensity_scale: int = 1, + ): + + if factor_values is None: + # default is a weekend factor + factor_values = {4: 1.15, 5: 1.3, 6: 1.3} + + if type(factor_values) is not dict: + raise ValueError(f"WeekdayFactor factor_values should be a dictionary") + + self._factor_values = factor_values + self._intensity_scale = intensity_scale + + super().__init__(col_name=col_name) + + def generate( + self, + start_date: Union[Timestamp, str, int, float], + end_date: Union[Timestamp, str, int, float] = None, + ) -> DataFrame: + df: DataFrame = self.get_datetime_index( + start_date=start_date, end_date=end_date + ).to_frame(index=False, name=self._date_col_name) + + df["weekday"] = df[self._date_col_name].dt.dayofweek + df[self._col_name] = df["weekday"].apply( + lambda day_number: self._factor_values.get(day_number, 1) + * self._intensity_scale + ) + + df = df.drop(axis=1, columns="weekday") + + if end_date is None: + df_sel = df[(df[self._date_col_name] >= start_date)] + else: + df_sel = df[ + (df[self._date_col_name] >= start_date) + & (df[self._date_col_name] < end_date) + ] + + # reindex to rangelist + return df_sel.reset_index().drop(axis=1, columns="index") diff --git a/timeseries_generator/white_noise.py b/timeseries_generator/white_noise.py new file mode 100644 index 0000000..b2c6a0c --- /dev/null +++ b/timeseries_generator/white_noise.py @@ -0,0 +1,126 @@ +import itertools +from typing import Optional, Dict + +from numpy.random.mtrand import randn +from pandas import DataFrame +from pandas._libs.tslibs.timestamps import Timestamp + +from timeseries_generator.base_factor import BaseFactor +from timeseries_generator.utils import get_cartesian_product + + +FeatureValues = Dict[str, Dict[str, float]] + + +class WhiteNoise(BaseFactor): + def __init__( + self, + stdev_factor: float = 0.05, + feature_values: Optional[FeatureValues] = None, + col_name: str = "white_noise", + ): + """ + Add white noise to the timeseries. The noise component will have a bell-shaped distribution, based on the input + standard deviation. + + Args: + stdev_factor: standard deviation of the factor random noise component. Do not supply when specifying + feature_values + feature_values: dictionary with the feature name as key and a dictionaty as value. This dictionaty contains + the feature values as keys and the stdev_factors as values. + col_name: name of the factor column. + + Raises: + AttributeError when stdev_factor and feature_values are set, or when more then one feature gets a unique + noise factor. + + Examples: + This can either act on all features in the same way: + >>> WhiteNoise(stdev_factor=0.05, col_name="White Noise") + + Or can be specified per feature value: + >>> WhiteNoise(feature_values={ + ... "my_feature": { + ... "feature1": 0.05, + ... "feature2": 0.10 + ... } + ... }) + """ + + if (stdev_factor and feature_values) or not (stdev_factor or feature_values): + raise AttributeError("Either set `stdev_factor` or `feature_values`") + if feature_values: + if len(feature_values) > 1: + raise AttributeError( + f"{self.__class__.__name__}, can only set feature values on one feature" + ) + features = feature_values + apply_to_all = False + else: + features = None + apply_to_all = True # random noise applied to all factors + + super().__init__( + col_name=col_name, features=features, apply_to_all=apply_to_all + ) + self._stdev_factor = stdev_factor + self._feature_values = feature_values + + @property + def stdev_factor(self): + return self._stdev_factor + + @stdev_factor.setter + def stdev_factor(self, f): + self._stdev_factor = f + + @property + def feature_values(self) -> Optional[FeatureValues]: + return self._feature_values + + @feature_values.setter + def feature_values(self, feature_values: Optional[FeatureValues]): + if len(feature_values) > 1: + raise AttributeError( + f"{self.__class__.__name__}, can only set feature values on one feature" + ) + self._feature_values = feature_values + + def generate(self, start_date: Timestamp, end_date: Timestamp = None) -> DataFrame: + dr: DataFrame = self.get_datetime_index( + start_date=start_date, end_date=end_date + ).to_frame(index=False, name=self._date_col_name) + + if self._features: + # Using self.features here gets all the features from the generator + df: DataFrame = DataFrame( + itertools.product(*self._features.values()), + columns=list(self._features.keys()), + ) + factor_df = get_cartesian_product(dr, df) + if self._feature_values: + feature: str = iter( + self._feature_values + ).__next__() # len(self._features is always 1) + factor_df["noise_1"] = randn(len(factor_df)) + + def get_factor_col(row): + stdev_factor: float = self._feature_values[feature][row[feature]] + return stdev_factor * row["noise1"] + 1 + + factor_df[self._col_name] = factor_df.apply( + get_factor_col, axis=1 + ).drop("noise1", axis=1) + else: + factor_df[self._col_name] = ( + self._stdev_factor * randn(len(factor_df)) + 1 + ) + + else: + # self._features can be none if used outside of generator + df: DataFrame = DataFrame( + self._stdev_factor * randn(len(dr)) + 1, columns=[self._col_name] + ) + factor_df = dr.join(df) + + return factor_df