From 4eb466830d76c261f102582e108a5ac13adb1b92 Mon Sep 17 00:00:00 2001 From: Yousen59 <155991677+Yousen59@users.noreply.github.com> Date: Mon, 12 Feb 2024 13:34:46 +0000 Subject: [PATCH] Add files via upload --- Lab05.ipynb | 1060 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1060 insertions(+) create mode 100644 Lab05.ipynb diff --git a/Lab05.ipynb b/Lab05.ipynb new file mode 100644 index 0000000..6aa3224 --- /dev/null +++ b/Lab05.ipynb @@ -0,0 +1,1060 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "id": "69fb876c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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latlondatecitycountry
051.481292-0.45101105/14/2014 09:07West DraytonUnited Kingdom
151.474005-0.45099905/14/2014 09:22HounslowUnited Kingdom
251.478199-0.44608105/14/2014 10:51HounslowUnited Kingdom
351.478199-0.44608105/14/2014 11:24HounslowUnited Kingdom
451.474146-0.45156205/14/2014 11:38HounslowUnited Kingdom
\n", + "
" + ], + "text/plain": [ + " lat lon date city country\n", + "0 51.481292 -0.451011 05/14/2014 09:07 West Drayton United Kingdom\n", + "1 51.474005 -0.450999 05/14/2014 09:22 Hounslow United Kingdom\n", + "2 51.478199 -0.446081 05/14/2014 10:51 Hounslow United Kingdom\n", + "3 51.478199 -0.446081 05/14/2014 11:24 Hounslow United Kingdom\n", + "4 51.474146 -0.451562 05/14/2014 11:38 Hounslow United Kingdom" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd, numpy as np, matplotlib. pyplot as plt\n", + "\n", + "df = pd.read_csv('D:/anaconda working/text/summer-travel-gps-full.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "de684cfb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " city country\n", + "0 West Drayton United Kingdom\n", + "1 Hounslow United Kingdom\n", + "2 Hounslow United Kingdom\n", + "3 Hounslow United Kingdom\n", + "4 Hounslow United Kingdom\n", + "... ... ...\n", + "1754 Munich Germany\n", + "1755 Munich Germany\n", + "1756 Munich Germany\n", + "1757 Munich Germany\n", + "1758 Munich Germany\n", + "\n", + "[1759 rows x 2 columns]\n" + ] + } + ], + "source": [ + "# Q1 \n", + "df = pd.read_csv('D:/anaconda working/text/summer-travel-gps-full.csv')\n", + "selected_columns = df[['city', 'country']]\n", + "\n", + "print(selected_columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "083176d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 West Drayton\n", + "1 Hounslow\n", + "2 Hounslow\n", + "3 Hounslow\n", + "4 Hounslow\n", + "Name: city, dtype: object\n" + ] + } + ], + "source": [ + "# Q2 \n", + "first_five_cities = df['city'].head(5)\n", + "print(first_five_cities)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "54b18035", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "lat 51.478199\n", + "lon -0.446081\n", + "date 05/14/2014 10:51\n", + "city Hounslow\n", + "country United Kingdom\n", + "Name: 2, dtype: object\n" + ] + } + ], + "source": [ + "# Q3\n", + "third_row = df.loc[2]\n", + "print(third_row)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a602ee6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "United Kingdom\n" + ] + } + ], + "source": [ + "# Q4 \n", + "first_country = df.loc[0, 'country']\n", + "print(first_country)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2103552a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " city date\n", + "0 West Drayton 05/14/2014 09:07\n", + "1 Hounslow 05/14/2014 09:22\n", + "2 Hounslow 05/14/2014 10:51\n", + "3 Hounslow 05/14/2014 11:24\n" + ] + } + ], + "source": [ + "# Q5\n", + "first_four_rows = df.loc[0:3, ['city', 'date']]\n", + "print(first_four_rows)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6774a8ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.446081\n" + ] + } + ], + "source": [ + "# Q6\n", + "value = df.iloc[2, 1]\n", + "print(value)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b32a0c7e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lat lon date city country\n", + "0 51.481292 -0.451011 05/14/2014 09:07 West Drayton United Kingdom\n", + "300 41.377091 2.151175 05/20/2014 03:18 Barcelona Spain\n", + "600 50.052338 19.946220 05/31/2014 21:10 Krakow Poland\n", + "900 48.561181 9.059672 06/09/2014 15:12 Tübingen Germany\n", + "1200 41.378301 2.187443 06/17/2014 16:37 Barcelona Spain\n", + "1500 42.208201 20.735993 06/30/2014 08:27 Prizren Kosovo\n" + ] + } + ], + "source": [ + "# Q7\n", + "every_300th_row = df.iloc[::300]\n", + "print(every_300th_row)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e7045728", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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latlondatecitycountry
17341.04455628.98328607/08/2014 16:44IstanbulTurkey
17441.00899228.96826807/08/2014 20:03IstanbulTurkey
17541.04348728.98548807/08/2014 22:18IstanbulTurkey
17640.97763728.82387907/09/2014 09:03IstanbulTurkey
17748.35711011.79134607/09/2014 13:20MunichGermany
\n", + "
" + ], + "text/plain": [ + " lat lon date city country\n", + "173 41.044556 28.983286 07/08/2014 16:44 Istanbul Turkey\n", + "174 41.008992 28.968268 07/08/2014 20:03 Istanbul Turkey\n", + "175 41.043487 28.985488 07/08/2014 22:18 Istanbul Turkey\n", + "176 40.977637 28.823879 07/09/2014 09:03 Istanbul Turkey\n", + "177 48.357110 11.791346 07/09/2014 13:20 Munich Germany" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('D:/anaconda working/text/summer-travel-gps-simplified.csv')\n", + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e353887a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + " ... \n", + "173 False\n", + "174 False\n", + "175 False\n", + "176 False\n", + "177 True\n", + "Name: city, Length: 178, dtype: bool\n" + ] + } + ], + "source": [ + "# Q9\n", + "is_munich = df['city'] == 'Munich'\n", + "print(is_munich)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "dca0edb9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lat lon date city country\n", + "24 41.303911 2.105931 05/18/2014 22:35 El Prat de Llobregat Spain\n", + "25 41.289946 2.064590 05/18/2014 23:04 Viladecans Spain\n", + "126 41.306752 2.097624 06/12/2014 17:19 El Prat de Llobregat Spain\n", + "127 41.304333 2.072728 06/12/2014 17:49 El Prat de Llobregat Spain\n", + "131 41.358460 2.128701 06/13/2014 11:35 Hospitalet de Llobregat Spain\n", + "138 41.294761 2.059722 06/20/2014 22:15 Viladecans Spain\n" + ] + } + ], + "source": [ + "# Q10\n", + "mask = (df['country'] == 'Spain') & (df['city'] != 'Barcelona')\n", + "cities_in_spain_not_barcelona = df[mask]\n", + "\n", + "print(cities_in_spain_not_barcelona)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "26a72cf6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lat lon date city country\n", + "139 44.821164 20.289821 06/21/2014 01:59 Belgrade Serbia\n", + "140 44.820414 20.463465 06/21/2014 18:44 Belgrade Serbia\n", + "141 44.761583 19.577904 06/22/2014 07:58 Slepčević Serbia\n", + "177 48.357110 11.791346 07/09/2014 13:20 Munich Germany\n" + ] + } + ], + "source": [ + "# Q11\n", + "mask = (df['city'] == 'Munich') | (df['country'] == 'Serbia')\n", + "selected_rows = df[mask]\n", + "print(selected_rows)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "352450e8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of observations west of the prime meridian: 24\n" + ] + } + ], + "source": [ + "# Q12\n", + "observations_west_of_prime_meridian = df[df['lon'] < 0].shape[0]\n", + "print(\"Number of observations west of the prime meridian:\", observations_west_of_prime_meridian)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "fbf4dc06", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lat lon date city country\n", + "62 50.273632 18.729429 06/02/2014 06:39 Gliwice Poland\n", + "114 48.282940 8.199630 06/10/2014 13:33 Gutach Germany\n", + "115 48.389029 8.021342 06/10/2014 13:48 Gengenbach Germany\n", + "152 40.187825 20.079303 07/04/2014 17:42 Gjirokastër Albania\n" + ] + } + ], + "source": [ + "# Q13\n", + "mask = df['city'].str.startswith('G')\n", + "cities_starting_with_g = df[mask]\n", + "print(cities_starting_with_g)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "bf9e8755", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique_cities: 91\n", + "unique_countries: 15\n", + "missing_values_number: 0\n" + ] + } + ], + "source": [ + "# Q14\n", + "unique_cities = df['city'].nunique()\n", + "unique_countries = df['country'].nunique()\n", + "print(\"unique_cities:\",unique_cities)\n", + "print(\"unique_countries:\",unique_countries)\n", + "\n", + "missing_values = df.isnull().sum()\n", + "total_missing_values = missing_values.sum()\n", + "print(\"missing_values_number:\",total_missing_values)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "99a63a07", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Albania\n", + "151 Berat\n", + "152 Gjirokastër\n", + "Name: city, dtype: object\n", + "\n", + "Bosnia and Herzegovina\n", + "142 Sarajevo\n", + "143 Sarajevo\n", + "144 Mostar\n", + "Name: city, dtype: object\n", + "\n", + "Croatia\n", + "145 Ploče\n", + "146 Split\n", + "147 Dubrovnik\n", + "Name: city, dtype: object\n", + "\n", + "Czech Republic\n", + "65 Novy Bohumin\n", + "66 Hranice\n", + "67 Prerov\n", + "68 Ústí nad Orlicí\n", + "69 Kojice\n", + "70 Sadská\n", + "71 Prague\n", + "72 Prague\n", + "73 Prague\n", + "74 Prague\n", + "75 Úvaly\n", + "76 Poříčany\n", + "77 Kolin\n", + "78 Kutna Hora\n", + "79 Kutna Hora\n", + "80 Kutna Hora\n", + "81 Kutna Hora\n", + "82 Nové Dvory (Kutná Hora District)\n", + "83 Kolin\n", + "84 Český Brod\n", + "85 Prague\n", + "86 Prague\n", + "87 Prague\n", + "88 Prague\n", + "89 Prague\n", + "90 Prague\n", + "91 Mýto\n", + "92 Plzen\n", + "93 Blatnice\n", + "94 Rozvadov\n", + "Name: city, dtype: object\n", + "\n", + "Germany\n", + "95 Kümmersbruck\n", + "96 Winkelhaid\n", + "97 Kammerstein\n", + "98 Ellhofen\n", + "99 Obersulm\n", + "100 Stuttgart\n", + "101 Stuttgart\n", + "102 Wernau\n", + "103 Riederich\n", + "104 Tübingen\n", + "105 Tübingen\n", + "106 Tübingen\n", + "107 Tübingen\n", + "108 Tübingen\n", + "109 Rottenburg\n", + "110 Horb am Neckar\n", + "111 Eutingen im Gäu\n", + "112 Freudenstadt\n", + "113 Alpirsbach\n", + "114 Gutach\n", + "115 Gengenbach\n", + "116 Offenburg\n", + "117 Freiburg\n", + "118 Freiburg\n", + "119 Freiburg\n", + "120 Freiburg\n", + "121 Titisee-Neustadt\n", + "122 Villingen-Schwenningen\n", + "123 Horb am Neckar\n", + "124 Kirchentellinsfurt\n", + "125 Stuttgart\n", + "177 Munich\n", + "Name: city, dtype: object\n", + "\n", + "Greece\n", + "153 Kakavia\n", + "154 Dytiki Ellada\n", + "155 Dytiki Ellada\n", + "156 Dytiki Ellada\n", + "157 Dytiki Ellada\n", + "158 Dytiki Ellada\n", + "159 Dytiki Ellada\n", + "160 Peloponnese\n", + "161 Athens\n", + "162 Athens\n", + "163 Athens\n", + "164 Attica\n", + "Name: city, dtype: object\n", + "\n", + "Kosovo\n", + "149 Prizren\n", + "Name: city, dtype: object\n", + "\n", + "Macedonia (FYROM)\n", + "150 Ohrid\n", + "Name: city, dtype: object\n", + "\n", + "Montenegro\n", + "148 Kotor\n", + "Name: city, dtype: object\n", + "\n", + "Poland\n", + "45 Zendek\n", + "46 Silesian Voivodeship\n", + "47 Dabrowa Gornicza\n", + "48 Olkusz County\n", + "49 Krakow\n", + "50 Oswiecim\n", + "51 Babice, Oświęcim County\n", + "52 Wygiełzów, Lesser Poland Voivodeship\n", + "53 Chrzanów County\n", + "54 Krakow\n", + "55 Krakow\n", + "56 Wieliczka\n", + "57 Krakow\n", + "58 Balice, Lesser Poland Voivodeship\n", + "59 Trzebinia\n", + "60 Imielin\n", + "61 Ruda Slaska\n", + "62 Gliwice\n", + "63 Tarnowskie Góry County\n", + "64 Bytom\n", + "Name: city, dtype: object\n", + "\n", + "Portugal\n", + "1 Lisbon\n", + "2 Lisbon\n", + "3 Lisbon\n", + "4 Lisbon\n", + "5 Lisbon\n", + "6 Lisbon\n", + "7 Lisbon\n", + "8 Algueirão-Mem Martins\n", + "9 Sintra\n", + "10 Sintra\n", + "11 Sintra\n", + "12 Sintra\n", + "13 Lisbon\n", + "14 Lisbon\n", + "15 Azambuja\n", + "16 Santarém\n", + "17 Zibreira\n", + "18 São Mamede\n", + "19 Leiria District\n", + "20 Coimbra Municipality\n", + "21 Aveiro District\n", + "22 Vila Nova de Gaia\n", + "23 Maia\n", + "Name: city, dtype: object\n", + "\n", + "Serbia\n", + "139 Belgrade\n", + "140 Belgrade\n", + "141 Slepčević\n", + "Name: city, dtype: object\n", + "\n", + "Spain\n", + "24 El Prat de Llobregat\n", + "25 Viladecans\n", + "26 Barcelona\n", + "27 Barcelona\n", + "28 Barcelona\n", + "29 Barcelona\n", + "30 Barcelona\n", + "31 Barcelona\n", + "32 Barcelona\n", + "33 Barcelona\n", + "34 Barcelona\n", + "35 Barcelona\n", + "36 Barcelona\n", + "37 Barcelona\n", + "38 Barcelona\n", + "39 Barcelona\n", + "40 Barcelona\n", + "41 Barcelona\n", + "42 Barcelona\n", + "43 Barcelona\n", + "44 Barcelona\n", + "126 El Prat de Llobregat\n", + "127 El Prat de Llobregat\n", + "128 Barcelona\n", + "129 Barcelona\n", + "130 Barcelona\n", + "131 Hospitalet de Llobregat\n", + "132 Barcelona\n", + "133 Barcelona\n", + "134 Barcelona\n", + "135 Barcelona\n", + "136 Barcelona\n", + "137 Barcelona\n", + "138 Viladecans\n", + "Name: city, dtype: object\n", + "\n", + "Turkey\n", + "165 Istanbul\n", + "166 Istanbul\n", + "167 Istanbul\n", + "168 Istanbul\n", + "169 Istanbul\n", + "170 Istanbul\n", + "171 Istanbul\n", + "172 Istanbul\n", + "173 Istanbul\n", + "174 Istanbul\n", + "175 Istanbul\n", + "176 Istanbul\n", + "Name: city, dtype: object\n", + "\n", + "United Kingdom\n", + "0 West Drayton\n", + "Name: city, dtype: object\n", + "\n" + ] + } + ], + "source": [ + "# Q15\n", + "grouped_by_country = df.groupby('country')\n", + "for country, group in grouped_by_country:\n", + " print(country)\n", + " print(group['city'])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "f1a7b933", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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latloncitycountry
date
2014-05-14 09:07:0051.481292-0.451011West DraytonUnited Kingdom
2014-05-14 09:22:0051.474005-0.450999HounslowUnited Kingdom
2014-05-14 10:51:0051.478199-0.446081HounslowUnited Kingdom
2014-05-14 11:24:0051.478199-0.446081HounslowUnited Kingdom
2014-05-14 11:38:0051.474146-0.451562HounslowUnited Kingdom
\n", + "
" + ], + "text/plain": [ + " lat lon city country\n", + "date \n", + "2014-05-14 09:07:00 51.481292 -0.451011 West Drayton United Kingdom\n", + "2014-05-14 09:22:00 51.474005 -0.450999 Hounslow United Kingdom\n", + "2014-05-14 10:51:00 51.478199 -0.446081 Hounslow United Kingdom\n", + "2014-05-14 11:24:00 51.478199 -0.446081 Hounslow United Kingdom\n", + "2014-05-14 11:38:00 51.474146 -0.451562 Hounslow United Kingdom" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dt = pd.read_csv('D:/anaconda working/text/summer-travel-gps-full.csv', index_col='date', parse_dates=True)\n", + "dt.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "a9747a37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "index is unique？ True\n" + ] + } + ], + "source": [ + "# Q16\n", + "is_index_unique = df.index.is_unique\n", + "print(\"index is unique？\", is_index_unique)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "681b9413", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " lat lon date city country\n", + "0 51.481292 -0.451011 05/14/2014 09:07 West Drayton United Kingdom\n", + "1 38.781775 -9.137544 05/14/2014 15:11 Lisbon Portugal\n", + "2 38.711050 -9.139739 05/14/2014 16:40 Lisbon Portugal\n", + "3 38.715637 -9.120558 05/14/2014 18:25 Lisbon Portugal\n", + "4 38.711977 -9.141788 05/14/2014 19:26 Lisbon Portugal\n", + ".. ... ... ... ... ...\n", + "173 41.044556 28.983286 07/08/2014 16:44 Istanbul Turkey\n", + "174 41.008992 28.968268 07/08/2014 20:03 Istanbul Turkey\n", + "175 41.043487 28.985488 07/08/2014 22:18 Istanbul Turkey\n", + "176 40.977637 28.823879 07/09/2014 09:03 Istanbul Turkey\n", + "177 48.357110 11.791346 07/09/2014 13:20 Munich Germany\n", + "\n", + "[178 rows x 5 columns]\n" + ] + } + ], + "source": [ + "# Q17\n", + "df_unique_index = df[~df.index.duplicated(keep='first')]\n", + "print(df_unique_index)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "af99eaf3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "weekday DataFrame:\n", + " lat lon date \\\n", + "1970-01-01 00:00:00.000000000 51.481292 -0.451011 05/14/2014 09:07 \n", + "1970-01-01 00:00:00.000000001 38.781775 -9.137544 05/14/2014 15:11 \n", + "1970-01-01 00:00:00.000000002 38.711050 -9.139739 05/14/2014 16:40 \n", + "1970-01-01 00:00:00.000000003 38.715637 -9.120558 05/14/2014 18:25 \n", + "1970-01-01 00:00:00.000000004 38.711977 -9.141788 05/14/2014 19:26 \n", + "... ... ... ... \n", + "1970-01-01 00:00:00.000000173 41.044556 28.983286 07/08/2014 16:44 \n", + "1970-01-01 00:00:00.000000174 41.008992 28.968268 07/08/2014 20:03 \n", + "1970-01-01 00:00:00.000000175 41.043487 28.985488 07/08/2014 22:18 \n", + "1970-01-01 00:00:00.000000176 40.977637 28.823879 07/09/2014 09:03 \n", + "1970-01-01 00:00:00.000000177 48.357110 11.791346 07/09/2014 13:20 \n", + "\n", + " city country \n", + "1970-01-01 00:00:00.000000000 West Drayton United Kingdom \n", + "1970-01-01 00:00:00.000000001 Lisbon Portugal \n", + "1970-01-01 00:00:00.000000002 Lisbon Portugal \n", + "1970-01-01 00:00:00.000000003 Lisbon Portugal \n", + "1970-01-01 00:00:00.000000004 Lisbon Portugal \n", + "... ... ... \n", + "1970-01-01 00:00:00.000000173 Istanbul Turkey \n", + "1970-01-01 00:00:00.000000174 Istanbul Turkey \n", + "1970-01-01 00:00:00.000000175 Istanbul Turkey \n", + "1970-01-01 00:00:00.000000176 Istanbul Turkey \n", + "1970-01-01 00:00:00.000000177 Munich Germany \n", + "\n", + "[178 rows x 5 columns]\n", + "weedend DataFrame:\n", + "Empty DataFrame\n", + "Columns: [lat, lon, date, city, country]\n", + "Index: []\n" + ] + } + ], + "source": [ + "# Q18\n", + "df.index = pd.to_datetime(df.index)\n", + "weekday_index = df.index[df.index.weekday < 5] \n", + "weekend_index = df.index[df.index.weekday >= 5]\n", + "weekday_df = df.loc[weekday_index]\n", + "weekend_df = df.loc[weekend_index]\n", + "print(\"weekday DataFrame:\")\n", + "print(weekday_df)\n", + "print(\"weedend DataFrame:\")\n", + "print(weekend_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c0199fb5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Q19\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df.index = pd.to_datetime(df.index)\n", + "observations_per_day = df.groupby(df.index.dayofweek).size()\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "observations_per_day.plot(kind='bar', color='skyblue')\n", + "plt.title('Number of Observations per Day of the Week')\n", + "plt.xlabel('Day of the Week (0=Monday, 1=Tuesday, ..., 6=Sunday)')\n", + "plt.ylabel('Number of Observations')\n", + "plt.xticks(rotation=0)\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "595b6117", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}