Explore_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore meteorological data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "import glob\n",
    "import keplergl\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import csv\n",
    "from datetime import datetime\n",
    "import re\n",
    "import numpy as np\n",
    "from pyproj import Proj\n",
    "%run Meteo_utils.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drive folder with original data: https://drive.google.com/drive/folders/1me2IpDY3om6IRKv_WMT5W6Qw0r9DI74C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.DS_Store',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto-20210505T205931Z-001.zip',\n",
       " 'Estaciones_meteorologicas.csv',\n",
       " 'Estaciones_meteorologicas_SW.csv',\n",
       " '02-HGPT-MT-06.- Estacion Baños-20210426T162523Z-001.zip',\n",
       " 'Inventario Estaciones Meteo.xls',\n",
       " '.ipynb_checkpoints',\n",
       " 'Estaciones_meteorologicas_SW.numbers',\n",
       " '02-HGPT-MT-06.- Estacion Baños']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read a sheet from an excel file\n",
    "file = glob.glob('data/*.xls')[0]\n",
    "sheets = pd.read_excel(file, None)\n",
    "list(sheets.keys())\n",
    "df = pd.read_excel(io=file, sheet_name=list(sheets.keys())[0], engine ='xlrd',skiprows = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuente</th>\n",
       "      <th>numero</th>\n",
       "      <th>codigo</th>\n",
       "      <th>nombre</th>\n",
       "      <th>tipo</th>\n",
       "      <th>provincia</th>\n",
       "      <th>estado</th>\n",
       "      <th>fecha_instalacion</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "      <th>parametros_medicion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>1</td>\n",
       "      <td>M0029</td>\n",
       "      <td>BAÑOS</td>\n",
       "      <td>CP</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>20-jun-62</td>\n",
       "      <td>12329.0</td>\n",
       "      <td>782505.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>2</td>\n",
       "      <td>M0258</td>\n",
       "      <td>QUEROCHACA(UTA)</td>\n",
       "      <td>CP</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>2-nov-85</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>783620.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>3</td>\n",
       "      <td>M1069</td>\n",
       "      <td>CALAMACA CONVENIO INAMHI HCPT</td>\n",
       "      <td>CP</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>4-jul-88</td>\n",
       "      <td>11634.0</td>\n",
       "      <td>784908.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>4</td>\n",
       "      <td>M0126</td>\n",
       "      <td>PATATE</td>\n",
       "      <td>CO</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>1-may-63</td>\n",
       "      <td>11801.0</td>\n",
       "      <td>783000.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>5</td>\n",
       "      <td>M0127</td>\n",
       "      <td>PILLARO</td>\n",
       "      <td>CO</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>1-Dec-63</td>\n",
       "      <td>11010.0</td>\n",
       "      <td>783310.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fuente  numero codigo                         nombre tipo   provincia  \\\n",
       "0  esta_inamhi       1  M0029                          BAÑOS   CP  TUNGURAHUA   \n",
       "1  esta_inamhi       2  M0258                QUEROCHACA(UTA)   CP  TUNGURAHUA   \n",
       "2  esta_inamhi       3  M1069  CALAMACA CONVENIO INAMHI HCPT   CP  TUNGURAHUA   \n",
       "3  esta_inamhi       4  M0126                         PATATE   CO  TUNGURAHUA   \n",
       "4  esta_inamhi       5  M0127                        PILLARO   CO  TUNGURAHUA   \n",
       "\n",
       "        estado fecha_instalacion  latitud  longitud  \\\n",
       "0  FUNCIONANDO         20-jun-62  12329.0  782505.0   \n",
       "1  FUNCIONANDO          2-nov-85  12202.0  783620.0   \n",
       "2  FUNCIONANDO          4-jul-88  11634.0  784908.0   \n",
       "3  FUNCIONANDO          1-may-63  11801.0  783000.0   \n",
       "4  FUNCIONANDO          1-Dec-63  11010.0  783310.0   \n",
       "\n",
       "                                 parametros_medicion  \n",
       "0  Temperatura Max, temperatura Min, Temperatura,...  \n",
       "1  Temperatura Max, temperatura Min, Temperatura,...  \n",
       "2  Temperatura Max, temperatura Min, Temperatura,...  \n",
       "3  Temperatura Max, temperatura Min, Temperatura,...  \n",
       "4  Temperatura Max, temperatura Min, Temperatura,...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/Estaciones_meteorologicas.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['esta_inamhi', 'esta_hgpt', 'esta_hgpt_sen'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fuente.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conversion from UTM to degrees\n",
    "# https://ocefpaf.github.io/python4oceanographers/blog/2013/12/16/utm/\n",
    "# EPSG:32717 (UTM 17S) (copy Proj4 format)\n",
    "north = Proj(\"+proj=utm +zone=17 +north +ellps=WGS84 +datum=WGS84 +units=m +no_defs\")\n",
    "south = Proj(\"+proj=utm +zone=17 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# UTM info : http://geokov.com/education/utm.aspx\n",
    "# Northing coordinates use Equator as reference. For NH, Equator= 0. For SH , Equator = 10.000.000\n",
    "# Easting coordinates use the false origin 500000 meters to the west of the central meridian of the UTM zone\n",
    "\n",
    "# ESTA INAMHI data had lat = xxxS and long = xxxW \n",
    "## 17N\n",
    "df.loc[df['fuente'] == 'esta_inamhi','lon_deg'], df.loc[df['fuente'] == 'esta_inamhi','lat_deg']= north(df[df['fuente'] == 'esta_inamhi']['longitud'].values, \n",
    "                                                                                          df[df['fuente'] == 'esta_inamhi']['latitud'].values, inverse=True)\n",
    "\n",
    "## 17S\n",
    "df.loc[df['fuente'] != 'esta_inamhi','lon_deg'], df.loc[df['fuente'] != 'esta_inamhi','lat_deg']= south(df[df['fuente'] != 'esta_inamhi']['longitud'].values, \n",
    "                                                                                          df[df['fuente'] != 'esta_inamhi']['latitud'].values, inverse=True)\n",
    "                               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuente</th>\n",
       "      <th>numero</th>\n",
       "      <th>codigo</th>\n",
       "      <th>nombre</th>\n",
       "      <th>tipo</th>\n",
       "      <th>provincia</th>\n",
       "      <th>estado</th>\n",
       "      <th>fecha_instalacion</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "      <th>parametros_medicion</th>\n",
       "      <th>lon_deg</th>\n",
       "      <th>lat_deg</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>1</td>\n",
       "      <td>M0029</td>\n",
       "      <td>BAÑOS</td>\n",
       "      <td>CP</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>20-jun-62</td>\n",
       "      <td>12329.0</td>\n",
       "      <td>782505.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "      <td>-78.462030</td>\n",
       "      <td>0.111434</td>\n",
       "      <td>POINT (-78.46203 0.11143)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>2</td>\n",
       "      <td>M0258</td>\n",
       "      <td>QUEROCHACA(UTA)</td>\n",
       "      <td>CP</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>2-nov-85</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>783620.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "      <td>-78.452020</td>\n",
       "      <td>0.110285</td>\n",
       "      <td>POINT (-78.45202 0.11029)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>3</td>\n",
       "      <td>M1069</td>\n",
       "      <td>CALAMACA CONVENIO INAMHI HCPT</td>\n",
       "      <td>CP</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>4-jul-88</td>\n",
       "      <td>11634.0</td>\n",
       "      <td>784908.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "      <td>-78.440457</td>\n",
       "      <td>0.105151</td>\n",
       "      <td>POINT (-78.44046 0.10515)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>4</td>\n",
       "      <td>M0126</td>\n",
       "      <td>PATATE</td>\n",
       "      <td>CO</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>1-may-63</td>\n",
       "      <td>11801.0</td>\n",
       "      <td>783000.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "      <td>-78.457586</td>\n",
       "      <td>0.106662</td>\n",
       "      <td>POINT (-78.45759 0.10666)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>5</td>\n",
       "      <td>M0127</td>\n",
       "      <td>PILLARO</td>\n",
       "      <td>CO</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>1-Dec-63</td>\n",
       "      <td>11010.0</td>\n",
       "      <td>783310.0</td>\n",
       "      <td>Temperatura Max, temperatura Min, Temperatura,...</td>\n",
       "      <td>-78.454804</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>POINT (-78.45480 0.09951)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        fuente  numero codigo                         nombre tipo   provincia  \\\n",
       "0  esta_inamhi       1  M0029                          BAÑOS   CP  TUNGURAHUA   \n",
       "1  esta_inamhi       2  M0258                QUEROCHACA(UTA)   CP  TUNGURAHUA   \n",
       "2  esta_inamhi       3  M1069  CALAMACA CONVENIO INAMHI HCPT   CP  TUNGURAHUA   \n",
       "3  esta_inamhi       4  M0126                         PATATE   CO  TUNGURAHUA   \n",
       "4  esta_inamhi       5  M0127                        PILLARO   CO  TUNGURAHUA   \n",
       "\n",
       "        estado fecha_instalacion  latitud  longitud  \\\n",
       "0  FUNCIONANDO         20-jun-62  12329.0  782505.0   \n",
       "1  FUNCIONANDO          2-nov-85  12202.0  783620.0   \n",
       "2  FUNCIONANDO          4-jul-88  11634.0  784908.0   \n",
       "3  FUNCIONANDO          1-may-63  11801.0  783000.0   \n",
       "4  FUNCIONANDO          1-Dec-63  11010.0  783310.0   \n",
       "\n",
       "                                 parametros_medicion    lon_deg   lat_deg  \\\n",
       "0  Temperatura Max, temperatura Min, Temperatura,... -78.462030  0.111434   \n",
       "1  Temperatura Max, temperatura Min, Temperatura,... -78.452020  0.110285   \n",
       "2  Temperatura Max, temperatura Min, Temperatura,... -78.440457  0.105151   \n",
       "3  Temperatura Max, temperatura Min, Temperatura,... -78.457586  0.106662   \n",
       "4  Temperatura Max, temperatura Min, Temperatura,... -78.454804  0.099512   \n",
       "\n",
       "                    geometry  \n",
       "0  POINT (-78.46203 0.11143)  \n",
       "1  POINT (-78.45202 0.11029)  \n",
       "2  POINT (-78.44046 0.10515)  \n",
       "3  POINT (-78.45759 0.10666)  \n",
       "4  POINT (-78.45480 0.09951)  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.lon_deg = df.lon_deg.replace({float(\"inf\"): np.nan})  \n",
    "df.lat_deg = df.lat_deg.replace({float(\"inf\"): np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### There is a problem with the coordinates. I can extract the coordinates from ESTA_INAMHI but not form the other 2. UTM format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gpd.GeoDataFrame(\n",
    "    df, geometry=gpd.points_from_xy(df.lon_deg, df.lat_deg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gdf.set_crs(epsg=4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "SA = world.query('name == \"Ecuador\"')\n",
    "gdf4326 = gdf.to_crs(world.crs)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.set_aspect('equal')\n",
    "SA.plot(ax=ax, color='white', edgecolor='black')\n",
    "gdf4326.plot(ax=ax)\n",
    "# for i in range(df.shape[0]):\n",
    "#     plt.text(x=gdf4326.lon_deg[i]+0.1,y=gdf4326.lat_deg[i]+0.1,s=gdf4326.nombre[i], \n",
    "#           fontdict=dict(color='black',size=10),\n",
    "#           bbox=dict(facecolor='yellow',alpha=0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuente</th>\n",
       "      <th>numero</th>\n",
       "      <th>codigo</th>\n",
       "      <th>nombre</th>\n",
       "      <th>tipo</th>\n",
       "      <th>provincia</th>\n",
       "      <th>estado</th>\n",
       "      <th>fecha_instalacion</th>\n",
       "      <th>latitud</th>\n",
       "      <th>longitud</th>\n",
       "      <th>lon_deg</th>\n",
       "      <th>lat_deg</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>esta_inamhi</td>\n",
       "      <td>1</td>\n",
       "      <td>M0029</td>\n",
       "      <td>BAÑOS</td>\n",
       "      <td>CP</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>20-jun-62</td>\n",
       "      <td>12329.0</td>\n",
       "      <td>782505.0</td>\n",
       "      <td>-78.462030</td>\n",
       "      <td>0.111434</td>\n",
       "      <td>POINT (-78.46203 0.11143)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>esta_hgpt</td>\n",
       "      <td>6</td>\n",
       "      <td>HGPT_MT-0006</td>\n",
       "      <td>Baños</td>\n",
       "      <td>MA</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>February-13</td>\n",
       "      <td>9845439.0</td>\n",
       "      <td>791471.0</td>\n",
       "      <td>-78.380768</td>\n",
       "      <td>-1.396889</td>\n",
       "      <td>POINT (-78.38077 -1.39689)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>esta_hgpt_sen</td>\n",
       "      <td>6</td>\n",
       "      <td>HGPT_MT-0006</td>\n",
       "      <td>Baños</td>\n",
       "      <td>MA</td>\n",
       "      <td>TUNGURAHUA</td>\n",
       "      <td>FUNCIONANDO</td>\n",
       "      <td>February-13</td>\n",
       "      <td>9845439.0</td>\n",
       "      <td>791471.0</td>\n",
       "      <td>-78.380768</td>\n",
       "      <td>-1.396889</td>\n",
       "      <td>POINT (-78.38077 -1.39689)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fuente  numero        codigo nombre tipo   provincia       estado  \\\n",
       "0     esta_inamhi       1         M0029  BAÑOS   CP  TUNGURAHUA  FUNCIONANDO   \n",
       "16      esta_hgpt       6  HGPT_MT-0006  Baños   MA  TUNGURAHUA  FUNCIONANDO   \n",
       "35  esta_hgpt_sen       6  HGPT_MT-0006  Baños   MA  TUNGURAHUA  FUNCIONANDO   \n",
       "\n",
       "   fecha_instalacion    latitud  longitud    lon_deg   lat_deg  \\\n",
       "0          20-jun-62    12329.0  782505.0 -78.462030  0.111434   \n",
       "16       February-13  9845439.0  791471.0 -78.380768 -1.396889   \n",
       "35       February-13  9845439.0  791471.0 -78.380768 -1.396889   \n",
       "\n",
       "                      geometry  \n",
       "0    POINT (-78.46203 0.11143)  \n",
       "16  POINT (-78.38077 -1.39689)  \n",
       "35  POINT (-78.38077 -1.39689)  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.nombre.str.match('^[Bb][Aa][Ññ][Oo][Ss]')].drop(columns ='parametros_medicion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Automate data extraction from Drive downloads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/Users/tamarahuete/Documents/Github_repos/TFM21/data'\n",
    "ziplist = glob.glob(f'{path}/*.zip')\n",
    "#ziplist = glob.glob(f'data/*.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/tamarahuete/Documents/Github_repos/TFM21/data/07-HGPT-MT-07.- Estacion Aeropuerto-20210505T205931Z-001.zip',\n",
       " '/Users/tamarahuete/Documents/Github_repos/TFM21/data/02-HGPT-MT-06.- Estacion Baños-20210426T162523Z-001.zip']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ziplist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['07-HGPT-MT-07.- Estacion Aeropuerto/2013-06-13/Aeropuerto_5min_20130624.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2018-01-11/8310_5min_20180116.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2013-06-13/8310_1min_20170404.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2015-07-02/8310_5min_20150714_subir.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2018-01-11/8310_5min_20180116_subir.csv']"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zf = zipfile.ZipFile(f'{ziplist[0]}')\n",
    "zf.namelist()[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "## order files and skip resumen and xls sheets\n",
    "files_by_date = order_meteo_zip(path, folder = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['07-HGPT-MT-07.- Estacion Aeropuerto/2013-03-26/8310_1min_20170404.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2013-03-26/Aeropuerto_1hora_20130517.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2013-05-07/8310_1min_20170404.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2013-05-07/Aeropuerto_5min_20140417.csv',\n",
       " '07-HGPT-MT-07.- Estacion Aeropuerto/2013-06-13/Aeropuerto_5min_20130624.csv']"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_by_date[0:5]  ### the filenames may be wrong, the order is done following the folder date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success regular csv, no header, latin encoding\n",
      "file =07-HGPT-MT-07.- Estacion Aeropuerto/2013-03-26/8310_1min_20170404.csv, cols = 6\n"
     ]
    }
   ],
   "source": [
    "df = read_meteo_csv(path =path,folder = 0,file = files_by_date[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Variable</th>\n",
       "      <th>Value</th>\n",
       "      <th>type</th>\n",
       "      <th>Freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03/19/2013</td>\n",
       "      <td>21:18:00</td>\n",
       "      <td>TB1minAcc</td>\n",
       "      <td>0.00</td>\n",
       "      <td>G</td>\n",
       "      <td>5min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03/19/2013</td>\n",
       "      <td>21:18:00</td>\n",
       "      <td>RHAvg</td>\n",
       "      <td>91.10</td>\n",
       "      <td>G</td>\n",
       "      <td>5min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/19/2013</td>\n",
       "      <td>21:18:00</td>\n",
       "      <td>ATAvg</td>\n",
       "      <td>10.07</td>\n",
       "      <td>G</td>\n",
       "      <td>5min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03/19/2013</td>\n",
       "      <td>21:19:00</td>\n",
       "      <td>TB1minAcc</td>\n",
       "      <td>0.00</td>\n",
       "      <td>G</td>\n",
       "      <td>5min</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03/19/2013</td>\n",
       "      <td>21:19:00</td>\n",
       "      <td>RHAvg</td>\n",
       "      <td>92.39</td>\n",
       "      <td>G</td>\n",
       "      <td>5min</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time   Variable  Value type  Freq\n",
       "0  03/19/2013  21:18:00  TB1minAcc   0.00    G  5min\n",
       "1  03/19/2013  21:18:00      RHAvg  91.10    G  5min\n",
       "2  03/19/2013  21:18:00      ATAvg  10.07    G  5min\n",
       "3  03/19/2013  21:19:00  TB1minAcc   0.00    G  5min\n",
       "4  03/19/2013  21:19:00      RHAvg  92.39    G  5min"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success regular csv, no header, latin encoding\n",
      "file =07-HGPT-MT-07.- Estacion Aeropuerto/2013-03-26/8310_1min_20170404.csv, cols = 5\n",
      "Success sep \";\", latin encoding\n",
      "file =07-HGPT-MT-07.- Estacion Aeropuerto/2013-03-26/Aeropuerto_1hora_20130517.csv, cols = 5\n",
      "Success regular csv, no header, latin encoding\n",
      "file =07-HGPT-MT-07.- Estacion Aeropuerto/2013-05-07/8310_1min_20170404.csv, cols = 5\n",
      "Success sep \";\", latin encoding\n",
      "file =07-HGPT-MT-07.- Estacion Aeropuerto/2013-05-07/Aeropuerto_5min_20140417.csv, cols = 5\n",
      "Success sep \";\", latin encoding\n",
      "file =07-HGPT-MT-07.- Estacion Aeropuerto/2013-06-13/Aeropuerto_5min_20130624.csv, cols = 5\n"
     ]
    }
   ],
   "source": [
    "all_var = get_unique_variables(files_by_date, export_name='summary_aeropuerto.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Variable names:\n",
    "\n",
    "# What are 'TB1hrAcc','TB1minAcc','TB5minAcc','BAT'?\n",
    "\n",
    "replace_values ={\n",
    "# Temperature\n",
    "    'ATAvg' :['AT1HrAvg', 'AT5minAvg', 'ATAvg','Temperatura'],\n",
    "    'ATMin' : ['AT1HrMin', 'AT5minMin','T_Min'],\n",
    "    'ATMax' : ['AT1HrMax','AT5minMax', 'T_Max'],\n",
    " \n",
    " # Relative Humidity\n",
    "    'RHAvg' : ['Humedad','RH5minAvg','RHAvg','RelHumidAvg'],\n",
    "    'RHMin' : ['H_Min','RH5minMin','RelHumidMin'],\n",
    "    'RHMax' : ['H_Max', 'RH5minMax', 'RelHumidMax'],\n",
    "\n",
    " # Wind Speed\n",
    "    'WSAvg' : ['Velocidad','WS5minAvg','WSAvg','WindMnSpdSclr'],\n",
    "    'WSMin' : ['WindMinSpdSclr','WS5minMin'],\n",
    "    'WSMax' : ['WindMaxSpdSclr','WS5minMax'],\n",
    "\n",
    " # Wind Direction\n",
    "    'WDAvg' : ['Direccion', 'WDAvg','WD5minAvg','WindMnDirUnit'],\n",
    "    'WDMin' : ['WD5minMin'],\n",
    "    'WDMax' : ['WD5minMax','WindMaxDir'],\n",
    "\n",
    " # Rain\n",
    "    'PAvg' : ['Precipitacion']\n",
    "}\n",
    "\n",
    "var_list = ['ATAvg','ATMin','ATMax','RHAvg','RHMin','RHMax', 'WSAvg','WSMin','WSMax','WDAvg','WDMin', 'WDMax','PAvg','TB1hrAcc','TB1minAcc','TB5minAcc','BAT']\n",
    "sorted(var_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reformat_df(df, replace_values):\n",
    "    \n",
    "    ## replace variables\n",
    "    for variable in list(replace_values.keys()):\n",
    "        df =df.replace(to_replace =replace_values[variable], value = variable)\n",
    "    \n",
    "    ## delete any empty rows\n",
    "    df.dropna(inplace=True)\n",
    "        \n",
    "    ## merge date + time\n",
    "    df['Date_Time']= pd.to_datetime(df['Date'] + ' ' + df['Time'])\n",
    "    df.drop(columns =['Date','Time'],inplace =True)\n",
    "    \n",
    "    #Convert to long format\n",
    "    df = df.pivot(index='Date_Time', columns='Variable', values='Value').reset_index()\n",
    "    \n",
    "    ## Add variables that are not in the df but are in the general list\n",
    "    not_in_df = list(set(var_list)-set(df.columns))\n",
    "    for i in not_in_df:\n",
    "        df[i]=np.nan\n",
    "    \n",
    "    ## Order columns so all dfs have the same structure\n",
    "    var_order = ['Date_Time']\n",
    "    var_order.extend(sorted(df.loc[:,df.columns != 'Date_Time'].columns))\n",
    "    df.reindex(var_order, axis=1)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = reformat_df(df=df, replace_values=replace_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Variable</th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>ATAvg</th>\n",
       "      <th>RHAvg</th>\n",
       "      <th>TB1minAcc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-19 21:18:00</td>\n",
       "      <td>10.07</td>\n",
       "      <td>91.10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-19 21:19:00</td>\n",
       "      <td>10.17</td>\n",
       "      <td>92.39</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-19 21:20:00</td>\n",
       "      <td>10.20</td>\n",
       "      <td>91.66</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-19 21:21:00</td>\n",
       "      <td>10.05</td>\n",
       "      <td>91.80</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-19 21:22:00</td>\n",
       "      <td>10.03</td>\n",
       "      <td>92.58</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9504</th>\n",
       "      <td>2013-03-26 11:42:00</td>\n",
       "      <td>15.81</td>\n",
       "      <td>80.41</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9505</th>\n",
       "      <td>2013-03-26 11:43:00</td>\n",
       "      <td>15.84</td>\n",
       "      <td>80.65</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9506</th>\n",
       "      <td>2013-03-26 11:44:00</td>\n",
       "      <td>15.80</td>\n",
       "      <td>80.85</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9507</th>\n",
       "      <td>2013-03-26 11:45:00</td>\n",
       "      <td>15.86</td>\n",
       "      <td>81.30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9508</th>\n",
       "      <td>2013-03-26 11:46:00</td>\n",
       "      <td>15.85</td>\n",
       "      <td>78.21</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9509 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Variable           Date_Time  ATAvg  RHAvg  TB1minAcc\n",
       "0        2013-03-19 21:18:00  10.07  91.10        0.0\n",
       "1        2013-03-19 21:19:00  10.17  92.39        0.0\n",
       "2        2013-03-19 21:20:00  10.20  91.66        0.0\n",
       "3        2013-03-19 21:21:00  10.05  91.80        0.0\n",
       "4        2013-03-19 21:22:00  10.03  92.58        0.0\n",
       "...                      ...    ...    ...        ...\n",
       "9504     2013-03-26 11:42:00  15.81  80.41        0.0\n",
       "9505     2013-03-26 11:43:00  15.84  80.65        0.0\n",
       "9506     2013-03-26 11:44:00  15.80  80.85        0.0\n",
       "9507     2013-03-26 11:45:00  15.86  81.30        0.0\n",
       "9508     2013-03-26 11:46:00  15.85  78.21        0.0\n",
       "\n",
       "[9509 rows x 4 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date_Time']= pd.to_datetime(df['Date'] + ' ' + df['Time'])\n",
    "df.pivot(index='Date_Time', columns='Variable', values='Value').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Date_Time'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-202-cd3f5c300f4b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date_Time'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Freq'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Variable'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Value'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(self, index, columns, values)\u001b[0m\n\u001b[1;32m   5921\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5923\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5924\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5925\u001b[0m     _shared_docs[\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/reshape/pivot.py\u001b[0m in \u001b[0;36mpivot\u001b[0;34m(data, index, columns, values)\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m             \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2804\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2805\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2806\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2808\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1552\u001b[0m         self._validate_read_indexer(\n\u001b[0;32m-> 1553\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1554\u001b[0m         )\n\u001b[1;32m   1555\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1644\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"loc\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1645\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1646\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m             \u001b[0;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Date_Time'] not in index\""
     ]
    }
   ],
   "source": [
    "df.pivot(index=['Date_Time','Freq'], columns='Variable', values='Value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### next step: How to keep all columns with pivoy or use group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Convert to long format\n",
    "#df.melt(id_vars='Fecha', value_vars=list(df.columns)[1:], var_name='Variable', value_name='value')"
   ]
  }
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