{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse historical data from the Climate Change Knowledge Portal \n",
    "\n",
    "Author: [Giuseppe La Rocca] (mailto: giuseppe.larocca@egi.eu)\n",
    "\n",
    "Creation date: 03-Sept-2019\n",
    "\n",
    "Last updated: 04-Sept-2019\n",
    "\n",
    "---\n",
    "\n",
    "## Exercise: \n",
    "\n",
    "Calculate the historical precipitation data derived from the Climate Research Unit (Mitchell et at, 2003) aggregated to country and basin levels.\n",
    "\n",
    "* Visit the [World Data Catalogue](https://datacatalog.worldbank.org/dataset/climate-change-knowledge-portal-historical-data)\n",
    "* Click on the \"Data & Resources\" tab\n",
    "* Download a copy of the \"Climate Chnage Knowledge Portal: Historical Data\" and safe the .xls spreadsheet in your computer\n",
    "* Upload the .xls spreadsheet in the Notebook user's workspace\n",
    "\n",
    "The \"Climate Chnage Knowledge Portal: Historical Data\" spreadsheet contains the following tabs:\n",
    "\n",
    "* <u>Country_temperatureCRU</u>: mean monthly and annual temperatures by country for the period 1961-1999.  Values are in degrees Celsius.\n",
    "* <u>Country_precipitationCRU</u>: mean monthly and annual precipitation by country for the period 1961-1999.  Values are in millimeters (mm).\n",
    "\n",
    "For this exercise the dataset in the <u>Country_temperatureCRU</u> tab will be used.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide the ISO_3DIGIT of the country you are interested to analyse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ISO_3DIGIT=\"ITA\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load historical datasets from local and create a DataFrame object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_excel('./cckp_historical_data_0.xls', sheet_name='Country_temperatureCRU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show keys() and datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ISO_3DIGIT', 'Jan_Temp', 'Feb_temp', 'Mar_temp', 'Apr_Temp',\n",
       "       'May_temp', 'Jun_Temp', 'July_Temp', 'Aug_Temp', 'Sept_temp',\n",
       "       'Oct_temp', 'Nov_Temp', 'Dec_temp', 'Annual_temp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show available keys()\n",
    "raw_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>ISO_3DIGIT</th>\n",
       "      <th>Jan_Temp</th>\n",
       "      <th>Feb_temp</th>\n",
       "      <th>Mar_temp</th>\n",
       "      <th>Apr_Temp</th>\n",
       "      <th>May_temp</th>\n",
       "      <th>Jun_Temp</th>\n",
       "      <th>July_Temp</th>\n",
       "      <th>Aug_Temp</th>\n",
       "      <th>Sept_temp</th>\n",
       "      <th>Oct_temp</th>\n",
       "      <th>Nov_Temp</th>\n",
       "      <th>Dec_temp</th>\n",
       "      <th>Annual_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>0.073058</td>\n",
       "      <td>2.109406</td>\n",
       "      <td>7.596974</td>\n",
       "      <td>13.370239</td>\n",
       "      <td>18.216310</td>\n",
       "      <td>23.201315</td>\n",
       "      <td>25.258537</td>\n",
       "      <td>23.766517</td>\n",
       "      <td>19.034575</td>\n",
       "      <td>12.992646</td>\n",
       "      <td>7.004094</td>\n",
       "      <td>2.433517</td>\n",
       "      <td>12.921455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO</td>\n",
       "      <td>22.582236</td>\n",
       "      <td>22.683536</td>\n",
       "      <td>22.784139</td>\n",
       "      <td>22.353582</td>\n",
       "      <td>20.739575</td>\n",
       "      <td>18.373315</td>\n",
       "      <td>17.946175</td>\n",
       "      <td>19.902466</td>\n",
       "      <td>22.187548</td>\n",
       "      <td>23.181599</td>\n",
       "      <td>22.786151</td>\n",
       "      <td>22.610858</td>\n",
       "      <td>21.510933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB</td>\n",
       "      <td>2.023067</td>\n",
       "      <td>3.218058</td>\n",
       "      <td>6.035317</td>\n",
       "      <td>9.917867</td>\n",
       "      <td>14.442750</td>\n",
       "      <td>17.927750</td>\n",
       "      <td>20.538917</td>\n",
       "      <td>20.479667</td>\n",
       "      <td>17.159083</td>\n",
       "      <td>12.265775</td>\n",
       "      <td>7.575808</td>\n",
       "      <td>3.653617</td>\n",
       "      <td>11.269800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARE</td>\n",
       "      <td>18.427522</td>\n",
       "      <td>19.428044</td>\n",
       "      <td>22.612913</td>\n",
       "      <td>26.578435</td>\n",
       "      <td>30.623652</td>\n",
       "      <td>32.457087</td>\n",
       "      <td>33.796130</td>\n",
       "      <td>33.550869</td>\n",
       "      <td>31.743130</td>\n",
       "      <td>28.343218</td>\n",
       "      <td>24.061783</td>\n",
       "      <td>20.283739</td>\n",
       "      <td>26.825609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG</td>\n",
       "      <td>20.803533</td>\n",
       "      <td>19.899523</td>\n",
       "      <td>17.514634</td>\n",
       "      <td>14.046284</td>\n",
       "      <td>10.647732</td>\n",
       "      <td>7.657256</td>\n",
       "      <td>7.421399</td>\n",
       "      <td>9.015955</td>\n",
       "      <td>11.529476</td>\n",
       "      <td>14.674651</td>\n",
       "      <td>17.544032</td>\n",
       "      <td>19.828148</td>\n",
       "      <td>14.215225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ARM</td>\n",
       "      <td>-8.663131</td>\n",
       "      <td>-6.652077</td>\n",
       "      <td>-0.566410</td>\n",
       "      <td>6.619723</td>\n",
       "      <td>11.432308</td>\n",
       "      <td>15.579385</td>\n",
       "      <td>19.819307</td>\n",
       "      <td>19.284308</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>7.922046</td>\n",
       "      <td>1.618720</td>\n",
       "      <td>-4.872554</td>\n",
       "      <td>6.374362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AUS</td>\n",
       "      <td>27.784496</td>\n",
       "      <td>27.229410</td>\n",
       "      <td>25.368697</td>\n",
       "      <td>21.874739</td>\n",
       "      <td>17.858305</td>\n",
       "      <td>14.832909</td>\n",
       "      <td>13.954677</td>\n",
       "      <td>15.707861</td>\n",
       "      <td>18.890749</td>\n",
       "      <td>22.457086</td>\n",
       "      <td>25.127116</td>\n",
       "      <td>26.994101</td>\n",
       "      <td>21.506676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AUT</td>\n",
       "      <td>-3.516328</td>\n",
       "      <td>-1.985213</td>\n",
       "      <td>1.418378</td>\n",
       "      <td>5.509071</td>\n",
       "      <td>10.118082</td>\n",
       "      <td>13.286418</td>\n",
       "      <td>15.255667</td>\n",
       "      <td>14.979069</td>\n",
       "      <td>12.092438</td>\n",
       "      <td>7.550790</td>\n",
       "      <td>1.733680</td>\n",
       "      <td>-2.209830</td>\n",
       "      <td>6.186013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AZE</td>\n",
       "      <td>-0.200881</td>\n",
       "      <td>0.803707</td>\n",
       "      <td>4.970370</td>\n",
       "      <td>11.643364</td>\n",
       "      <td>16.801467</td>\n",
       "      <td>21.729355</td>\n",
       "      <td>24.763133</td>\n",
       "      <td>24.111133</td>\n",
       "      <td>19.831755</td>\n",
       "      <td>12.881762</td>\n",
       "      <td>7.325707</td>\n",
       "      <td>2.032077</td>\n",
       "      <td>12.224344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BDI</td>\n",
       "      <td>20.243000</td>\n",
       "      <td>20.388000</td>\n",
       "      <td>20.429700</td>\n",
       "      <td>20.368500</td>\n",
       "      <td>20.046100</td>\n",
       "      <td>19.372300</td>\n",
       "      <td>19.356400</td>\n",
       "      <td>20.443000</td>\n",
       "      <td>21.159100</td>\n",
       "      <td>20.983400</td>\n",
       "      <td>20.239100</td>\n",
       "      <td>20.165700</td>\n",
       "      <td>20.266100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ISO_3DIGIT   Jan_Temp   Feb_temp   Mar_temp   Apr_Temp   May_temp  \\\n",
       "0        AFG   0.073058   2.109406   7.596974  13.370239  18.216310   \n",
       "1        AGO  22.582236  22.683536  22.784139  22.353582  20.739575   \n",
       "2        ALB   2.023067   3.218058   6.035317   9.917867  14.442750   \n",
       "3        ARE  18.427522  19.428044  22.612913  26.578435  30.623652   \n",
       "4        ARG  20.803533  19.899523  17.514634  14.046284  10.647732   \n",
       "5        ARM  -8.663131  -6.652077  -0.566410   6.619723  11.432308   \n",
       "6        AUS  27.784496  27.229410  25.368697  21.874739  17.858305   \n",
       "7        AUT  -3.516328  -1.985213   1.418378   5.509071  10.118082   \n",
       "8        AZE  -0.200881   0.803707   4.970370  11.643364  16.801467   \n",
       "9        BDI  20.243000  20.388000  20.429700  20.368500  20.046100   \n",
       "\n",
       "    Jun_Temp  July_Temp   Aug_Temp  Sept_temp   Oct_temp   Nov_Temp  \\\n",
       "0  23.201315  25.258537  23.766517  19.034575  12.992646   7.004094   \n",
       "1  18.373315  17.946175  19.902466  22.187548  23.181599  22.786151   \n",
       "2  17.927750  20.538917  20.479667  17.159083  12.265775   7.575808   \n",
       "3  32.457087  33.796130  33.550869  31.743130  28.343218  24.061783   \n",
       "4   7.657256   7.421399   9.015955  11.529476  14.674651  17.544032   \n",
       "5  15.579385  19.819307  19.284308  14.970000   7.922046   1.618720   \n",
       "6  14.832909  13.954677  15.707861  18.890749  22.457086  25.127116   \n",
       "7  13.286418  15.255667  14.979069  12.092438   7.550790   1.733680   \n",
       "8  21.729355  24.763133  24.111133  19.831755  12.881762   7.325707   \n",
       "9  19.372300  19.356400  20.443000  21.159100  20.983400  20.239100   \n",
       "\n",
       "    Dec_temp  Annual_temp  \n",
       "0   2.433517    12.921455  \n",
       "1  22.610858    21.510933  \n",
       "2   3.653617    11.269800  \n",
       "3  20.283739    26.825609  \n",
       "4  19.828148    14.215225  \n",
       "5  -4.872554     6.374362  \n",
       "6  26.994101    21.506676  \n",
       "7  -2.209830     6.186013  \n",
       "8   2.032077    12.224344  \n",
       "9  20.165700    20.266100  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group datasets based on the \"ISO_3DIGIT\" code and check data structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_annual_temperature = raw_data.groupby(['ISO_3DIGIT'])\n",
    "#average_annual_temperature.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ISO_3DIGIT</th>\n",
       "      <th>Jan_Temp</th>\n",
       "      <th>Feb_temp</th>\n",
       "      <th>Mar_temp</th>\n",
       "      <th>Apr_Temp</th>\n",
       "      <th>May_temp</th>\n",
       "      <th>Jun_Temp</th>\n",
       "      <th>July_Temp</th>\n",
       "      <th>Aug_Temp</th>\n",
       "      <th>Sept_temp</th>\n",
       "      <th>Oct_temp</th>\n",
       "      <th>Nov_Temp</th>\n",
       "      <th>Dec_temp</th>\n",
       "      <th>Annual_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>ITA</td>\n",
       "      <td>3.318792</td>\n",
       "      <td>4.34936</td>\n",
       "      <td>6.522076</td>\n",
       "      <td>9.612692</td>\n",
       "      <td>13.777579</td>\n",
       "      <td>17.524698</td>\n",
       "      <td>20.305087</td>\n",
       "      <td>20.144344</td>\n",
       "      <td>17.185813</td>\n",
       "      <td>12.856856</td>\n",
       "      <td>8.012446</td>\n",
       "      <td>4.398412</td>\n",
       "      <td>11.500694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ISO_3DIGIT  Jan_Temp  Feb_temp  Mar_temp  Apr_Temp   May_temp   Jun_Temp  \\\n",
       "79        ITA  3.318792   4.34936  6.522076  9.612692  13.777579  17.524698   \n",
       "\n",
       "    July_Temp   Aug_Temp  Sept_temp   Oct_temp  Nov_Temp  Dec_temp  \\\n",
       "79  20.305087  20.144344  17.185813  12.856856  8.012446  4.398412   \n",
       "\n",
       "    Annual_temp  \n",
       "79    11.500694  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter datasets by ISO_3DIGIT\n",
    "iso_3digit_average_annual_temperature = average_annual_temperature.get_group(ISO_3DIGIT)\n",
    "iso_3digit_average_annual_temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the DataFrame to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean monthly and annual temperature for period 1961-1999</th>\n",
       "      <th>Temperatures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>3.318792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>4.349360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>6.522076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>9.612692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>13.777579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>17.524698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jul</td>\n",
       "      <td>20.305087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>20.144344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sept</td>\n",
       "      <td>17.185813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>12.856856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>8.012446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>4.398412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mean monthly and annual temperature for period 1961-1999  Temperatures\n",
       "0                                                 Jan            3.318792\n",
       "1                                                 Feb            4.349360\n",
       "2                                                 Mar            6.522076\n",
       "3                                                 Apr            9.612692\n",
       "4                                                 May           13.777579\n",
       "5                                                 Jun           17.524698\n",
       "6                                                 Jul           20.305087\n",
       "7                                                 Aug           20.144344\n",
       "8                                                Sept           17.185813\n",
       "9                                                 Oct           12.856856\n",
       "10                                                Nov            8.012446\n",
       "11                                                Dec            4.398412"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = {\n",
    "    'Mean monthly and annual temperature for period 1961-1999': [\n",
    "          'Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sept','Oct','Nov','Dec'\n",
    "    ],\n",
    "    \n",
    "    'Temperatures': [\n",
    "          iso_3digit_average_annual_temperature['Jan_Temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Feb_temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Mar_temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Apr_Temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['May_temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Jun_Temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['July_Temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Aug_Temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Sept_temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Oct_temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Nov_Temp'].values[0],\n",
    "          iso_3digit_average_annual_temperature['Dec_temp'].values[0]\n",
    "    ]\n",
    "}\n",
    "\n",
    "data_frame=DataFrame(Data, columns=['Mean monthly and annual temperature for period 1961-1999', 'Temperatures'])\n",
    "data_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_frame.plot(\n",
    "    x='Mean monthly and annual temperature for period 1961-1999', \n",
    "    y='Temperatures',\n",
    "    color='lightblue', \n",
    "    figsize=(10,5),\n",
    "    linewidth='3')\n",
    "\n",
    "# Add legend, grid and show the plot\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "\n",
    "# Saving the final plot\n",
    "plt.savefig(\"temperatures.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}