{ "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: 13-March-2023\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 file in your computer\n", "* Upload the .xlx spreadsheet in the user's workspace\n", "\n", "The spreadsheet contains the following tabs:\n", "\n", "* Country_temperatureCRU: mean monthly and annual temperatures by country for the period 1961-1999. Values are in degrees Celsius.\n", "* Country_precipitationCRU: 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 Country_precipitationCRU 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": 4, "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_excel('./cckp_historical_data_0.xls', sheet_name='Country_precipitationCRU')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show keys() and datasets" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ISO_3DIGIT', 'Jan_precip', 'Feb_precip', 'Mar_precip', 'Apr_precip',\n", " 'May_precip', 'Jun_precip', 'July_precip', 'Aug_precip', 'Sept_precip',\n", " 'Oct_precip', 'Nov_precip', 'Dec_precip', 'Annual_precip'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show available keys()\n", "raw_data.keys()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ISO_3DIGITJan_precipFeb_precipMar_precipApr_precipMay_precipJun_precipJuly_precipAug_precipSept_precipOct_precipNov_precipDec_precipAnnual_precip
0AFG43.27564149.18457864.97141849.40662424.1824884.5591858.0343267.7364033.6674209.35527315.40912731.539374311.321856
1AGO154.987404148.544214169.914693109.07226715.1426750.3723320.3211373.41340520.10227469.766698144.510161155.158423991.305683
2ALB107.999418102.62583493.43308386.16250074.27391754.57916839.07500045.69633367.568500105.261417138.264167138.2958341053.235184
3ARE5.95538722.17800016.5876098.9614701.0805741.0940612.2840591.4055040.4959430.4285521.7405765.60029167.812025
4ARG68.37044063.65408869.53103746.48114836.28215428.08004625.89438524.09676830.07355447.71548755.56666463.300102559.045871
5ARM20.84030825.92638540.10553859.73115482.84869168.04715443.06392335.96969229.42953842.12738530.79407723.397923502.281769
6AUS80.55664675.80913061.97969931.12384730.00329323.82774423.29561820.26907617.84230224.36599333.09903150.919120473.091500
7AUT67.70153965.91356475.92148790.704667113.363616137.655924136.999410131.41753794.90541077.15084592.21007777.2397701161.183869
8AZE24.67986727.42884440.52277848.43735656.82755650.86251125.20357123.08684534.15297845.41275630.50388926.764289433.883241
9BDI154.085002139.050999170.240001193.73199994.36899912.2234303.74901013.85004056.13410096.152401171.953999157.2719991262.812000
\n", "
" ], "text/plain": [ " ISO_3DIGIT Jan_precip Feb_precip Mar_precip Apr_precip May_precip \\\n", "0 AFG 43.275641 49.184578 64.971418 49.406624 24.182488 \n", "1 AGO 154.987404 148.544214 169.914693 109.072267 15.142675 \n", "2 ALB 107.999418 102.625834 93.433083 86.162500 74.273917 \n", "3 ARE 5.955387 22.178000 16.587609 8.961470 1.080574 \n", "4 ARG 68.370440 63.654088 69.531037 46.481148 36.282154 \n", "5 ARM 20.840308 25.926385 40.105538 59.731154 82.848691 \n", "6 AUS 80.556646 75.809130 61.979699 31.123847 30.003293 \n", "7 AUT 67.701539 65.913564 75.921487 90.704667 113.363616 \n", "8 AZE 24.679867 27.428844 40.522778 48.437356 56.827556 \n", "9 BDI 154.085002 139.050999 170.240001 193.731999 94.368999 \n", "\n", " Jun_precip July_precip Aug_precip Sept_precip Oct_precip Nov_precip \\\n", "0 4.559185 8.034326 7.736403 3.667420 9.355273 15.409127 \n", "1 0.372332 0.321137 3.413405 20.102274 69.766698 144.510161 \n", "2 54.579168 39.075000 45.696333 67.568500 105.261417 138.264167 \n", "3 1.094061 2.284059 1.405504 0.495943 0.428552 1.740576 \n", "4 28.080046 25.894385 24.096768 30.073554 47.715487 55.566664 \n", "5 68.047154 43.063923 35.969692 29.429538 42.127385 30.794077 \n", "6 23.827744 23.295618 20.269076 17.842302 24.365993 33.099031 \n", "7 137.655924 136.999410 131.417537 94.905410 77.150845 92.210077 \n", "8 50.862511 25.203571 23.086845 34.152978 45.412756 30.503889 \n", "9 12.223430 3.749010 13.850040 56.134100 96.152401 171.953999 \n", "\n", " Dec_precip Annual_precip \n", "0 31.539374 311.321856 \n", "1 155.158423 991.305683 \n", "2 138.295834 1053.235184 \n", "3 5.600291 67.812025 \n", "4 63.300102 559.045871 \n", "5 23.397923 502.281769 \n", "6 50.919120 473.091500 \n", "7 77.239770 1161.183869 \n", "8 26.764289 433.883241 \n", "9 157.271999 1262.812000 " ] }, "execution_count": 6, "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": 7, "metadata": {}, "outputs": [], "source": [ "average_annual_rainfall = raw_data.groupby(['ISO_3DIGIT'])\n", "#average_annual_rainfall.describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ISO_3DIGITJan_precipFeb_precipMar_precipApr_precipMay_precipJun_precipJuly_precipAug_precipSept_precipOct_precipNov_precipDec_precipAnnual_precip
79ITA77.58259571.87711974.60104878.99883374.15334267.46072656.39023871.92112575.89435795.069706101.23637385.008159930.193617
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" ], "text/plain": [ " ISO_3DIGIT Jan_precip Feb_precip Mar_precip Apr_precip May_precip \\\n", "79 ITA 77.582595 71.877119 74.601048 78.998833 74.153342 \n", "\n", " Jun_precip July_precip Aug_precip Sept_precip Oct_precip Nov_precip \\\n", "79 67.460726 56.390238 71.921125 75.894357 95.069706 101.236373 \n", "\n", " Dec_precip Annual_precip \n", "79 85.008159 930.193617 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Filter datasets by ISO_3DIGIT\n", "iso_3digit_average_annual_rainfall = average_annual_rainfall.get_group(ISO_3DIGIT)\n", "iso_3digit_average_annual_rainfall" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create the DataFrame to plot" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Mean monthly and annual precipitation for period 1961-1999Precipitations
0Jan77.582595
1Feb71.877119
2Mar74.601048
3Apr78.998833
4May74.153342
5Jun67.460726
6Jul56.390238
7Aug71.921125
8Sept75.894357
9Oct95.069706
10Nov101.236373
11Dec85.008159
\n", "
" ], "text/plain": [ " Mean monthly and annual precipitation for period 1961-1999 Precipitations\n", "0 Jan 77.582595\n", "1 Feb 71.877119\n", "2 Mar 74.601048\n", "3 Apr 78.998833\n", "4 May 74.153342\n", "5 Jun 67.460726\n", "6 Jul 56.390238\n", "7 Aug 71.921125\n", "8 Sept 75.894357\n", "9 Oct 95.069706\n", "10 Nov 101.236373\n", "11 Dec 85.008159" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Data = {\n", " 'Mean monthly and annual precipitation for period 1961-1999': [\n", " 'Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sept','Oct','Nov','Dec'\n", " ],\n", " \n", " 'Precipitations': [\n", " iso_3digit_average_annual_rainfall['Jan_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Feb_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Mar_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Apr_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['May_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Jun_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['July_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Aug_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Sept_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Oct_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Nov_precip'].values[0],\n", " iso_3digit_average_annual_rainfall['Dec_precip'].values[0]\n", " ]\n", "}\n", "\n", "data_frame=DataFrame(Data, columns=['Mean monthly and annual precipitation for period 1961-1999', 'Precipitations'])\n", "data_frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the DataFrame " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_frame.plot(\n", " x='Mean monthly and annual precipitation for period 1961-1999', \n", " y='Precipitations',\n", " color='darkblue', \n", " figsize=(10,5),\n", " linewidth='3')\n", "\n", "# Add legend, grid and show the plot\n", "plt.grid()\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Save the final plot\n", "plt.savefig(\"./rainfalls.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 }