{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "62ad30ef", "metadata": { "ExecuteTime": { "end_time": "2022-12-23T07:52:51.688213Z", "start_time": "2022-12-23T07:52:51.685959Z" } }, "outputs": [], "source": [ "# Source of data:\n", "# National Centers for Environmental Information (NCEI)\n", "# Global Surface Summary of the Day - GSOD\n", "# https://www.ncei.noaa.gov\n", "# https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00516" ] }, { "cell_type": "code", "execution_count": 2, "id": "98bc1a92", "metadata": { "ExecuteTime": { "end_time": "2022-12-23T07:54:39.802223Z", "start_time": "2022-12-23T07:53:12.738755Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2001 01 31\n", "2001 02 28\n", "2001 03 31\n", "2001 04 30\n", "2001 05 31\n", "2001 06 30\n", "2001 07 31\n", "2001 08 31\n", "2001 09 30\n", "2001 10 31\n", "2001 11 30\n", "2001 12 31\n", "2002 01 31\n", "2002 02 28\n", "2002 03 31\n", "2002 04 30\n", "2002 05 31\n", "2002 06 30\n", "2002 07 31\n", "2002 08 31\n", "2002 09 30\n", "2002 10 31\n", "2002 11 30\n", "2002 12 31\n", "2003 01 31\n", "2003 02 28\n", "2003 03 31\n", "2003 04 30\n", "2003 05 31\n", "2003 06 30\n", "2003 07 31\n", "2003 08 31\n", "2003 09 30\n", "2003 10 31\n", "2003 11 30\n", "2003 12 31\n", "2004 01 31\n", "2004 02 29\n", "2004 03 31\n", "2004 04 30\n", "2004 05 31\n", "2004 06 30\n", "2004 07 31\n", "2004 08 31\n", "2004 09 30\n", "2004 10 31\n", "2004 11 30\n", "2004 12 31\n", "2005 01 31\n", "2005 02 28\n", "2005 03 31\n", "2005 04 30\n", "2005 05 31\n", "2005 06 30\n", "2005 07 31\n", "2005 08 31\n", "2005 09 30\n", "2005 10 31\n", "2005 11 30\n", "2005 12 31\n", "2006 01 31\n", "2006 02 28\n", "2006 03 31\n", "2006 04 30\n", "2006 05 31\n", "2006 06 30\n", "2006 07 31\n", "2006 08 31\n", "2006 09 30\n", "2006 10 31\n", "2006 11 30\n", "2006 12 31\n", "2007 01 31\n", "2007 02 28\n", "2007 03 31\n", "2007 04 30\n", "2007 05 31\n", "2007 06 30\n", "2007 07 31\n", "2007 08 31\n", "2007 09 30\n", "2007 10 31\n", "2007 11 30\n", "2007 12 31\n", "2008 01 31\n", "2008 02 29\n", "2008 03 31\n", "2008 04 30\n", "2008 05 31\n", "2008 06 30\n", "2008 07 31\n", "2008 08 31\n", "2008 09 30\n", "2008 10 31\n", "2008 11 30\n", "2008 12 31\n", "2009 01 31\n", "2009 02 28\n", "2009 03 31\n", "2009 04 30\n", "2009 05 31\n", "2009 06 30\n", "2009 07 31\n", "2009 08 31\n", "2009 09 30\n", "2009 10 31\n", "2009 11 30\n", "2009 12 31\n", "2010 01 31\n", "2010 02 28\n", "2010 03 31\n", "2010 04 30\n", "2010 05 31\n", "2010 06 30\n", "2010 07 31\n", "2010 08 31\n", "2010 09 30\n", "2010 10 31\n", "2010 11 30\n", "2010 12 31\n", "2011 01 31\n", "2011 02 28\n", "2011 03 31\n", "2011 04 30\n", "2011 05 31\n", "2011 06 30\n", "2011 07 31\n", "2011 08 31\n", "2011 09 30\n", "2011 10 31\n", "2011 11 30\n", "2011 12 31\n", "2012 01 31\n", "2012 02 29\n", "2012 03 31\n", "2012 04 30\n", "2012 05 31\n", "2012 06 30\n", "2012 07 31\n", "2012 08 31\n", "2012 09 30\n", "2012 10 31\n", "2012 11 30\n", "2012 12 31\n", "2013 01 31\n", "2013 02 28\n", "2013 03 31\n", "2013 04 30\n", "2013 05 31\n", "2013 06 30\n", "2013 07 31\n", "2013 08 31\n", "2013 09 30\n", "2013 10 31\n", "2013 11 30\n", "2013 12 31\n", "2014 01 31\n", "2014 02 28\n", "2014 03 31\n", "2014 04 30\n", "2014 05 31\n", "2014 06 30\n", "2014 07 31\n", "2014 08 31\n", "2014 09 30\n", "2014 10 31\n", "2014 11 30\n", "2014 12 31\n", "2015 01 31\n", "2015 02 28\n", "2015 03 31\n", "2015 04 30\n", "2015 05 31\n", "2015 06 30\n", "2015 07 31\n", "2015 08 31\n", "2015 09 30\n", "2015 10 31\n", "2015 11 30\n", "2015 12 31\n", "2016 01 31\n", "2016 02 29\n", "2016 03 31\n", "2016 04 30\n", "2016 05 31\n", "2016 06 30\n", "2016 07 31\n", "2016 08 31\n", "2016 09 30\n", "2016 10 31\n", "2016 11 30\n", "2016 12 31\n", "2017 01 31\n", "2017 02 28\n", "2017 03 31\n", "2017 04 30\n", "2017 05 31\n", "2017 06 30\n", "2017 07 31\n", "2017 08 31\n", "2017 09 30\n", "2017 10 31\n", "2017 11 30\n", "2017 12 31\n", "2018 01 31\n", "2018 02 28\n", "2018 03 31\n", "2018 04 30\n", "2018 05 31\n", "2018 06 30\n", "2018 07 31\n", "2018 08 31\n", "2018 09 30\n", "2018 10 31\n", "2018 11 30\n", "2018 12 31\n", "2019 01 31\n", "2019 02 28\n", "2019 03 31\n", "2019 04 30\n", "2019 05 31\n", "2019 06 30\n", "2019 07 31\n", "2019 08 31\n", "2019 09 30\n", "2019 10 31\n", "2019 11 30\n", "2019 12 31\n", "2020 01 31\n", "2020 02 29\n", "2020 03 31\n", "2020 04 30\n", "2020 05 31\n", "2020 06 30\n", "2020 07 31\n", "2020 08 31\n", "2020 09 30\n", "2020 10 31\n", "2020 11 30\n", "2020 12 31\n" ] } ], "source": [ "# Import packages\n", "import requests\n", "import pandas as pd\n", "import calendar\n", "\n", "# Input information\n", "st_name = 'Sabang-CutBau'\n", "st_id = '960010'\n", "yr_strt = 2001\n", "yr_ends = 2020\n", "\n", "# Scraping and appending data\n", "appended_data = []\n", "for year in range(yr_strt, yr_ends+1):\n", " for month in range(1,12+1):\n", " month = format(month, '02d')\n", " nday = calendar.monthrange(int(year), int(month))[1]\n", " print(year, month, nday)\n", " url = 'https://www.ogimet.com/cgi-bin/gsodres?lang=en&ind='\\\n", " +st_id+'-99999&ord=DIR&ano='+str(year)+'&mes='+month+'&day='+str(nday)+'&ndays='+str(nday)\n", " html = requests.get(url).content\n", " df_list = pd.read_html(html, header=0)\n", " df = df_list[3]\n", " data = df[['Date', 'Temperature(°C)', 'Temperature(°C).1', 'Temperature(°C).2', \n", " 'Hr.Med(%)','Prec(mm)']].copy()\n", " data.rename(columns={'Temperature(°C)': 'Temp_max_C', 'Temperature(°C).1' : 'Temp_min_C', \n", " 'Temperature(°C).2': 'Temp_mean_C', 'Hr.Med(%)' : 'Rel_Hum', \n", " 'Prec(mm)': 'Prec_mm'}, inplace=True)\n", " data.drop(index=data.index[0], axis=0, inplace=True)\n", " data['Date'] = pd.to_datetime(data.Date)\n", " data.replace('---', np.nan, inplace=True)\n", " data.replace('----', np.nan, inplace=True)\n", " data.replace('-----', np.nan, inplace=True)\n", " appended_data.append(data)\n", "appended_data = pd.concat(appended_data)\n", "appended_data.reset_index(drop=True, inplace=True)\n", "\n", "appended_data.dtypes\n", "cols = appended_data.columns.drop('Date')\n", "appended_data[cols] = appended_data[cols].apply(pd.to_numeric)" ] }, { "cell_type": "code", "execution_count": 3, "id": "d82e656e", "metadata": { "ExecuteTime": { "end_time": "2022-12-23T07:56:57.443581Z", "start_time": "2022-12-23T07:56:57.423067Z" } }, "outputs": [ { "data": { "text/html": [ "
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DateTemp_max_CTemp_min_CTemp_mean_CRel_HumPrec_mm
02001-01-0129.020.627.078.8NaN
12001-01-0228.620.826.185.30.0
22001-01-0329.825.226.981.40.0
32001-01-0429.821.426.677.60.0
42001-01-0528.222.026.377.10.0
.....................
73002020-12-2730.024.026.886.211.9
73012020-12-28NaNNaNNaNNaNNaN
73022020-12-29NaNNaNNaNNaNNaN
73032020-12-30NaNNaNNaNNaNNaN
73042020-12-31NaNNaNNaNNaNNaN
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7305 rows × 6 columns

\n", "
" ], "text/plain": [ " Date Temp_max_C Temp_min_C Temp_mean_C Rel_Hum Prec_mm\n", "0 2001-01-01 29.0 20.6 27.0 78.8 NaN\n", "1 2001-01-02 28.6 20.8 26.1 85.3 0.0\n", "2 2001-01-03 29.8 25.2 26.9 81.4 0.0\n", "3 2001-01-04 29.8 21.4 26.6 77.6 0.0\n", "4 2001-01-05 28.2 22.0 26.3 77.1 0.0\n", "... ... ... ... ... ... ...\n", "7300 2020-12-27 30.0 24.0 26.8 86.2 11.9\n", "7301 2020-12-28 NaN NaN NaN NaN NaN\n", "7302 2020-12-29 NaN NaN NaN NaN NaN\n", "7303 2020-12-30 NaN NaN NaN NaN NaN\n", "7304 2020-12-31 NaN NaN NaN NaN NaN\n", "\n", "[7305 rows x 6 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "appended_data" ] }, { "cell_type": "code", "execution_count": 4, "id": "69a884d3", "metadata": { "ExecuteTime": { "end_time": "2022-12-23T07:57:01.262367Z", "start_time": "2022-12-23T07:57:01.260126Z" } }, "outputs": [], "source": [ "# appended_data.to_csv('/bog/amuttaqin/Datasets/GroundObs/'+st_id+'_'+st_name+'.csv')" ] }, { "cell_type": "code", "execution_count": 47, "id": "8d363534", "metadata": { "ExecuteTime": { "end_time": "2022-12-23T08:38:49.164685Z", "start_time": "2022-12-23T08:38:49.020481Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.pyplot import figure\n", "\n", "font = {'family': 'serif',\n", " 'color': 'black', \n", " 'weight': 'normal', \n", " 'size': 16}\n", "\n", "df = appended_data.set_index(['Date'])\n", "df = df.loc['2013-01-01':'2015-01-01']\n", "\n", "figure(figsize=(10,5), dpi=100)\n", "x = df.index\n", "y = df.Prec_mm\n", "\n", "plt.plot(x, y, 'b')\n", "plt.title('Title', fontsize=16, fontname='Serif')\n", "plt.xticks(fontsize=12, fontname='Serif')\n", "plt.yticks(fontsize=12, fontname='Serif')\n", "plt.xlabel('Date', fontdict=font)\n", "plt.ylabel('Precipitation (mm)', fontdict=font)\n", "\n", "plt.ylim(bottom=0)\n", "\n", "plt.subplots_adjust(left=0.15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "id": "a82ba9eb", "metadata": { "ExecuteTime": { "end_time": "2022-12-23T08:07:48.291450Z", "start_time": "2022-12-23T08:07:48.280493Z" } }, "outputs": [ { "data": { "text/html": [ "
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Temp_max_CTemp_min_CTemp_mean_CRel_HumPrec_mm
Date
2012-01-0128.818.026.194.215.0
2012-01-0230.216.026.593.90.8
2012-01-0330.219.025.995.23.0
2012-01-0430.418.827.393.04.1
2012-01-0528.019.825.195.589.2
..................
2016-12-2729.123.828.084.90.0
2016-12-28NaNNaNNaNNaNNaN
2016-12-29NaNNaNNaNNaNNaN
2016-12-30NaNNaNNaNNaNNaN
2016-12-31NaNNaNNaNNaNNaN
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1827 rows × 5 columns

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" ], "text/plain": [ " Temp_max_C Temp_min_C Temp_mean_C Rel_Hum Prec_mm\n", "Date \n", "2012-01-01 28.8 18.0 26.1 94.2 15.0\n", "2012-01-02 30.2 16.0 26.5 93.9 0.8\n", "2012-01-03 30.2 19.0 25.9 95.2 3.0\n", "2012-01-04 30.4 18.8 27.3 93.0 4.1\n", "2012-01-05 28.0 19.8 25.1 95.5 89.2\n", "... ... ... ... ... ...\n", "2016-12-27 29.1 23.8 28.0 84.9 0.0\n", "2016-12-28 NaN NaN NaN NaN NaN\n", "2016-12-29 NaN NaN NaN NaN NaN\n", "2016-12-30 NaN NaN NaN NaN NaN\n", "2016-12-31 NaN NaN NaN NaN NaN\n", "\n", "[1827 rows x 5 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "id": "16119734", "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": "3.7.12" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 5 }