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Parsing daily climate data in a xlsx, downloaded from BMKG's Data Online
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| |
} | |
}, | |
"cell_type": "markdown", | |
"id": "cae879c7", | |
"metadata": {}, | |
"source": [ | |
"# Parsing daily climate data in a xlsx, downloaded from BMKG's Data Online\n", | |
"\n", | |
"If you have spreadsheet file of daily climate data, downloaded from [BMKG Data Online](https://dataonline.bmkg.go.id/data_iklim). then you need to parse it before the data ready to use. \n", | |
"\n", | |
"Somehow, the file's structure is a bit unconventional for a typical Excel sheet. The values for ID WMO, Nama Stasiun, Lintang, etc. are actually in the header names themselves, not in the cell values under those headers. The climate data itself started at row 9 for the header.\n", | |
"\n", | |
"See below screenshot!\n", | |
"\n", | |
"![image.png](attachment:image.png)\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "6bbd4940", | |
"metadata": {}, | |
"source": [ | |
"## Extract weather station information from data collection\n", | |
"\n", | |
"Currently I have around 180 xlsx files (1 xlsx for 1 station) in a folder. Let's extract the station information from each file, compile it, and save it as single csv file." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 33, | |
"id": "15d86ed7", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Processing files: 100%|██████████████████████████████████████████████| 182/182 [05:40<00:00, 1.87s/it]" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Error processing file ~$Balai Besar Meteorologi Klimatologi dan Geofisika Wilayah I.xlsx: Excel file format cannot be determined, you must specify an engine manually.\n", | |
"Error processing file ~$Halim Perdana Kusuma Jakarta.xlsx: Excel file format cannot be determined, you must specify an engine manually.\n" | |
] | |
}, | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"\n" | |
] | |
}, | |
{ | |
"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>ID</th>\n", | |
" <th>ID_WMO</th>\n", | |
" <th>Station</th>\n", | |
" <th>Lon</th>\n", | |
" <th>Lat</th>\n", | |
" <th>Elevation</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1</td>\n", | |
" <td>96001</td>\n", | |
" <td>Stasiun Meteorologi Maimun Saleh</td>\n", | |
" <td>95.33785</td>\n", | |
" <td>5.87655</td>\n", | |
" <td>126.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>2</td>\n", | |
" <td>96009</td>\n", | |
" <td>Stasiun Meteorologi Malikussaleh</td>\n", | |
" <td>96.94749</td>\n", | |
" <td>5.22869</td>\n", | |
" <td>28.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>3</td>\n", | |
" <td>96011</td>\n", | |
" <td>Stasiun Meteorologi Sultan Iskandar Muda</td>\n", | |
" <td>95.41700</td>\n", | |
" <td>5.52244</td>\n", | |
" <td>20.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>4</td>\n", | |
" <td>96013</td>\n", | |
" <td>Stasiun Geofisika Aceh Besar</td>\n", | |
" <td>95.29600</td>\n", | |
" <td>5.49600</td>\n", | |
" <td>32.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>4</th>\n", | |
" <td>5</td>\n", | |
" <td>96015</td>\n", | |
" <td>Stasiun Meteorologi Cut Nyak Dhien Nagan Raya</td>\n", | |
" <td>96.24796</td>\n", | |
" <td>4.04928</td>\n", | |
" <td>3.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>...</th>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" <td>...</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>175</th>\n", | |
" <td>176</td>\n", | |
" <td>97812</td>\n", | |
" <td>Stasiun Geofisika Nabire</td>\n", | |
" <td>132.75000</td>\n", | |
" <td>-5.64000</td>\n", | |
" <td>22.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>176</th>\n", | |
" <td>177</td>\n", | |
" <td>97876</td>\n", | |
" <td>Stasiun Meteorologi Tanah Merah</td>\n", | |
" <td>140.31000</td>\n", | |
" <td>-6.10000</td>\n", | |
" <td>97.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>177</th>\n", | |
" <td>178</td>\n", | |
" <td>97900</td>\n", | |
" <td>Stasiun Meteorologi Mathilda Batlayeri</td>\n", | |
" <td>131.30000</td>\n", | |
" <td>-7.98000</td>\n", | |
" <td>24.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>178</th>\n", | |
" <td>179</td>\n", | |
" <td>97978</td>\n", | |
" <td>Stasiun Klimatologi Merauke</td>\n", | |
" <td>140.51700</td>\n", | |
" <td>-8.38700</td>\n", | |
" <td>5.0</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>179</th>\n", | |
" <td>180</td>\n", | |
" <td>97980</td>\n", | |
" <td>Stasiun Meteorologi Mopah</td>\n", | |
" <td>140.41568</td>\n", | |
" <td>-8.52019</td>\n", | |
" <td>0.0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>180 rows × 6 columns</p>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" ID ID_WMO Station Lon \\\n", | |
"0 1 96001 Stasiun Meteorologi Maimun Saleh 95.33785 \n", | |
"1 2 96009 Stasiun Meteorologi Malikussaleh 96.94749 \n", | |
"2 3 96011 Stasiun Meteorologi Sultan Iskandar Muda 95.41700 \n", | |
"3 4 96013 Stasiun Geofisika Aceh Besar 95.29600 \n", | |
"4 5 96015 Stasiun Meteorologi Cut Nyak Dhien Nagan Raya 96.24796 \n", | |
".. ... ... ... ... \n", | |
"175 176 97812 Stasiun Geofisika Nabire 132.75000 \n", | |
"176 177 97876 Stasiun Meteorologi Tanah Merah 140.31000 \n", | |
"177 178 97900 Stasiun Meteorologi Mathilda Batlayeri 131.30000 \n", | |
"178 179 97978 Stasiun Klimatologi Merauke 140.51700 \n", | |
"179 180 97980 Stasiun Meteorologi Mopah 140.41568 \n", | |
"\n", | |
" Lat Elevation \n", | |
"0 5.87655 126.0 \n", | |
"1 5.22869 28.0 \n", | |
"2 5.52244 20.0 \n", | |
"3 5.49600 32.0 \n", | |
"4 4.04928 3.0 \n", | |
".. ... ... \n", | |
"175 -5.64000 22.0 \n", | |
"176 -6.10000 97.0 \n", | |
"177 -7.98000 24.0 \n", | |
"178 -8.38700 5.0 \n", | |
"179 -8.52019 0.0 \n", | |
"\n", | |
"[180 rows x 6 columns]" | |
] | |
}, | |
"execution_count": 33, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"import pandas as pd\n", | |
"import os\n", | |
"import re\n", | |
"from tqdm import tqdm\n", | |
"\n", | |
"# Function to extract data after the colon\n", | |
"def get_data_after_colon(cell_value):\n", | |
" match = re.search(r':\\s+(.*)', str(cell_value))\n", | |
" if match:\n", | |
" return match.group(1).strip()\n", | |
" else:\n", | |
" return str(cell_value).strip()\n", | |
"\n", | |
"# Function to extract only numeric characters\n", | |
"def get_only_numbers(cell_value):\n", | |
" return ''.join(re.findall(r'\\d+', str(cell_value)))\n", | |
"\n", | |
"# Directory containing the .xlsx files\n", | |
"folder_path = './input/BMKG/'\n", | |
"\n", | |
"# Get a list of all xlsx files in the directory\n", | |
"xlsx_files = [f for f in os.listdir(folder_path) if f.endswith('.xlsx')]\n", | |
"\n", | |
"all_data = []\n", | |
"\n", | |
"# Loop through each file and extract data\n", | |
"for file in tqdm(xlsx_files, desc=\"Processing files\"):\n", | |
" try:\n", | |
" # Load the spreadsheet\n", | |
" xls = pd.ExcelFile(folder_path + file)\n", | |
" sheet = xls.parse(xls.sheet_names[0])\n", | |
"\n", | |
" # Extract data\n", | |
" ID_WMO = get_only_numbers(sheet.columns[2]) # From column header\n", | |
" Station = get_data_after_colon(sheet.iloc[0, 2])\n", | |
" Lat = float(get_data_after_colon(sheet.iloc[1, 2]))\n", | |
" Lon = float(get_data_after_colon(sheet.iloc[2, 2]))\n", | |
" Elevation = float(get_data_after_colon(sheet.iloc[3, 2]))\n", | |
"\n", | |
" all_data.append([ID_WMO, Station, Lon, Lat, Elevation])\n", | |
" except Exception as e:\n", | |
" print(f\"Error processing file {file}: {e}\")\n", | |
"\n", | |
"# Convert to dataframe\n", | |
"df = pd.DataFrame(all_data, columns=['ID_WMO', 'Station', 'Lon', 'Lat', 'Elevation'])\n", | |
"\n", | |
"# Sort by ID_WMO\n", | |
"df = df.sort_values(by=['ID_WMO'])\n", | |
"\n", | |
"# Add ID column as index\n", | |
"df = df.reset_index(drop=True)\n", | |
"df['ID'] = df.index + 1\n", | |
"\n", | |
"# Reorder columns\n", | |
"df = df[['ID', 'ID_WMO', 'Station', 'Lon', 'Lat', 'Elevation']]\n", | |
"\n", | |
"# Save to csv\n", | |
"df.to_csv('./output/idn_cli_weatherstation_location_bmkg.csv', index=False)\n", | |
"\n", | |
"# Preview the output\n", | |
"df\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "74e08506", | |
"metadata": {}, | |
"source": [ | |
"## Plot the station\n", | |
"\n", | |
"Let's plot the station coordinate into a map, to see the spatial distribution accross the country" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 50, | |
"id": "c2ce341a", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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", | |
"text/plain": [ | |
"<Figure size 1000x1500 with 1 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"import pandas as pd\n", | |
"import matplotlib.pyplot as plt\n", | |
"from mpl_toolkits.basemap import Basemap\n", | |
"\n", | |
"# Load the CSV data\n", | |
"df = pd.read_csv('./output/idn_cli_weatherstation_location_bmkg.csv')\n", | |
"\n", | |
"# Initialize a new map\n", | |
"fig, ax = plt.subplots(figsize=(10,15))\n", | |
"m = Basemap(resolution='i', projection='merc', llcrnrlat=-11, urcrnrlat=6, llcrnrlon=95, urcrnrlon=141, ax=ax)\n", | |
"m.drawcoastlines()\n", | |
"m.drawcountries()\n", | |
"m.drawmapboundary(fill_color='white') # Set sea color to white\n", | |
"m.fillcontinents(color='grey',lake_color='white') # Set land color to grey\n", | |
"\n", | |
"# Plot each weather station on the map\n", | |
"for _, row in df.iterrows():\n", | |
" x, y = m(row['Lon'], row['Lat'])\n", | |
" m.plot(x, y, 'ro', markersize=5) # Change marker to red\n", | |
" #plt.text(x, y, row['ID_WMO'], fontsize=9)\n", | |
"\n", | |
"# Save the map as a PNG\n", | |
"plt.savefig('./output/idn_cli_weatherstation_map_bmkg.png', dpi=300)\n", | |
"\n", | |
"# Show the map\n", | |
"plt.show()\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "e54508e0", | |
"metadata": {}, | |
"source": [ | |
"## Extract climate variables\n", | |
"\n", | |
"Next, extract climate variables (in this case is RR or Rainfall), from each xlsx and compile it as single csv file.\n", | |
"\n", | |
"For this case, I would like to extract daily rainfall from each of station with start date 1 Jun 2000, and arrange in long format with column structure: ID, Date, Julian Date, ID_WMO(1), ID_WMO(2), ..., ID_WMO(n). I'd like to pivot the data so that each unique ID_WMO value forms a column in the resultant DataFrame. The RR values will populate these columns." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 36, | |
"id": "3f6a2429", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"Processing files: 100%|██████████████████████████████████████████████| 182/182 [35:40<00:00, 11.76s/it]\n" | |
] | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"Error processing file ~$Balai Besar Meteorologi Klimatologi dan Geofisika Wilayah I.xlsx: Excel file format cannot be determined, you must specify an engine manually.\n", | |
"Error processing file ~$Halim Perdana Kusuma Jakarta.xlsx: Excel file format cannot be determined, you must specify an engine manually.\n" | |
] | |
}, | |
{ | |
"data": { | |
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"<div>\n", | |
"<style scoped>\n", | |
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"\n", | |
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"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>ID</th>\n", | |
" <th>Date</th>\n", | |
" <th>JD</th>\n", | |
" <th>96041</th>\n", | |
" <th>96747</th>\n", | |
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" <td>7459</td>\n", | |
" <td>02-06-2000</td>\n", | |
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" <td>13.2</td>\n", | |
" <td>33.0</td>\n", | |
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" <td>7460</td>\n", | |
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" <td>3.6</td>\n", | |
" <td>7.0</td>\n", | |
" <td>NaN</td>\n", | |
" <td>4.9</td>\n", | |
" <td>0.0</td>\n", | |
" <td>NaN</td>\n", | |
" <td>NaN</td>\n", | |
" <td>NaN</td>\n", | |
" <td>2.0</td>\n", | |
" <td>NaN</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>3</th>\n", | |
" <td>7461</td>\n", | |
" <td>04-06-2000</td>\n", | |
" <td>156</td>\n", | |
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" <td>12.5</td>\n", | |
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" <td>8888.0</td>\n", | |
" <td>NaN</td>\n", | |
" <td>26.0</td>\n", | |
" <td>5.7</td>\n", | |
" <td>NaN</td>\n", | |
" <td>NaN</td>\n", | |
" <td>NaN</td>\n", | |
" <td>...</td>\n", | |
" <td>2.6</td>\n", | |
" <td>13.2</td>\n", | |
" <td>10.0</td>\n", | |
" <td>20.5</td>\n", | |
" <td>40.3</td>\n", | |
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" <td>8888.0</td>\n", | |
" <td>17.8</td>\n", | |
" <td>9.7</td>\n", | |
" <td>NaN</td>\n", | |
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" <th>7881</th>\n", | |
" <td>15339</td>\n", | |
" <td>29-12-2021</td>\n", | |
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" <td>0.0</td>\n", | |
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" <tr>\n", | |
" <th>7883</th>\n", | |
" <td>15341</td>\n", | |
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" <td>8.3</td>\n", | |
" <td>NaN</td>\n", | |
" <td>0.6</td>\n", | |
" <td>17.7</td>\n", | |
" <td>NaN</td>\n", | |
" <td>NaN</td>\n", | |
" <td>NaN</td>\n", | |
" <td>...</td>\n", | |
" <td>4.0</td>\n", | |
" <td>0.2</td>\n", | |
" <td>8888.0</td>\n", | |
" <td>2.9</td>\n", | |
" <td>2.9</td>\n", | |
" <td>0.0</td>\n", | |
" <td>9.3</td>\n", | |
" <td>7.5</td>\n", | |
" <td>NaN</td>\n", | |
" <td>NaN</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"<p>7884 rows × 183 columns</p>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" ID Date JD 96041 96747 96793 97234 96013 96039 \\\n", | |
"0 7458 01-06-2000 153 NaN NaN NaN 0.0 NaN 0.0 \n", | |
"1 7459 02-06-2000 154 NaN NaN NaN 0.0 NaN 0.3 \n", | |
"2 7460 03-06-2000 155 NaN NaN NaN 0.0 NaN 0.0 \n", | |
"3 7461 04-06-2000 156 NaN NaN NaN 1.0 NaN 0.0 \n", | |
"4 7462 05-06-2000 157 NaN 11.2 NaN 0.0 NaN 0.0 \n", | |
"... ... ... ... ... ... ... ... ... ... \n", | |
"7879 15337 27-12-2021 361 0.0 NaN NaN NaN NaN NaN \n", | |
"7880 15338 28-12-2021 362 8888.0 NaN 26.0 5.7 NaN NaN \n", | |
"7881 15339 29-12-2021 363 0.0 NaN 18.5 11.0 NaN NaN \n", | |
"7882 15340 30-12-2021 364 0.0 NaN 0.4 NaN NaN NaN \n", | |
"7883 15341 31-12-2021 365 8.3 NaN 0.6 17.7 NaN NaN \n", | |
"\n", | |
" 97728 ... 96655 97630 96939 96805 97340 97760 97686 96505 \\\n", | |
"0 NaN ... 0.5 9.0 NaN 0.0 0.0 NaN NaN NaN \n", | |
"1 NaN ... 13.2 33.0 NaN 0.0 0.0 NaN NaN NaN \n", | |
"2 NaN ... 3.6 7.0 NaN 4.9 0.0 NaN NaN NaN \n", | |
"3 NaN ... 0.0 17.0 NaN 40.3 0.0 NaN NaN NaN \n", | |
"4 NaN ... 0.0 4.0 NaN 0.0 0.0 NaN NaN NaN \n", | |
"... ... ... ... ... ... ... ... ... ... ... \n", | |
"7879 NaN ... 12.5 1.7 56.0 8888.0 8888.0 0.2 4.0 0.2 \n", | |
"7880 NaN ... 2.6 13.2 10.0 20.5 40.3 3.2 8888.0 17.8 \n", | |
"7881 NaN ... 0.0 40.6 25.0 3.9 9.1 11.9 17.8 27.4 \n", | |
"7882 NaN ... 3.8 0.0 0.0 1.4 17.2 NaN 1.4 NaN \n", | |
"7883 NaN ... 4.0 0.2 8888.0 2.9 2.9 0.0 9.3 7.5 \n", | |
"\n", | |
" 97240 96169 \n", | |
"0 0.0 NaN \n", | |
"1 0.0 NaN \n", | |
"2 2.0 NaN \n", | |
"3 0.0 NaN \n", | |
"4 0.0 NaN \n", | |
"... ... ... \n", | |
"7879 2.5 NaN \n", | |
"7880 9.7 NaN \n", | |
"7881 13.0 NaN \n", | |
"7882 0.0 NaN \n", | |
"7883 NaN NaN \n", | |
"\n", | |
"[7884 rows x 183 columns]" | |
] | |
}, | |
"execution_count": 36, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"import pandas as pd\n", | |
"import os\n", | |
"from tqdm import tqdm\n", | |
"\n", | |
"# Function to convert a date in DD-MM-YYYY format to its Julian date\n", | |
"def to_julian_date(date_string):\n", | |
" try:\n", | |
" date_obj = pd.to_datetime(date_string, format='%d-%m-%Y')\n", | |
" return int(date_obj.strftime('%j'))\n", | |
" except:\n", | |
" return None\n", | |
"\n", | |
"# Directory containing the .xlsx files\n", | |
"folder_path = './input/BMKG/'\n", | |
"\n", | |
"# Get a list of all xlsx files in the directory\n", | |
"xlsx_files = [f for f in os.listdir(folder_path) if f.endswith('.xlsx')]\n", | |
"\n", | |
"# Dictionary to store the data\n", | |
"data_dict = {\"ID\": [], \"Date\": [], \"JD\": []}\n", | |
"\n", | |
"# Loop through each file and extract data\n", | |
"for file in tqdm(xlsx_files, desc=\"Processing files\"):\n", | |
" try:\n", | |
" # Load the spreadsheet\n", | |
" xls = pd.ExcelFile(folder_path + file)\n", | |
" sheet = xls.parse(xls.sheet_names[0], skiprows=8) # Start reading from row 9\n", | |
" \n", | |
" # Extract ID_WMO value from column C1\n", | |
" ID_WMO = ''.join(filter(str.isdigit, xls.parse(xls.sheet_names[0]).columns[2]))\n", | |
" \n", | |
" # Check and initialize ID_WMO in data_dict if not already present\n", | |
" if ID_WMO not in data_dict:\n", | |
" data_dict[ID_WMO] = [None for _ in range(len(data_dict['ID']))]\n", | |
" \n", | |
" # Loop through each row in the sheet starting from the 10th row\n", | |
" for index, row in sheet.iterrows():\n", | |
" date_string = row['Tanggal']\n", | |
" \n", | |
" # Check if date_string is empty, and if so, break out of the loop for this sheet\n", | |
" if pd.isna(date_string):\n", | |
" break\n", | |
" \n", | |
" # Check if the date is 01-06-2000 or later\n", | |
" if pd.to_datetime(date_string, format='%d-%m-%Y') >= pd.to_datetime('01-06-2000', format='%d-%m-%Y'):\n", | |
" julian_date = to_julian_date(date_string)\n", | |
" rr_value = row['RR']\n", | |
"\n", | |
" # If date_string is new, append to data_dict\n", | |
" if date_string not in data_dict[\"Date\"]:\n", | |
" data_dict[\"ID\"].append(index + 1)\n", | |
" data_dict[\"Date\"].append(date_string)\n", | |
" data_dict[\"JD\"].append(julian_date)\n", | |
" for key in data_dict:\n", | |
" if key not in [\"ID\", \"Date\", \"JD\"]:\n", | |
" data_dict[key].append(None) # Add None for other ID_WMOs\n", | |
" \n", | |
" # Update the RR value for the current ID_WMO\n", | |
" idx = data_dict[\"Date\"].index(date_string)\n", | |
" data_dict[ID_WMO][idx] = rr_value\n", | |
" \n", | |
" except Exception as e:\n", | |
" print(f\"Error processing file {file}: {e}\")\n", | |
"\n", | |
"# Convert data dictionary to dataframe\n", | |
"df = pd.DataFrame(data_dict)\n", | |
"\n", | |
"# Save to csv\n", | |
"df.to_csv('./output/idn_cli_weatherstation_data_bmkg.csv', index=False)\n", | |
"\n", | |
"# Preview the output\n", | |
"df\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "b7b248e8", | |
"metadata": {}, | |
"source": [ | |
"## Annual summary of the data availability\n", | |
"\n", | |
"Let's calculate number of day per year where the data is NaN, so we can see if this is good enough reason to drop the data or not." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 45, | |
"id": "bc76c226", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
" Year 96001 96009 96011 96013 96015 96017 96031 96033 96037 ... \\\n", | |
"0 2000 214 0 0 214 214 0 0 214 0 ... \n", | |
"1 2001 365 0 0 365 365 0 0 365 0 ... \n", | |
"2 2002 365 0 0 365 365 0 0 365 0 ... \n", | |
"3 2003 365 0 0 365 365 0 0 365 0 ... \n", | |
"4 2004 366 0 0 366 366 0 31 366 0 ... \n", | |
"5 2005 365 0 0 365 365 0 0 365 0 ... \n", | |
"6 2006 365 0 0 365 365 0 0 365 0 ... \n", | |
"7 2007 365 0 0 365 365 0 0 365 0 ... \n", | |
"8 2008 366 0 0 366 366 0 0 366 0 ... \n", | |
"9 2009 365 0 0 365 365 0 0 365 0 ... \n", | |
"10 2010 0 0 0 365 0 0 12 0 0 ... \n", | |
"11 2011 0 0 0 365 0 0 0 0 0 ... \n", | |
"12 2012 1 335 6 366 0 1 95 11 2 ... \n", | |
"13 2013 0 160 180 365 0 0 0 24 84 ... \n", | |
"14 2014 187 199 1 341 2 0 0 0 1 ... \n", | |
"15 2015 188 46 235 30 86 121 222 203 233 ... \n", | |
"16 2016 54 8 200 58 16 118 117 47 173 ... \n", | |
"17 2017 171 3 226 53 97 133 106 69 138 ... \n", | |
"18 2018 202 33 224 52 117 130 147 96 135 ... \n", | |
"19 2019 35 57 122 200 98 70 66 115 109 ... \n", | |
"20 2020 51 68 145 366 93 90 118 48 120 ... \n", | |
"21 2021 61 3 130 365 71 67 154 4 117 ... \n", | |
"\n", | |
" 97760 97780 97790 97796 97810 97812 97876 97900 97978 97980 \n", | |
"0 214 0 0 214 214 214 214 0 214 214 \n", | |
"1 365 61 0 1 365 365 0 0 365 0 \n", | |
"2 365 0 31 1 365 365 123 0 365 0 \n", | |
"3 365 272 31 0 365 365 1 0 365 0 \n", | |
"4 366 122 0 0 366 366 0 0 366 0 \n", | |
"5 365 365 0 0 365 365 0 0 365 0 \n", | |
"6 365 0 0 0 365 365 0 0 365 0 \n", | |
"7 365 92 0 0 365 365 31 0 365 0 \n", | |
"8 366 275 0 0 366 366 29 0 366 0 \n", | |
"9 365 48 0 1 365 365 37 0 365 91 \n", | |
"10 365 132 0 0 0 365 120 0 365 30 \n", | |
"11 365 79 0 0 0 365 61 0 365 0 \n", | |
"12 366 184 0 0 5 366 31 0 366 31 \n", | |
"13 364 149 322 46 182 311 96 73 365 0 \n", | |
"14 235 0 335 40 62 72 245 32 365 0 \n", | |
"15 181 274 250 58 186 159 364 177 365 349 \n", | |
"16 129 232 110 48 8 366 127 91 366 196 \n", | |
"17 141 161 102 27 51 365 164 89 365 29 \n", | |
"18 163 4 123 35 38 365 193 180 365 85 \n", | |
"19 130 140 131 52 5 365 201 365 227 91 \n", | |
"20 82 115 92 28 57 366 245 43 242 46 \n", | |
"21 23 34 14 14 5 365 38 16 155 28 \n", | |
"\n", | |
"[22 rows x 181 columns]\n" | |
] | |
} | |
], | |
"source": [ | |
"import pandas as pd\n", | |
"\n", | |
"# Load the previously generated CSV file\n", | |
"df = pd.read_csv('./output/idn_cli_weatherstation_data_bmkg.csv')\n", | |
"\n", | |
"# Extract the year from the Date column\n", | |
"df['Year'] = pd.to_datetime(df['Date'], format='%d-%m-%Y').dt.year\n", | |
"\n", | |
"# Create a dataframe to store annual NaN counts\n", | |
"nan_count_df = df.groupby('Year').apply(lambda x: x.isna().sum()).drop(columns=['ID', 'Date', 'JD', 'Year'])\n", | |
"\n", | |
"# Reset the index for the new dataframe\n", | |
"nan_count_df = nan_count_df.reset_index()\n", | |
"\n", | |
"# Sort columns by their names (ID_WMO values)\n", | |
"sorted_columns = sorted(nan_count_df.columns[1:], key=lambda x: int(x)) # Convert ID_WMO to integers for sorting\n", | |
"nan_count_df = nan_count_df[['Year'] + sorted_columns]\n", | |
"\n", | |
"# Display the result\n", | |
"print(nan_count_df)\n", | |
"\n", | |
"# Save the summarized dataframe to CSV\n", | |
"nan_count_df.to_csv('./output/idn_cli_annual_nan_precip_count_summary_bmkg.csv', index=False)\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "e5ce6ac5", | |
"metadata": {}, | |
"source": [ | |
"## Heat map\n", | |
"\n", | |
"Let's visualise as a heat map.\n", | |
"\n", | |
"Each column represents a year. Each row represents an ID_WMO. Color intensity indicates the count of NaN values.\n", | |
"\n", | |
"This will let you quickly spot patterns across years and stations" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 43, | |
"id": "88b9ddd0", | |
"metadata": { | |
"scrolled": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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", | |
"text/plain": [ | |
"<Figure size 2000x4000 with 2 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"import pandas as pd\n", | |
"import matplotlib.pyplot as plt\n", | |
"import numpy as np\n", | |
"\n", | |
"# Read the csv file\n", | |
"df = pd.read_csv('./output/idn_cli_annual_nan_precip_count_summary_bmkg.csv')\n", | |
"\n", | |
"# Set the 'Year' column as the index\n", | |
"df.set_index('Year', inplace=True)\n", | |
"\n", | |
"# Check and drop the 'ID' column if it exists\n", | |
"if 'ID' in df.columns:\n", | |
" df.drop('ID', axis=1, inplace=True)\n", | |
"\n", | |
"# Transpose the DataFrame\n", | |
"df = df.transpose()\n", | |
"\n", | |
"# Sort the DataFrame by its index (ID_WMO)\n", | |
"df = df.sort_index()\n", | |
"\n", | |
"# Generate the heatmap using only matplotlib\n", | |
"fig, ax = plt.subplots(figsize=(20, 40))\n", | |
"\n", | |
"# Plot heatmap using pcolormesh\n", | |
"cax = ax.pcolormesh(df, cmap=\"viridis\", shading=\"auto\")\n", | |
"\n", | |
"# Create a colorbar and adjust its height\n", | |
"cbar = fig.colorbar(cax, ax=ax, aspect=50)\n", | |
"cbar.ax.tick_params(labelsize=14) # Font size for colorbar tick labels\n", | |
"cbar.set_label('Number of NaN Days', size=16) # Font size for colorbar label\n", | |
"\n", | |
"# Set x-axis labels to be the columns of df (i.e., years)\n", | |
"ax.set_xticks(np.arange(len(df.columns)) + 0.5)\n", | |
"ax.set_xticklabels(df.columns, rotation=45, ha='right', fontsize=14) # Font size for x-axis labels\n", | |
"\n", | |
"# Set y-axis labels to be the index of df (i.e., the ID_WMO values)\n", | |
"ax.set_yticks(np.arange(len(df.index)) + 0.5)\n", | |
"ax.set_yticklabels(df.index, fontsize=14) # Font size for y-axis labels\n", | |
"\n", | |
"# Set title and x/y axis labels with specified font sizes\n", | |
"ax.set_title(\"Number of Days with NaN Values per Year\", fontsize=20)\n", | |
"ax.set_xlabel(\"Year\", fontsize=16)\n", | |
"ax.set_ylabel(\"ID_WMO\", fontsize=16)\n", | |
"\n", | |
"# Save the map as a PNG\n", | |
"plt.savefig('./output/idn_cli_weatherstation_heatmap_nancount_bmkg.png', dpi=300)\n", | |
"\n", | |
"plt.tight_layout()\n", | |
"plt.show()\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"id": "43fe08e7", | |
"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": "3.10.4" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 5 | |
} |
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