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Convert GHCN weather data files to pandas dataframes
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| import pandas as pd | |
| import numpy as np | |
| def transform_elements(df): | |
| # TMAX/MIN are given in _tenths_; convert to an approrpiate float for now | |
| for t in ['TMAX', 'TMIN', 'TAVG']: | |
| df.loc[df.element == t, 'value'] = df.loc[df.element == t, 'value'] / 10 | |
| def get_df_for_dly(file): | |
| """ Parse awkward .dly GHCN data format into a clean pandas time-series dataframe """ | |
| xform = [] | |
| with open(file, 'r') as f: | |
| for l in f.readlines(): | |
| base = { | |
| 'country': l[0:2], | |
| 'station': l[0:11], | |
| 'element': l[17:21], | |
| 'year': int(l[11:15]), | |
| 'month': int(l[15:17]), | |
| } | |
| for m in np.arange(0, 30): | |
| value = int(l[21 + m *8:26 + m * 8]) | |
| if value == -9999: | |
| continue | |
| d = base.copy() | |
| d.update({ | |
| 'day': m+1, | |
| 'value': value, | |
| 'mflag': l[26 + m *8], | |
| 'qflag': l[27 + m *8], | |
| 'sflag': l[28 + m *8] | |
| }) | |
| xform.append(d) | |
| df = pd.DataFrame.from_records(xform) | |
| df.index = pd.to_datetime(df[['year','month','day']]) | |
| transform_elements(df) | |
| return df | |
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Learn more about bidirectional Unicode characters
| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd\n", | |
| "import numpy as np" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## GHCND\n", | |
| "The Global Historical Climatology Network has a wonderful set of daily data available for weather stations around the world and is frequently updated. Unfortunately, GHCN data is in a strange format. This notebook has a couple of utility functions for converting these files into pandas dataframes that are much easier to work with. \n", | |
| "\n", | |
| "Documentation available here:\n", | |
| "\n", | |
| "https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf\n", | |
| "https://www.ncdc.noaa.gov/ghcnd-data-access\n", | |
| "https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt\n", | |
| "\n", | |
| "Each record in a file contains one month of daily data. Files can be downloaded directly from FTP, eg:\n", | |
| "ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/all/CA006158350.dly\n", | |
| "\n", | |
| "### Data format\n", | |
| "\n", | |
| "The variables on each line include the following:\n", | |
| "\n", | |
| " ------------------------------\n", | |
| " Variable Columns Type\n", | |
| " ------------------------------\n", | |
| " ID 1-11 Character\n", | |
| " YEAR 12-15 Integer\n", | |
| " MONTH 16-17 Integer\n", | |
| " ELEMENT 18-21 Character\n", | |
| " VALUE1 22-26 Integer\n", | |
| " MFLAG1 27-27 Character\n", | |
| " QFLAG1 28-28 Character\n", | |
| " SFLAG1 29-29 Character\n", | |
| " VALUE2 30-34 Integer\n", | |
| " MFLAG2 35-35 Character\n", | |
| " QFLAG2 36-36 Character\n", | |
| " SFLAG2 37-37 Character\n", | |
| " . . .\n", | |
| " . . .\n", | |
| " . . .\n", | |
| " VALUE31 262-266 Integer\n", | |
| " MFLAG31 267-267 Character\n", | |
| " QFLAG31 268-268 Character\n", | |
| " SFLAG31 269-269 Character\n", | |
| " ------------------------------\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The `ghcnd-stations.txt` file contains the list of all the stations:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "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>station</th>\n", | |
| " <th>lat</th>\n", | |
| " <th>long</th>\n", | |
| " <th>elev</th>\n", | |
| " <th>name</th>\n", | |
| " <th>Unnamed: 5</th>\n", | |
| " <th>src</th>\n", | |
| " <th>st_id</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>station</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>CA006158350</th>\n", | |
| " <td>CA006158350</td>\n", | |
| " <td>43.6667</td>\n", | |
| " <td>-79.4</td>\n", | |
| " <td>113.0</td>\n", | |
| " <td>TORONTO</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>71266.0</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>CA006158355</th>\n", | |
| " <td>CA006158355</td>\n", | |
| " <td>43.6667</td>\n", | |
| " <td>-79.4</td>\n", | |
| " <td>113.0</td>\n", | |
| " <td>TORONTO CITY</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>71508.0</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " station lat long elev name Unnamed: 5 src \\\n", | |
| "station \n", | |
| "CA006158350 CA006158350 43.6667 -79.4 113.0 TORONTO NaN NaN \n", | |
| "CA006158355 CA006158355 43.6667 -79.4 113.0 TORONTO CITY NaN NaN \n", | |
| "\n", | |
| " st_id \n", | |
| "station \n", | |
| "CA006158350 71266.0 \n", | |
| "CA006158355 71508.0 " | |
| ] | |
| }, | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "st = pd.read_fwf('ghcnd-stations.txt')\n", | |
| "st['station'] = st['station']\n", | |
| "st.set_index('station', inplace=True)\n", | |
| "st[\n", | |
| " (st.name.str.contains('TORONTO')) &\n", | |
| " (st.st_id)\n", | |
| "].head(2)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Converting the data" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def transform_elements(df):\n", | |
| " # TMAX/MIN are given in _tenths_; convert to an approrpiate float for now\n", | |
| " for t in ['TMAX', 'TMIN', 'TAVG']:\n", | |
| " df.loc[df.element == t, 'value'] = df.loc[df.element == t, 'value'] / 10\n", | |
| "\n", | |
| "def get_df_for_dly(file):\n", | |
| " \"\"\" Parse awkward .dly GHCN data format into a clean pandas time-series dataframe \"\"\"\n", | |
| " xform = []\n", | |
| " with open(file, 'r') as f:\n", | |
| " for l in f.readlines():\n", | |
| " base = {\n", | |
| " 'country': l[0:2],\n", | |
| " 'station': l[0:11],\n", | |
| " 'element': l[17:21],\n", | |
| " 'year': int(l[11:15]),\n", | |
| " 'month': int(l[15:17]),\n", | |
| "\n", | |
| " }\n", | |
| " for m in np.arange(0, 30):\n", | |
| " value = int(l[21 + m *8:26 + m * 8])\n", | |
| " if value == -9999:\n", | |
| " continue\n", | |
| " d = base.copy()\n", | |
| " d.update({\n", | |
| " 'day': m+1,\n", | |
| " 'value': value,\n", | |
| " 'mflag': l[26 + m *8],\n", | |
| " 'qflag': l[27 + m *8],\n", | |
| " 'sflag': l[28 + m *8] \n", | |
| " })\n", | |
| " xform.append(d)\n", | |
| " df = pd.DataFrame.from_records(xform) \n", | |
| " df.index = pd.to_datetime(df[['year','month','day']])\n", | |
| " transform_elements(df)\n", | |
| " return df " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "old_tor = get_df_for_dly('CA006158350.dly')\n", | |
| "new_tor = get_df_for_dly('CA006158355.dly')\n", | |
| "df = pd.concat([old_tor, new_tor])" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "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>country</th>\n", | |
| " <th>day</th>\n", | |
| " <th>element</th>\n", | |
| " <th>mflag</th>\n", | |
| " <th>month</th>\n", | |
| " <th>qflag</th>\n", | |
| " <th>sflag</th>\n", | |
| " <th>station</th>\n", | |
| " <th>value</th>\n", | |
| " <th>year</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1840-03-01</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>1</td>\n", | |
| " <td>TMAX</td>\n", | |
| " <td></td>\n", | |
| " <td>3</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158350</td>\n", | |
| " <td>8.3</td>\n", | |
| " <td>1840</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1840-03-02</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>2</td>\n", | |
| " <td>TMAX</td>\n", | |
| " <td></td>\n", | |
| " <td>3</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158350</td>\n", | |
| " <td>7.8</td>\n", | |
| " <td>1840</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1840-03-03</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>3</td>\n", | |
| " <td>TMAX</td>\n", | |
| " <td></td>\n", | |
| " <td>3</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158350</td>\n", | |
| " <td>11.1</td>\n", | |
| " <td>1840</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1840-03-04</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>4</td>\n", | |
| " <td>TMAX</td>\n", | |
| " <td></td>\n", | |
| " <td>3</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158350</td>\n", | |
| " <td>15.0</td>\n", | |
| " <td>1840</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1840-03-05</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>5</td>\n", | |
| " <td>TMAX</td>\n", | |
| " <td></td>\n", | |
| " <td>3</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158350</td>\n", | |
| " <td>6.7</td>\n", | |
| " <td>1840</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " country day element mflag month qflag sflag station value \\\n", | |
| "1840-03-01 CA 1 TMAX 3 C CA006158350 8.3 \n", | |
| "1840-03-02 CA 2 TMAX 3 C CA006158350 7.8 \n", | |
| "1840-03-03 CA 3 TMAX 3 C CA006158350 11.1 \n", | |
| "1840-03-04 CA 4 TMAX 3 C CA006158350 15.0 \n", | |
| "1840-03-05 CA 5 TMAX 3 C CA006158350 6.7 \n", | |
| "\n", | |
| " year \n", | |
| "1840-03-01 1840 \n", | |
| "1840-03-02 1840 \n", | |
| "1840-03-03 1840 \n", | |
| "1840-03-04 1840 \n", | |
| "1840-03-05 1840 " | |
| ] | |
| }, | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "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>country</th>\n", | |
| " <th>day</th>\n", | |
| " <th>element</th>\n", | |
| " <th>mflag</th>\n", | |
| " <th>month</th>\n", | |
| " <th>qflag</th>\n", | |
| " <th>sflag</th>\n", | |
| " <th>station</th>\n", | |
| " <th>value</th>\n", | |
| " <th>year</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>2020-11-02</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>2</td>\n", | |
| " <td>TAVG</td>\n", | |
| " <td></td>\n", | |
| " <td>11</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158355</td>\n", | |
| " <td>3.3</td>\n", | |
| " <td>2020</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2020-11-03</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>3</td>\n", | |
| " <td>TAVG</td>\n", | |
| " <td></td>\n", | |
| " <td>11</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158355</td>\n", | |
| " <td>6.2</td>\n", | |
| " <td>2020</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2020-11-04</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>4</td>\n", | |
| " <td>TAVG</td>\n", | |
| " <td></td>\n", | |
| " <td>11</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158355</td>\n", | |
| " <td>11.0</td>\n", | |
| " <td>2020</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2020-11-05</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>5</td>\n", | |
| " <td>TAVG</td>\n", | |
| " <td></td>\n", | |
| " <td>11</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158355</td>\n", | |
| " <td>13.9</td>\n", | |
| " <td>2020</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2020-11-06</th>\n", | |
| " <td>CA</td>\n", | |
| " <td>6</td>\n", | |
| " <td>TAVG</td>\n", | |
| " <td></td>\n", | |
| " <td>11</td>\n", | |
| " <td></td>\n", | |
| " <td>C</td>\n", | |
| " <td>CA006158355</td>\n", | |
| " <td>13.9</td>\n", | |
| " <td>2020</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " country day element mflag month qflag sflag station value \\\n", | |
| "2020-11-02 CA 2 TAVG 11 C CA006158355 3.3 \n", | |
| "2020-11-03 CA 3 TAVG 11 C CA006158355 6.2 \n", | |
| "2020-11-04 CA 4 TAVG 11 C CA006158355 11.0 \n", | |
| "2020-11-05 CA 5 TAVG 11 C CA006158355 13.9 \n", | |
| "2020-11-06 CA 6 TAVG 11 C CA006158355 13.9 \n", | |
| "\n", | |
| " year \n", | |
| "2020-11-02 2020 \n", | |
| "2020-11-03 2020 \n", | |
| "2020-11-04 2020 \n", | |
| "2020-11-05 2020 \n", | |
| "2020-11-06 2020 " | |
| ] | |
| }, | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.tail()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now it's easy to work with the dataset. Eg. what's the mean and maximum high temperature in November in Toronto going back to 1840?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x1da3444bdd8>" | |
| ] | |
| }, | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "df[\n", | |
| " (df.month == 11) & \n", | |
| " (df.element == 'TMAX')\n", | |
| "].groupby('year').agg({'value': ['mean', 'max']}).plot()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "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": "3.7.3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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