Precipitation_with_NCEI_Tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a35011c5",
   "metadata": {},
   "source": [
    "# Precipation Data with NCEI Tutorial\n",
    "\n",
    "This notebook has been developed to access point-based (by weather station) precipation data in the Global Historical Climatology Network Daily (GHCND) dataset available by API in NCEI's Climate Data Online (CDO).  \n",
    "\n",
    "You will first need to obtain a token which can be requested here: https://www.ncdc.noaa.gov/cdo-web/token (response is quick, if not immediate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "16c6a896",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d0f02f84",
   "metadata": {},
   "outputs": [],
   "source": [
    "api_token=\"your_token_here\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9454386e",
   "metadata": {},
   "source": [
    "## Exploring the Datasets\n",
    "\n",
    "There are a variety of useful datasets in CDO, let's take a look.  Alternatively, you can explore the datasets at https://www.ncei.noaa.gov/cdo-web/datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fcd96e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First let's get a list of all datasets using the Base URL for the datasets endpoint\n",
    "url = \"https://www.ncei.noaa.gov/cdo-web/api/v2/datasets\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8a4dea73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the headers with the token\n",
    "headers = {\n",
    "    'token': api_token\n",
    "}\n",
    "\n",
    "# Make the request to the API\n",
    "response = requests.get(url, headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5f532afa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: GHCND\n",
      "Name: Daily Summaries\n",
      "Description: No description available\n",
      "Min Date: 1750-02-01\n",
      "Max Date: 2024-08-21\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: GSOM\n",
      "Name: Global Summary of the Month\n",
      "Description: No description available\n",
      "Min Date: 1750-02-01\n",
      "Max Date: 2024-08-01\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: GSOY\n",
      "Name: Global Summary of the Year\n",
      "Description: No description available\n",
      "Min Date: 1763-01-01\n",
      "Max Date: 2024-01-01\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: NEXRAD2\n",
      "Name: Weather Radar (Level II)\n",
      "Description: No description available\n",
      "Min Date: 1991-06-05\n",
      "Max Date: 2024-08-22\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: NEXRAD3\n",
      "Name: Weather Radar (Level III)\n",
      "Description: No description available\n",
      "Min Date: 1994-05-20\n",
      "Max Date: 2024-08-20\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: NORMAL_ANN\n",
      "Name: Normals Annual/Seasonal\n",
      "Description: No description available\n",
      "Min Date: 2010-01-01\n",
      "Max Date: 2010-01-01\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: NORMAL_DLY\n",
      "Name: Normals Daily\n",
      "Description: No description available\n",
      "Min Date: 2010-01-01\n",
      "Max Date: 2010-12-31\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: NORMAL_HLY\n",
      "Name: Normals Hourly\n",
      "Description: No description available\n",
      "Min Date: 2010-01-01\n",
      "Max Date: 2010-12-31\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: NORMAL_MLY\n",
      "Name: Normals Monthly\n",
      "Description: No description available\n",
      "Min Date: 2010-01-01\n",
      "Max Date: 2010-12-01\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: PRECIP_15\n",
      "Name: Precipitation 15 Minute\n",
      "Description: No description available\n",
      "Min Date: 1970-05-12\n",
      "Max Date: 2014-01-01\n",
      "Coverage: N/A\n",
      "-----------\n",
      "ID: PRECIP_HLY\n",
      "Name: Precipitation Hourly\n",
      "Description: No description available\n",
      "Min Date: 1900-01-01\n",
      "Max Date: 2014-01-01\n",
      "Coverage: N/A\n",
      "-----------\n"
     ]
    }
   ],
   "source": [
    "# Define the headers with the token\n",
    "headers = {\n",
    "    'token': api_token\n",
    "}\n",
    "\n",
    "# Make the request to the API\n",
    "response = requests.get(url, headers=headers)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Parse the JSON response\n",
    "    data = response.json()\n",
    "    \n",
    "    # Ensure the 'results' key exists in the response\n",
    "    if 'results' in data:\n",
    "        datasets = data['results']\n",
    "        \n",
    "        # Print the available datasets\n",
    "        for dataset in datasets:\n",
    "            print(f\"ID: {dataset.get('id', 'N/A')}\")\n",
    "            print(f\"Name: {dataset.get('name', 'N/A')}\")\n",
    "            print(f\"Description: {dataset.get('description', 'No description available')}\")\n",
    "            print(f\"Min Date: {dataset.get('mindate', 'N/A')}\")\n",
    "            print(f\"Max Date: {dataset.get('maxdate', 'N/A')}\")\n",
    "            print(f\"Coverage: {dataset.get('coverage', 'N/A')}\")\n",
    "            print(\"-----------\")\n",
    "    else:\n",
    "        print(\"No datasets found in the response.\")\n",
    "else:\n",
    "    print(f\"Failed to retrieve data: {response.status_code}, {response.text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b81113",
   "metadata": {},
   "source": [
    "### Let's look closer at the variables in GHCND (Global Historical Climatology Network-Daily)\n",
    "\n",
    "Besides precipitation, what else is available in the GHCND?  Full documentation on GHCND is also available at https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "186c1de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: ACMC - Name: Average cloudiness midnight to midnight from 30-second ceilometer data - Description: No description available\n",
      "ID: ACMH - Name: Average cloudiness midnight to midnight from manual observations - Description: No description available\n",
      "ID: ACSC - Name: Average cloudiness sunrise to sunset from 30-second ceilometer data - Description: No description available\n",
      "ID: ACSH - Name: Average cloudiness sunrise to sunset from manual observations - Description: No description available\n",
      "ID: AWND - Name: Average wind speed - Description: No description available\n",
      "ID: DAEV - Name: Number of days included in the multiday evaporation total (MDEV) - Description: No description available\n",
      "ID: DAPR - Name: Number of days included in the multiday precipitation total (MDPR) - Description: No description available\n",
      "ID: DASF - Name: Number of days included in the multiday snow fall total (MDSF)  - Description: No description available\n",
      "ID: DATN - Name: Number of days included in the multiday minimum temperature (MDTN) - Description: No description available\n",
      "ID: DATX - Name: Number of days included in the multiday maximum temperature (MDTX) - Description: No description available\n",
      "ID: DAWM - Name: Number of days included in the multiday wind movement (MDWM) - Description: No description available\n",
      "ID: DWPR - Name: Number of days with non-zero precipitation included in multiday precipitation total (MDPR) - Description: No description available\n",
      "ID: EVAP - Name: Evaporation of water from evaporation pan - Description: No description available\n",
      "ID: FMTM - Name: Time of fastest mile or fastest 1-minute wind - Description: No description available\n",
      "ID: FRGB - Name: Base of frozen ground layer - Description: No description available\n",
      "ID: FRGT - Name: Top of frozen ground layer - Description: No description available\n",
      "ID: FRTH - Name: Thickness of frozen ground layer - Description: No description available\n",
      "ID: GAHT - Name: Difference between river and gauge height - Description: No description available\n",
      "ID: MDEV - Name: Multiday evaporation total (use with DAEV) - Description: No description available\n",
      "ID: MDPR - Name: Multiday precipitation total (use with DAPR and DWPR, if available) - Description: No description available\n",
      "ID: MDSF - Name: Multiday snowfall total  - Description: No description available\n",
      "ID: MDTN - Name: Multiday minimum temperature (use with DATN) - Description: No description available\n",
      "ID: MDTX - Name: Multiday maximum temperature (use with DATX) - Description: No description available\n",
      "ID: MDWM - Name: Multiday wind movement - Description: No description available\n",
      "ID: MNPN - Name: Daily minimum temperature of water in an evaporation pan - Description: No description available\n"
     ]
    }
   ],
   "source": [
    "# Example dataset ID (replace with the dataset ID you want to explore)\n",
    "dataset_id = \"GHCND\"  # Replace with actual dataset ID\n",
    "\n",
    "# Base URL for the datatypes endpoint\n",
    "datatypes_url = f\"https://www.ncei.noaa.gov/cdo-web/api/v2/datatypes\"\n",
    "\n",
    "# Define the parameters to filter by the dataset ID\n",
    "params = {\n",
    "    'datasetid': dataset_id\n",
    "}\n",
    "\n",
    "# Make the request to the API to get datatypes for the specified dataset\n",
    "response = requests.get(datatypes_url, headers=headers, params=params)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Parse the JSON response\n",
    "    datatypes = response.json().get('results', [])\n",
    "    \n",
    "    # Print available datatypes for the specified dataset\n",
    "    for datatype in datatypes:\n",
    "        print(f\"ID: {datatype.get('id', 'N/A')} - Name: {datatype.get('name', 'N/A')} - Description: {datatype.get('description', 'No description available')}\")\n",
    "else:\n",
    "    print(f\"Failed to retrieve datatypes: {response.status_code}, {response.text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dda3fe",
   "metadata": {},
   "source": [
    "### What Geographic Categories are available in the GHCND?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "af70d7bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: CITY - Name: City - Description: No description available\n",
      "ID: CLIM_DIV - Name: Climate Division - Description: No description available\n",
      "ID: CLIM_REG - Name: Climate Region - Description: No description available\n",
      "ID: CNTRY - Name: Country - Description: No description available\n",
      "ID: CNTY - Name: County - Description: No description available\n",
      "ID: HYD_ACC - Name: Hydrologic Accounting Unit - Description: No description available\n",
      "ID: HYD_CAT - Name: Hydrologic Cataloging Unit - Description: No description available\n",
      "ID: HYD_REG - Name: Hydrologic Region - Description: No description available\n",
      "ID: HYD_SUB - Name: Hydrologic Subregion - Description: No description available\n",
      "ID: ST - Name: State - Description: No description available\n",
      "ID: US_TERR - Name: US Territory - Description: No description available\n",
      "ID: ZIP - Name: Zip Code - Description: No description available\n"
     ]
    }
   ],
   "source": [
    "# Base URL for the locationcategories endpoint\n",
    "locationcategories_url = f\"https://www.ncei.noaa.gov/cdo-web/api/v2/locationcategories\"\n",
    "\n",
    "# Define the parameters to filter by the dataset ID\n",
    "params = {\n",
    "    'datasetid': dataset_id\n",
    "}\n",
    "\n",
    "# Make the request to the API to get location categories for the specified dataset\n",
    "response = requests.get(locationcategories_url, headers=headers, params=params)\n",
    "\n",
    "# Check if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Parse the JSON response\n",
    "    locationcategories = response.json().get('results', [])\n",
    "    \n",
    "    # Print available location categories for the specified dataset\n",
    "    for locationcategory in locationcategories:\n",
    "        print(f\"ID: {locationcategory.get('id', 'N/A')} - Name: {locationcategory.get('name', 'N/A')} - Description: {locationcategory.get('description', 'No description available')}\")\n",
    "else:\n",
    "    print(f\"Failed to retrieve location categories: {response.status_code}, {response.text}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908092fb",
   "metadata": {},
   "source": [
    "### Gathering Data\n",
    "\n",
    "Let's pull some precipipation (PRCP) data for the zip codes covered by the longitudinal survey.  First, let's loop through all zip codes included in the survey administration area.  This can create problems for the server some times, so we are batching the zip codes and putting a 1 second delay between requests.  It may take a while to download the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8296f538",
   "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>date</th>\n",
       "      <th>datatype</th>\n",
       "      <th>station</th>\n",
       "      <th>attributes</th>\n",
       "      <th>value</th>\n",
       "      <th>zip_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-01T00:00:00</td>\n",
       "      <td>PRCP</td>\n",
       "      <td>GHCND:USC00042713</td>\n",
       "      <td>,,7,0800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-01T00:00:00</td>\n",
       "      <td>PRCP</td>\n",
       "      <td>GHCND:USW00023199</td>\n",
       "      <td>,,W,</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-02T00:00:00</td>\n",
       "      <td>PRCP</td>\n",
       "      <td>GHCND:USC00042713</td>\n",
       "      <td>,,7,0800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-03T00:00:00</td>\n",
       "      <td>PRCP</td>\n",
       "      <td>GHCND:USC00042713</td>\n",
       "      <td>,,7,0800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-03T00:00:00</td>\n",
       "      <td>PRCP</td>\n",
       "      <td>GHCND:USW00023199</td>\n",
       "      <td>,,W,</td>\n",
       "      <td>0.3</td>\n",
       "      <td>92243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date datatype            station attributes  value  zip_code\n",
       "0  2024-01-01T00:00:00     PRCP  GHCND:USC00042713   ,,7,0800    0.0     92243\n",
       "1  2024-01-01T00:00:00     PRCP  GHCND:USW00023199       ,,W,    0.0     92243\n",
       "2  2024-01-02T00:00:00     PRCP  GHCND:USC00042713   ,,7,0800    0.0     92243\n",
       "3  2024-01-03T00:00:00     PRCP  GHCND:USC00042713   ,,7,0800    0.0     92243\n",
       "4  2024-01-03T00:00:00     PRCP  GHCND:USW00023199       ,,W,    0.3     92243"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Base URL for the data endpoint\n",
    "base_url = \"https://www.ncei.noaa.gov/cdo-web/api/v2/data\"\n",
    "\n",
    "# Define the headers with the token\n",
    "headers = {\n",
    "    'token': api_token\n",
    "}\n",
    "\n",
    "# List of zip codes\n",
    "zip_codes = [92243]\n",
    "\n",
    "# Define the parameters\n",
    "params = {\n",
    "    'datasetid': 'GHCND',\n",
    "    'startdate': '2024-01-01',\n",
    "    'enddate': '2024-04-30',\n",
    "    'limit': 1000,\n",
    "    'datatypeid': ['PRCP'],\n",
    "    'units': 'metric'\n",
    "}\n",
    "\n",
    "# List to store all the data entries\n",
    "all_data = []\n",
    "\n",
    "# Loop through each ZIP code and make the API request\n",
    "batch_size = 5  # Number of requests per second allowed\n",
    "for i in range(0, len(zip_codes), batch_size):\n",
    "    batch = zip_codes[i:i + batch_size]\n",
    "    for zip_code in batch:\n",
    "        params['locationid'] = f'ZIP:{zip_code}'\n",
    "        response = requests.get(base_url, headers=headers, params=params)\n",
    "        \n",
    "        if response.status_code == 200:\n",
    "            data = response.json().get('results', [])\n",
    "            for entry in data:\n",
    "                entry['zip_code'] = zip_code  # Add ZIP code to each entry\n",
    "                all_data.append(entry)\n",
    "        else:\n",
    "            print(f\"Failed to retrieve data for ZIP code {zip_code}: {response.status_code}\")\n",
    "    \n",
    "    # Delay after each batch\n",
    "    time.sleep(1)  # Sleep for 1 second to ensure we don't exceed 5 requests per second\n",
    "\n",
    "# Convert the collected data to a DataFrame\n",
    "df = pd.DataFrame(all_data)\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c45f3ac4",
   "metadata": {},
   "source": [
    "## Zip Codes\n",
    "\n",
    "Now you try it using the zip code for the survey area in Southern California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "482b6b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of zip codes\n",
    "zip_codes = [\n",
    "    90001, 90002, 90003, 90004, 90005, 90006, 90210, 90211, 90212, 90230, 90232, \n",
    "    91701, 91708, 91709, 91710, 91730, 91737, 91739, 91743, 91759, 91901, 91902, \n",
    "    91905, 91910, 91911, 91913, 92007, 92008, 92009, 92010, 92011, 92220, 92222, \n",
    "    92223, 92225, 92227, 92230, 92231, 92232, 92233, 92234, 92236, 92240, 92241, \n",
    "    92243, 92244, 92249, 92250, 92251, 92252, 92253, 92258, 92301, 92307, 92602, \n",
    "    92603, 92604, 92606, 92610, 92612, 92614, 92617, 92618, 92620, 92624, 93001, \n",
    "    93003, 93004, 93010, 93012, 93013, 93015, 93021, 93030, 93033, 93036, 93101, \n",
    "    93105, 93110, 93201, 93202, 93203, 93205, 93206, 93207, 93208, 93212, 93215, \n",
    "    93216, 93219, 93221, 93222, 93223, 93224, 93225, 93226, 93227, 93230, 93232, \n",
    "    93235, 93237, 93238, 93240, 93245, 93247, 93401, 93402, 93405, 93420, 93422, \n",
    "    93424, 93426, 93427, 93428, 93430, 93432, 93433, 93436, 93454, 93460, 93463, \n",
    "    93513, 93514, 93515, 93526, 93541, 93545, 93549, 93601, 93602, 93606, 93607, \n",
    "    93608, 93609, 93610, 93611, 93612, 93616, 93619, 93620, 93623, 93635, 93636, \n",
    "    93637, 93638, 93639, 93661, 93665, 93901, 93905, 93906, 93907, 93908, 93920, \n",
    "    93923, 93924, 93925, 93927, 95023, 95024, 95043, 95301, 95303, 95306, 95312, \n",
    "    95315, 95317, 95322, 95324, 95333, 95338, 95345\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006ec19e",
   "metadata": {},
   "source": [
    "### Use the code in Gather Data to pull data for all the zip codes  for the survey area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1a6ab93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy and paste from cell \"Gathering Data\" and make the necessary changes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "431677d7",
   "metadata": {},
   "source": [
    "### How many stations per zip code?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "f25abce0",
   "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>ZIP_CODE</th>\n",
       "      <th>STATION_COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92243</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ZIP_CODE  STATION_COUNT\n",
       "0     92243              2"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_count_by_zip = df.groupby('zip_code')['station'].nunique().reset_index()\n",
    "\n",
    "# Rename columns for clarity\n",
    "stations_count_by_zip.columns = ['ZIP_CODE', 'STATION_COUNT']\n",
    "\n",
    "# Display the result\n",
    "stations_count_by_zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7948e989",
   "metadata": {},
   "source": [
    "### Where are stations located (by zip code)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e46f937e",
   "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>zip_code</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92243</td>\n",
       "      <td>[GHCND:USC00042713, GHCND:USW00023199, GHCND:U...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   zip_code                                            station\n",
       "0     92243  [GHCND:USC00042713, GHCND:USW00023199, GHCND:U..."
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_by_zip = df.groupby('zip_code')['station'].agg(list).reset_index()\n",
    "stations_by_zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a564cb00",
   "metadata": {},
   "source": [
    "### Visualizing the data\n",
    "\n",
    "This will get a little messy with 74 zip codes, but it should show general precipitation trends for this geographic area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2046a08a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert DATE to datetime format\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(12, 6))\n",
    "for station in df['station'].unique():\n",
    "    station_data = df[df['station'] == station]\n",
    "    plt.plot(station_data['date'], station_data['value'], marker='o', label=station)\n",
    "\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Precipitation (mm)')\n",
    "plt.title('Daily Precipitation by Station')\n",
    "plt.legend(title='Station')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651a14e3",
   "metadata": {},
   "source": [
    "## Closing\n",
    "\n",
    "The GHCND is one way to collect point-based precipation amounts, and other weather variables, as recorded daily by weather stations.  Obviously, a certain amount of cleaning is still necessary (perhaps selecting one station per zip code; alternatively, one couple identify stations by zip and pull data by station), but this data might be useful for long-term case studies.  In this case, we can see actual precipitation amounts experienced by survey respondents."
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}