NWIS_USGS_Data_Acquisition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0912ee47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first import the functions for downloading data from NWIS, https://github.com/USGS-python/dataretrieval\n",
    "import dataretrieval.nwis as nwis\n",
    "\n",
    "# specify the USGS site code for which we want data.\n",
    "site = '03339000'\n",
    "\n",
    "\n",
    "# get instantaneous values (iv)\n",
    "df = nwis.get_record(sites=site, service='iv', start='2017-12-31', end='2018-01-01')\n",
    "\n",
    "# get water quality samples (qwdata)\n",
    "df2 = nwis.get_record(sites=site, service='qwdata', start='2017-12-31', end='2018-01-01')\n",
    "\n",
    "# get basic info about the site\n",
    "df3 = nwis.get_record(sites=site, service='site')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d34c5853",
   "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>00060_Mean</th>\n",
       "      <th>00060_Mean_cd</th>\n",
       "      <th>site_no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-31 00:00:00+00:00</th>\n",
       "      <td>137.0</td>\n",
       "      <td>A, e</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01 00:00:00+00:00</th>\n",
       "      <td>142.0</td>\n",
       "      <td>A, e</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02 00:00:00+00:00</th>\n",
       "      <td>156.0</td>\n",
       "      <td>A, e</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03 00:00:00+00:00</th>\n",
       "      <td>173.0</td>\n",
       "      <td>A, e</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04 00:00:00+00:00</th>\n",
       "      <td>180.0</td>\n",
       "      <td>A, e</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-26 00:00:00+00:00</th>\n",
       "      <td>753.0</td>\n",
       "      <td>P</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-27 00:00:00+00:00</th>\n",
       "      <td>714.0</td>\n",
       "      <td>P</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-28 00:00:00+00:00</th>\n",
       "      <td>976.0</td>\n",
       "      <td>P</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-29 00:00:00+00:00</th>\n",
       "      <td>997.0</td>\n",
       "      <td>P</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-05-30 00:00:00+00:00</th>\n",
       "      <td>865.0</td>\n",
       "      <td>P</td>\n",
       "      <td>03339000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1611 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           00060_Mean 00060_Mean_cd   site_no\n",
       "datetime                                                     \n",
       "2017-12-31 00:00:00+00:00       137.0          A, e  03339000\n",
       "2018-01-01 00:00:00+00:00       142.0          A, e  03339000\n",
       "2018-01-02 00:00:00+00:00       156.0          A, e  03339000\n",
       "2018-01-03 00:00:00+00:00       173.0          A, e  03339000\n",
       "2018-01-04 00:00:00+00:00       180.0          A, e  03339000\n",
       "...                               ...           ...       ...\n",
       "2022-05-26 00:00:00+00:00       753.0             P  03339000\n",
       "2022-05-27 00:00:00+00:00       714.0             P  03339000\n",
       "2022-05-28 00:00:00+00:00       976.0             P  03339000\n",
       "2022-05-29 00:00:00+00:00       997.0             P  03339000\n",
       "2022-05-30 00:00:00+00:00       865.0             P  03339000\n",
       "\n",
       "[1611 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwis.get_record(sites='03339000', service='dv', start='2017-12-31', parameterCd='00060')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c681b3cb",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}