exercise5_timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>ELEVATION</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>QGAG</th>\n",
       "      <th>QPCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12192.000000</td>\n",
       "      <td>12192.000000</td>\n",
       "      <td>12192.000000</td>\n",
       "      <td>12192.000000</td>\n",
       "      <td>12192.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>274.402657</td>\n",
       "      <td>42.297762</td>\n",
       "      <td>-83.714192</td>\n",
       "      <td>-4977.626605</td>\n",
       "      <td>-506.243985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.225965</td>\n",
       "      <td>0.002615</td>\n",
       "      <td>0.002895</td>\n",
       "      <td>5005.592632</td>\n",
       "      <td>2244.553990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>274.300000</td>\n",
       "      <td>42.294700</td>\n",
       "      <td>-83.716670</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>-9999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>274.300000</td>\n",
       "      <td>42.294700</td>\n",
       "      <td>-83.716670</td>\n",
       "      <td>-9999.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>274.300000</td>\n",
       "      <td>42.300000</td>\n",
       "      <td>-83.716670</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.300000</td>\n",
       "      <td>42.300000</td>\n",
       "      <td>-83.710800</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>274.900000</td>\n",
       "      <td>42.300000</td>\n",
       "      <td>-83.710800</td>\n",
       "      <td>999.990000</td>\n",
       "      <td>999.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ELEVATION      LATITUDE     LONGITUDE          QGAG          QPCP\n",
       "count  12192.000000  12192.000000  12192.000000  12192.000000  12192.000000\n",
       "mean     274.402657     42.297762    -83.714192  -4977.626605   -506.243985\n",
       "std        0.225965      0.002615      0.002895   5005.592632   2244.553990\n",
       "min      274.300000     42.294700    -83.716670  -9999.000000  -9999.000000\n",
       "25%      274.300000     42.294700    -83.716670  -9999.000000      0.100000\n",
       "50%      274.300000     42.300000    -83.716670      0.000000      0.100000\n",
       "75%      274.300000     42.300000    -83.710800      0.870000      0.100000\n",
       "max      274.900000     42.300000    -83.710800    999.990000    999.990000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "#ftp://ftp.ncdc.noaa.gov/pub/data/cdo/documentation/PRECIP_15_documentation.pdf\n",
    "\n",
    "'''\n",
    "QPCP: The amount of precipitation recorded at the station for the 15 minute period\n",
    "ending at the time specified for DATE above given in hundredths of inches or tenths of\n",
    "millimeters depending on user’s specification of standard or metric units.\n",
    "\n",
    "HT==hundredths of inches\n",
    "'''\n",
    "\n",
    "#What does the daily precipitation accumulation look like?\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df=pd.read_csv(\"datasets/1934033.csv\")\n",
    "df=df[df[\"STATION\"]==\"COOP:200230\"] #A2 campus\n",
    "df.head()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9daca2e48>"
      ]
     },
     "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=df.set_index(pd.to_datetime(df[\"DATE\"]))\n",
    "def clean(item):\n",
    "    if item == -9999:\n",
    "        return np.nan\n",
    "    if item == 999.99:\n",
    "        return np.nan\n",
    "    return item\n",
    "\n",
    "df[\"QPCP\"]=df[\"QPCP\"].apply(clean)\n",
    "\n",
    "new_df=df[\"QPCP\"].interpolate().resample(\"1D\").apply(np.sum)\n",
    "new_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9dada31d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,8))\n",
    "new_df[new_df.index>\"2013\"].plot(style=\".\")"
   ]
  }
 ],
 "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
}