NIAPythonDay4_v2.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. [sdfgf](https://catalog.data.gov/dataset/vsrr-state-and-national-provisional-counts-for-live-births-deaths-and-infant-deaths) VSRR - State and National Provisional Counts for Live Births, Deaths, and Infant Deaths\n",
    "2. https://data.cdc.gov/api/views/hc4f-j6nb/rows.csv - 93 rows 14 columns\n",
    "3. https://www.mdch.state.mi.us/osr/Provisional/MonthlyDeaths.csv\n",
    "\n",
    "https://covidtracking.com/data/api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv( \"https://api.covidtracking.com/v1/states/daily.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18418 entries, 0 to 18417\n",
      "Data columns (total 55 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   date                         18418 non-null  int64  \n",
      " 1   state                        18418 non-null  object \n",
      " 2   positive                     18232 non-null  float64\n",
      " 3   probableCases                7890 non-null   float64\n",
      " 4   negative                     17971 non-null  float64\n",
      " 5   pending                      1929 non-null   float64\n",
      " 6   totalTestResultsSource       18418 non-null  object \n",
      " 7   totalTestResults             18316 non-null  float64\n",
      " 8   hospitalizedCurrently        15112 non-null  float64\n",
      " 9   hospitalizedCumulative       11295 non-null  float64\n",
      " 10  inIcuCurrently               9770 non-null   float64\n",
      " 11  inIcuCumulative              3288 non-null   float64\n",
      " 12  onVentilatorCurrently        7778 non-null   float64\n",
      " 13  onVentilatorCumulative       1125 non-null   float64\n",
      " 14  recovered                    13156 non-null  float64\n",
      " 15  dataQualityGrade             17103 non-null  object \n",
      " 16  lastUpdateEt                 17854 non-null  object \n",
      " 17  dateModified                 17854 non-null  object \n",
      " 18  checkTimeEt                  17854 non-null  object \n",
      " 19  death                        17550 non-null  float64\n",
      " 20  hospitalized                 11295 non-null  float64\n",
      " 21  dateChecked                  17854 non-null  object \n",
      " 22  totalTestsViral              12258 non-null  float64\n",
      " 23  positiveTestsViral           7022 non-null   float64\n",
      " 24  negativeTestsViral           4189 non-null   float64\n",
      " 25  positiveCasesViral           13359 non-null  float64\n",
      " 26  deathConfirmed               8208 non-null   float64\n",
      " 27  deathProbable                6439 non-null   float64\n",
      " 28  totalTestEncountersViral     4315 non-null   float64\n",
      " 29  totalTestsPeopleViral        8095 non-null   float64\n",
      " 30  totalTestsAntibody           4094 non-null   float64\n",
      " 31  positiveTestsAntibody        3132 non-null   float64\n",
      " 32  negativeTestsAntibody        1289 non-null   float64\n",
      " 33  totalTestsPeopleAntibody     1601 non-null   float64\n",
      " 34  positiveTestsPeopleAntibody  926 non-null    float64\n",
      " 35  negativeTestsPeopleAntibody  846 non-null    float64\n",
      " 36  totalTestsPeopleAntigen      789 non-null    float64\n",
      " 37  positiveTestsPeopleAntigen   590 non-null    float64\n",
      " 38  totalTestsAntigen            2564 non-null   float64\n",
      " 39  positiveTestsAntigen         1614 non-null   float64\n",
      " 40  fips                         18418 non-null  int64  \n",
      " 41  positiveIncrease             18418 non-null  int64  \n",
      " 42  negativeIncrease             18418 non-null  int64  \n",
      " 43  total                        18418 non-null  int64  \n",
      " 44  totalTestResultsIncrease     18418 non-null  int64  \n",
      " 45  posNeg                       18418 non-null  int64  \n",
      " 46  deathIncrease                18418 non-null  int64  \n",
      " 47  hospitalizedIncrease         18418 non-null  int64  \n",
      " 48  hash                         18418 non-null  object \n",
      " 49  commercialScore              18418 non-null  int64  \n",
      " 50  negativeRegularScore         18418 non-null  int64  \n",
      " 51  negativeScore                18418 non-null  int64  \n",
      " 52  positiveScore                18418 non-null  int64  \n",
      " 53  score                        18418 non-null  int64  \n",
      " 54  grade                        0 non-null      float64\n",
      "dtypes: float64(33), int64(14), object(8)\n",
      "memory usage: 7.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18418"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len( data )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>positive</th>\n",
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       "      <th>negative</th>\n",
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       "      <th>...</th>\n",
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       "      <th>hospitalizedIncrease</th>\n",
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       "      <th>negativeRegularScore</th>\n",
       "      <th>negativeScore</th>\n",
       "      <th>positiveScore</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>20210124</td>\n",
       "      <td>AK</td>\n",
       "      <td>51610.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1401860.0</td>\n",
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       "      <td>totalTestsViral</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20210124</td>\n",
       "      <td>AL</td>\n",
       "      <td>441170.0</td>\n",
       "      <td>91709.0</td>\n",
       "      <td>1737094.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>totalTestsPeopleViral</td>\n",
       "      <td>2086555.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>40514.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2178264</td>\n",
       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>20210124</td>\n",
       "      <td>AR</td>\n",
       "      <td>284066.0</td>\n",
       "      <td>56238.0</td>\n",
       "      <td>2119560.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>totalTestsViral</td>\n",
       "      <td>2347388.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>13267.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2403626</td>\n",
       "      <td>43</td>\n",
       "      <td>17</td>\n",
       "      <td>dcd628b60b2155bbe3365ea432671d94b2e69596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20210124</td>\n",
       "      <td>AS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>totalTestsViral</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3c81ee980bf91ef3fe383f82e1094d0d8fc76e2c</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20210124</td>\n",
       "      <td>AZ</td>\n",
       "      <td>722574.0</td>\n",
       "      <td>44099.0</td>\n",
       "      <td>2671771.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>totalTestsViral</td>\n",
       "      <td>6385219.0</td>\n",
       "      <td>4320.0</td>\n",
       "      <td>50264.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3394345</td>\n",
       "      <td>68</td>\n",
       "      <td>301</td>\n",
       "      <td>2453c8080bd6d93c891880b4084cd6e120bbad71</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       date state  positive  probableCases   negative  pending  \\\n",
       "0  20210124    AK   51610.0            NaN  1401860.0      NaN   \n",
       "1  20210124    AL  441170.0        91709.0  1737094.0      NaN   \n",
       "2  20210124    AR  284066.0        56238.0  2119560.0      NaN   \n",
       "3  20210124    AS       0.0            NaN     2140.0      NaN   \n",
       "4  20210124    AZ  722574.0        44099.0  2671771.0      NaN   \n",
       "\n",
       "  totalTestResultsSource  totalTestResults  hospitalizedCurrently  \\\n",
       "0        totalTestsViral         1453470.0                   57.0   \n",
       "1  totalTestsPeopleViral         2086555.0                 2254.0   \n",
       "2        totalTestsViral         2347388.0                 1080.0   \n",
       "3        totalTestsViral            2140.0                    NaN   \n",
       "4        totalTestsViral         6385219.0                 4320.0   \n",
       "\n",
       "   hospitalizedCumulative  ...   posNeg  deathIncrease  hospitalizedIncrease  \\\n",
       "0                  1185.0  ...  1453470              0                     1   \n",
       "1                 40514.0  ...  2178264              3                     0   \n",
       "2                 13267.0  ...  2403626             43                    17   \n",
       "3                     NaN  ...     2140              0                     0   \n",
       "4                 50264.0  ...  3394345             68                   301   \n",
       "\n",
       "                                       hash  commercialScore  \\\n",
       "0  657bf064457b28ae60abcd8b2ac6ccf4b4c11643                0   \n",
       "1  a5886e16177a22e9e711e0d36eafcaecba90307d                0   \n",
       "2  dcd628b60b2155bbe3365ea432671d94b2e69596                0   \n",
       "3  3c81ee980bf91ef3fe383f82e1094d0d8fc76e2c                0   \n",
       "4  2453c8080bd6d93c891880b4084cd6e120bbad71                0   \n",
       "\n",
       "  negativeRegularScore negativeScore positiveScore score  grade  \n",
       "0                    0             0             0     0    NaN  \n",
       "1                    0             0             0     0    NaN  \n",
       "2                    0             0             0     0    NaN  \n",
       "3                    0             0             0     0    NaN  \n",
       "4                    0             0             0     0    NaN  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "provis_data = pd.read_csv( \"https://data.cdc.gov/api/views/hmz2-vwda/rows.csv\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Period</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Data Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>2019</td>\n",
       "      <td>January</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>311000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>2019</td>\n",
       "      <td>February</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>2019</td>\n",
       "      <td>March</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>304000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>2019</td>\n",
       "      <td>April</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>299000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>2019</td>\n",
       "      <td>May</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>316000</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>1975</th>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>2020</td>\n",
       "      <td>June</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>7117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>WEST VIRGINIA</td>\n",
       "      <td>2020</td>\n",
       "      <td>June</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>1449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>WISCONSIN</td>\n",
       "      <td>2020</td>\n",
       "      <td>June</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>5249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>WYOMING</td>\n",
       "      <td>2020</td>\n",
       "      <td>June</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>PUERTO RICO</td>\n",
       "      <td>2020</td>\n",
       "      <td>June</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Live Births</td>\n",
       "      <td>1327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1980 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              State  Year     Month   Period              Indicator  \\\n",
       "0     UNITED STATES  2019   January  Monthly  Number of Live Births   \n",
       "1     UNITED STATES  2019  February  Monthly  Number of Live Births   \n",
       "2     UNITED STATES  2019     March  Monthly  Number of Live Births   \n",
       "3     UNITED STATES  2019     April  Monthly  Number of Live Births   \n",
       "4     UNITED STATES  2019       May  Monthly  Number of Live Births   \n",
       "...             ...   ...       ...      ...                    ...   \n",
       "1975     WASHINGTON  2020      June  Monthly  Number of Live Births   \n",
       "1976  WEST VIRGINIA  2020      June  Monthly  Number of Live Births   \n",
       "1977      WISCONSIN  2020      June  Monthly  Number of Live Births   \n",
       "1978        WYOMING  2020      June  Monthly  Number of Live Births   \n",
       "1979    PUERTO RICO  2020      June  Monthly  Number of Live Births   \n",
       "\n",
       "      Data Value  \n",
       "0         311000  \n",
       "1         280000  \n",
       "2         304000  \n",
       "3         299000  \n",
       "4         316000  \n",
       "...          ...  \n",
       "1975        7117  \n",
       "1976        1449  \n",
       "1977        5249  \n",
       "1978         526  \n",
       "1979        1327  \n",
       "\n",
       "[1980 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1980 entries, 0 to 1979\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   State       1980 non-null   object\n",
      " 1   Year        1980 non-null   int64 \n",
      " 2   Month       1980 non-null   object\n",
      " 3   Period      1980 non-null   object\n",
      " 4   Indicator   1980 non-null   object\n",
      " 5   Data Value  1980 non-null   int64 \n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 92.9+ KB\n"
     ]
    }
   ],
   "source": [
    "provis_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Year</th>\n",
       "      <th>Data Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1980.000000</td>\n",
       "      <td>1.980000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2019.333333</td>\n",
       "      <td>7.049803e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.471524</td>\n",
       "      <td>4.463652e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>3.370000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.554750e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>4.042000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>7.114750e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>3.786000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year    Data Value\n",
       "count  1980.000000  1.980000e+03\n",
       "mean   2019.333333  7.049803e+04\n",
       "std       0.471524  4.463652e+05\n",
       "min    2019.000000  3.370000e+02\n",
       "25%    2019.000000  1.554750e+03\n",
       "50%    2019.000000  4.042000e+03\n",
       "75%    2020.000000  7.114750e+03\n",
       "max    2020.000000  3.786000e+06"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       UNITED STATES\n",
       "1       UNITED STATES\n",
       "2       UNITED STATES\n",
       "3       UNITED STATES\n",
       "4       UNITED STATES\n",
       "            ...      \n",
       "1975       WASHINGTON\n",
       "1976    WEST VIRGINIA\n",
       "1977        WISCONSIN\n",
       "1978          WYOMING\n",
       "1979      PUERTO RICO\n",
       "Name: State, Length: 1980, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data[\"State\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [strptime format codes](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "provis_data['Date'] = provis_data[ 'Year' ].astype( str ) + \" \" + provis_data[ 'Month' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2019 January\n",
       "1       2019 February\n",
       "2          2019 March\n",
       "3          2019 April\n",
       "4            2019 May\n",
       "            ...      \n",
       "1975        2020 June\n",
       "1976        2020 June\n",
       "1977        2020 June\n",
       "1978        2020 June\n",
       "1979        2020 June\n",
       "Name: Date, Length: 1980, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "provis_data['Date'] = pd.to_datetime( provis_data['Date'], format=\"%Y %B\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1980 entries, 0 to 1979\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   State       1980 non-null   object        \n",
      " 1   Year        1980 non-null   int64         \n",
      " 2   Month       1980 non-null   object        \n",
      " 3   Period      1980 non-null   object        \n",
      " 4   Indicator   1980 non-null   object        \n",
      " 5   Data Value  1980 non-null   int64         \n",
      " 6   Date        1980 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2), object(4)\n",
      "memory usage: 108.4+ KB\n"
     ]
    }
   ],
   "source": [
    "provis_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using dt Accessor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.indexes.accessors.DatetimeProperties object at 0x10db963a0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data['Date'].dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "maryland_data = provis_data[ provis_data[ 'State' ] == 'MARYLAND' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "maryland_deaths = maryland_data[ maryland_data[ 'Indicator' ] == 'Number of Deaths' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Period</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Data Value</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>January</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4573</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>February</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4068</td>\n",
       "      <td>2019-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>March</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4636</td>\n",
       "      <td>2019-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>April</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4241</td>\n",
       "      <td>2019-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>May</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4138</td>\n",
       "      <td>2019-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>June</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>3992</td>\n",
       "      <td>2019-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>July</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4047</td>\n",
       "      <td>2019-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>August</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4040</td>\n",
       "      <td>2019-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>September</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>3878</td>\n",
       "      <td>2019-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>October</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4274</td>\n",
       "      <td>2019-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>November</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4366</td>\n",
       "      <td>2019-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2019</td>\n",
       "      <td>December</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4474</td>\n",
       "      <td>2019-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2020</td>\n",
       "      <td>January</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4531</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2020</td>\n",
       "      <td>February</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4238</td>\n",
       "      <td>2020-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2020</td>\n",
       "      <td>March</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4725</td>\n",
       "      <td>2020-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2020</td>\n",
       "      <td>April</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>6073</td>\n",
       "      <td>2020-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2020</td>\n",
       "      <td>May</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>6102</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2020</td>\n",
       "      <td>June</td>\n",
       "      <td>Monthly</td>\n",
       "      <td>Number of Deaths</td>\n",
       "      <td>4709</td>\n",
       "      <td>2020-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         State  Year      Month   Period         Indicator  Data Value  \\\n",
       "128   MARYLAND  2019    January  Monthly  Number of Deaths        4573   \n",
       "180   MARYLAND  2019   February  Monthly  Number of Deaths        4068   \n",
       "232   MARYLAND  2019      March  Monthly  Number of Deaths        4636   \n",
       "284   MARYLAND  2019      April  Monthly  Number of Deaths        4241   \n",
       "336   MARYLAND  2019        May  Monthly  Number of Deaths        4138   \n",
       "388   MARYLAND  2019       June  Monthly  Number of Deaths        3992   \n",
       "440   MARYLAND  2019       July  Monthly  Number of Deaths        4047   \n",
       "492   MARYLAND  2019     August  Monthly  Number of Deaths        4040   \n",
       "544   MARYLAND  2019  September  Monthly  Number of Deaths        3878   \n",
       "596   MARYLAND  2019    October  Monthly  Number of Deaths        4274   \n",
       "648   MARYLAND  2019   November  Monthly  Number of Deaths        4366   \n",
       "700   MARYLAND  2019   December  Monthly  Number of Deaths        4474   \n",
       "752   MARYLAND  2020    January  Monthly  Number of Deaths        4531   \n",
       "804   MARYLAND  2020   February  Monthly  Number of Deaths        4238   \n",
       "856   MARYLAND  2020      March  Monthly  Number of Deaths        4725   \n",
       "908   MARYLAND  2020      April  Monthly  Number of Deaths        6073   \n",
       "960   MARYLAND  2020        May  Monthly  Number of Deaths        6102   \n",
       "1012  MARYLAND  2020       June  Monthly  Number of Deaths        4709   \n",
       "\n",
       "           Date  \n",
       "128  2019-01-01  \n",
       "180  2019-02-01  \n",
       "232  2019-03-01  \n",
       "284  2019-04-01  \n",
       "336  2019-05-01  \n",
       "388  2019-06-01  \n",
       "440  2019-07-01  \n",
       "492  2019-08-01  \n",
       "544  2019-09-01  \n",
       "596  2019-10-01  \n",
       "648  2019-11-01  \n",
       "700  2019-12-01  \n",
       "752  2020-01-01  \n",
       "804  2020-02-01  \n",
       "856  2020-03-01  \n",
       "908  2020-04-01  \n",
       "960  2020-05-01  \n",
       "1012 2020-06-01  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maryland_deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 41,
     "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": [
    "maryland_deaths.plot( x='Date', y=\"Data Value\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='Data Value'>"
      ]
     },
     "execution_count": 55,
     "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": [
    "sns.lineplot( x='Month', y='Data Value', hue='Year', data=maryland_deaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1050x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots( figsize=(7,4), dpi=150 )\n",
    "maryland_deaths_2019.plot( x='Date', y=\"Data Value\", ax=ax)\n",
    "maryland_deaths_2020.plot( x='Date', y=\"Data Value\", ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ALABAMA                  36\n",
       "ALASKA                   36\n",
       "ARIZONA                  36\n",
       "ARKANSAS                 36\n",
       "CALIFORNIA               36\n",
       "COLORADO                 36\n",
       "CONNECTICUT              36\n",
       "DELAWARE                 36\n",
       "DISTRICT OF COLUMBIA     36\n",
       "FLORIDA                  36\n",
       "GEORGIA                  36\n",
       "HAWAII                   36\n",
       "IDAHO                    36\n",
       "ILLINOIS                 36\n",
       "INDIANA                  36\n",
       "IOWA                     36\n",
       "KANSAS                   36\n",
       "KENTUCKY                 36\n",
       "LOUISIANA                36\n",
       "MAINE                    36\n",
       "MARYLAND                 36\n",
       "MASSACHUSETTS            36\n",
       "MICHIGAN                 36\n",
       "MINNESOTA                36\n",
       "MISSISSIPPI              36\n",
       "MISSOURI                 36\n",
       "MONTANA                  36\n",
       "NEBRASKA                 36\n",
       "NEVADA                   36\n",
       "NEW HAMPSHIRE            36\n",
       "NEW JERSEY               36\n",
       "NEW MEXICO               36\n",
       "NEW YORK                 36\n",
       "NORTH CAROLINA           36\n",
       "NORTH DAKOTA             36\n",
       "OHIO                     36\n",
       "OKLAHOMA                 36\n",
       "OREGON                   36\n",
       "PENNSYLVANIA             36\n",
       "PUERTO RICO              36\n",
       "RHODE ISLAND             36\n",
       "SOUTH CAROLINA           36\n",
       "SOUTH DAKOTA             36\n",
       "TENNESSEE                36\n",
       "TEXAS                    36\n",
       "UNITED STATES           108\n",
       "UTAH                     36\n",
       "VERMONT                  36\n",
       "VIRGINIA                 36\n",
       "WASHINGTON               36\n",
       "WEST VIRGINIA            36\n",
       "WISCONSIN                36\n",
       "WYOMING                  36\n",
       "Name: State, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data['State'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Monthly            1926\n",
       "12 Month-ending      54\n",
       "Name: Period, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data['Period'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of Live Births      972\n",
       "Number of Deaths           972\n",
       "Number of Infant Deaths     36\n",
       "Name: Indicator, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provis_data['Indicator'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len( provis_data['State'].unique() )"
   ]
  }
 ],
 "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}