{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "plain-hampton",
   "metadata": {},
   "source": [
    "# <center>Time Series Data</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "appreciated-leeds",
   "metadata": {},
   "source": [
    "<center>Dr. W.J.B. Mattingly</center>\n",
    "\n",
    "<center>Smithsonian Data Science Lab and United States Holocaust Memorial Museum</center>\n",
    "\n",
    "<center>August 2021</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "competent-iraqi",
   "metadata": {},
   "source": [
    "## Covered in this Chapter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mature-officer",
   "metadata": {},
   "source": [
    "1) How to Work with Time Series Data\n",
    "2) How to Work with NaN in Time Series Data\n",
    "3) How to Convert Floats and Ints into Time Series Data\n",
    "4) How to Plot Time Series Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comparative-helmet",
   "metadata": {},
   "source": [
    "## What is Time Series Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chubby-internet",
   "metadata": {},
   "source": [
    "**Time series data** is data that reflects either time or dates. In Pandas this type of data is known as datetime. If you are working with time series data, as we shall see, there are significant reasons to ensure that Pandas understands that the data at hand is a date or a time. It allows for easily manipulation and cleaning of inconsistent data formatting. Let us consider a simple example. Imagine we were given dates from one source as 01/02/2002 and another as 01.02.2002. Both are valid date formats, but they are structured entirely differently. Imagine now you had a third dataset that organized the data as 2 January 2002. Your task is to merge all these datasets together.\n",
    "\n",
    "If you wanted to do that, you could write out some Python and script them into alignment, but Pandas offers the ability to do that automatically. In order to leverage that ability, however, you must tell Pandas that the data at hand is datetime data. Exactly how you do that, we will learn in this chapter.\n",
    "\n",
    "Time series data is important for many different aspects of industry and academia. In the financial sector, time series data allows for one to understand the past performance of a stock. This is particularly useful in machine learning predictions which need to understand the past to predict accurately the future. More importantly, they need to understand the past a sequence of data. In the humanities, time series data is important for understand historical context and, as we shall see, plotting data temporally. Understanding how to work with time series data, therefore, in Pandas is absolutely essential."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "premium-separate",
   "metadata": {},
   "source": [
    "## About the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-cleveland",
   "metadata": {},
   "source": [
    "In this chapter, we will be working with an early version of a dataset I helped cultivate at the *Bitter Aloe Project*, a digital humanities project that explores apartheid violence in South Africa during the 20th century. This dataset comes from Vol. 7 of the Truth and Reconciliation Commission's *Final Report*. I am using not our final, well-cleaned version of this dataset, rather an earlier version for one key reason. It contains problematic cells and structure. This is more reflective of real-world data, which will often times come from multiple sources and need to be cleaned and structured. As such, it is good practice in this chapter to try and address some of the common problems that you will encounter with time series data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "controlling-revolution",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ObjectId</th>\n",
       "      <th>Last</th>\n",
       "      <th>First</th>\n",
       "      <th>Description</th>\n",
       "      <th>Place</th>\n",
       "      <th>Yr</th>\n",
       "      <th>Homeland</th>\n",
       "      <th>Province</th>\n",
       "      <th>Long</th>\n",
       "      <th>Lat</th>\n",
       "      <th>HRV</th>\n",
       "      <th>ORG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>AARON</td>\n",
       "      <td>Thabo Simon</td>\n",
       "      <td>An ANCYL member who was shot and severely inju...</td>\n",
       "      <td>Bethulie</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Orange Free State</td>\n",
       "      <td>25.97552</td>\n",
       "      <td>-30.503290</td>\n",
       "      <td>shoot|injure</td>\n",
       "      <td>ANC|ANCYL|Police|SAP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>ABBOTT</td>\n",
       "      <td>Montaigne</td>\n",
       "      <td>A member of the SADF who was severely injured ...</td>\n",
       "      <td>Messina</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Transvaal</td>\n",
       "      <td>30.039597</td>\n",
       "      <td>-22.351308</td>\n",
       "      <td>injure</td>\n",
       "      <td>SADF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>ABRAHAM</td>\n",
       "      <td>Nzaliseko Christopher</td>\n",
       "      <td>A COSAS supporter who was kicked and beaten wi...</td>\n",
       "      <td>Mdantsane</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Ciskei</td>\n",
       "      <td>Cape of Good Hope</td>\n",
       "      <td>27.6708791</td>\n",
       "      <td>-32.958623</td>\n",
       "      <td>beat</td>\n",
       "      <td>COSAS|Police</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>ABRAHAMS</td>\n",
       "      <td>Achmat Fardiel</td>\n",
       "      <td>Was shot and blinded in one eye by members of ...</td>\n",
       "      <td>Athlone</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cape of Good Hope</td>\n",
       "      <td>18.50214</td>\n",
       "      <td>-33.967220</td>\n",
       "      <td>shoot|blind</td>\n",
       "      <td>SAP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ABRAHAMS</td>\n",
       "      <td>Annalene Mildred</td>\n",
       "      <td>Was shot and injured by members of the SAP in ...</td>\n",
       "      <td>Robertson</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cape of Good Hope</td>\n",
       "      <td>19.883611</td>\n",
       "      <td>-33.802220</td>\n",
       "      <td>shoot|injure</td>\n",
       "      <td>Police|SAP</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20829</th>\n",
       "      <td>20888</td>\n",
       "      <td>XUZA</td>\n",
       "      <td>Mandla</td>\n",
       "      <td>Was severely injured when he was stoned by a f...</td>\n",
       "      <td>Carletonville</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Transvaal</td>\n",
       "      <td>27.397673</td>\n",
       "      <td>-26.360943</td>\n",
       "      <td>injure|stone</td>\n",
       "      <td>ANC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20830</th>\n",
       "      <td>20889</td>\n",
       "      <td>YAKA</td>\n",
       "      <td>Mbangomuni</td>\n",
       "      <td>An IFP supporter and acting induna who was sho...</td>\n",
       "      <td>Mvutshini</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>KwaZulu</td>\n",
       "      <td>Natal</td>\n",
       "      <td>30.28172</td>\n",
       "      <td>-30.868900</td>\n",
       "      <td>shoot</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20831</th>\n",
       "      <td>20890</td>\n",
       "      <td>YALI</td>\n",
       "      <td>Khayalethu</td>\n",
       "      <td>Was shot by members of the SAP in Lingelihle, ...</td>\n",
       "      <td>Cradock</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cape of Good Hope</td>\n",
       "      <td>25.619176</td>\n",
       "      <td>-32.164221</td>\n",
       "      <td>shoot</td>\n",
       "      <td>SAP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20832</th>\n",
       "      <td>20891</td>\n",
       "      <td>YALO</td>\n",
       "      <td>Bikiwe</td>\n",
       "      <td>An IFP supporter whose house and possessions w...</td>\n",
       "      <td>Port Shepstone</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Natal</td>\n",
       "      <td>30.4297304</td>\n",
       "      <td>-30.752126</td>\n",
       "      <td>destroy</td>\n",
       "      <td>ANC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20833</th>\n",
       "      <td>20892</td>\n",
       "      <td>YALOLO-BOOYSEN</td>\n",
       "      <td>Geoffrey Yali</td>\n",
       "      <td>An ANC supporter and youth activist who was to...</td>\n",
       "      <td>George</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cape of Good Hope</td>\n",
       "      <td>22.459722</td>\n",
       "      <td>-33.964440</td>\n",
       "      <td>torture|detain|torture</td>\n",
       "      <td>ANC|SAP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20834 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ObjectId            Last                  First  \\\n",
       "0             1           AARON            Thabo Simon   \n",
       "1             2          ABBOTT              Montaigne   \n",
       "2             3         ABRAHAM  Nzaliseko Christopher   \n",
       "3             4        ABRAHAMS         Achmat Fardiel   \n",
       "4             5        ABRAHAMS       Annalene Mildred   \n",
       "...         ...             ...                    ...   \n",
       "20829     20888            XUZA                 Mandla   \n",
       "20830     20889            YAKA             Mbangomuni   \n",
       "20831     20890            YALI             Khayalethu   \n",
       "20832     20891            YALO                 Bikiwe   \n",
       "20833     20892  YALOLO-BOOYSEN          Geoffrey Yali   \n",
       "\n",
       "                                             Description           Place  \\\n",
       "0      An ANCYL member who was shot and severely inju...        Bethulie   \n",
       "1      A member of the SADF who was severely injured ...         Messina   \n",
       "2      A COSAS supporter who was kicked and beaten wi...       Mdantsane   \n",
       "3      Was shot and blinded in one eye by members of ...         Athlone   \n",
       "4      Was shot and injured by members of the SAP in ...       Robertson   \n",
       "...                                                  ...             ...   \n",
       "20829  Was severely injured when he was stoned by a f...   Carletonville   \n",
       "20830  An IFP supporter and acting induna who was sho...       Mvutshini   \n",
       "20831  Was shot by members of the SAP in Lingelihle, ...         Cradock   \n",
       "20832  An IFP supporter whose house and possessions w...  Port Shepstone   \n",
       "20833  An ANC supporter and youth activist who was to...          George   \n",
       "\n",
       "           Yr Homeland           Province        Long        Lat  \\\n",
       "0      1991.0      NaN  Orange Free State    25.97552 -30.503290   \n",
       "1      1987.0      NaN          Transvaal   30.039597 -22.351308   \n",
       "2      1985.0   Ciskei  Cape of Good Hope  27.6708791 -32.958623   \n",
       "3      1985.0      NaN  Cape of Good Hope    18.50214 -33.967220   \n",
       "4      1990.0      NaN  Cape of Good Hope   19.883611 -33.802220   \n",
       "...       ...      ...                ...         ...        ...   \n",
       "20829  1991.0      NaN          Transvaal   27.397673 -26.360943   \n",
       "20830  1993.0  KwaZulu              Natal    30.28172 -30.868900   \n",
       "20831  1986.0      NaN  Cape of Good Hope   25.619176 -32.164221   \n",
       "20832  1994.0      NaN              Natal  30.4297304 -30.752126   \n",
       "20833  1986.0      NaN  Cape of Good Hope   22.459722 -33.964440   \n",
       "\n",
       "                          HRV                   ORG  \n",
       "0                shoot|injure  ANC|ANCYL|Police|SAP  \n",
       "1                      injure                  SADF  \n",
       "2                        beat          COSAS|Police  \n",
       "3                 shoot|blind                   SAP  \n",
       "4                shoot|injure            Police|SAP  \n",
       "...                       ...                   ...  \n",
       "20829            injure|stone                   ANC  \n",
       "20830                   shoot                   NaN  \n",
       "20831                   shoot                   SAP  \n",
       "20832                 destroy                   ANC  \n",
       "20833  torture|detain|torture               ANC|SAP  \n",
       "\n",
       "[20834 rows x 12 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"data/trc.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "median-present",
   "metadata": {},
   "source": [
    "As we can see, we have a few different columns which are relatively straight forward. In this notebook, however, I want to focus on Yr, which is a column that contains a single year referenced within the description. This corresponds to the year in which the violence described occurred. Notice, however, that we have a problem. Year is being recognized as a float (a number with a decimal place), or floating number. To confirm our suspicion, let's take a look at the data types by using the following command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ceramic-fifty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectId         int64\n",
       "Last            object\n",
       "First           object\n",
       "Description     object\n",
       "Place           object\n",
       "Yr             float64\n",
       "Homeland        object\n",
       "Province        object\n",
       "Long            object\n",
       "Lat            float64\n",
       "HRV             object\n",
       "ORG             object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "natural-cliff",
   "metadata": {},
   "source": [
    "Here, we can see all the different columns and their corresponding data types. Notice that Yr has float64. This confirms our suspicion. Why is this a problem? Well, if we were to try and plot the data by year (see the bar graph below), we would have floating numbers in that graph. This does not look clean. We could manually adjust these years to have no decimal place, but that requires effort on a case-by-case basis. Instead, it is best practice to convert these floats either to integers or to datetime data. Both have their advantages, but if your end goal is larger data analysis on time series data (not just plotting the years), I would opt for the latter. In order to do either, however, we must clean the data to get it into the correct format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "hired-savings",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Yr'].value_counts().sort_index().plot.bar(figsize=(20,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "independent-hello",
   "metadata": {},
   "source": [
    "## Cleaning the Data from Float to Int"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-script",
   "metadata": {},
   "source": [
    "Let's first try and convert our float column into an integer column. If we execute the command below which would normally achieve this task, we get the following error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "identified-addiction",
   "metadata": {},
   "outputs": [
    {
     "ename": "IntCastingNaNError",
     "evalue": "Cannot convert non-finite values (NA or inf) to integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIntCastingNaNError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-dc1db5c67903>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Yr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Yr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   5804\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5805\u001b[0m             \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5806\u001b[1;33m             \u001b[0mnew_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5807\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5808\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 414\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    415\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    416\u001b[0m     def convert(\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[0;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    326\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 327\u001b[1;33m                     \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    328\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    329\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mignore_failures\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    590\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 592\u001b[1;33m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    593\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    594\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[1;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   1307\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1308\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1309\u001b[1;33m         \u001b[0mnew_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1310\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1311\u001b[0m         \u001b[1;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m   1255\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1256\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1257\u001b[1;33m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1259\u001b[0m     \u001b[1;31m# in pandas we don't store numpy str dtypes, so convert to object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m   1166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1167\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloating\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1168\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mastype_float_to_int_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1169\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1170\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_float_to_int_nansafe\u001b[1;34m(values, dtype, copy)\u001b[0m\n\u001b[0;32m   1211\u001b[0m     \"\"\"\n\u001b[0;32m   1212\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1213\u001b[1;33m         raise IntCastingNaNError(\n\u001b[0m\u001b[0;32m   1214\u001b[0m             \u001b[1;34m\"Cannot convert non-finite values (NA or inf) to integer\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1215\u001b[0m         )\n",
      "\u001b[1;31mIntCastingNaNError\u001b[0m: Cannot convert non-finite values (NA or inf) to integer"
     ]
    }
   ],
   "source": [
    "df['Yr'] = df['Yr'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "professional-platinum",
   "metadata": {},
   "source": [
    "At the very bottom, we see why the error was returned. \"IntCastingNaNError: Cannot convert non-finite values (NA or inf) to integer\". This means that somewhere in our data, there are a few blank cells in the Year column. We need to fill in these blank cells. To do that, we can use the fillna function that we met earlier in this textbook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "competent-upper",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graduate-planning",
   "metadata": {},
   "source": [
    "If we try and rerun our same command as above, you will notice we have no errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "pressing-placement",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Yr'] = df['Yr'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marked-float",
   "metadata": {},
   "source": [
    "Now, let's see if it worked by displaying the data types again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "atomic-planet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectId         int64\n",
       "Last            object\n",
       "First           object\n",
       "Description     object\n",
       "Place           object\n",
       "Yr               int32\n",
       "Homeland        object\n",
       "Province        object\n",
       "Long            object\n",
       "Lat            float64\n",
       "HRV             object\n",
       "ORG             object\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "satellite-soviet",
   "metadata": {},
   "source": [
    "Notice that Yr is now int32. Success! Now that we have the data in the correct format, let's plot it out. We can plot out the frequency of violence based on year by using value counts. This will go through the entire Yr column and count all the values identified and store them as a dictionary of frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "veterinary-fraud",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1993    2835\n",
       "1992    2648\n",
       "1990    2556\n",
       "1986    2056\n",
       "1994    1867\n",
       "1991    1793\n",
       "1985    1665\n",
       "1988    1015\n",
       "1989     935\n",
       "1987     744\n",
       "1980     438\n",
       "1983     352\n",
       "1976     319\n",
       "1984     301\n",
       "1960     280\n",
       "1977     128\n",
       "1981     124\n",
       "1982     123\n",
       "1975     111\n",
       "1963      88\n",
       "0         84\n",
       "1962      69\n",
       "1978      60\n",
       "1979      53\n",
       "1964      37\n",
       "1961      32\n",
       "1965      19\n",
       "1969      14\n",
       "1968      14\n",
       "1974      12\n",
       "1966      11\n",
       "1970      10\n",
       "1971      10\n",
       "1967       8\n",
       "1972       6\n",
       "1973       5\n",
       "1959       3\n",
       "1998       3\n",
       "1996       3\n",
       "1997       2\n",
       "1958       1\n",
       "Name: Yr, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Yr'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cleared-journey",
   "metadata": {},
   "source": [
    "This looks great, but let's try and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "developing-bearing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Yr'].value_counts().plot.bar(figsize=(20,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quantitative-athens",
   "metadata": {},
   "source": [
    "What do you notice that is horribly wrong about our bar graph? If you noticed that it is not chronological, you'd be right. It would be quite odd to present our data in this format. When we are examining time series data, we need to visualize that data chronologically (usually). We can fix this, by adding sort_index()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "differential-silence",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         84\n",
       "1958       1\n",
       "1959       3\n",
       "1960     280\n",
       "1961      32\n",
       "1962      69\n",
       "1963      88\n",
       "1964      37\n",
       "1965      19\n",
       "1966      11\n",
       "1967       8\n",
       "1968      14\n",
       "1969      14\n",
       "1970      10\n",
       "1971      10\n",
       "1972       6\n",
       "1973       5\n",
       "1974      12\n",
       "1975     111\n",
       "1976     319\n",
       "1977     128\n",
       "1978      60\n",
       "1979      53\n",
       "1980     438\n",
       "1981     124\n",
       "1982     123\n",
       "1983     352\n",
       "1984     301\n",
       "1985    1665\n",
       "1986    2056\n",
       "1987     744\n",
       "1988    1015\n",
       "1989     935\n",
       "1990    2556\n",
       "1991    1793\n",
       "1992    2648\n",
       "1993    2835\n",
       "1994    1867\n",
       "1996       3\n",
       "1997       2\n",
       "1998       3\n",
       "Name: Yr, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Yr'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instructional-horror",
   "metadata": {},
   "source": [
    "Notice that we have now preserved the value counts, but organized them in their correct order. We can now try plotting that data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "architectural-turner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Yr'].value_counts().sort_index().plot.bar(figsize=(20,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moved-spotlight",
   "metadata": {},
   "source": [
    "We have a potential issue, however. That first row, 0, is throwing off our bar graph. What if I didn't want to represent 0, or no date, in the graph. I can solve this problem a few different ways. Let's first create a new dataframe called val_year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "athletic-allocation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         84\n",
       "1958       1\n",
       "1959       3\n",
       "1960     280\n",
       "1961      32\n",
       "1962      69\n",
       "1963      88\n",
       "1964      37\n",
       "1965      19\n",
       "1966      11\n",
       "1967       8\n",
       "1968      14\n",
       "1969      14\n",
       "1970      10\n",
       "1971      10\n",
       "1972       6\n",
       "1973       5\n",
       "1974      12\n",
       "1975     111\n",
       "1976     319\n",
       "1977     128\n",
       "1978      60\n",
       "1979      53\n",
       "1980     438\n",
       "1981     124\n",
       "1982     123\n",
       "1983     352\n",
       "1984     301\n",
       "1985    1665\n",
       "1986    2056\n",
       "1987     744\n",
       "1988    1015\n",
       "1989     935\n",
       "1990    2556\n",
       "1991    1793\n",
       "1992    2648\n",
       "1993    2835\n",
       "1994    1867\n",
       "1996       3\n",
       "1997       2\n",
       "1998       3\n",
       "Name: Yr, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_year = df[\"Yr\"].value_counts().sort_index()\n",
    "val_year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capable-compound",
   "metadata": {},
   "source": [
    "With this new dataframe, I can simply start at index 1 and then graph the data. Notice that the 0 value is now gone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "exceptional-singing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_year.iloc[1:].plot.bar(figsize=(20,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "several-revolution",
   "metadata": {},
   "source": [
    "Although we have been able to now plot our time series data chronologically, Pandas has not seen this as a datetime type. Instead, it has viewed these years solely as integers. In order to work with the years as time series data formally, we need to convert the integers into datetime format."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cultural-diabetes",
   "metadata": {},
   "source": [
    "## Convert to Time Series DateTime in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "experimental-harvey",
   "metadata": {},
   "source": [
    "Our goal here will be to create a new column that will store Yr as a datetime type. One might think that we could easily just convert everything to datetime. Normally the following command would work, but instead we get this error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dominant-photograph",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "time data '0' does not match format '%Y' (match)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_to_datetime_with_format\u001b[1;34m(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m    508\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 509\u001b[1;33m             \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    510\u001b[0m             \u001b[0mdta\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDatetimeArray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtz_to_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\_libs\\tslibs\\conversion.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.conversion.datetime_to_datetime64\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Unrecognized value type: <class 'int'>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-cc2c68a810bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Dates'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Yr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'%Y'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[0;32m    881\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtz_localize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 883\u001b[1;33m         \u001b[0mcache_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_maybe_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    884\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcache_array\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    885\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_maybe_cache\u001b[1;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[0;32m    193\u001b[0m         \u001b[0munique_dates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munique_dates\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 195\u001b[1;33m             \u001b[0mcache_dates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0munique_dates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m             \u001b[0mcache_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_dates\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munique_dates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m             \u001b[1;31m# GH#39882 and GH#35888 in case of None and NaT we get duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[1;34m(arg, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[0;32m    391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mformat\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 393\u001b[1;33m         res = _to_datetime_with_format(\n\u001b[0m\u001b[0;32m    394\u001b[0m             \u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morig_arg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfer_datetime_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m         )\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_to_datetime_with_format\u001b[1;34m(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m    511\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdta\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    512\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 513\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    514\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_to_datetime_with_format\u001b[1;34m(arg, orig_arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m    498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    499\u001b[0m         \u001b[1;31m# fallback\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 500\u001b[1;33m         res = _array_strptime_with_fallback(\n\u001b[0m\u001b[0;32m    501\u001b[0m             \u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfer_datetime_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    502\u001b[0m         )\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py\u001b[0m in \u001b[0;36m_array_strptime_with_fallback\u001b[1;34m(arg, name, tz, fmt, exact, errors, infer_datetime_format)\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 436\u001b[1;33m         \u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimezones\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray_strptime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexact\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    437\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m\"%Z\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfmt\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"%z\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfmt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    438\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0m_return_parsed_timezone_results\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimezones\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\wma22\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\pandas\\_libs\\tslibs\\strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: time data '0' does not match format '%Y' (match)"
     ]
    }
   ],
   "source": [
    "df['Dates'] = pd.to_datetime(df['Yr'], format='%Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portuguese-belize",
   "metadata": {},
   "source": [
    "Just as the NaN cells plagued us above, so too has the 0s that we filled them with. Fortunately, we can fix this issue by passing the keyword argument errors=\"coerce\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "driven-surveillance",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Dates'] = pd.to_datetime(df['Yr'], format='%Y', errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "loved-arbor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectId                int64\n",
       "Last                   object\n",
       "First                  object\n",
       "Description            object\n",
       "Place                  object\n",
       "Yr                      int32\n",
       "Homeland               object\n",
       "Province               object\n",
       "Long                   object\n",
       "Lat                   float64\n",
       "HRV                    object\n",
       "ORG                    object\n",
       "Dates          datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alone-conspiracy",
   "metadata": {},
   "source": [
    "And like magic, we have not only created a new column, but notice that it is in datetime64[ns] format. We should also understand the keyword argument passed here, format. Format takes a formatted string that will tell Pandas how to interpret the data being passed to it. Because our integer referred to a single year, we use %Y. Let's try and plot this data now to see how it looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "noted-metro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IAAAAgCR72Siqqpur6jNVdWVVXTYse2JVfaSqbhy+HjQsr6r65aq6qaquqqqjxxgAAAAAAOMYY4+iF7XWjmqtHTPcPj3JR1trRyb56HA7SV6a5MjhclqSd4+QDQAAAMBIpvjo2UlJ3jdcf1+Sl69Zfk5buSTJE6rqqRPkAwAAALAH9rZR1JJcXFWXV9Vpw7JDWmtfGK5/Mckhw/VDk9y65mdvG5YBAAAAsA/Yspc//4LW2u1V9eQkH6mqz669s7XWqqo9nAccGk6nJckRRxyxl08PAAAAgN21V3sUtdZuH75+KcmHkhyb5I7tHykbvn5p+Pbbkxy+5scPG5bt+Jjvaa0d01o75uCDD96bpwcAAADAw7DHjaKqemxVPW779STHJbk6yflJThm+7ZQk5w3Xz09y8nD2s+cl+cqaj6gBAAAAsMH25qNnhyT5UFVtf5zfbK19uKouTfLBqnptkluSvHL4/guTvCzJTUm+luQ1e5ENAAAAwMj2uFHUWvtcku9bZ/mXk7x4neUtyev2NA8AAACAae3tWc8AAAAA6MTenvUMAIBNbuvpF+zRz9185okjPxMAYG/ZowgAAACAJBpFAAAAAAw0igAAAABIolEEAAAAwECjCAAAAIAkGkUAAAAADDSKAAAAAEiSbNnoJwAAADxg6+kX7NHP3XzmiSM/EwA2I3sUAQAAAJBEowgAAACAgY+eAQD7BB+3AQDYeBpFAACwiWnSArCWj54BAAAAkMQeRfss/9kBAAAA5maPIgAAAACSaBQBAAAAMNAoAgAAACCJRhEAAAAAA40iAAAAAJJoFAEAAAAw0CgCAAAAIIlGEQAAAAADjSIAAAAAkmgUAQAAADDQKAIAAAAgiUYRAAAAAAONIgAAAACSJFs2+gkAAGyEradf8LB/5uYzT5zgmfBw7Untkj2v39x5ALCR7FEEAAAAQBJ7FAEAO2EvCgCAzcceRQAAAAAksUcRm4T/igMAAMCuaRSxITRuAAAAYN+jUQQAMDH/IAEAlkKjCACgMxpTAMCemr1RVFUnJPmlJPsn+bXW2plzPweYmjfoAAAALNGsjaKq2j/JryR5SZLbklxaVee31q6d83nwrTQ2AAAAgLn3KDo2yU2ttc8lSVWdm+SkJBpFsBc0+sYz91yqHQAAsC+Zu1F0aJJb19y+LclzZ34Oe8Qfc/CAJTRTvPbWt4Taydt38gAAlqL392Vz/k1UrbU9+sE9Cqv6n5Kc0Fr78eH2jyV5bmvt9Wu+57Qkpw03/36S6/cg6klJ/mIvn648efLkbWSWPHnyNk9ez2OTJ0/exuX1PDZ58uTtfd53tNYOXu+Oufcouj3J4WtuHzYs+6bW2nuSvGdvQqrqstbaMXvzGPLkyZO3kVny5MnbPHk9j02ePHkbl9fz2OTJkzdt3n5jPdBuujTJkVX19Kp6ZJJXJTl/5ucAAAAAwDpm3aOotXZvVb0+yUVJ9k9ydmvtmjmfAwAAAADrm/ujZ2mtXZjkwolj9uqja/LkyZO3D2TJkydv8+T1PDZ58uRtXF7PY5MnT96EebMezBoAAACAfdfcxygCAAAAYB+lUQQAAABAkg04RtHYqqqSHJvk0GHR7Uk+2Sb6TF1VHZjkhB3yLmqt3TNRXu/j630+1W+heZugdr2Pr/f5VL8FZg15XdduyDxkbV5r7Y4Js7p+7Q2Zs83n3Hm9128D8tRuvLxut3tDnvotO2/y+Vz0HkVVdVySG5OckeRlw+UtSW4c7hs77+QkVyR5YZLHDJcXJbl8uG/svN7H1/t8qt9C8zZB7XofX+/zqX4LzBryeq/dUVV1SZKPJfmF4fLxqrqkqo6eIK/3197c86l+C81Tu9Hzut3uDXnqt+y8eeaztbbYS5LrkmxdZ/nTk1w3Qd71SZ6wzvKDktxgfPt83tzzqX4LzdsEtet9fL3Pp/otMGuT1O7KJM9dZ/nzkny6g/mcO2/u+VS/heap3eh53W731E/9dvey6D2Ksvro3G3rLL89ySMmyKsk6+3Odf9w39h6H1/v86l+y83rvXa9j6/3+VS/ZWYl/dfusa21T+y4sLV2SZLHTpDX+2tv7vlUv+Xmqd24et7uJeq39LxZ5nPpxyg6O8mlVXVukluHZYcneVWS906Q9/YkV1TVxWvyjkjykiRvmyCv9/H1Pp/qt9y83mvX+/h6n0/1W2ZW0n/tfr+qLkhyTh48vpOTfHiCvN5fe3PPp/otN0/txtXzdi9Rv6XnzTKfNeymtFhV9ewkP5wHH8jp/NbatRPlHZTk+Hzrgaruniiv9/H1Pp/qt9C8TVC73sfX+3yq3wKzhrzea/fSJCflW8d34UR5vb/25p5P9VtontqNntftdm/IU79l500+n4tvFG1XVU9MktbaXTNkzXr2iSGz2/H1Pp9z5/Vevw3I67Z2Q2a34+t9PufO67l+vf+ubMT45tbza28z6L1+Pf++9DyXPW/31mSq30LzhszJ5nPRjaKqOiKro/r/wyRfyeozgI9P8gdJTm+t3Txy3lFJfjXJgVl9LrCSHJbkniQ/0Vq7YuS83sc3d97c86l+C83bBLXrfXxz51m3LDRvE/yuHJV5x3dgkjdltVfDIVkds+FLSc5LcmYb+TTBm+C1N/d8qt9C89Ru8evOufPUb9l588xnG/ko3HNekvy3JD+aZP81y/bP6vN5l0yQd2XmPaNA7+PrfT7Vb6F5m6B2vY+v9/lUvwVmbZLaXZTkjUmesmbZU5KcnuTiDuZz7ry551P9FpqndqPndbvdUz/12+2csZ/4nJckN+7JfRPl3WR8i86bez7Vbx/O2+S16318vc+n+u2jWZukdtfvyX2dzOcUeXPPp/otNE/tZs1b9HZvH5xP9dtH53PpZz27vKreleR9efARv09J8qkJ8uY+o0Dv4+t9PtVvuXm916738fU+n+q3zKyk/9rdUlU/neR9bTg2w3DMhlPX5I+p99fe3POpfsvNU7tx9bzdS9Rv6XmzzOfSj1H0yCSvzYOP8H9bkt9N8t7W2t9OkDnbGQV6H9/ceXPPp/otN6/32vU+vrnzrFvGN3P9uv1dGTLnHN9BWX3U5aQ8cJyUO5Kcn+QdbeSDbfb+2tuA+VS/heap3bLXnXPnqd/4eqzfohtFAAAAAIxnv41+AmOpqh96qNsT5J32ULcnyOt9fL3Pp/otNG8T1K738fU+n+q3wKzh8Xuv3dEPdXuCvN5fe3PPp/otNE/tRs/rdrs3PL76LTtvsvnsplGU5Dm7uD222sXtsfU+vt7nU/2Wm9d77XofX+/zqX7LzEr6r92/2sXtsfX+2pt7PtVvuXlqN66et3uJ+i09b7L59NEzAAAAAJJk8Wc9S1U9K+sfOOq6ifKOT/LyHfLOa61NcUTzzTC+3udT/Raatwlq1/v4ep9P9Vtg1pDXe+0OTHLCDnkXtdbumSiv99fe3POpfgvNU7vR87rd7g156rfsvMnnc9F7FFXVG5O8Osm5WR3pO0kOS/KqJOe21s4cOe+sJM/M6tR3a/NOTnJja+0NI+f1Pr658+aeT/VbaN4mqF3v45s7z7ploXmb4HflrMw7vpOTvDnJxVm9ad2e95Ikb2mtnTNyXu+vvbnnU/0Wmqd2i193zp2nfsvOm2c+W2uLvSS5Ickj1ln+yKyKMnreTpbXVHm9j6/3+VS/ZeZthtr1Pr7e51P9lpe1SWp3fZInrLP8oJ09l6XN58x5c8+n+i00T+3Gz9vJ8sVv99Svj7w55nPpB7O+P8nT1ln+1OG+sX29qtY7QNRzknx9grzex9f7fKrfcvN6r13v4+t9PtVvmVlJ/7WrJOvtqn5/pjmgZ++vvbnnU/2Wm6d24+p5u5eo39LzZpnPpR+jaFuSj1bVjUluHZYdkeQZSV4/Qd6pSd5dVY/LA7t5HZ7kK8N9Y9uWvsc3d962zDufc+edmr7rN2fetvRdu23pe3xz522LdctS8+bMSvqv3duTXFFVF+fB43tJkrdNkLctfb/25p5P9VtuntqN69T0u91L1G/pedsyw3wu+hhFSVJV+yU5Ng8+kNOlrbX7Jsx8ytq81toXJ8zqenxz5809n+q33Lzea9f7+ObOs25Zdl7PvytD5pzjOyjJ8fnWA+rePVFe16+9DZhP9VtontpNktnzdk/9Fpw3x3wufY+iZLWb5fbL9ttT7MKW5JtnFPjBrClKVU12RoF0Pr7e53PuvN7rN3Ne17VL5+PrfT7nzuu5fr3/rsw9vtba3VX1h3nwm+VJ/lDdHpmOX3tzz6f6LTdP7cbV83ZvoH4LzssM87noYxRV1XFJbkxyRpKXDZe3JLlxuG/svJOTXJHkhUkeM1xelOTy4b6x83ofX+/zqX4LzdsEtet9fL3Pp/otMGvI6712R1XVJUk+luQdSX4hycer6pKqOnqCvN5fe3PPp/otNE/tRs/rdrs35KnfsvPmmc+xj8I95yXJdUm2rrP86UmumyBv7jMK9D6+3udT/Raatwlq1/v4ep9P9Vtg1iap3ZVJnrvO8ucl+XQH8zl33tzzqX4LzVO70fO63e6pn/rt7mXRexRl9dG529ZZfnuSR0yQN/cZBXofX+/zqX7Lzeu9dr2Pr/f5VL9lZiX91+6xrbVP7LiwtXZJksdOkNf7a2/u+VS/5eap3bh63u4l6rf0vFnmc+nHKDo7yaVVdW4eOOL34UleleS9E+TNfUaB3sfX+3yq33Lzeq9d7+PrfT7Vb5lZSf+1+/2quiDJOXnw+E5O8uEJ8np/7c09n+q33Dy1G1fP271E/ZaeN8t89nDWs+9KclIefMTv81tr106UN/cZBXofX+/zqX4LzdsEtet9fL3Pp/otMGvI6712L83647tworzeX3tzz6f6LTRP7UbP63a7N+Sp37LzJp/PxTeKAAAAABjH0o9R9E1VdcZD3Z4g7z0PdXuCvDMe6vYEeXOPr/f5nDuv9/rNlrcJanfGQ92eIK/b35Xh8c94qNsd5HVbv03wuzL3+E57qNsT5J3xULc7yJt7PtVvoXlqN3pet9u94fHPeKjbHeSp3x7qplGU5PJd3B7b/72L22PrfXy9z6f6LTev99r1Pr7e51P9lpmV9F+7HQ/gOcUBPdfq/bU393yq33Lz1G5cPW/3EvVbet5k8+mjZwAAAAAkWfhZz6pqS5LXJnlFkqcNi29Pcl6S97bWvjFy3oFJ3pTk5UmenNVp8L405J3ZWrtn5Lzex9f7fKrfQvM2Qe16H1/v86l+C8wa8rqu3ZB5/JC39gCb57XWRj/zUu+vvSFztvmcO6/3+m1AntqNl9ftdm/IU79l580yn4veo6iq3p/kniTvS3LbsPiwJKckeWJr7UdHzrsoyR8keV9r7YvDsqcMeS9urR03cl7v4+t9PtVvoXmboHa9j6/3+VS/BWYNj9177c5K8sysTtG9dnwnJ7mxtfaGkfN6f+2dlXnnc+683us3W57aLX7d2fu2SP3GzZtnPltri70kuWFP7tuLvOv35D7j22fy5p5P9Vto3iaoXe/j630+1W+BWZukduuOIatjpNzYwXzuE3lzz6f67ft5ajd6XrfbPfVTv929LP1g1ndV1Y9U1TfHUVX7VdWPJrl7grxbquqnq+qQNXmHVNUbk9w6QV7v4+t9PtVvuXm916738fU+n+q3zKyk/9p9vaqes87y5yT5+gR5vb/25p5P9VtuntqNq+ftXqJ+S8+bZz7H7nDNeUmyNckHktyZ5IYkNw7XP5Dk6RPkHZTkHUk+OxTh7iTXDcueaHz7fN7c86l+C83bBLXrfXy9z6f6LTBrk9Tu6CSfSHJtkouHy3VJLknyAx3M59x5c8+n+i00T+1Gz+t2u6d+6re7l0Ufo2itqvr2JGmtfXmjn8sUeh/f3OaeT/Vbrt5r1/v45mbdwu7quXa1OjbDNw+o24ZjNkyc2e1rb+75VL/l5qkdD5f6LduU87n4RlFVPSvJSfnWI/x/dqK8uc8+0fv4ep9P9Vto3iaoXe/j630+1W+BWUNe77U7MMkJO+Rd1CY4w9qQ1/trb+75VL+F5qnd6HndbveGPPVbdt7k87noRtHwub9XJzk3Dz7i96uSnNtaO3PkvLMy7xkFeh/f3Hlzz6f6LTRvE9Su9/HNnWfdstC8TfC7clbmHd/JSd6c1cdebl+T95Ikb2mtnTNyXu+vvbnnU/0Wmqd2i193zp2nfsvOm2c+x/oM20ZcsvpM3iPWWf7IdHI2iN7H1/t8qt8y8zZD7XofX+/zqX7Ly9oktbs+yRPWWX7Qzp7L0uZz5ry551P9FpqnduPn7WT54rd76tdH3hzzufSznt2f5GnrLH/qcN/Y5j6jQO/j630+1W+5eb3Xrvfx9T6f6rfMrKT/2lWS9XZVv3+4b2y9v/bmnk/1W26e2o2r5+1eon5Lz5tlPreM9UAbZFuSj1bVjXng1HNHJHlGktdPkHdqkndX1ePywG5ehyf5ynDf2Lal7/HNnbct887n3Hmnpu/6zZm3LX3Xblv6Ht/cedti3bLUvDmzkv5r9/YkV1TVxXnw+F6S5G0T5G1L36+9uedT/Zabp3bjOjX9bvcS9Vt63rbMMJ+LPkZRklTVfkmOzYMP5HRpa+2+CTOfsjavTXhGgd7HN3fe3POpfsvN6712vY9v7jzrlmXn9fy7MmTOOb6Dkhyfbz2g7t0T5XX92tuA+VS/heap3SSZPW/31G/BeXPM5+IbRQAAAACMY+nHKAIAAABgJBpFAAAAACTRKBpNVR2w0c9hSnOPr/f5nFvv9ev596XnsSV+V5au5/r1/rvS+/gAptDzdm8zUL/d122jqKo+M3PktXOG9T6+ufPmnk/1W25e77XrfXxz51m3LDqv69+VzD++3585r+vX3gbMp/otNE/tRtfzdk/9Fp435nxuGeuBNkJV/ZOd3ZXkKRPk/dRD5I3eLdwE4+t9PtVvoXmboHa9j6/3+VS/BWYNeb3X7uiHyDtqgrzeX3tzz6f6LTRP7UbP63a7N+Sp37LzZpnPRTeKknwgyW8kWe/Ubd82Qd7PJfmPSe5d574p9s7qfXy9z6f6LTev99r1Pr7e51P9lpmV9F+7S5N8PKs3qzt6wgR5vb/25p5P9VtuntqNq+ftXqJ+S8+bZz5ba4u9JLk8yffs5L5bJ8j70yQ/MGNe7+PrfT7Vb6F5m6B2vY+v9/lUvwVmbZLaXZ3kyI7nc+68uedT/Raap3aj53W73VO/LvJmmc+l71G0LclXd3LfKybIe02Su3Zy3zET5G1L3+PrfT7nzuu9fnPmbUvftduWvsfX+3zOnddz/Xr/XZl7fGdk5/89/dcT5G1L36+9MzLvfM6dty1912/OvDOidmPqebuXqN/S87ZlhvmsofMEAAAAwCa36D2KqmpLktdm1Tl72rD49iTnJXlva+0bI+cdmORNSV6e5MlZfS7wS0Pema21e0bO6318vc+n+i00bxPUrvfx9T6f6rfArCGv69oNmccPeYcOi25Pcl5r7cMTZHX92hsyZ5vPufN6r98G5KndeHndbveGPPVbdt4s87noPYqq6v1J7knyviS3DYsPS3JKkie21n505LyLkvxBkve11r44LHvKkPfi1tpxI+f1Pr7e51P9Fpq3CWrX+/h6n0/1W2DW8Ni91+6sJM9Mck4ePL6Tk9zYWnvDyHm9v/bOyrzzOXde7/WbLU/tFr/u7H1bpH7j5s0zn2Md7GgjLklu2JP79iLv+j25z/j2mby551P9Fpq3CWrX+/h6n0/1W2DWJqndumPI6kxMN3Ywn/tE3tzzqX77fp7ajZ7X7XZP/dRvdy9TnK5tTndV1Y9U1TfHUVX7VdWPJrl7grxbquqnq+qQNXmHVNUbk9w6QV7v4+t9PtVvuXm916738fU+n+q3zKyk/9p9vaqes87y5yT5+gR5vb/25p5P9VtuntqNq+ftXqJ+S8+bZz7H7nDNeUmyNckHktyZ5Ibhcuew7OkT5B2U5B1JPpvVkc3vSnLdsOyJxrfP5809n+q30LxNULvex9f7fKrfArM2Se2OTvKJJNcmuXi4XJfkkuzk1MELm8+58+aeT/VbaJ7ajZ7X7XZP/dRvdy+LPkbRWlX17UnSWvvyRj+XKfQ+vrnNPZ/qt1y916738c3NuoXd1XPtanVshm8eULcNx2yYOLPb197c86l+y81TOx4u9Vu2Kedz6R89+6bW2pdba1+uqvfMmVtVvzdHTu/jmztv7vlUv+Xm9V673sc3d551y/Lzev1d2W6m9eYXW2uXt9YuT/Ivp84bMrt97c09n+q33Dy1m06v2z31W3belPPZTaNojWNmzjt0198yqt7H1/t8qt9y83qvXe/j630+1W+ZWUn/tfvhmfN6f+3NPZ/qt9w8tRtXz9u9RP2Wnjf6fPbYKPrSzHmfmjmv9/H1Pp/qt9y83mvX+/h6n0/1W2ZW0n/taua83l97c8+n+i03T+3G1fN2L1G/peeNPp/dHKMIAIB9S1Xt11q7f6OfRy/mnk/1Wy61A/ZGj3sUJUk24HOPvz9zXu/j630+1W+heZugdr2Pr/f5VL8FZg15XdZu+x+qVfXv58jbrtfX3tzzqX7LzVO7yfK63e4Neeq37LzR5nPLWA+0EarqiTu7K8nLJsg7+iHyjpogr/fx9T6f6rfQvE1Qu97H1/t8qt8Cs4a8rmu3Cz+e5K1jPmDvr71dGH0+587rvX770O+L2j38vG63e0Oe+i07b5b5XHSjKMmdSW7Jgz+D24bbT54g79IkH98hb7snTJDX+/h6n0/1W25e77XrfXy9z6f6LTMr6bx2VfXVnd2V5NFj56Xz197c86l+y81Tu9H1vN1L1G/pefPMZ2ttsZckNyY5Yif33TpB3tVJjpwxr/fx9T6f6rfQvE1Qu97H1/t8qt8CszZJ7T6f5JCO53PuvLnnU/0Wmqd2o+d1u91Tvy7yZpnPpR+j6KwkB+3kvl+YIO+M7Py4Tv96gryz0vf45s47K/PO59x5Z6Tv+s2Zd1b6rt1Z6Xt8c+edFeuWpebNmZX0X7tzknzHTu77zQnyzkrfr72551P9lpunduM6I/1u9xL1W3reWZlhPp31DAAAAIAkyz9GUarqWUlOSnLosOj2JOe31q6bKO/4JC/fIe+81tqHJ8rrfXy9z6f6LTRvE9Su9/H1Pp/qt8CsIa/32h2Y5IQd8i5qrd0zUV7vr72551P9FpqndqPndbvdG/LUb9l5k8/novcoqqo3Jnl1knOT3DYsPizJq5Kc21o7c+S8s5I8M6vdO9fmnZzkxtbaG0bO6318c+fNPZ/qt9C8TVC73sc3d551y0LzNsHvylmZd3wnJ3lzkouzetO6Pe8lSd7SWjtn5LzeX3tzz6f6LTRP7Ra/7pw7T/2WnTfPfI59cKU5L0luSPKIdZY/MquijJ63k+U1VV7v4+t9PtVvmXmboXa9j6/3+VS/5WVtktpdn+QJ6yw/aGfPZWnzOXPe3POpfgvNU7vx83ayfPHbPfXrI2+O+Vz6wazvT/K0dZY/dbhvbF+vquess/w5Sb4+QV7v4+t9PtVvuXm916738fU+n+q3zKyk/9pVVqfo3dH9Wf+0wXur99fe3POpfsvNU7tx9bzdS9Rv6XmzzOfSj1G0LclHq+rGJLcOy45I8owkr58g79Qk766qx+WB3bwOT/KV4b6xbUvf45s7b1vmnc+5805N3/WbM29b+q7dtvQ9vrnztsW6Zal5c2Yl/dfu7UmuqKqL8+DxvSTJ2ybI25a+X3tzz6f6LTdP7cZ1avrd7iXqt/S8bZlhPhd9jKIkqar9khybBx/I6dLW2n0TZj5lbV5r7YsTZnU9vrnz5p5P9VtuXu+16318c+dZtyw7r+fflSFzzvEdlOT4fOsBde+eKK/r194GzKf6LTRP7SbJ7Hm7p34LzptlPsf+zNxGX5KcNnPeGca36Ly551P9Fpq3CWrX+/h6n0/1W2DWJqndD3U+n3PnzT2f6rfQPLUbPe+MzvPUb9l5o8/n0o9RtJ5/OXPeD8+c1/v4ep9P9VtuXu+16318vc+n+i0zK+m/dm+dOa/3197c86l+y81Tu3H1vN1L1G/peaPPZ4+NoikO2CZPnjx5PY9Nnjx5G5MlT548eT1kyZMnr7O8xR+jaEdVdVhr7bZdf+doefu11qY4WvvO8nofX+/z2Xv9qs24UplzfJugdr2Pr/f57L1+s61bNsHvytzjO7a19skZ83p/7c09n+q30Dy1Gz3P+5Zl53X7N8qQN/p8LnqPoqp6YlX9+6r68Vr52SS/WlX/cTig2xSZL6qq/6uqzquq30nyc1X1jCmyhrzjq+rdVXV+VZ2f5Ger6oSp8na0/Re8qv79FI8/jO+1VbV1h7x/NkFWVdUrq+pHhusvTvLTVfUTwwHB5nDOVA9cVU/a4fb/kuSsqjqtqsbvMle9oqqeOFw/uKrOSXJVVX2gqg6bIO+dVfX8tcumWgFbt0zPumV01i17ljXbemXI2wzrlmdV1Rur6per6peTvKiqvmuqvB1tf6NcVa+Z4vGH8b24qg7YIW+S9WdVHVvDaZer6tlJXlBVL5siayemOCPRuqrqBUleWVXHTfT4z62qxw/XH11Vb8nqTEXvqKoDJ8j7yao6fO2yqf4wrqpHVtXJVfWPhtv/NMnJVfW6qnrERJnfWVX/a1X9UlW9M8kPbZ/fifK8bxkvq/f3LbO9jxgyunwvseg9iqrqwiSfSfL4JN81XP9gVqeC/L7W2kkj5/18kqck+WiSlyf58yQ3JPmJJD/XWvutkfPOSvLMrF5I2zcshyU5OcmNrbU3jJm3i+fy+dbaESM/5s8leUGSK5L84yRntdb+z+G+K1prR4+c964kT07yyCRfTfKoJOcnOTHJHWPPZ1VdteOirOp5fZK01r535LxvzllV/bsk/0OS30zyQ0lua639m5Hzrm2tPXu4/oEklyT5rST/KMn/3Fp7ych5dya5JcnBST6Q5P2ttU+NmbEmy7plJtYte5Rn3TJe1mzrlSGv93XLG5O8Osm5efC65VVJzm2tnTlm3i6eyxTrlp9M8rok1yU5KskbWmvnDfdNsW55c5KXJtmS5CNJnpvkD7P6fbmotfb2kfPO33FRkhcl+YMkaa2NesyNqvpka+3Y4fo/z2puP5TkuCS/O/bvS1Vdk9Xr7N6qek+SryX5L0lePCz/JyPnfSXJXyf5syTvT/JbrbU7x8xYk/UbWf2ePCbJPUkOSPI7WY2tWmunjJz3k1ltA/4oycuSfGrIfUWSn2itfWzkvLPifcuYeb2/b+n2b5Qhb573EntyBOx95ZLkyuFrZXUKum+5b+S8z6y5viXJnwzXD0py9QR5N+xkeWW1Uhw776s7ufxlknunmM8kW4brT0hyYZJfHG5/aqr6JXlEki8neeSaWl41Qd75Sf7fJM9K8h1Jtia5dbj+HRPkfWrN9SuSPHbNeD8zQd71a65fvsN9V041vqw2LP97kmuSfDbJm5M8c+SsK4ev1i3j5Fm3jJtn3TLy2OZYr6x9/j2vW5I8Yp3lj5xo3XLVTi6fSfK3U8xnkgOG61uTXJZVs2iydUuS/bP64/+rSR4/LH/0ROuWK4Z1ywuT/ODw9QvD9R+cIO9Ta65fmuTg4fpjJ1q3XLd2rDvcd+UU48vq0xvHJXlvkjuTfDjJKUkeN3LWVcPXLUnuSLL/cLsm+l35zJqMxyT52HD9iIleC963jJw3fO31fUu3f6OsHUMmfi+x6I+eJdlv2L3q8CQHbN9Vr6q+Pas3JWO7f/tubEmeltXGO621u5NJDlj19e27G+/gOUm+PkHePUmObK09fofL47J6ozC2La21e5OktXZPVh30x1fVb2Wa+m3P+kaSS1trfzfcvjfJ6LsHttV/3n47yXuy6u7enOQbrbVbWmu3jJ2X5NFV9f1V9QNZbbz/enge30hy3wR5H6uqt1bVo4frr0hWH3NI8pUJ8lqStNZuaK29rbX23UlemeTbstqgjsm6ZVz3xLplNNYto5pzvZL0v265f8jZ0VMzwWshySFZ7VHwj9e5fHmCvP1aa3+VJMPr7oVJXjp87GaK+by3tXZfa+1rSf6stfbVIftvMs18HpPk8iQ/m+QrbbVXyN+01j7eWvv4BHn7VdVBw+9/tWFvm2Edc+8EeVfXAx9J/HRVHZMkVfXMJN+YIK+11u5vrV3cWnttVq+NdyU5IcnnRs7ar6oemeRxWTVuDhyWPyqrZsAUtqzJ2P5RzM9PlOd9y7h6f9/S898oyUzvJbbs+lv2aT+fVbcuSf5Zkl+rqpbk2UneMkHezyX5VFXdkOTvJ/lXyeqzj0k+PUHeqVl9dvpxeWA3y8Oz+gU/dYK8c7Lq7N6xzn2/OUHen1XVD25/89Fauy/Ja6vqPyT5HyfI+2JVHdBa+6vW2jc/01xVT0nydxPkpbX2oaq6OMnbquq1mWZlv90XkrxzuH5XVT21tfaFYaUxxRuu12f1ZvL64fa/qaq/TvK7SX5sgrxveRPeWtv+3+M3jZy13rolWe3e2cO65TVJ3mXdMpre1y1fTL/rljnXK0n/71u2JfloVd2Y1X+Lk9UeBs/INMe6+b2s9vC5csc7qupjE+TdUVVHbc9rrf1VVf1QkrOT/HcT5P1dVT1maBT9wPaFtTqezhR/zN2f5BeHP05/saruyLR/KxyYVWOqkrQ165YDMk3j7ceT/NLwEdq/SPLfqurWrH5Xf3yCvAeNYfij/Pwk51fVY0bOem9W65b9s1p//lZVfS7J87L6KOjYfi3JpVX1iaw+jvyO5JvrlrsmyDs1/iYaU+/vW3r+GyWZ6b3Eoo9RlCRVtX9W47i3qrZk9Znx21trU3R7M/xn7juT3DR0fCc3vGgPHW7e3lr74hy5Uxu6vNv/M7bjfYe21m6f6Xk8NquPUnxp4pzvS/Lft9Z+dcqcdXL3S/JtwxvNqTIOzOq/IVP8B3d7xgHb/5M7B+uW5bJumcfwGnnUktctc69Xhsyu1y3DNufYrFm3ZPUf6yn2PptVrQ6Ceu9668qqen5r7U9GzntUa+1v11n+pCRPba19Zsy8dXJOTPL81trPTJmzTu5jkhzSWvvziR7/8UmenlUT7LbW2nrNgDFyntlau2GKx95J3tOSpLX2/1XVE7I6Hsvn20RnPquq787qH2hXt9Y+u6vvHynT+5Zpn0d371t6/BtlyJz8vcTiG0VrDf+BeGaSz830ZmjSvGEX0m+0oUjD7nJHJ7mmtfbhGfOuba39vjx5myWv57ENj/+9w386ZiFP3r6a1/PYNiJvyDwiyVdba/fUanf4Y7I6Nsw1M+Z9trV2tTx5mymv57GtyTwmqz2J7svquEWTNqjkydvUeW3kgyvNeUnyrjXXX5Dk81mdDeLWJC/rIO/TSQ4arv9vSf40yb/L6swXPz9z3pmdj6/3PPXbR7M2qHb3JbkxyduSPHvsx5cnbyl5PY9tg/JOz+rMap/N6qM8n83qIzHXJPkpefLkTZPX89iGvB/M6uDx/zXJ3Vl97PRPknwsyeHy5MkbP2/UJz33JWvOWJBVw+bo4fp3Jrmsg7yr11y/LMmjh+tTHZFenjx5nY9teNxPJfmeJG9PclNWjarTk2wdO0uevH05r+exbVDeNVmdkevbszo70NqzWE1xljV58uR1PrbhcT+1JuPpST40XH9JkovlyZM3ft7Sz3q21uNba1ckSWvtc8nkY5sj76tV9T3D9b/I6sjpyeqPR3ny5E2X1/PYktWZWK5urf1sa+0ZSf55kicn+eOq+lN58jZRXs9j24i8+9rqGBv3JPmbDGcea8OZ8uTJkzdZXs9jS1Zn3LxzuP75rA40ndbaR/LAMYvkyZM3Yt6ij1FUVV/L6j9klWRrkiNaa3cPB1K8qrX2PQ/18wvI+94k/08eODPJ85P8UVZn1nhna23Uo+7Lkydv/qwNyvtUa+3711leSf5BG/k0yPLk7at5PY9tg/J+Pasz2Tw2ydeyOiveh5P8wySPa629Up48eePn9Ty2Ie/srE5B/gdJfjirg/b+VK0OfH5Fa+1Z8uTJGzdv6Y2i79hh0Rdaa39Xq7NB/IPW2u8sOW/I3D/JcVkdNHtLVqeEvKhNdLBuefLkzZ81d15V/dOxm0/y5C0xr+exbVDeliQ/ktUb2P+S5LlJXp3Vfzx/Zey9DeTJkzd/1gblPSKrPSKfndU/1c5urd1Xq7OFPbm1dos8efLGzVt0owgAAACA8Sz6GEVVdUBVvbWqrqmqr1TVnVV1SVWdKk+ePHlLyJInT97G5fU8tn0s7xR58uRNl9fz2HbIu3rmdZk8eZs2b9F7FFXVeUk+lNWp4V6Z1edkz83qtNK3t9Z+Rp48efL25Sx58uRtXF7PY5MnT97myet5bPLkydugvDby6drmvCT59A63Lx2+7pfks/LkyZO3r2fJkydv4/J6Hps8efI2T17PY5MnT97G5C36o2dJ/rqqXpAkVfXDSe5Kktba/UlKnjx58haQJU+evI3L63ls8uTJ2zx5PY9Nnjx5G5E3dodrzkuS703yySR3J/njJM8clh+c5CflyZMnb1/PkidP3sbl9Tw2efLkbZ68nscmT568jclb9DGKAAAAABjP0j96tlNV9Rp58uTJW3KWPHnyNi6v57HJkydv8+T1PDZ58uRNl9ftHkVV9fnW2hHy5MmTt9QsefLkbVxez2OTJ0/e5snreWzy5MmbLm/LGA+yUarqqp3dleQQefLkydvXs+TJk7dxeT2PTZ48eZsnr+exyZMnb2PyFt0oymoijs/qQE5rVZI/lSdPnrwFZMmTJ2/j8noemzx58jZPXs9jkydP3gbkLb1R9HtJDmitXbnjHVX1MXny5MlbQJY8efI2Lq/nscmTJ2/z5PU8Nnny5G1AXrfHKAIAAADg4en2rGcAAAAAPDwaRQAAAAAk0SgCAAAAYKBRBAAAAEASjSIAAAAABhpFAAAAACRJ/n+C9X5FovY4wwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Dates'].value_counts().sort_index().plot.bar(figsize=(20,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "creative-debut",
   "metadata": {},
   "source": [
    "While this data is now plotted as Pandas-structured time series data, it does not look good. Our dates are rendered in the long, full format that has both the date (in its entirety) and the time. Let's fix this by first, extracting the relevant data. In this case, the year and the counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "attempted-yahoo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1958-01-01       1\n",
       "1959-01-01       3\n",
       "1960-01-01     280\n",
       "1961-01-01      32\n",
       "1962-01-01      69\n",
       "1963-01-01      88\n",
       "1964-01-01      37\n",
       "1965-01-01      19\n",
       "1966-01-01      11\n",
       "1967-01-01       8\n",
       "1968-01-01      14\n",
       "1969-01-01      14\n",
       "1970-01-01      10\n",
       "1971-01-01      10\n",
       "1972-01-01       6\n",
       "1973-01-01       5\n",
       "1974-01-01      12\n",
       "1975-01-01     111\n",
       "1976-01-01     319\n",
       "1977-01-01     128\n",
       "1978-01-01      60\n",
       "1979-01-01      53\n",
       "1980-01-01     438\n",
       "1981-01-01     124\n",
       "1982-01-01     123\n",
       "1983-01-01     352\n",
       "1984-01-01     301\n",
       "1985-01-01    1665\n",
       "1986-01-01    2056\n",
       "1987-01-01     744\n",
       "1988-01-01    1015\n",
       "1989-01-01     935\n",
       "1990-01-01    2556\n",
       "1991-01-01    1793\n",
       "1992-01-01    2648\n",
       "1993-01-01    2835\n",
       "1994-01-01    1867\n",
       "1996-01-01       3\n",
       "1997-01-01       2\n",
       "1998-01-01       3\n",
       "Name: Dates, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df['Dates'].value_counts().sort_index()\n",
    "new_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classical-skating",
   "metadata": {},
   "source": [
    "Next, we need to convert that data into a new DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "sporting-netscape",
   "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>Dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-01-01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Dates\n",
       "1958-01-01      1\n",
       "1959-01-01      3\n",
       "1960-01-01    280\n",
       "1961-01-01     32\n",
       "1962-01-01     69"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.DataFrame(new_df)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "municipal-recording",
   "metadata": {},
   "source": [
    "Now that we have that new DataFrame created, let's fix our column name and change Dates to ViolentActs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "opening-spring",
   "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>ViolentActs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-01-01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-01</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ViolentActs\n",
       "1958-01-01            1\n",
       "1959-01-01            3\n",
       "1960-01-01          280\n",
       "1961-01-01           32\n",
       "1962-01-01           69"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df.rename(columns={\"Dates\": \"ViolentActs\"})\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fatal-bullet",
   "metadata": {},
   "source": [
    "With the new DataFrame, we can also fix the index so that it is strictly the year. Because Pandas knows that the index is a datetime type, then we can use the extra method, year, to grab just the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "starting-french",
   "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>ViolentActs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ViolentActs\n",
       "1958            1\n",
       "1959            3\n",
       "1960          280\n",
       "1961           32\n",
       "1962           69"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.index = new_df.index.year\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relevant-breakdown",
   "metadata": {},
   "source": [
    "Notice that our data is now just the year, the only piece of data in the time series data that matters to us. With that new DataFrame in the correct format, we can now plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "voluntary-cable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.plot.bar(figsize=(20,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thick-removal",
   "metadata": {},
   "source": [
    "And thus we have successfully plotted our datetime data after properly formatting it in Pandas. While working with time series data in Pandas as a datetime can be a bit more complex in the beginning, it allows for you to more advanced things, such as we saw above by calling the year with .year. As we will see in the next few chapters, there are other advantages as well."
   ]
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}