{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/gisynw/ssj-302/blob/main/docs/Lectures/Week04_Pandas.ipynb\" target=\"_blank\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" style=\"height: 30px;\"/>\n",
    "</a>\n",
    "&nbsp &nbsp\n",
    "<a href=\"./Week04_Pandas.ipynb\" target=\"_blank\"> \n",
    "  <img src=\"https://img.icons8.com/?size=100&id=108635&format=png&color=000000\" alt=\"Download\" style=\"height: 40px;\"/>\n",
    " </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h2 style=\"color:  #526cfe; font-family: Arial, sans-serif; font-size: 32px; padding: 10px;\"> Week 4: Pandas</h2></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is pandas?\n",
    "\n",
    "pandas is a Python library used for data manipulation and analysis that buil on top of the Python programming language.\n",
    "\n",
    "Pandas work well with CSV file (e.g., excel, SQL)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key features of Python Pandas\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Pandas in Python\n",
    "It is recommended to install and run pandas from a virtual environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a virtual environment\n",
    "Right click the Anaconda prompt and __Run as Administrator__ (This step will ensure that your package is installed in _Anaconda/env_ folder) \n",
    "\n",
    "Create a virtual environment named as 'pandas_v' with python version 3.9\n",
    "```bash\n",
    "conda create -n pda python=3.9\n",
    "```\n",
    "\n",
    "#### List virtual environment\n",
    "now if you list virgual envionment, you can see the virtual environment that you just create \n",
    "```bash\n",
    "conda env list\n",
    "```\n",
    "\n",
    "#### Activate virtual environment\n",
    "if you virtual environment named as 'arcgis_python'\n",
    "```bash\n",
    "activate pda\n",
    "```\n",
    "\n",
    "#### Install pandas\n",
    "```bash\n",
    "conda install pandas\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data in CSV format\n",
    "\n",
    "[Data Source: The city of Worcester](https://opendata.worcesterma.gov/search?collection=dataset&sort=Date%20Updated%7Cmodified%7Cdesc)\n",
    "\n",
    "Search: Worcester Police Use of Force Incidents (July 2024)\n",
    "\n",
    "Download CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading a Pandas DataFrame from CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ArrowDtype', 'BooleanDtype', 'Categorical', 'CategoricalDtype', 'CategoricalIndex', 'DataFrame', 'DateOffset', 'DatetimeIndex', 'DatetimeTZDtype', 'ExcelFile', 'ExcelWriter', 'Flags', 'Float32Dtype', 'Float64Dtype', 'Grouper', 'HDFStore', 'Index', 'IndexSlice', 'Int16Dtype', 'Int32Dtype', 'Int64Dtype', 'Int8Dtype', 'Interval', 'IntervalDtype', 'IntervalIndex', 'MultiIndex', 'NA', 'NaT', 'NamedAgg', 'Period', 'PeriodDtype', 'PeriodIndex', 'RangeIndex', 'Series', 'SparseDtype', 'StringDtype', 'Timedelta', 'TimedeltaIndex', 'Timestamp', 'UInt16Dtype', 'UInt32Dtype', 'UInt64Dtype', 'UInt8Dtype', '__all__', '__builtins__', '__cached__', '__doc__', '__docformat__', '__file__', '__git_version__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', '_built_with_meson', '_config', '_is_numpy_dev', '_libs', '_pandas_datetime_CAPI', '_pandas_parser_CAPI', '_testing', '_typing', '_version_meson', 'annotations', 'api', 'array', 'arrays', 'bdate_range', 'compat', 'concat', 'core', 'crosstab', 'cut', 'date_range', 'describe_option', 'errors', 'eval', 'factorize', 'from_dummies', 'get_dummies', 'get_option', 'infer_freq', 'interval_range', 'io', 'isna', 'isnull', 'json_normalize', 'lreshape', 'melt', 'merge', 'merge_asof', 'merge_ordered', 'notna', 'notnull', 'offsets', 'option_context', 'options', 'pandas', 'period_range', 'pivot', 'pivot_table', 'plotting', 'qcut', 'read_clipboard', 'read_csv', 'read_excel', 'read_feather', 'read_fwf', 'read_gbq', 'read_hdf', 'read_html', 'read_json', 'read_orc', 'read_parquet', 'read_pickle', 'read_sas', 'read_spss', 'read_sql', 'read_sql_query', 'read_sql_table', 'read_stata', 'read_table', 'read_xml', 'reset_option', 'set_eng_float_format', 'set_option', 'show_versions', 'test', 'testing', 'timedelta_range', 'to_datetime', 'to_numeric', 'to_pickle', 'to_timedelta', 'tseries', 'unique', 'util', 'value_counts', 'wide_to_long']\n"
     ]
    }
   ],
   "source": [
    "## use the dir() function to list all the attributes (include functions) in python\n",
    "print(dir(pd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Incident_No</th>\n",
       "      <th>Date___Time</th>\n",
       "      <th>Location</th>\n",
       "      <th>Officer</th>\n",
       "      <th>Narr</th>\n",
       "      <th>Implement</th>\n",
       "      <th>Injury</th>\n",
       "      <th>Supervisor</th>\n",
       "      <th>Notified</th>\n",
       "      <th>ObjectId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024000078279</td>\n",
       "      <td>7/29/24 1:09 PM</td>\n",
       "      <td>9 LINCOLN SQ Worcester, MA</td>\n",
       "      <td>Daniel Adjei</td>\n",
       "      <td>1</td>\n",
       "      <td>R. Empty Hand Compliance Techniques / Level 3</td>\n",
       "      <td>Visible Injury</td>\n",
       "      <td>Neil F O'Connor</td>\n",
       "      <td>7/29/24 4:27 PM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024000078796</td>\n",
       "      <td>7/30/24 4:38 PM</td>\n",
       "      <td>6 SHANNON ST Apt #: 2 Worcester, MA</td>\n",
       "      <td>Rachel E Frisch</td>\n",
       "      <td>3</td>\n",
       "      <td>R. Empty Hand Compliance Techniques / Level 3</td>\n",
       "      <td>_None</td>\n",
       "      <td>Michael A Cappabianca Jr</td>\n",
       "      <td>7/30/24 6:57 PM</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024000078796</td>\n",
       "      <td>7/30/24 4:38 PM</td>\n",
       "      <td>6 SHANNON ST Apt #: 2 Worcester, MA</td>\n",
       "      <td>Michael Genese</td>\n",
       "      <td>2</td>\n",
       "      <td>R. Empty Hand Compliance Techniques / Level 3</td>\n",
       "      <td>_None</td>\n",
       "      <td>Michael A Cappabianca Jr</td>\n",
       "      <td>7/30/24 6:55 PM</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024000078953</td>\n",
       "      <td>7/30/24 9:50 PM</td>\n",
       "      <td>79 MAYFIELD ST Worcester, MA</td>\n",
       "      <td>David Green</td>\n",
       "      <td>3</td>\n",
       "      <td>I. Display of Firearm</td>\n",
       "      <td>_None</td>\n",
       "      <td>Stephen L Roche</td>\n",
       "      <td>7/30/24 10:26 PM</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024000079115</td>\n",
       "      <td>7/31/24 11:05 AM</td>\n",
       "      <td>70 JACKSON ST Worcester, MA</td>\n",
       "      <td>DUY CHAU</td>\n",
       "      <td>6</td>\n",
       "      <td>I. Display of Firearm</td>\n",
       "      <td>_None</td>\n",
       "      <td>Shawn M Barbale</td>\n",
       "      <td>7/31/24 1:20 PM</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Incident_No       Date___Time                              Location  \\\n",
       "0  2024000078279   7/29/24 1:09 PM           9 LINCOLN SQ Worcester, MA    \n",
       "1  2024000078796   7/30/24 4:38 PM  6 SHANNON ST Apt #: 2 Worcester, MA    \n",
       "2  2024000078796   7/30/24 4:38 PM  6 SHANNON ST Apt #: 2 Worcester, MA    \n",
       "3  2024000078953   7/30/24 9:50 PM         79 MAYFIELD ST Worcester, MA    \n",
       "4  2024000079115  7/31/24 11:05 AM          70 JACKSON ST Worcester, MA    \n",
       "\n",
       "            Officer  Narr                                      Implement  \\\n",
       "0     Daniel Adjei      1  R. Empty Hand Compliance Techniques / Level 3   \n",
       "1  Rachel E Frisch      3  R. Empty Hand Compliance Techniques / Level 3   \n",
       "2   Michael Genese      2  R. Empty Hand Compliance Techniques / Level 3   \n",
       "3      David Green      3                          I. Display of Firearm   \n",
       "4         DUY CHAU      6                          I. Display of Firearm   \n",
       "\n",
       "           Injury                Supervisor          Notified  ObjectId  \n",
       "0  Visible Injury          Neil F O'Connor    7/29/24 4:27 PM         1  \n",
       "1           _None  Michael A Cappabianca Jr   7/30/24 6:57 PM         2  \n",
       "2           _None  Michael A Cappabianca Jr   7/30/24 6:55 PM         3  \n",
       "3           _None          Stephen L Roche   7/30/24 10:26 PM         4  \n",
       "4           _None          Shawn M Barbale    7/31/24 1:20 PM         5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents = pd.read_csv(r'D:\\Teaching_Data\\Worcester_Police_Use_of_Force_Incidents_July_2024.csv')\n",
    "incidents.head(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A concise summary of a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 74 entries, 0 to 73\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Incident_No  74 non-null     int64 \n",
      " 1   Date___Time  74 non-null     object\n",
      " 2   Location     74 non-null     object\n",
      " 3   Officer      74 non-null     object\n",
      " 4   Narr         74 non-null     int64 \n",
      " 5   Implement    74 non-null     object\n",
      " 6   Injury       74 non-null     object\n",
      " 7   Supervisor   74 non-null     object\n",
      " 8   Notified     74 non-null     object\n",
      " 9   ObjectId     74 non-null     int64 \n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "incidents.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Series and DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series: A one-dimensional labeled array capable of holding data of any type (integers, strings, floating-point numbers, etc.).\n",
    "\n",
    "DataFrame: A two-dimensional tabular data structure with labeled axes (rows and columns)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Access a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     1\n",
      "1     3\n",
      "2     2\n",
      "3     3\n",
      "4     6\n",
      "     ..\n",
      "69    5\n",
      "70    2\n",
      "71    1\n",
      "72    4\n",
      "73    2\n",
      "Name: Narr, Length: 74, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access a column as series\n",
    "col_series = incidents['Narr']\n",
    "print(col_series)\n",
    "type(col_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     3\n",
       "2     2\n",
       "3     3\n",
       "4     6\n",
       "     ..\n",
       "69    5\n",
       "70    2\n",
       "71    1\n",
       "72    4\n",
       "73    2\n",
       "Name: Narr, Length: 74, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access a column as Series (2nd syntax)\n",
    "col_series02 = incidents.Narr\n",
    "print(type(col_series02))\n",
    "col_series02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Narr\n",
      "0      1\n",
      "1      3\n",
      "2      2\n",
      "3      3\n",
      "4      6\n",
      "..   ...\n",
      "69     5\n",
      "70     2\n",
      "71     1\n",
      "72     4\n",
      "73     2\n",
      "\n",
      "[74 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Access a column as DataFrame\n",
    "col_df = incidents[['Narr']]\n",
    "print(col_df)\n",
    "type(col_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Access a row\n",
    "The iloc() function is an indexed-based selecting method which means that we have to pass an integer index in the method to select a specific row/column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incident_No                                    2024000078279\n",
      "Date___Time                                  7/29/24 1:09 PM\n",
      "Location                         9 LINCOLN SQ Worcester, MA \n",
      "Officer                                        Daniel Adjei \n",
      "Narr                                                       1\n",
      "Implement      R. Empty Hand Compliance Techniques / Level 3\n",
      "Injury                                        Visible Injury\n",
      "Supervisor                                  Neil F O'Connor \n",
      "Notified                                     7/29/24 4:27 PM\n",
      "ObjectId                                                   1\n",
      "Name: 0, dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access a row as Series\n",
    "row_series = incidents.iloc[0,:] \n",
    "print(row_series)\n",
    "type(row_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>Incident_No</th>\n",
       "      <th>Date___Time</th>\n",
       "      <th>Location</th>\n",
       "      <th>Officer</th>\n",
       "      <th>Narr</th>\n",
       "      <th>Implement</th>\n",
       "      <th>Injury</th>\n",
       "      <th>Supervisor</th>\n",
       "      <th>Notified</th>\n",
       "      <th>ObjectId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024000078279</td>\n",
       "      <td>7/29/24 1:09 PM</td>\n",
       "      <td>9 LINCOLN SQ Worcester, MA</td>\n",
       "      <td>Daniel Adjei</td>\n",
       "      <td>1</td>\n",
       "      <td>R. Empty Hand Compliance Techniques / Level 3</td>\n",
       "      <td>Visible Injury</td>\n",
       "      <td>Neil F O'Connor</td>\n",
       "      <td>7/29/24 4:27 PM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Incident_No      Date___Time                     Location        Officer  \\\n",
       "0  2024000078279  7/29/24 1:09 PM  9 LINCOLN SQ Worcester, MA   Daniel Adjei    \n",
       "\n",
       "   Narr                                      Implement          Injury  \\\n",
       "0     1  R. Empty Hand Compliance Techniques / Level 3  Visible Injury   \n",
       "\n",
       "         Supervisor         Notified  ObjectId  \n",
       "0  Neil F O'Connor   7/29/24 4:27 PM         1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access a row as DataFrame\n",
    "row_df = incidents.iloc[[0],:] \n",
    "print(type(row_df))\n",
    "row_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access multiple rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>Incident_No</th>\n",
       "      <th>Date___Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024000078279</td>\n",
       "      <td>7/29/24 1:09 PM</td>\n",
       "      <td>9 LINCOLN SQ Worcester, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024000078796</td>\n",
       "      <td>7/30/24 4:38 PM</td>\n",
       "      <td>6 SHANNON ST Apt #: 2 Worcester, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024000078796</td>\n",
       "      <td>7/30/24 4:38 PM</td>\n",
       "      <td>6 SHANNON ST Apt #: 2 Worcester, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024000078953</td>\n",
       "      <td>7/30/24 9:50 PM</td>\n",
       "      <td>79 MAYFIELD ST Worcester, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024000079115</td>\n",
       "      <td>7/31/24 11:05 AM</td>\n",
       "      <td>70 JACKSON ST Worcester, MA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Incident_No       Date___Time                              Location\n",
       "0  2024000078279   7/29/24 1:09 PM           9 LINCOLN SQ Worcester, MA \n",
       "1  2024000078796   7/30/24 4:38 PM  6 SHANNON ST Apt #: 2 Worcester, MA \n",
       "2  2024000078796   7/30/24 4:38 PM  6 SHANNON ST Apt #: 2 Worcester, MA \n",
       "3  2024000078953   7/30/24 9:50 PM         79 MAYFIELD ST Worcester, MA \n",
       "4  2024000079115  7/31/24 11:05 AM          70 JACKSON ST Worcester, MA "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Access multiple row using iloc()\n",
    "sub_inci = incidents.iloc[0:5,0:3]\n",
    "print(type(sub_inci))\n",
    "sub_inci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Access columns or rows using loc()\n",
    "The loc() function is label based data selecting method which means that we have to pass the name of the row or column which we want to select."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Incident_No</th>\n",
       "      <th>Date___Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024000078279</td>\n",
       "      <td>7/29/24 1:09 PM</td>\n",
       "      <td>9 LINCOLN SQ Worcester, MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024000078796</td>\n",
       "      <td>7/30/24 4:38 PM</td>\n",
       "      <td>6 SHANNON ST Apt #: 2 Worcester, MA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Incident_No      Date___Time                              Location\n",
       "0  2024000078279  7/29/24 1:09 PM           9 LINCOLN SQ Worcester, MA \n",
       "1  2024000078796  7/30/24 4:38 PM  6 SHANNON ST Apt #: 2 Worcester, MA "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rst = incidents.loc[0:1,['Incident_No', 'Date___Time', 'Location']]\n",
    "rst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1: Get record as series and Datafram from incidents\n",
    "1. Get the record as series: 'Location' column\n",
    "\n",
    "2. Get the record as DataFrame using index based method (.iloc): row 1 - 5 and column 2 - 3\n",
    "\n",
    "3. Get the record as DataFrame using label based method (.loc): row 1-5 and column ['Supervisor', 'Notified']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning\n",
    "#### Rename the column name\n",
    "Check the columns names based on `incidents.columns`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "column name are Index(['IncidentNo', 'DateTime', 'Location', 'Officer', 'Narr', 'Implement',\n",
      "       'Injury', 'Supervisor', 'Notified', 'ObjectId'],\n",
      "      dtype='object') \n",
      "updated column name are Index(['IncidentNo', 'DateTime', 'Location', 'Officer', 'Narr', 'Implement',\n",
      "       'Injury', 'Supervisor', 'Notified', 'ObjectId'],\n",
      "      dtype='object') \n"
     ]
    }
   ],
   "source": [
    "#format of columns can be the dictionary: old column name: new column name\n",
    "print('column name are {} '.format(incidents.columns))\n",
    "\n",
    "incidents.rename(columns={'Date___Time':'DateTime', 'Incident_No':\"IncidentNo\"}, inplace=True)\n",
    "\n",
    "print('updated column name are {} '.format(incidents.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Null value\n",
    "[pandas.DataFrame.isnull](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.isnull.html) check missing value\n",
    "\n",
    "[pandas.DataFrame.sum](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sum.html) the sum of the values over the requested axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>IncidentNo</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Location</th>\n",
       "      <th>Officer</th>\n",
       "      <th>Narr</th>\n",
       "      <th>Implement</th>\n",
       "      <th>Injury</th>\n",
       "      <th>Supervisor</th>\n",
       "      <th>Notified</th>\n",
       "      <th>ObjectId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IncidentNo  DateTime  Location  Officer   Narr  Implement  Injury  \\\n",
       "0       False     False     False    False  False      False   False   \n",
       "1       False     False     False    False  False      False   False   \n",
       "2       False     False     False    False  False      False   False   \n",
       "3       False     False     False    False  False      False   False   \n",
       "4       False     False     False    False  False      False   False   \n",
       "\n",
       "   Supervisor  Notified  ObjectId  \n",
       "0       False     False     False  \n",
       "1       False     False     False  \n",
       "2       False     False     False  \n",
       "3       False     False     False  \n",
       "4       False     False     False  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check null value in each cell\n",
    "incidents.isnull().head(n = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IncidentNo    0\n",
       "DateTime      0\n",
       "Location      0\n",
       "Officer       0\n",
       "Narr          0\n",
       "Implement     0\n",
       "Injury        0\n",
       "Supervisor    0\n",
       "Notified      0\n",
       "ObjectId      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calcualte the sum for each column\n",
    "incidents.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get unique value of the columns\n",
    "unique() function in pandas\n",
    " \n",
    "[pandas.unique](https://pandas.pydata.org/docs/reference/api/pandas.unique.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yanawu\\AppData\\Local\\Temp\\ipykernel_8664\\3584398706.py:2: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  pd.unique(multi_col)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not broadcast input array from shape (74,2) into shape (74,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m multi_col \u001b[38;5;241m=\u001b[39m incidents\u001b[38;5;241m.\u001b[39mloc[:,[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInjury\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNarr\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[1;32m----> 2\u001b[0m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmulti_col\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\pandas_vp\\lib\\site-packages\\pandas\\core\\algorithms.py:401\u001b[0m, in \u001b[0;36munique\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m    307\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munique\u001b[39m(values):\n\u001b[0;32m    308\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    309\u001b[0m \u001b[38;5;124;03m    Return unique values based on a hash table.\u001b[39;00m\n\u001b[0;32m    310\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    399\u001b[0m \u001b[38;5;124;03m    array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)\u001b[39;00m\n\u001b[0;32m    400\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 401\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munique_with_mask\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\pandas_vp\\lib\\site-packages\\pandas\\core\\algorithms.py:429\u001b[0m, in \u001b[0;36munique_with_mask\u001b[1;34m(values, mask)\u001b[0m\n\u001b[0;32m    427\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munique_with_mask\u001b[39m(values, mask: npt\u001b[38;5;241m.\u001b[39mNDArray[np\u001b[38;5;241m.\u001b[39mbool_] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    428\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"See algorithms.unique for docs. Takes a mask for masked arrays.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 429\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43m_ensure_arraylike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43munique\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    431\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype):\n\u001b[0;32m    432\u001b[0m         \u001b[38;5;66;03m# Dispatch to extension dtype's unique.\u001b[39;00m\n\u001b[0;32m    433\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m values\u001b[38;5;241m.\u001b[39munique()\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\pandas_vp\\lib\\site-packages\\pandas\\core\\algorithms.py:238\u001b[0m, in \u001b[0;36m_ensure_arraylike\u001b[1;34m(values, func_name)\u001b[0m\n\u001b[0;32m    236\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(values, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m    237\u001b[0m         values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(values)\n\u001b[1;32m--> 238\u001b[0m     values \u001b[38;5;241m=\u001b[39m \u001b[43mconstruct_1d_object_array_from_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    239\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    240\u001b[0m     values \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(values)\n",
      "File \u001b[1;32md:\\Anaconda\\envs\\pandas_vp\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1601\u001b[0m, in \u001b[0;36mconstruct_1d_object_array_from_listlike\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m   1598\u001b[0m \u001b[38;5;66;03m# numpy will try to interpret nested lists as further dimensions, hence\u001b[39;00m\n\u001b[0;32m   1599\u001b[0m \u001b[38;5;66;03m# making a 1D array that contains list-likes is a bit tricky:\u001b[39;00m\n\u001b[0;32m   1600\u001b[0m result \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(\u001b[38;5;28mlen\u001b[39m(values), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 1601\u001b[0m \u001b[43mresult\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m values\n\u001b[0;32m   1602\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (74,2) into shape (74,)"
     ]
    }
   ],
   "source": [
    "multi_col = incidents.loc[:,['Injury', 'Narr']]\n",
    "pd.unique(multi_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Injury': array(['Visible Injury', '_None', 'Complaint of Injury'], dtype=object),\n",
       " 'Narr': array([ 1,  3,  2,  6,  4, 12,  7,  5,  8,  9, 10], dtype=int64)}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_col = {}\n",
    "for col in multi_col.columns:\n",
    "    unique_col[col] = pd.unique(multi_col[col])\n",
    "unique_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame Manipulation\n",
    "#### Data sorting\n",
    "[pandas.DataFrame.sort_values](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html#pandas.DataFrame.sort_values)\n",
    "\n",
    "[pandas.Series.sort_values](https://pandas.pydata.org/docs/reference/api/pandas.Series.sort_values.html#pandas.Series.sort_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Incident_No</th>\n",
       "      <th>Date___Time</th>\n",
       "      <th>Location</th>\n",
       "      <th>Officer</th>\n",
       "      <th>Narr</th>\n",
       "      <th>Implement</th>\n",
       "      <th>Injury</th>\n",
       "      <th>Supervisor</th>\n",
       "      <th>Notified</th>\n",
       "      <th>ObjectId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2024000067866</td>\n",
       "      <td>7/6/24 12:33 AM</td>\n",
       "      <td>466 HARDING ST Worcester, MA</td>\n",
       "      <td>Santino Simone</td>\n",
       "      <td>12</td>\n",
       "      <td>G. Display of Taser or Sparks Display</td>\n",
       "      <td>_None</td>\n",
       "      <td>Terrence G Cahill</td>\n",
       "      <td>8/18/24 12:39 AM</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2024000074652</td>\n",
       "      <td>7/20/24 11:52 PM</td>\n",
       "      <td>3 JEFFERSON ST Worcester, MA</td>\n",
       "      <td>Tyler R Sterner</td>\n",
       "      <td>10</td>\n",
       "      <td>G. Display of Taser or Sparks Display</td>\n",
       "      <td>_None</td>\n",
       "      <td>Michael A Cappabianca Jr</td>\n",
       "      <td>7/24/24 9:23 PM</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2024000072086</td>\n",
       "      <td>7/15/24 10:27 AM</td>\n",
       "      <td>442 GRAFTON ST Worcester, MA</td>\n",
       "      <td>Dimitrios Gaitanidis</td>\n",
       "      <td>9</td>\n",
       "      <td>I. Display of Firearm</td>\n",
       "      <td>_None</td>\n",
       "      <td>Jeffrey P Carlson</td>\n",
       "      <td>7/15/24 1:05 PM</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2024000072443</td>\n",
       "      <td>7/16/24 6:23 AM</td>\n",
       "      <td>5 TOWNSEND ST Apt #: 2 Worcester, MA</td>\n",
       "      <td>Terrence O Gaffney</td>\n",
       "      <td>9</td>\n",
       "      <td>I. Display of Firearm</td>\n",
       "      <td>_None</td>\n",
       "      <td>Terrence G Cahill</td>\n",
       "      <td>7/16/24 7:41 PM</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2024000072086</td>\n",
       "      <td>7/15/24 10:27 AM</td>\n",
       "      <td>442 GRAFTON ST Worcester, MA</td>\n",
       "      <td>Jose M Lugo-Gardner</td>\n",
       "      <td>8</td>\n",
       "      <td>I. Display of Firearm</td>\n",
       "      <td>_None</td>\n",
       "      <td>Shawn M Barbale</td>\n",
       "      <td>7/15/24 2:01 PM</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Incident_No       Date___Time                               Location  \\\n",
       "24  2024000067866   7/6/24 12:33 AM          466 HARDING ST Worcester, MA    \n",
       "55  2024000074652  7/20/24 11:52 PM          3 JEFFERSON ST Worcester, MA    \n",
       "47  2024000072086  7/15/24 10:27 AM          442 GRAFTON ST Worcester, MA    \n",
       "48  2024000072443   7/16/24 6:23 AM  5 TOWNSEND ST Apt #: 2 Worcester, MA    \n",
       "43  2024000072086  7/15/24 10:27 AM          442 GRAFTON ST Worcester, MA    \n",
       "\n",
       "                  Officer  Narr                              Implement Injury  \\\n",
       "24        Santino Simone     12  G. Display of Taser or Sparks Display  _None   \n",
       "55       Tyler R Sterner     10  G. Display of Taser or Sparks Display  _None   \n",
       "47  Dimitrios Gaitanidis      9                  I. Display of Firearm  _None   \n",
       "48    Terrence O Gaffney      9                  I. Display of Firearm  _None   \n",
       "43   Jose M Lugo-Gardner      8                  I. Display of Firearm  _None   \n",
       "\n",
       "                  Supervisor          Notified  ObjectId  \n",
       "24        Terrence G Cahill   8/18/24 12:39 AM        25  \n",
       "55  Michael A Cappabianca Jr   7/24/24 9:23 PM        56  \n",
       "47        Jeffrey P Carlson    7/15/24 1:05 PM        48  \n",
       "48        Terrence G Cahill    7/16/24 7:41 PM        49  \n",
       "43          Shawn M Barbale    7/15/24 2:01 PM        44  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inct_sort_narr = incidents.sort_values(by=\"Narr\", ascending=False)\n",
    "inct_sort_narr.head(n = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2: sort values based on Officer (String)\n",
    "sort_values() function sort the number from smallest to largest (ascending) descending\n",
    "\n",
    "how about sort the String?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data query: single condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique value: ['Visible Injury' '_None' 'Complaint of Injury']\n"
     ]
    }
   ],
   "source": [
    "unique_injury = incidents.loc[:,\"Injury\"].unique()\n",
    "print('unique value: {}'.format(unique_injury))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(68, 10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "none_incident = incidents.query('Injury == \"_None\"')\n",
    "print(none_incident.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3: How many incidents' injury type are 'Visible Injury'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data query: multiple condition\n",
    "Query how many incidents's Narr are larger than 6 and Implement are 'I. Display of Firearm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "none_firearm = incidents.query('Narr > 6 & Implement == \"I. Display of Firearm\"')\n",
    "none_firearm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DateTime\n",
    "Pandas provides the `pd.to_datetime()` function to convert string data into a datetime object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7/29/24 1:09 PM\n",
       "1      7/30/24 4:38 PM\n",
       "2      7/30/24 4:38 PM\n",
       "3      7/30/24 9:50 PM\n",
       "4     7/31/24 11:05 AM\n",
       "            ...       \n",
       "69    7/27/24 12:43 AM\n",
       "70    7/29/24 11:41 AM\n",
       "71    7/29/24 11:41 AM\n",
       "72     7/29/24 1:09 PM\n",
       "73     7/29/24 1:09 PM\n",
       "Name: DateTime, Length: 74, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents['DateTime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column to store the DateTime\n",
    "incidents['DT'] = pd.to_datetime(incidents['DateTime'], format = \"%m/%d/%y %I:%M %p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>IncidentNo</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Location</th>\n",
       "      <th>Officer</th>\n",
       "      <th>Narr</th>\n",
       "      <th>Implement</th>\n",
       "      <th>Injury</th>\n",
       "      <th>Supervisor</th>\n",
       "      <th>Notified</th>\n",
       "      <th>ObjectId</th>\n",
       "      <th>DT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024000065816</td>\n",
       "      <td>7/1/24 9:46 AM</td>\n",
       "      <td>25 QUEEN ST Worcester, MA</td>\n",
       "      <td>Joseph L Ford</td>\n",
       "      <td>1</td>\n",
       "      <td>R. Empty Hand Compliance Techniques / Level 3</td>\n",
       "      <td>_None</td>\n",
       "      <td>Justin Bennes</td>\n",
       "      <td>7/1/24 4:00 PM</td>\n",
       "      <td>8</td>\n",
       "      <td>2024-07-01 09:46:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024000066207</td>\n",
       "      <td>7/2/24 10:11 AM</td>\n",
       "      <td>275 PLEASANT ST Apt #: 713 Worcester, MA</td>\n",
       "      <td>John Biancaniello</td>\n",
       "      <td>1</td>\n",
       "      <td>G. Display of Taser or Sparks Display</td>\n",
       "      <td>_None</td>\n",
       "      <td>MIGUEL DIAZ</td>\n",
       "      <td>7/2/24 11:45 AM</td>\n",
       "      <td>10</td>\n",
       "      <td>2024-07-02 10:11:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024000066207</td>\n",
       "      <td>7/2/24 10:11 AM</td>\n",
       "      <td>275 PLEASANT ST Apt #: 713 Worcester, MA</td>\n",
       "      <td>David C McAtee</td>\n",
       "      <td>2</td>\n",
       "      <td>I. Display of Firearm</td>\n",
       "      <td>_None</td>\n",
       "      <td>MIGUEL DIAZ</td>\n",
       "      <td>7/2/24 12:11 PM</td>\n",
       "      <td>9</td>\n",
       "      <td>2024-07-02 10:11:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024000066256</td>\n",
       "      <td>7/2/24 12:02 PM</td>\n",
       "      <td>25 TOBIAS BOLAND WAY Worcester, MA</td>\n",
       "      <td>James P Ciru</td>\n",
       "      <td>1</td>\n",
       "      <td>R. Empty Hand Compliance Techniques / Level 3</td>\n",
       "      <td>_None</td>\n",
       "      <td>Jeffrey P Carlson</td>\n",
       "      <td>7/2/24 1:04 PM</td>\n",
       "      <td>11</td>\n",
       "      <td>2024-07-02 12:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024000066380</td>\n",
       "      <td>7/2/24 5:16 PM</td>\n",
       "      <td>810 MAIN ST Worcester, MA</td>\n",
       "      <td>Stephen J Mitchell</td>\n",
       "      <td>2</td>\n",
       "      <td>R. Empty Hand Compliance Techniques / Level 3</td>\n",
       "      <td>_None</td>\n",
       "      <td>Christopher A Panarello</td>\n",
       "      <td>7/2/24 5:11 PM</td>\n",
       "      <td>12</td>\n",
       "      <td>2024-07-02 17:16:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       IncidentNo         DateTime                                   Location  \\\n",
       "7   2024000065816   7/1/24 9:46 AM                 25 QUEEN ST Worcester, MA    \n",
       "9   2024000066207  7/2/24 10:11 AM  275 PLEASANT ST Apt #: 713 Worcester, MA    \n",
       "8   2024000066207  7/2/24 10:11 AM  275 PLEASANT ST Apt #: 713 Worcester, MA    \n",
       "10  2024000066256  7/2/24 12:02 PM        25 TOBIAS BOLAND WAY Worcester, MA    \n",
       "11  2024000066380   7/2/24 5:16 PM                 810 MAIN ST Worcester, MA    \n",
       "\n",
       "                Officer  Narr                                      Implement  \\\n",
       "7        Joseph L Ford      1  R. Empty Hand Compliance Techniques / Level 3   \n",
       "9    John Biancaniello      1          G. Display of Taser or Sparks Display   \n",
       "8       David C McAtee      2                          I. Display of Firearm   \n",
       "10        James P Ciru      1  R. Empty Hand Compliance Techniques / Level 3   \n",
       "11  Stephen J Mitchell      2  R. Empty Hand Compliance Techniques / Level 3   \n",
       "\n",
       "   Injury                Supervisor         Notified  ObjectId  \\\n",
       "7   _None            Justin Bennes    7/1/24 4:00 PM         8   \n",
       "9   _None              MIGUEL DIAZ   7/2/24 11:45 AM        10   \n",
       "8   _None              MIGUEL DIAZ   7/2/24 12:11 PM         9   \n",
       "10  _None        Jeffrey P Carlson    7/2/24 1:04 PM        11   \n",
       "11  _None  Christopher A Panarello    7/2/24 5:11 PM        12   \n",
       "\n",
       "                    DT  \n",
       "7  2024-07-01 09:46:00  \n",
       "9  2024-07-02 10:11:00  \n",
       "8  2024-07-02 10:11:00  \n",
       "10 2024-07-02 12:02:00  \n",
       "11 2024-07-02 17:16:00  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort value based on 'DateTime' or 'DT' to see the difference\n",
    "incidents.sort_values(by='DT', ascending=True).head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 74 entries, 0 to 73\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   IncidentNo  74 non-null     int64         \n",
      " 1   DateTime    74 non-null     object        \n",
      " 2   Location    74 non-null     object        \n",
      " 3   Officer     74 non-null     object        \n",
      " 4   Narr        74 non-null     int64         \n",
      " 5   Implement   74 non-null     object        \n",
      " 6   Injury      74 non-null     object        \n",
      " 7   Supervisor  74 non-null     object        \n",
      " 8   Notified    74 non-null     object        \n",
      " 9   ObjectId    74 non-null     int64         \n",
      " 10  DT          74 non-null     datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(3), object(7)\n",
      "memory usage: 6.5+ KB\n"
     ]
    }
   ],
   "source": [
    "incidents.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get weekday from the DateTime\n",
    "[pandas.Series.dt](https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidents['wday'] = incidents.DT.dt.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents['wday'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4: Get year and hour from the DateTime and assign to a new column\n",
    "\n",
    "incidents['yr']\n",
    "\n",
    "incidents[hr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "incidents['yr'] = incidents.DT.dt.year\n",
    "incidents['hr'] = incidents.DT.dt.hour"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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39KN3aTtoOp4NlXHb+oljaXpadkQZ1+G0iF18FtNvpDdWi3TjjIs2P2QuS9PnCCCAAAIIbDOBzqUxbgYQv/TWA702p/8FwHy96KRX3cgj1gshrbcVp/KFIJUsbQdZIRj19uJ5lXAsy8s6A8JFkcZ1rtd513vev5PntvWR5jvfftwUJgbB6bIXXNLt569jcBeRgYgChyBFO4lBYFwPGm0rrcNVEp0P+DcpjesOgEMy07ItW3/T+01eP23eLzt1vOkGV23Sje+0lZK26dXfizuZ1nfnjFMk47jymOOefNQ8rov2oTqN+nmdY0S9Tv3c9ng2VMZt6yePrre5RrlmtOo55x8/WsRny252tio9/0cAAQQQQGCbCXQujfnpTuucXpdfO7fqup/8+rQ4TTVOuytZ2g6yIpJZD8p3I6n5gGTZ4D7Kkktb3EJ+2Z00F5U9v2azzn/p86JpUfaCS1rWuIPoFV+9cnZr/ZgeIxfDprJGXTQteyWN69ZzlKFNu9qL/aaJb9P/8ptO1fUY8h9S1mZakUXpt5WSReumn8X2Y7qPOM7VUy7UeWx6Hoo0RlmGyLht/eSnznd5CnPU7aJpjWK/jLutxs3FLAgggAACCEyFQOfSmF+bGO/bLvl1Vquui8tlqs1AbFle2kpj2+8t2079eZvBff3dvJzrynGa56aBbNv/LTrdLd3Gov/XZVn1vA6XSCsiil/44pd25ltrW4b0e0OVxhKObfjl7WkT+82qem/6f9ME6/XclfGDwTqnDraVkmX5qqf2iO2nbant6zbM29Tlsvytuz8OjXHb+sl/pIxyd7lE1Di9SVtav/HDVEjlxZd+tnGqny7zIy0EEEAAAQT2ikCn0pjfDCXtYEtfN0nnXgx+1x2MLavYdQaEuy1nmufSekjXWyQz6TYW/X8Zh/zzdbiEJMSdcpcN3O95nwfNTg2MvEVUqp54/a1nnj030I9tNi17FWks4diG327bU8qqq3pP01z0upa0dFqFtE3G6xjAt50Go62ULMpLTENz3JOeNteG6rxEW4zpeOJU6DgFOE6NjlOkY2qY9OZgQ5PGKOeQGLetn76lMbjE6agxxUpEGOt6zp9jntj48WqdHy4WtS2fIYAAAgggMFQCnUpjfhe7vGMted90euteDH67GiS3GdzXjWa35UzzHAOfz37uC7NrEkOGSh5x84w4xTFd0m2UyE6d1jpcYmCZSkT9y/9Fn770qIm66/TjOd9GvG9aSONyOl3V+/ItzP8nThk886x3Lh3Ah7TF//P2OZ9K+TWNsS/+8XNeMCcP9emK1157eGnEKW9DQ5TGmtFeM458DEkaay7f+96R2anIcWr0or4sjj9xU574ngUBBBBAAIGxEehMGmOQls6XuKhTLfmsaY7H3cpUWpltB79tv5emveh1Li4xSFq27LacXeV5Wf7i86620ZZLPjdbyOP+j57b6pf+fBuk8egbGzXVdfq/ruo9TbPN6zjeXP7lr8wELr9+Nd6vmqevrZTkeYmbc6Xbe/oznlPdfPPf51876v02SWOd+b1iHNtvWz95pHHRdEB1ebp6juunI3L8spefuvAsh1Ne9dqVP1p0lRfpIIAAAgggsCkCnUljPjdjTDIeHXh0oOs+4m6DqWAum14ivy36ve734OrAVQfXZpdLWVOkLL2pTAweL7n0c2tvL1ZIB9tR1j6lse0dNYsK8g8rbZpLPsVH/GCxKrpUl2/q0rgX+03Nvo/nOCX1SU95+twx49cf+MjG+VPbSkma32hfcXOu+tgUx7iYKqjNso3SmJZrU4zrbbatn/xGOIumA6rT7OP5hm/fWD3vhX+20yaibcQPWHEqvAUBBBBAAIExEehMGvM7n64ziM+B5qe5Nk1kn8pKdNhxDdG6Sz7waJLGdPqK2F7JvJD5beIjnT6lMabniG3Uj5I8r2K6aS75FB/r1HveZqYWaYy6zBmsw69uC+vsN/U6fT1/5zu3zc21Gm29aTqctlKS5je/ZnsdQYkfs+JHrXofHPLpqWmZ09ebYFxvr2395HPINh2767S7fo5rHk9+xat26jbqOK5ntSCAAAIIIDAmAp1IY+kci8tALpKqZYPa+LweiMXzi//s5a0jTvX2c+FtGnjkEa64+UZcA7TOkktx5LtPaczvSvvYJzyliusSu1w2zSWPFjbxS8sZ5Y7yp21mitK46f0mrYO+Xuc/jjQN3NtKSZrXPFq4znyd+TFmG6UxWPTNuOa9Tv2kc47Gfv3mv357q9PU62118RxTIKU3yuly6o8u8icNBBBAAAEEdkugE2mMU7TiVK16IN7FBMv51B1/8twXLZxUOReidU8Niluq5xLRJI15tCFOUf3Ah/a3rodFN9LoWxoXSX3cdbTLZdNccmmMwWybJWQpvSYt2K8rjSf84bNnd5pss738mqs4LblpSU9bbmqHy9LIuSyT6U3vN8vy2+Xn+cA9oqnLllxK4hTuVUsujW3bwaL5/kqksW0bj3Lsth0tY9E343q7ef007aN5/xNTNa0zj229zd08Rz+S3h037p4bp4FbEEAAAQQQGAuBTqQxndg9BuFxB7nd3no8H2wvu34orjPKb8Bz7ON/t4rb4q9aQqby0/Qi/6sG67nQth2kxA0U3vHOfUdJS9/SGBzyG3hEnttejxXrR33GdTr/96PnLsW6SS4lkbIYSEa5g3f6aBqQRmFzIX7gbx5bxamZbZa8HQ9FGvdiv2nDK/1O7C/rHEfyNhGnTC9bPveFy6q4yVbdDuJaxVXXxOZnQMQx6UuXf3nZJmafR5pxTXe9nfq5jTTmp2Cvc1p5W2kcGuMa5jrSGIzjDJOabTy37QPq7eXP0e6CTdslP9Oi6QeLtmn6HgIIIIAAAkMisGtpzE/3a7rb6ToFz6NjMRBYNmfjlV+/6igZiOjhVQe/sXST6Q0MQhIf+qjH7ww6Vknjouhk3HgjpGrZIDeuvXn1aafPhDEY5dHNZRGhKEB+o542A8684IsinPf4tfvOJHDV4ChucvSCF79slvcmwdokl1zGIsIcpwAu43/Zl/5up45TWYh21VSm4HjnnXdWcbpZOigN+V+2rZR9ns+hSGPkcdP7Tcqlzetg94xnPW82/92qNnrNtYdnolDXUfw4EPMjLlvySGvs81d89cplX599HvUdP4jV24jnuK4x2v2iJaZeeM0/7PP/4l//p7kfi9rsw7nYPvIxx1exL7ZZ2krj0BjXZVtHGmOdqP+YHzOtmzimr5o7MdpVfOe01/3V3DQ9EVWO04/Pv+CTK39MiGs9o53W2+6qD6xZeEYAAQQQQGAIBHYtjdG5p6f7hQx1db1cHjlYlnYM5uIW+2k+ogOPOdue/dwXVxd84qJZ5DEGd3G30xhQhWTEd2KdWDdun153+qukMSouThWr06jXi7TiGsf9HzuvOnTNtbM5EL942eXVa1//prnrXSLyEHPJ1evFc9/SGHmO6Gv8Ap9uN17HvGNvPOOtVeQ1BkvBKQQ4IhtxJ9uU6yrB2hSXRdGFmn+0myhrPOL1H/7xc3fKEG0o5D1lsKpMwS6/Ji3a1itPPW12GlzwijaWDzxjvSFL417sN8Gk7ZKyCwmMm41EfYYMRjuN+v3kpy6eRfLiB5C0TmOfa5L6RT9K3ft+D6ne874Pzfbd2H/POntf9aGPfHQuuxFZTE/Fj23GtWyxvYhkR77iOfanSC/+H+0y/h+iWOexjTTmP8jFujGJfBxfouzBIa7nC7nMl3WkMY53kfZQGEdZ1pXGWOfiSz87O0ukZlw/B7Ooy/r4Vreb2F/rORfz61OjHuv6irYVkeiP7D9np47rY2Raz/X2nvuCly68lCKvI+8RQAABBBDYJgK7ksYYuOenhjbdfGJdMPk0Hk2/4MYvxstO/aw78/w5BnOvf+NbZr8ktx1k1WWIAWn8Cp2LY76N/P2LXnpyFXMMhqik/9uENEbev37g4E7ELd1+m9fBKwZOTcsmucTgb5EELytLDIpjQJ+zbyONMUjMo8P5dvKBZ3BKxSe+H+2saVm3HeZp5WVralex7qb3mzy/Te9zdjnvZe9Pevmft5pgPdis2n/ztrFMtJflJT6PH4mirdYSEp+1kcZgE9cex37XlP6iOm7bjobIOModZUrLnNfDsnYTUcM46yNdt83rfN9NpbHN+vV32s7buSz/PkcAAQQQQGCoBHYljfkpXiF1i371Li18DNDy08GapvKohaX+hb/uyBc9x3fOO//CnetW2g6y8rLEaY/xS/aibaSfxa/VEdGMCEcs6wzuuzg9Nc13TEYe1wBFtCzNY9PrmAcvoizBuM2yCS6Rj28duuaoOfoWlSPyH9+NJWffdkD6tSu/3jggzQeesa18UD40aYw87sV+M6uIFX9iAvU6ErSoTvPPYp+OyOCqU1nrzUa5Q8qa9oNFbSN+LIt9eZVwxv/je/H9XELaSmOs+6q/eH2jOO5GGofKuFQao27j0oN1jm9RT3HH1WBdLxHlPe5JT2t9fIzj+1ve9o6d43udjmcEEEAAAQTGQmBX0pifsrfOvGVtAebTU7S5CUmI2bnnX1jFHezueZ8H7XT88fqpJzxzdopbXG9ULzF4POnkU3a+F/OpxbxqbZcYpMapUXEK030f8LCddGIg8cQnnzAbmObTcsTdGutBb0QS4rTZZUvX0lhv5/obvj0b1EYeI69pfmKwHvxiUB0R37ayWKcdz31zqbdVbyfusJvXd3wWdZOKRKk0xvbi2tQYHMYdgusIUAw6f+/3/6j61EWXHMVpG6Sx5rjp/abebtNzDOTjVOk4NfURxx4/J2oheyFf8b/4Tjrob0oz/19MXP+Sk15xVNuJz+ofGvJ14n2I4BlvefvsR6NaPOM5fkSKz+P/9VIqjbF+7Hvxg0205XQ/fcBDHj27ZjJ+BMqXdX4EGyLj3UhjzSKOb3H8imN+elyIOorrH+N4HaeVp31BvW48xzEjrnON01jjNP2Ufez7cYxclUaantcIIIAAAghsM4FdSeM2FzzNe19Slm4jf73OoC5fd8zvcdme2t2L/WZ76MgpAggggAACCCAwHgKksapmN35J59hqO/9aaTOIaFVso47sxbbjmrmpL7hsVwuINrvJ/Wa76MgtAggggAACCCAwHgKksaqq/BTYvufYirsexk1Zamk0EfSPdihctuvAsun9ZrvoyC0CCCCAAAIIIDAeApOXxrieJ70DbFyrcv7HP9FrDcd8k7UwxvOy+Sd7zcQAE8dlgJWyJEt7sd8syYqPEUAAAQQQQAABBHomMHlpzOddWzYXZFf1kE9CHRHHiLBNfcFlu1rApveb7aIjtwgggAACCCCAwLgIjEIa47bx7//gR9a+3XlMGfLIxxy/dtQvbscek0XHNXjrLHGXw5jHK40yNk0hsk7aQ/guLkOohfZ52PR+0z5nvokAAggggAACCCAwJAKjkMZ6WoOYpy0mn192C/UafJxad855F1T5fI4xL1eIz6qlvn1+3IL9rWeeXeXTaeTrx63bY0qAfD7HmIR6TFFGXPKaH/b7Te83w6YhdwgggAACCCCAAALLCIxKGusIXsyb94xnPW8WfQxZi7s8fvGyy2eiGHO6pTehqdeJz776tSuXcZr7vJajet2Y9yvmqIyJvGN+seuuv2GW1vkXfHI2x9eDH/7YuehirBd5/MSnPj2X7ra/wWW7arCWxrod973fbBcduUUAAQQQQAABBBCoCYxSGutBcNvnuI4xTlVtu+Ry1HY79fciwviFL36p7ea25nu4bE1VzTKaS2PdPts+r7vfbBcduUUAAQQQQAABBBCoCYxCGuN01L9689uqY+51/6Miek0D4Pj+29/xtytPZ61h1c9xeuv7PvDh6v4PftRa24tIzmtOO7265dZb66RG9YzLdlXnpveb7aIjtwgggAACCCCAAAI1gVFIY12YkJavXPG16nVvOKN64pNPqO55nwfNSV1cg/iY455c/fmr/3J2jWF8fzdLXAMqymAAACAASURBVKsYEcq3vO0d1VNPeGZ13wc8bG57IYmPOPb46qSTT6ku+MRFa8vpbvK2l+vispf019/2pveb9XNoDQQQQAABBBBAAIG9JDAqadxLkLaNAAIIIIAAAggggAACCIyRAGkcY60qEwIIIIAAAggggAACCCDQEQHS2BFIySCAAAIIIIAAAggggAACYyRAGsdYq8qEAAIIIIAAAggggAACCHREgDR2BFIyCCCAAAIIIIAAAggggMAYCZDGMdaqMiGAAAIIIIAAAggggAACHREgjR2BlAwCCCCAAAIIIIAAAgggMEYCpHGMtapMCCCAAAIIIIAAAggggEBHBEhjRyAlgwACCCCAAAIIIIAAAgiMkQBpHGOtKhMCCCCAAAIIIIAAAggg0BEB0tgRSMkggAACCCCAAAIIIIAAAmMkQBrHWKvKhAACCCCAAAIIIIAAAgh0RIA0dgRSMggggAACCCCAAAIIIIDAGAmQxjHWqjIhgAACCCCAAAIIIIAAAh0RII0dgZQMAggggAACCCCAAAIIIDBGAqRxjLWqTAgggAACCCCAAAIIIIBARwRIY0cgJYMAAggggAACCCCAAAIIjJEAaRxjrSoTAggggAACCCCAAAIIINARAdLYEUjJIIAAAggggAACCCCAAAJjJEAax1iryoQAAggggAACCCCAAAIIdESANHYEUjIIIIAAAggggAACCCCAwBgJkMYx1qoyIYAAAggggAACCCCAAAIdESCNHYGUDAIIIIAAAggggAACCCAwRgKkcYy1qkwIIIAAAggggAACCCCAQEcESGNHICWDAAIIIIAAAggggAACCIyRAGkcY60qEwIIIIAAAggggAACCCDQEQHS2BFIySCAAAIIIIAAAggggAACYyRAGsdYq8qEAAIIIIAAAggggAACCHREgDR2BFIyCCCAAAIIIIAAAggggMAYCZDGMdaqMiGAAAIIIIAAAggggAACHREgjR2BlAwCCCCAAAIIIIAAAgggMEYCpHGMtapMCCCAAAIIIIAAAggggEBHBEhjRyAlgwACCCCAAAIIIIAAAgiMkQBpHGOtKhMCCCCAAAIIIIAAAggg0BEB0tgRSMkggAACCCCAAAIIIIAAAmMkQBrHWKvKhAACCCCAAAIIIIAAAgh0RIA0dgRSMggggAACCCCAAAIIIIDAGAmQxjHW6l6X6c7rqsqjnEFSf0eOHKk8yhkkKHHcZVtKWXqNAAIIIIAAAtMiQBqnVd+bKe0X7lZVF9/Fo4TB1x42V0fnfexj1b6zz/YoYPDxc8+dY/mpCy/EsYBjtL/9H/7wHEtvEEAAAQQQQGBaBEjjtOp7M6UljeXCTBo7EzvS2N2PDaRxM4dOW0EAAQQQQGCoBEjjUGtmm/NFGkljYUSry6gqaSSN23wYlXcEEEAAAQSGRIA0Dqk2xpIX0kgaSWNnEdMuRbo0LZHGsRyclQMBBBBAAIEyAqSxjJu1mgiQRtJIGklj0zHC/xBAAAEEEEBgqwiQxq2qri3JLGkkjaSRNG7J4Uo2EUAAAQQQQGA1AdK4mpFvrEuANJJG0kga1z1u+D4CCCCAAAIIDJYAaRxs1WxxxkgjaSSNpHGLD2GyjgACCCCAAALzBEjjPA/vuiBAGkkjaSSNXRxLpIEAAggggAACgyBAGgdRDSPLBGkkjaSRNI7ssKY4CCCAAAIITJkAaZxy7fdVdtJIGkkjaezr+CJdBBBAAAEEENg4AdK4ceQT2CBpJI2kkTRO4FCniAgggAACCEyFAGmcSk1vspykkTSSRtK4yWOObSGAAAIIIIBArwRIY694J5o4aSSNpJE0TvTwp9gIIIAAAgiMkQBpHGOt7nWZSCNpJI2kca+PQ7aPAAIIIIAAAp0RII2doZTQDgHSSBpJI2ncOSB4gQACCCCAAALbToA0bnsNDjH/pJE0kkbSOMRjkzwhgAACCCCAQBEB0liEzUqNBEgjaSSNpLHxIOGfCCCAAAIIILBNBEjjNtXWtuSVNJJG0kgat+V4JZ8IIIAAAgggsJIAaVyJyBfWJkAaSSNpJI1rHzisgAACCCCAAAJDJUAah1oz25wv0kgaSSNp3OZjmLwjgAACCCCAwBwB0jiHw5tOCJBG0kgaSWMnBxOJIIAAAggggMAQCJDGIdTC2PJAGkkjaSSNYzuuKQ8CCCCAAAITJkAaJ1z5vRWdNJJG0kgaezvASBgBBBBAAAEENk2ANG6a+BS2RxpJI2kkjVM41ikjAggggAACEyFAGidS0RstJmkkjaSRNG70oGNjCCCAAAIIINAnAdLYJ92ppk0aSSNpJI1TPf4pNwIIIIAAAiMkQBpHWKl7XiTSSBpJI2nc8wORDCCAAAIIIIBAVwRIY1ckpfOPBEgjaSSNpPEfjwheIYAAAggggMCWEyCNW16Bg8w+aSSNpJE0DvLgJFMIIIAAAgggUEKANJZQs04zAdJIGkkjaWw+SvgvAggggAACCGwRAdK4RZW1NVkljaSRNJLGrTlgySgCCCCAAAIIrCJAGlcR8v/1CZBG0kgaSeP6Rw5rIIAAAggggMBACZDGgVbMVmeLNJJG0kgat/ogJvMIIIAAAgggkBIgjSkNr7shQBpJI2kkjd0cTaSCAAIIIIAAAgMgQBoHUAmjywJpJI2kkTSO7sCmQAgggAACCEyXAGmcbt33V3LSSBpJI2ns7wgjZQQQQAABBBDYMAHSuGHgk9hcSONn/olHCYOvPWyuiZz3sY9V73/3uz0KGHz83HPnWH7qwgtxLOAY7W//hz88x9IbBBBAAAEEEJgWAdI4rfpWWgQQQAABBBBAAAEEEEBgLQKkcS1cvowAAggggAACCCCAAAIITIsAaZxWfSstAggggAACCCCAAAIIILAWAdK4Fi5fRgABBBBAAAEEEEAAAQSmRYA0Tqu+lRYBBBBAAAEEEEAAAQQQWIsAaVwLly8jgAACCCCAAAIIIIAAAtMiMFlpvPnmmyuP3TFYuqv8vxuqyqOcQQL2jjvuqDzKGSQocdxlW0pZeo0AAggggAAC0yIwWWm87vDhUU2+vW/Dk6kHv6XLjWdV1fWne5QwuGX/HNZrDx2qDh444FHA4NprrpljGW0Wy7K2dOib35xj6Q0CCCCAAAIITIsAadywbG1a7vraHmnsSYpJY2diRxrLBHGRWJPGaQ0MlBYBBBBAAIGcAGkkjUURV9JIGhfJxZA+I42kMe/wvEcAAQQQQACBMgKkkTSSxpLTSPtaR6RRpLHgVNy+ZV2ksayDtRYCCCCAAAJjIUAaSSNp7EsAS9IljaSRNI6lf1UOBBBAAAEERkOANJJG0lgid32tQxpJI2kcTQerIAgggAACCIyFAGkkjaSxLwEsSZc0kkbSOJb+VTkQQAABBBAYDQHSSBpJY4nc9bUOaSSNpHE0HayCIIAAAgggMBYCpJE0ksa+BLAkXdJIGknjWPpX5UAAAQQQQGA0BEgjaSSNJXLX1zqkkTSSxtF0sAqCAAIIIIDAWAiQRtJIGvsSwJJ0SSNpJI1j6V+VAwEEEEAAgdEQII2kkTSWyF1f65BG0kgaR9PBKggCCCCAAAJjIUAaSSNp7EsAS9IljaSRNI6lf1UOBBBAAAEERkOANJJG0lgid32tQxpJI2kcTQerIAgggAACCIyFAGkkjaSxLwEsSZc0kkbSOJb+VTkQQAABBBAYDQHSSBpJY4nc9bUOaSSNpHE0HayCIIAAAgggMBYCpJE0ksa+BLAkXdJIGknjWPpX5UAAAQQQQGA0BEgjaSSNJXLX1zqkkTSSxtF0sAqCAAIIIIDAWAiQRtJIGvsSwJJ0SSNpJI1j6V+VAwEEEEAAgdEQII2kkTSWyF1f65BG0kgaR9PBKggCCCCAAAJjIUAaSSNp7EsAS9IljaSRNI6lf1UOBBBAAAEERkOANJJG0lgid32tQxpJI2kcTQerIAgggAACCIyFAGkkjaSxLwEsSZc0kkbSOJb+VTkQQAABBBAYDQHSSBpJY4nc9bUOaSSNpHE0HayCIIAAAgggMBYCpJE0ksa+BLAkXdJIGknjWPpX5UAAAQQQQGA0BEgjaSSNJXLX1zqkkTSSxtF0sAqCAAIIIIDAWAiQRtJIGvsSwJJ0SSNpJI1j6V+VAwEEEEAAgdEQII2kkTSWyF1f65BG0kgaR9PBKggCCCCAAAJjIUAaSSNp7EsAS9IljaSRNI6lf1UOBBBAAAEERkOANJJG0lgid32tQxpJI2kcTQerIAgggAACCIyFAGkkjaSxLwEsSZc0kkbSOJb+VTkQQAABBBAYDQHSSBpJY4nc9bUOaSSNpHE0HayCIIAAAgggMBYCpJE0ksa+BLAkXdJIGknjWPpX5UAAAQQQQGA0BEgjaSSNJXLX1zqkkTSSxtF0sAqCAAIIIIDAWAiQRtJIGvsSwJJ0SSNpJI1j6V+VAwEEEEAAgdEQII2kkTSWyF1f65BG0kgaR9PBKggCCCCAAAJjIUAaSSNp7EsAS9IljaSRNI6lf1UOBBBAAAEERkNg0tL4sY98pPIoY3Dd4cPLd4Kb3llVHmUMFkjjoW99q/JYn8G111wz10ajzeK4Psea2RxMbxBAAAEEEEBgUgQmK42TqmWFRQABBBBAAAEEEEAAAQQKCZDGQnBWQwABBBBAAAEEEEAAAQSmQIA0TqGWlREBBBBAAAEEEEAAAQQQKCQwWWmM65suvfhij0IGjdc03npeVXmUM0h25huuv77yKGeQoMRxl20pZek1AggggAACCEyLwKSlcZ87pxbdOTW4NUrjjWdVVcmdQ61TVQtuhHNwgHfT3IY8LboRzjbke4h5PPTNb06rZ1RaBBBAAAEEEJgjQBqJY5E4ksbT+xFj0mjKjQH+SEAa5/pNbxBAAAEEEJgcAdJIGknjkCKcpJE0ksbJdcQKjAACCCCAwNAJkEbSSBpJY2eiNqRTK52eeqCzehVpHHpXLn8IIIAAAgj0S4A0kkbSSBo7kwvS2J2oDYklaey3I5Y6AggggAACQydAGkkjaSSNpHGAp4SSxqF3n/KHAAIIIIDAdAiQRtJIGkkjaSSNjW1ApHE6gwIlRQABBBBAYBEB0kgaSSNpbBSGIUW81smLaxq7O1WWNC7qPn2GAAIIIIDAdAiQRtJIGkkjaRRpbGwDpHE6gwIlRQABBBBAYBEB0kgaSSNpbBSGdaJ7Q/quSKNI46JOz2cIIIAAAgggsD4B0kgaSSNpJI0ijY1tQKRx/c7VGggggAACCIyJAGkkjaSRNDYKw5Cih+vkRaRRpHFMnbWyIIAAAgggsJcESCNpJI2kkTSKNDa2AZHGveymbRsBBBBAAIG9J0AaSSNpJI2NwrBOdG9I3xVpFGnc+y5WDhBAAAEEEBgHAdJIGkkjaSSNIo2NbUCkcRwdvlIggAACCCBQSoA0kkbSSBobhWFI0cN18iLSKNJY2jFaDwEEEEAAAQTmCZBG0kgaSSNpFGlsbAMijfMdp3cIIIAAAghMjQBpJI2kkTQ2CsM60b0hfVekUaRxah268iKAAAIIINAXAdJIGkkjaSSNIo2NbUCksa8uWLoIIIAAAkHg+9+6tbrz0ms9ChkEv74X0kgaSSNpbBSGIUUP18mLSKNIY98dqPQRQAABBLohENJz+xs+71HIgDR20w4XpnLd4cNFsrSPZM64Bb+ly41nVdWQRGyb8nLL/jms1x46NEqhW0f+Sr9LGknj3M7kDQIIIIDAYAmQxt0JM2nssWmTxrN3Jc2k8fR+xJg0dibJpJE09tiFSBoBBBBAoEMCpJE0dticuk2KNJLGQUZDSSNpHOD1la5p7Lb/kRoCCCCAwDwB0kga51vEgN6RRtJIGruLRJWeQtrneiKN3dUvaRxQ5yUrCCCAwAgJkEbSONhmTRpJI2nsTir6lL/StEljd/VLGgfblckYAgggMAoCpJE0DrYhk0bSSBq7k4pSsetzPdLYXf2SxsF2ZTKGAAIIjIIAaSSNg23IpJE0ksbupKJP+StNmzR2V7+kcbBdmYwhgAACoyBAGknjYBsyaSSNpLE7qSgVuz7XI43d1S9pHGxXJmMIIIDAKAiQRtI42IZMGkkjaexOKvqUv9K0SWN39UsaB9uVyRgCCCAwCgKkkTQOtiGTRtJIGruTilKx63M90thd/ZLGwXZlMoYAAgiMggBpJI2DbcikkTSSxu6kok/5K02bNHZXv6RxsF2ZjCEwSgK33XZb9epXv7o6dOjQKMunUEcTII2k8ehWMZBPSCNpJI3dSUWp2PW5Hmnsrn5J40A6LtlAYCIErrvuuuqud71rdZe73KU65phjqjPPPLM6cuTIREo/zWKSRtI42JZPGkkjaexOKvqUv9K0SWN39UsaB9uVyRgCoyXw/Oc/v/qFX/iFmTj+3M/9XPUzP/Mz1XHHHVddeOGFoy3zlAtGGknjYNs/aSSNpLE7qSgVuz7XI43d1S9pHGxXJmMIjJZAnKL6S7/0SzNpjIhjPH78x398Jo+//Mu/XL3oRS+qrr766tGWf2oFI42kcbBtnjSSRtLYnVT0KX+laZPG7uqXNA62K5MxBEZN4PGPf/ycNNbyGM8RefzJn/zJ6h73uEd1xhlnVCGZlu0lQBpJ42BbL2kkjaSxO6koFbs+1yON3dUvaRxsVyZjCIyaQMhgnJqayuKi1/Gdn/qpn6oe/ehHV+ecc071wx/+cNRcxlg40kgaB9uuSSNpJI3dSUWf8leaNmnsrn5J42C7sq3J2CWXXFJ5YLBuGzj77LOrX/zFX1wpjbVI/tiP/Vj10z/907PTWk888cTqwIEDW7OPTD2jpJE0DnYfCGm86sABj0IGwW/p8r2vVJVHOYME7HduvbXyKGeQoMRxl20pZek1AusSuNvd7tZ64F8LgOcfXceHQxmHn/iJn5i1uYMHD67bXH1/DwiQRtK4B83OJhFAAAEEEEBgSATOPffcygODddvAKaecUv38z/986x8c6khjRCdFGod0BFidF9JIGle3Et9AAAEEEEAAAQQQQCAj8MY3vnF2w5tV0VbXNGbgtvAtaSSNg222d95xR+WxOwZLK/eHR6rKo5xBAvYHP/hB5VHOIEGJ4y7bUsrSawQQQGATBO5973svjTLGtYtx99S73/3u7p66icroeRukkTT23MTKk3cjHDfCcSOc7m6UUnqzmj7XcyOc7urXjXDK+xprIoBAGYErrriiuutd7zonjek8jS984QvN01iGdpBrkUbSOMiGGZkijaSRNHYnFX3KX2napLG7+iWNg+3KZAyB0RI4/vjjq5/92Z+dSWOcfhrzMh533HHVhRdeONoyT7lgpJE0Drb9k0bSSBq7k4pSsetzPdLYXf2SxsF2ZTKGwCgJHD58uIqoYlzLeMwxx1RnnnlmdeTIkVGWVaF+RIA0ksbB7gukkTSSxu6kok/5K02bNHZXv6RxsF2ZjCEwSgK33357deqpp1aHDh0aZfkU6mgCpJE0Ht0qBvIJaSSNpLE7qSgVuz7XI43d1S9pHEjHJRsIIIDASAmQRtI42KZNGkkjaexOKvqUv9K0SWN39UsaB9uVyRgCCCAwCgKkkTQOtiGTRtJIGruTilKx63M90thd/ZLGwXZlMoYAAgiMggBpJI2DbcikkTSSxu6kok/5K02bNHZXv6RxsF2ZjCGAAAKjIEAaSeNgGzJpJI2ksTupKBW7Ptcjjd3VL2kcbFcmYwgggMAoCJBG0jjYhkwaSSNp7E4q+pS/0rRJY3f1SxoH25XJGAIIIDAKAqSRNA62IZNG0kgau5OKUrHrcz3S2F39ksbBdmUyhgACCIyCAGkkjYNtyKSRNJLG7qSiT/krTZs0dle/pHGwXZmMIYAAAqMgQBpJ42AbMmkkjaSxO6koFbs+1yON3dUvaRxsVyZjCCCAwCgIkEbSONiGTBpJI2nsTir6lL/StEljd/VLGgfblckYAgggMAoCpJE0DrYhk0bSSBq7k4pSsetzPdLYXf2SxsF2ZTKGAAIIjIIAaSSNg23IpJE0ksbupKJP+StNmzR2V7+kcbBdmYwhgAACoyAQ0njnpdd6FDIIfn0vd+l7A0NNnzSSRtLYnVSUil2f65HG7uqXNA61J5MvBBBAAAEENkOANJ69O3naN9H1Q7qXLjeeVQ1SyK4/ffj5umX/HNZrDx2q+hSrMadNGknj3M7kDQIIIIAAAggUEyCNE5W+3couaexJQEljZ5JMGkljcc9oRQQQQAABBBCYI0AaSWNVIpCkkTQOPUpJGknjXG/nDQIIIIAAAggUEyCNpJE0Dum0VZFGkcYD3cleV2LvmsbiPtaKCCCAAAIIjIIAaSSNpJE0diZqXUlKF+mINHYnn6RxFP29QiCAAAIIIFBMgDSSRtJIGknjAKN7XYhzV2mQxuI+1ooIIIAAAgiMggBpJI2kkTSSRtLY2AZI4yj6e4VAAAEEEECgmABpJI2kkTQ2CkNX0apNp+P0VKenFveMVkQAAQQQQACBOQKkkTSSRtJIGkUaG9uASONcv+kNAggggAACkyNAGkkjaSSNjcKw6QhhV9sTaRRpnFyPrsAIIIAAAgj0RIA0kkbSSBpJo0hjYxsQaeypB5YsAggggAACW0KANJJG0kgaG4Whq8jfptMRaRRp3JJ+WDYRQAABBBAYPAHSSBpJI2kkjSKNjW1ApHHwfbkMIoAAAggg0CsB0kgaSSNpbBSGTUcIu9qeSKNIY6+9p8QRQAABBBCYEAHSSBpJI2kkjSKNjW1ApHFCowJFRQABBBBAYAEB0kgaSSNpbBSGriJ/m05HpFGkcUGf5yMEEEAAAQQQKCBAGkkjaSSNpFGksbENiDQW9K5WQQABBBBAYEQESCNpJI2ksVEYNh0h7Gp7Io0ijSPqqxUFAQQQQACBPSVAGkkjaSSNpFGksbENiDTuaT9t4wgggAACCOw5AdJIGkkjaWwUhq4if5tOR6RRpHHPe1gZQAABBBBAYCQEJi2N7923r/IoY3Dd4cPLd4Ebz6qqG97kUcLglv1zXK89dKj6xlVXeRQwWCSNWJa1JZHGud3SGwQQQAABBCZHYLLSOLmaVmAEEEAAAQQQQAABBBBAoIAAaSyAZhUEEEAAAQQQQAABBBBAYCoESONUalo5EUAAAQQQQAABBBBAAIECApOVxrgm78uXX+5RyKDxmsbbPlNVHuUMkh355ptuqjzKGSQocdxlW0pZeo0AAggggAAC0yIwaWnc586pRXdODW6N0hg3whnSHUm3KS8LboSz6buOjmV7i26EM5aybbocboQzrYGB0iKAAAIIIJATII3EsUgcSePp/Ygxaexs+g/SaMqNvMPzHgEEEEAAAQTKCJBG0kgahxSJJI2k8UB3stdVRFKksayDtRYCCCCAAAJjIUAaSSNpJI2diVpXktJFOiKN3cknaRxLl68cCCCAAAIIlBEgjaSRNJJG0jjA6F4X4txVGqSxrIO1FgIIIIAAAmMhQBpJI2kkjaSRNDa2AdI4li5fORBAAAEEECgjQBpJI2kkjY3C0FW0atPpOD3V6all3aK1EEAAAQQQQCAnQBpJI2kkjaRRpLGxDYg05l2n9wgggAACCEyLAGkkjaSRNDYKw6YjhF1tT6RRpHFa3bnSIoAAAggg0B8B0kgaSSNpJI0ijY1tQKSxv05YyggggAACCGwDAdJIGkkjaWwUhq4if5tOR6RRpHEbOmF5RAABBBBAYBsIkEbSSBpJI2kUaWxsAyKN29CdyyMCCCCAAAL9ESCNpJE0ksZGYdh0hLCr7Yk0ijT213VKGQEEEEAAgWkRII2kkTSSRtIo0tjYBkQapzUwUFoEEEAAAQRyAqSRNJJG0tgoDF1F/jadjkijSGPe4XmPAAIIIIAAAmUESCNpJI2kkTSKNDa2AZHGsg7WWggggAACCIyFAGkkjaSRNDYKw6YjhF1tT6RRpHEsHbVyIIAAAgggsNcESCNpJI2kkTSKNDa2AZHGve6qbR8BBBBAAIG9JUAaSSNpJI2NwtBV5G/T6Yg0ijTubfdq6wgggAACCIyHAGkkjaSRNJJGkcbGNiDSOJ5OX0kQQAABBBAoIUAaSSNpJI2NwrDpCGFX2xNpFGks6RStgwACCCCAAAJHEyCNpJE0kkbSKNLY2AZEGo/uPH2CAAIIIIDAlAiQRtJIGkljozB0FfnbdDoijSKNU+rMlRUBBBBAAIE+CZBG0kgaSSNpFGlsbAMijX12w9JGAAEEEEBg+ARII2kkjaSxURg2HSHsansijSKNw++C5RABBBBAAIHtIEAaSSNpJI2kUaSxsQ2ING5Hhy6XCCCAAAII9EWANJJG0kgaG4Whq8jfptMRaRRp7KvjlC4CCCCAAAJTI0AaSSNpJI2kUaSxsQ2INE5taKC8CCCAAAIIzBMgjaSRNJLGRmHYdISwq+2JNIo0znd33iGAAAIIIIBAKQHSSBpJI2kkjSKNjW1ApLG0i7UeAggggAAC4yBAGkkjaSSNjcLQVeRv0+mINIo0jqObVgoEEEAAAQT2ngBpJI2kkTSSRpHGxjYg0rj3nbUcIIAAAgggsJcESCNpJI2ksVEYNh0h7Gp7Io0ijXvZudo2AggggAACYyJAGkkjaSSNpFGksbENiDSOqdtXFgQQQAABBNYnQBpJI2kkjY3C0FXkb9PpiDSKNK7fJVoDAQQQQAABBBYRII2kkTSSRtIo0tjYBkQaF3WfPkMAAQQQQGA6BEgjaSSNpLFRGDYdIexqeyKNIo3T6cqVFAEEEEAAgX4JTFoaL7/sssqjjMF1hw8vb5m3XVJVHuUMErI333RT5VHOIEGJ4y7bUsrSawQQQAABBBCYFoHJSuO0qllpEUAAAQQQQAABBBBAAIEyAqSxjJu1EEAAAQQQQAABBBBAAIFJECCNk6hmhUQAAQQQQAABBBBAAAEEyghMVhrjmrz3v/vdHoUMGq9pvPGsqrrhzR4lDG7ZP7cnX3vo102gBgAABlBJREFUUHX1wYMeBQwW3QgHy7K25O6pc7ulNwgggAACCEyOwKSlcZ87pxbdOTW4rZTGId2RdJvyskAau7qb6NTSWSSNU2PQVXlJ4+TGBgqMAAIIIIDAHAHSSByLxJE0nl5Vfcgoaexs+g/SaMqNud7OGwQQQAABBBAoJkAaSSNp7EP+StMkjaTxQHeyJ9JY3DdaEQEEEEAAAQQSAqSRNJLGUsHrYz3SSBpJY9JFeYkAAggggAACQyBAGkkjaexD/krTJI2kkTQOoW+UBwQQQAABBBBICJBG0kgaSwWvj/VII2kkjUkX5SUCCCCAAAIIDIEAaSSNpLEP+StNkzSSRtI4hL5RHhBAAAEEEEAgIUAaSSNpLBW8PtYjjaSRNCZdlJcIIIAAAgggMAQCpJE0ksY+5K80TdJIGknjEPpGeUAAAQQQQACBhABpJI2ksVTw+liPNJJG0ph0UV4igAACCCCAwBAIkEbSSBr7kL/SNEkjaSSNQ+gb5QEBBBBAAAEEEgKkkTSSxlLB62M90kgaSWPSRXmJAAIIIIAAAkMgQBpJI2nsQ/5K0ySNpJE0DqFvlAcEEEAAAQQQSAiQRtJIGksFr4/1SCNpJI1JF+UlAggggAACCAyBAGkkjaSxD/krTZM0kkbSOIS+UR4QQAABBBBAICFAGkkjaSwVvD7WI42kkTQmXZSXCCCAAAIIIDAEAqSRNJLGPuSvNE3SSBpJ4xD6RnlAAAEEEEAAgYQAaSSNpLFU8PpYjzSSRtKYdFFeIoAAAggggMAQCJBG0kga+5C/0jRJI2kkjUPoG+UBAQQQQAABBBICpJE0ksZSwetjPdJIGklj0kV5iQACCCCAAAJDIEAaSSNp7EP+StMkjaSRNA6hb5QHBBBAAAEEEEgIkEbSSBpLBa+P9UgjaSSNSRflJQIIIIAAAggMgQBpJI2ksQ/5K02TNJJG0jiEvlEeEEAAAQQQQCAhQBpJI2ksFbw+1iONpJE0Jl2UlwgggAACCCAwBAKkkTSSxj7krzRN0kgaSeMQ+kZ5QAABBBBAAIGEAGkkjaSxVPD6WI80kkbSmHRRXiKAAAIIIIDAEAiQRtJIGvuQv9I0SSNpJI1D6BvlAQEEEEAAAQQSAqSRNJLGUsHrYz3SSBpJY9JFeYkAAggggAACQyBAGkkjaexD/krTJI2kkTQOoW+UBwQQQAABBBBICJBG0kgaSwWvj/VII2kkjUkX5SUCCCCAAAIIDIEAaSSNpLEP+StNkzSSRtI4hL5RHhBAAAEEEEAgIUAaSSNpLBW8PtYjjaSRNCZdlJcIIIAAAgggMAQCpJE0ksY+5K80TdJIGknjEPpGeUAAAQQQQACBhABpJI2ksVTw+liPNJJG0ph0UV4igAACCCCAwBAIkEbSSBr7kL/SNEkjaSSNQ+gb5QEBBBBAAAEEEgKkkTSSxlLB62M90kgaSWPSRXmJAAIIIIAAAkMgQBpJI2nsQ/5K0ySNpJE0DqFvlAcEEEAAAQQQSAhMVhqPHDlSeeyOQdKO5l/+4LtV5VHOIKH5/e9/v/IoZ5CgxHGXbSll6TUCCCCAAAIITIvAZKVxWtWstAgggAACCCCAAAIIIIBAGQHSWMbNWggggAACCCCAAAIIIIDAJAiQxklUs0IigAACCCCAAAIIIIAAAmUESGMZN2shgAACCCCAAAIIIIAAApMgQBonUc0KiQACCCCAAAIIIIAAAgiUESCNZdyshQACCCCAAAIIIIAAAghMggBpnEQ1KyQCCCCAAAIIIIAAAgggUEaANJZxsxYCCCCAAAIIIIAAAgggMAkCpHES1ayQCCCAAAIIIIAAAggggEAZAdJYxs1aCCCAAAIIIIAAAggggMAkCJDGSVSzQiKAAAIIIIAAAggggAACZQRIYxk3ayGAAAIIIIAAAggggAACkyBAGidRzQqJAAIIIIAAAggggAACCJQRII1l3KyFAAIIIIAAAggggAACCEyCAGmcRDUrJAIIIIAAAggggAACCCBQRoA0lnGzFgIIIIAAAggggAACCCAwCQKkcRLVrJAIIIAAAggggAACCCCAQBkB0ljGzVoIIIAAAggggAACCCCAwCQIkMZJVLNCIoAAAggggAACCCCAAAJlBEhjGTdrIYAAAggggAACCCCAAAKTIPD/AQhSp8jg/d7sAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregating statistics\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregating Statistics of a single column\n",
    "\n",
    "[pandas.DataFrame.mean](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.mean.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.9324324324324325"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents['Narr'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Narr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.932432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.377584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Narr\n",
       "count  74.000000\n",
       "mean    2.932432\n",
       "std     2.377584\n",
       "min     1.000000\n",
       "25%     1.000000\n",
       "50%     2.000000\n",
       "75%     3.000000\n",
       "max    12.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents[[\"Narr\"]].describe()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Aggregating statistics grouped by category\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Narr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Narr\n",
       "wday          \n",
       "0     3.142857\n",
       "1     2.700000\n",
       "2     2.307692\n",
       "3     2.111111\n",
       "4     2.833333\n",
       "5     4.200000\n",
       "6     2.428571"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents[[\"Narr\", \"wday\"]].groupby(\"wday\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5: Count the incidents based on day of week\n",
    "using count() to count the incidents for each day of week\n",
    "\n",
    "Group IncidentNo based on wday, then count the occurrence of incidentNo for each wday\n",
    "\n",
    "Sort the wday based on the total number of incidents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>IncidentNo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      IncidentNo\n",
       "wday            \n",
       "0             14\n",
       "1             10\n",
       "2             13\n",
       "3              9\n",
       "4              6\n",
       "5             15\n",
       "6              7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incidents[[\"IncidentNo\", \"wday\"]].groupby(\"wday\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spatially Enabled Data Frame\n",
    "The Spatially Enabled DataFrame (SEDF) creates a simple, intutive object that can easily manipulate attribute and geometric data.\n",
    "\n",
    "Create SEDF from a Shapefile\n",
    "\n",
    "An SEDF still includes access to all the Pandas DataFrame functionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shapefile\n",
    "\n",
    "A shapefile is made up of multiple files and all files must be found in the same folder with the same name\n",
    "\n",
    "A shapefile must have the following:\n",
    "\n",
    "- .shp – this file stores the geometry of the feature\n",
    "- .shx – this file stores the index of the geometry\n",
    "- .dbf – this file stores the attribute information for the feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next, do the task about Process SEDF in the notebook in ArcGIS Pro"
   ]
  }
 ],
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   "display_name": "base",
   "language": "python",
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    "name": "ipython",
    "version": 3
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