02-911 Calls Data Capstone Project  (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this capstone project we will be analyzing some 911 call data from [Kaggle](https://www.kaggle.com/mchirico/montcoalert). The data contains the following fields:\n",
    "\n",
    "* lat : String variable, Latitude\n",
    "* lng: String variable, Longitude\n",
    "* desc: String variable, Description of the Emergency Call\n",
    "* zip: String variable, Zipcode\n",
    "* title: String variable, Title\n",
    "* timeStamp: String variable, YYYY-MM-DD HH:MM:SS\n",
    "* twp: String variable, Township\n",
    "* addr: String variable, Address\n",
    "* e: String variable, Dummy variable (always 1)\n",
    "\n",
    "Just go along with this notebook and try to complete the instructions or answer the questions in bold using your Python and Data Science skills!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Import numpy and pandas **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Import visualization libraries and set %matplotlib inline. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() of the df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 99492 entries, 0 to 99491\n",
      "Data columns (total 9 columns):\n",
      "lat          99492 non-null float64\n",
      "lng          99492 non-null float64\n",
      "desc         99492 non-null object\n",
      "zip          86637 non-null float64\n",
      "title        99492 non-null object\n",
      "timeStamp    99492 non-null object\n",
      "twp          99449 non-null object\n",
      "addr         98973 non-null object\n",
      "e            99492 non-null int64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 6.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 17:40:00</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 zipcodes for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19401.0    6979\n",
       "19464.0    6643\n",
       "19403.0    4854\n",
       "19446.0    4748\n",
       "19406.0    3174\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOWER MERION    8443\n",
       "ABINGTON        5977\n",
       "NORRISTOWN      5890\n",
       "UPPER MERION    5227\n",
       "CHELTENHAM      4575\n",
       "Name: twp, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Take a look at the 'title' column, how many unique title codes are there? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** In the titles column there are \"Reasons/Departments\" specified before the title code. These are EMS, Fire, and Traffic. Use .apply() with a custom lambda expression to create a new column called \"Reason\" that contains this string value.** \n",
    "\n",
    "**For example, if the title column value is EMS: BACK PAINS/INJURY , the Reason column value would be EMS. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Reason'] = df['title'].apply(lambda title: title.split(':')[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the most common Reason for a 911 call based off of this new column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS        48877\n",
       "Traffic    35695\n",
       "Fire       14920\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121757b70>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Yl5nb6vYNwNnNHoIkaaxGwyMzvwns7mia1fF1G+gDWsAzHe3PAvPHtLc72kbG7GP+xFYt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123c1b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Reason',data=df,palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "** Now let us begin to focus on time information. What is the data type of the objects in the timeStamp column? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['timeStamp'].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You should have seen that these timestamps are still strings. Use [pd.to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) to convert the column from strings to DateTime objects. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You can now grab specific attributes from a Datetime object by calling them. For example:**\n",
    "\n",
    "    time = df['timeStamp'].iloc[0]\n",
    "    time.hour\n",
    "\n",
    "**You can use Jupyter's tab method to explore the various attributes you can call. Now that the timestamp column are actually DateTime objects, use .apply() to create 3 new columns called Hour, Month, and Day of Week. You will create these columns based off of the timeStamp column, reference the solutions if you get stuck on this step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Hour'] = df['timeStamp'].apply(lambda time: time.hour)\n",
    "df['Month'] = df['timeStamp'].apply(lambda time: time.month)\n",
    "df['Day of Week'] = df['timeStamp'].apply(lambda time: time.dayofweek)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Notice how the Day of Week is an integer 0-6. Use the .map() with this dictionary to map the actual string names to the day of the week: **\n",
    "\n",
    "    dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dmap = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Day of Week'] = df['Day of Week'].map(dmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of the Day of Week column with the hue based off of the Reason column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x121762710>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fGMnMt9WvF1F1p50OfDIiRoC9gFX1LrPoks9/M7Hf21StOda7M/ORuu69VK2U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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b4e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Day of Week',data=df,hue='Reason',palette='viridis')\n",
    "\n",
    "# To relocate the legend\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now do the same for Month:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11fa7ad68>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dXl6dmc8DbmH7ewjfm5kvrPucl5nfo7pV3fvrOw8B7FP3eSWwajq+GEllWGil\nXTMGXAscXz8+Gfgy1Qj2IeBHmfn/6m2fBf605bk31B8HqUazO/J1gMz8CfCUzsWWNN0stNIuyswH\ngX+OiJcAx1AfNqZ6X7Weu92D7U/TPFx/HOPJ7xDVM/dDlrR7LLTS7vkq8GHgtpY78iwAXhgRz6wf\nn0F1L+HJbObJ50z0xO0aJe0aJ0NJu+da4FLgvfXjMeDfqYrrNRGxF9WtzN7Ssn1HvgusioiNO+jj\nLbakGczb5EmSVJCHjiVJKshCK0lSQRZaSZIKstBKklSQhVaSpIIstJIkFWShlSSpIAutJEkF/X+C\nYEO7LC7vVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b0bb668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Month',data=df,hue='Reason',palette='viridis')\n",
    "\n",
    "# To relocate the legend\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Did you notice something strange about the Plot? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# It is missing some months! 9,10, and 11 are not there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** You should have noticed it was missing some Months, let's see if we can maybe fill in this information by plotting the information in another way, possibly a simple line plot that fills in the missing months, in order to do this, we'll need to do some work with pandas...**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a gropuby object called byMonth, where you group the DataFrame by the month column and use the count() method for aggregation. Use the head() method on this returned DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>Reason</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day of Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>11527</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13203</td>\n",
       "      <td>13096</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "      <td>13205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>9930</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11465</td>\n",
       "      <td>11396</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "      <td>11467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>9755</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11092</td>\n",
       "      <td>11059</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "      <td>11101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>9895</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11323</td>\n",
       "      <td>11283</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "      <td>11326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>9946</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11420</td>\n",
       "      <td>11378</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "      <td>11423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
       "Month                                                                      \n",
       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
       "\n",
       "       Reason   Hour  Day of Week  \n",
       "Month                              \n",
       "1       13205  13205        13205  \n",
       "2       11467  11467        11467  \n",
       "3       11101  11101        11101  \n",
       "4       11326  11326        11326  \n",
       "5       11423  11423        11423  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byMonth = df.groupby('Month').count()\n",
    "byMonth.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a simple plot off of the dataframe indicating the count of calls per month. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fa06630>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+4dHuGfJDF0FEgRaLnbyfuVJ3q88yammDgBSEmO4pzyX1YumB9V1/zOyEklPiaOytpGe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\nvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fa7e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Could be any column\n",
    "byMonth['twp'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now see if you can use seaborn's lmplot() to create a linear fit on the number of calls per month. Keep in mind you may need to reset the index to a column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11bf002b0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EEqzZ3sy+I8YxORSpqqqqKC937j2hnV4fOL9Gp9c3EJlaotZ9Uq9rW2t9CHgM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\nhLCRNGEhhLCRNGEhhLCRNGEhhLDR/wE6wgpwSQi5YgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf00780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Month',y='twp',data=byMonth.reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create a new column called 'Date' that contains the date from the timeStamp column. You'll need to use apply along with the .date() method. ** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['Date']=df['timeStamp'].apply(lambda t: t.date())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now groupby this Date column with the count() aggregate and create a plot of counts of 911 calls.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IwnVKnppLZVnfGSQc8FS1oC6Y5zg47Gx9igxTj8fN9ds7GZtK8ozZOumds+Oc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\nAq5S1d+JyFzgVlw9wd+o6pdD39kfuE9V5wT/jwC+A0wDqoErVPX3Wa4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be729b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('Date').count()['twp'].plot()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now recreate this plot but create 3 separate plots with each plot representing a Reason for the 911 call**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+xYx7qcjmS8B/jgIOV82xrITxLoRmHHczjpnRjGNvxjEDK2vcdoaynuX6TwF8TxCEp9Xj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\ntKDKzdJ3TePNzdsi+0ZdbYpz37F/t+5RV5vKSBOVTdkVVPY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6SXtvzj5kjT8BXvE9KyKH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\nE/P1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11be68748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Traffic'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('Traffic')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zcnmd67d00RYNrlg5lVIbdcVx64tRIQn1YrWDcqkelMeDbR9Uc04M+bxx8rE4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\nGEZJYgrKMAzDKElMQRmGYRglyf8D0QfhuyNHPrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12303a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='Fire'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('Fire')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XgPE2x3eh0dSRtB8C4FygH7oq1F1GiM80TXzu2wdcAzOdlFBrWAocSmD6Fura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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123072668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['Reason']=='EMS'].groupby('Date').count()['twp'].plot()\n",
    "plt.title('EMS')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "** Now let's move on to creating  heatmaps with seaborn and our data. We'll first need to restructure the dataframe so that the columns become the Hours and the Index becomes the Day of the Week. There are lots of ways to do this, but I would recommend trying to combine groupby with an [unstack](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html) method. Reference the solutions if you get stuck on this!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>275</td>\n",
       "      <td>235</td>\n",
       "      <td>191</td>\n",
       "      <td>175</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>372</td>\n",
       "      <td>598</td>\n",
       "      <td>742</td>\n",
       "      <td>752</td>\n",
       "      <td>...</td>\n",
       "      <td>932</td>\n",
       "      <td>980</td>\n",
       "      <td>1039</td>\n",
       "      <td>980</td>\n",
       "      <td>820</td>\n",
       "      <td>696</td>\n",
       "      <td>667</td>\n",
       "      <td>559</td>\n",
       "      <td>514</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>282</td>\n",
       "      <td>221</td>\n",
       "      <td>201</td>\n",
       "      <td>194</td>\n",
       "      <td>204</td>\n",
       "      <td>267</td>\n",
       "      <td>397</td>\n",
       "      <td>653</td>\n",
       "      <td>819</td>\n",
       "      <td>786</td>\n",
       "      <td>...</td>\n",
       "      <td>869</td>\n",
       "      <td>913</td>\n",
       "      <td>989</td>\n",
       "      <td>997</td>\n",
       "      <td>885</td>\n",
       "      <td>746</td>\n",
       "      <td>613</td>\n",
       "      <td>497</td>\n",
       "      <td>472</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>375</td>\n",
       "      <td>301</td>\n",
       "      <td>263</td>\n",
       "      <td>260</td>\n",
       "      <td>224</td>\n",
       "      <td>231</td>\n",
       "      <td>257</td>\n",
       "      <td>391</td>\n",
       "      <td>459</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>789</td>\n",
       "      <td>796</td>\n",
       "      <td>848</td>\n",
       "      <td>757</td>\n",
       "      <td>778</td>\n",
       "      <td>696</td>\n",
       "      <td>628</td>\n",
       "      <td>572</td>\n",
       "      <td>506</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>383</td>\n",
       "      <td>306</td>\n",
       "      <td>286</td>\n",
       "      <td>268</td>\n",
       "      <td>242</td>\n",
       "      <td>240</td>\n",
       "      <td>300</td>\n",
       "      <td>402</td>\n",
       "      <td>483</td>\n",
       "      <td>620</td>\n",
       "      <td>...</td>\n",
       "      <td>684</td>\n",
       "      <td>691</td>\n",
       "      <td>663</td>\n",
       "      <td>714</td>\n",
       "      <td>670</td>\n",
       "      <td>655</td>\n",
       "      <td>537</td>\n",
       "      <td>461</td>\n",
       "      <td>415</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>278</td>\n",
       "      <td>202</td>\n",
       "      <td>233</td>\n",
       "      <td>159</td>\n",
       "      <td>182</td>\n",
       "      <td>203</td>\n",
       "      <td>362</td>\n",
       "      <td>570</td>\n",
       "      <td>777</td>\n",
       "      <td>828</td>\n",
       "      <td>...</td>\n",
       "      <td>876</td>\n",
       "      <td>969</td>\n",
       "      <td>935</td>\n",
       "      <td>1013</td>\n",
       "      <td>810</td>\n",
       "      <td>698</td>\n",
       "      <td>617</td>\n",
       "      <td>553</td>\n",
       "      <td>424</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Hour          0    1    2    3    4    5    6    7    8    9  ...    14   15  \\\n",
       "Day of Week                                                   ...              \n",
       "Fri          275  235  191  175  201  194  372  598  742  752 ...   932  980   \n",
       "Mon          282  221  201  194  204  267  397  653  819  786 ...   869  913   \n",
       "Sat          375  301  263  260  224  231  257  391  459  640 ...   789  796   \n",
       "Sun          383  306  286  268  242  240  300  402  483  620 ...   684  691   \n",
       "Thu          278  202  233  159  182  203  362  570  777  828 ...   876  969   \n",
       "\n",
       "Hour           16    17   18   19   20   21   22   23  \n",
       "Day of Week                                            \n",
       "Fri          1039   980  820  696  667  559  514  474  \n",
       "Mon           989   997  885  746  613  497  472  325  \n",
       "Sat           848   757  778  696  628  572  506  467  \n",
       "Sun           663   714  670  655  537  461  415  330  \n",
       "Thu           935  1013  810  698  617  553  424  354  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayHour = df.groupby(by=['Day of Week','Hour']).count()['Reason'].unstack()\n",
    "dayHour.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a HeatMap using this new DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12305acf8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lSZI6wcrjhPYC9ga+BTw3MwP4X8DyQu1JkiR1w1iv/2MWFUkeM3NNZq4G9sjM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\nzu4YAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11faa4208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(dayHour,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now create a clustermap using this DataFrame. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x103276748>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SVJBhS5IkqSDDliRJUkGGLUmSpIIMW5IkSQUZtiRJkgoybEmSJBVk2JIkSSpo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\nkGFLkiSpoP8PGwocjpNs7BAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10326d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayHour,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now repeat these same plots and operations, for a DataFrame that shows the Month as the column. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day of Week</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>1970</td>\n",
       "      <td>1581</td>\n",
       "      <td>1525</td>\n",
       "      <td>1958</td>\n",
       "      <td>1730</td>\n",
       "      <td>1649</td>\n",
       "      <td>2045</td>\n",
       "      <td>1310</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>1727</td>\n",
       "      <td>1964</td>\n",
       "      <td>1535</td>\n",
       "      <td>1598</td>\n",
       "      <td>1779</td>\n",
       "      <td>1617</td>\n",
       "      <td>1692</td>\n",
       "      <td>1511</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2291</td>\n",
       "      <td>1441</td>\n",
       "      <td>1266</td>\n",
       "      <td>1734</td>\n",
       "      <td>1444</td>\n",
       "      <td>1388</td>\n",
       "      <td>1695</td>\n",
       "      <td>1099</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>1960</td>\n",
       "      <td>1229</td>\n",
       "      <td>1102</td>\n",
       "      <td>1488</td>\n",
       "      <td>1424</td>\n",
       "      <td>1333</td>\n",
       "      <td>1672</td>\n",
       "      <td>1021</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>1584</td>\n",
       "      <td>1596</td>\n",
       "      <td>1900</td>\n",
       "      <td>1601</td>\n",
       "      <td>1590</td>\n",
       "      <td>2065</td>\n",
       "      <td>1646</td>\n",
       "      <td>1230</td>\n",
       "      <td>1266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month          1     2     3     4     5     6     7     8     12\n",
       "Day of Week                                                      \n",
       "Fri          1970  1581  1525  1958  1730  1649  2045  1310  1065\n",
       "Mon          1727  1964  1535  1598  1779  1617  1692  1511  1257\n",
       "Sat          2291  1441  1266  1734  1444  1388  1695  1099   978\n",
       "Sun          1960  1229  1102  1488  1424  1333  1672  1021   907\n",
       "Thu          1584  1596  1900  1601  1590  2065  1646  1230  1266"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dayMonth = df.groupby(by=['Day of Week','Month']).count()['Reason'].unstack()\n",
    "dayMonth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bcabf98>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D6RxKSZIkNWKHUpIkqSCHvCVJktTMAIe8x0YD3FxTkiRJ5TiHUpIkSY1YUEqS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1233352b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(dayMonth,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.matrix.ClusterGrid at 0x120341e80>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgN3k0939yUUuKLYAgN3kmqp6R5IPJ1lJku4+b+SCYgsA2E1+f9EL+m5EAGA3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\nYgsAYCCxBQAw0P8Hv0ppOs88huMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a66eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.clustermap(dayMonth,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Continue exploring the Data however you see fit!**\n",
    "# Great Job!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}