CAT1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### All The Best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is 8 to the power of 4?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4096"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow(8,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Given this nested list, use indexing to grab the word \"Zalego\" **\n",
    "List1=[1,2,[3,4],[5,[100,200,['Zalego']],23,11],1,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "List1=[1,2,[3,4],[5,[100,200,['Zalego']],23,11],1,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Zalego']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "List1[3][1][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Given this nest dictionary grab the word \"Zalego\". Be prepared, this will be annoying/tricky **\n",
    "d = {'k1':[1,2,3,{'tricky':['oh','man','Zalego',{'target':[1,2,3,'hello']}]}]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'k1':[1,2,3,{'tricky':['oh','man','Zalego',{'target':[1,2,3,'hello']}]}]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Zalego'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['k1'][3]['tricky'][2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What is the main difference between a tuple and a list? **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A list is mutable while a tuple is immutable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List a = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]. \n",
    "Write one line of Python that takes this list a and makes a new list \n",
    "that has only the even elements of this list in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 16, 36, 64, 100]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(filter(lambda x:x%2==0 , a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**You are driving a little too fast, and a police officer stops you. Write a function\n",
    "  to return one of 3 possible results: \"No ticket\", \"Small ticket\", or \"Big Ticket\". \n",
    "  If your speed is 80 or less, the result is \"No Ticket\". If speed is between 81 \n",
    "  and 90 inclusive, the result is \"Small Ticket\". If speed is 91 or more, the result is \"Big    Ticket\". Unless it is your birthday (encoded as a boolean value in the parameters of the function) -- on your birthday, your speed can be 5 higher in all \n",
    "  cases. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#A function to give Tickets depending on the speed limit on a given day\n",
    "#The last digit in range not included in the range specified\n",
    "def caught_speeding(speed, is_birthday):\n",
    "        if bool(is_birthday)==True:\n",
    "            if speed in range(0,81):\n",
    "                return \"No Ticket\"\n",
    "            elif speed in range(81,91):\n",
    "                return \"Small Ticket\"\n",
    "            else:\n",
    "                return \"Big Ticket\"\n",
    "        else:\n",
    "            if speed in range(0,86):\n",
    "                return \"No Ticket\" \n",
    "            elif speed in range(61,96):\n",
    "                return \"Small Ticket\"\n",
    "            else:\n",
    "                return \"Big Ticket\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 911 Calls "
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Read in the csv file as a dataframe called df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('911.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the info() of the df **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 326425 entries, 0 to 326424\n",
      "Data columns (total 9 columns):\n",
      "lat          326425 non-null float64\n",
      "lng          326425 non-null float64\n",
      "desc         326425 non-null object\n",
      "zip          286835 non-null float64\n",
      "title        326425 non-null object\n",
      "timeStamp    326425 non-null object\n",
      "twp          326310 non-null object\n",
      "addr         326425 non-null object\n",
      "e            326425 non-null int64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 22.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Check the head of df **"
   ]
  },
  {
   "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>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 17:10:52</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 17:29:21</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 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</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",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19401.0    22136\n",
       "19464.0    21588\n",
       "19403.0    16488\n",
       "19446.0    15793\n",
       "19406.0    10800\n",
       "Name: zip, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['zip'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What are the top 5 townships (twp) for 911 calls? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lat', 'lng', 'desc', 'zip', 'title', 'timeStamp', 'twp', 'addr', 'e'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LOWER MERION    28073\n",
       "ABINGTON        20206\n",
       "NORRISTOWN      18433\n",
       "UPPER MERION    17276\n",
       "CHELTENHAM      15026\n",
       "Name: twp, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['twp'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Take a look at the 'title' column, how many unique title codes are there? **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Code'] = df['title'].apply(lambda x:x.split(':')[1])\n",
    "np.sum(pd.value_counts(pd.unique(df['Code'])))"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     EMS\n",
       "1     EMS\n",
       "2    Fire\n",
       "3     EMS\n",
       "4     EMS\n",
       "Name: Reason, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Reason'] = df['title'].apply(lambda x:x.split(':')[0])\n",
    "df['Reason'].head()"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EMS    161441\n",
       "Name: Reason, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['Reason']).head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Now use seaborn to create a countplot of 911 calls by Reason. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x33f789d3c8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Reason',data=df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}