04_Data_Methods.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "pd.options.display.max_rows = 8\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Types\n",
      "You can get the type of a python variable using the `type` function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 1\n",
      "print(type(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'int'>\n"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = 'Eric'\n",
      "print(type(y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'str'>\n"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = True\n",
      "print(type(z))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'bool'>\n"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = [1, 2, 3]\n",
      "print(type(l))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<type 'list'>\n"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Type Conversion\n",
      "Also recall that we can cast between different types, for example:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(3 + 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4\n"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(str(3) + str(1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "31\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Custom Functions: Definition\n",
      "We can also define our own *functions* or *methods*. This is good practice because it makes the programmer's job easier and the resulting program less error-prone."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_type(var):\n",
      "    print(str(var) + ' is type' + str(type(var)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Where:\n",
      "- `def` is the keyword for defining a method\n",
      "- `print_type` is the method\n",
      "- `var` is the *argument*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Custom Functions: Calling\n",
      "Now we can call our function just like we would any other python function:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for var in [x,y,z,l]:\n",
      "    print_type(var)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 is type<type 'int'>\n",
        "Eric is type<type 'str'>\n",
        "True is type<type 'bool'>\n",
        "[1, 2, 3] is type<type 'list'>\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Custom Functions: Returning\n",
      "Functions can also have a *return* value, for example the `type()` function returns the type, the `int()` function returns the cast integer, etc. We can write our own functions that return values like so:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def return_max(x,y):\n",
      "    if x > y:\n",
      "        return x\n",
      "    else:\n",
      "        return y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = return_max(1,2) # now z is the max of 1 and 2\n",
      "print(z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Objects\n",
      "Besides the above built-in types, python has many other types. What about the data we read from our csv file?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('Current_Employee_Names__Salaries__and_Position_Titles.csv')\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER RATE TAKER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$88968.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AARON,  KIMBERLEI R</td>\n",
        "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "      <td>$84780.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$83616.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32180</th>\n",
        "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
        "      <td>CHIEF DATA BASE ANALYST</td>\n",
        "      <td>DoIT</td>\n",
        "      <td>$110352.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32181</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32182 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "                      Name            Position Title        Department  \\\n",
        "0          AARON,  ELVIA J          WATER RATE TAKER       WATER MGMNT   \n",
        "1        AARON,  JEFFERY M            POLICE OFFICER            POLICE   \n",
        "2           AARON,  KARINA            POLICE OFFICER            POLICE   \n",
        "3      AARON,  KIMBERLEI R  CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
        "...                    ...                       ...               ...   \n",
        "32178    ZYMANTAS,  MARK E            POLICE OFFICER            POLICE   \n",
        "32179  ZYRKOWSKI,  CARLO E            POLICE OFFICER            POLICE   \n",
        "32180  ZYSKOWSKI,  DARIUSZ   CHIEF DATA BASE ANALYST              DoIT   \n",
        "32181                  NaN                       NaN               NaN   \n",
        "\n",
        "      Employee Annual Salary  \n",
        "0                  $88968.00  \n",
        "1                  $80778.00  \n",
        "2                  $80778.00  \n",
        "3                  $84780.00  \n",
        "...                      ...  \n",
        "32178              $83616.00  \n",
        "32179              $86520.00  \n",
        "32180             $110352.00  \n",
        "32181                    NaN  \n",
        "\n",
        "[32182 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Objects"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(type(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A \"kind\" of object (like `int` or `float` or `DataFrame`) is called a *class* or a *type*. An instance or example of a type (1, 'Eric', `df`) are called *objects*."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Objects have members, including *functions* and *attributes*."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Objects: Methods"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.dropna(inplace=True)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER RATE TAKER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$88968.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AARON,  KIMBERLEI R</td>\n",
        "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "      <td>$84780.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32177</th>\n",
        "      <td>ZYGOWICZ,  PETER J</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$83616.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32180</th>\n",
        "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
        "      <td>CHIEF DATA BASE ANALYST</td>\n",
        "      <td>DoIT</td>\n",
        "      <td>$110352.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32181 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "                      Name            Position Title        Department  \\\n",
        "0          AARON,  ELVIA J          WATER RATE TAKER       WATER MGMNT   \n",
        "1        AARON,  JEFFERY M            POLICE OFFICER            POLICE   \n",
        "2           AARON,  KARINA            POLICE OFFICER            POLICE   \n",
        "3      AARON,  KIMBERLEI R  CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
        "...                    ...                       ...               ...   \n",
        "32177   ZYGOWICZ,  PETER J            POLICE OFFICER            POLICE   \n",
        "32178    ZYMANTAS,  MARK E            POLICE OFFICER            POLICE   \n",
        "32179  ZYRKOWSKI,  CARLO E            POLICE OFFICER            POLICE   \n",
        "32180  ZYSKOWSKI,  DARIUSZ   CHIEF DATA BASE ANALYST              DoIT   \n",
        "\n",
        "      Employee Annual Salary  \n",
        "0                  $88968.00  \n",
        "1                  $80778.00  \n",
        "2                  $80778.00  \n",
        "3                  $84780.00  \n",
        "...                      ...  \n",
        "32177              $86520.00  \n",
        "32178              $83616.00  \n",
        "32179              $86520.00  \n",
        "32180             $110352.00  \n",
        "\n",
        "[32181 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Objects: Methods"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort_values(by='Department')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2812</th>\n",
        "      <td>BREWER,  JAMIE</td>\n",
        "      <td>ADMINISTRATIVE ASST II</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$63708.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3019</th>\n",
        "      <td>BROWN,  ELOUISE V</td>\n",
        "      <td>ADMINISTRATIVE ASST II</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$66684.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3338</th>\n",
        "      <td>BURAGE,  LORRIE A</td>\n",
        "      <td>CUSTOMER ACCOUNT REPRESENTATIVE</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$55428.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4236</th>\n",
        "      <td>CASSELLA,  CATHERINE</td>\n",
        "      <td>ADMINISTRATIVE ASST II</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$60780.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32104</th>\n",
        "      <td>ZIZUMBO,  DANIEL</td>\n",
        "      <td>POOL MOTOR TRUCK DRIVER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$71780.80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32128</th>\n",
        "      <td>ZOTTA,  SANDINO</td>\n",
        "      <td>MECHANICAL ENGINEER IV</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$104736.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32157</th>\n",
        "      <td>ZUNO,  ERIK</td>\n",
        "      <td>LABORER - APPRENTICE</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$63232.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32170</th>\n",
        "      <td>ZWARYCZ,  THOMAS J</td>\n",
        "      <td>POOL MOTOR TRUCK DRIVER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$71780.80</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32181 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "                       Name                   Position Title    Department  \\\n",
        "2812         BREWER,  JAMIE           ADMINISTRATIVE ASST II  ADMIN HEARNG   \n",
        "3019      BROWN,  ELOUISE V           ADMINISTRATIVE ASST II  ADMIN HEARNG   \n",
        "3338      BURAGE,  LORRIE A  CUSTOMER ACCOUNT REPRESENTATIVE  ADMIN HEARNG   \n",
        "4236   CASSELLA,  CATHERINE           ADMINISTRATIVE ASST II  ADMIN HEARNG   \n",
        "...                     ...                              ...           ...   \n",
        "32104      ZIZUMBO,  DANIEL          POOL MOTOR TRUCK DRIVER   WATER MGMNT   \n",
        "32128       ZOTTA,  SANDINO           MECHANICAL ENGINEER IV   WATER MGMNT   \n",
        "32157           ZUNO,  ERIK             LABORER - APPRENTICE   WATER MGMNT   \n",
        "32170    ZWARYCZ,  THOMAS J          POOL MOTOR TRUCK DRIVER   WATER MGMNT   \n",
        "\n",
        "      Employee Annual Salary  \n",
        "2812               $63708.00  \n",
        "3019               $66684.00  \n",
        "3338               $55428.00  \n",
        "4236               $60780.00  \n",
        "...                      ...  \n",
        "32104              $71780.80  \n",
        "32128             $104736.00  \n",
        "32157              $63232.00  \n",
        "32170              $71780.80  \n",
        "\n",
        "[32181 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Objects: Documentation\n",
      "Learn about methods by reading the [pandas documentation](http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.sort_values.html)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort_values('Department', ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER RATE TAKER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$88968.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>ABAD JR,  VICENTE M</td>\n",
        "      <td>CIVIL ENGINEER IV</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$104736.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>ABDUL-KARIM,  MUHAMMAD A</td>\n",
        "      <td>ENGINEERING TECHNICIAN VI</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$106104.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>ABRAHAM,  GIRLEY T</td>\n",
        "      <td>CIVIL ENGINEER IV</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$104736.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28206</th>\n",
        "      <td>TAKATA,  NANCY M</td>\n",
        "      <td>EXEC ADMINISTRATIVE ASST II</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$67224.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30552</th>\n",
        "      <td>WEINERT,  DARCI N</td>\n",
        "      <td>SENIOR HEARING OFFICER</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$66696.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30907</th>\n",
        "      <td>WILKINS,  THADDEUS</td>\n",
        "      <td>SENIOR ADMINISTRATIVE LAW OFFICER</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$91980.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31532</th>\n",
        "      <td>WOODRIDGE,  RACHENETTE</td>\n",
        "      <td>ADMINISTRATIVE ASST II</td>\n",
        "      <td>ADMIN HEARNG</td>\n",
        "      <td>$58020.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32181 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "                           Name                     Position Title  \\\n",
        "0               AARON,  ELVIA J                   WATER RATE TAKER   \n",
        "4           ABAD JR,  VICENTE M                  CIVIL ENGINEER IV   \n",
        "18     ABDUL-KARIM,  MUHAMMAD A          ENGINEERING TECHNICIAN VI   \n",
        "29           ABRAHAM,  GIRLEY T                  CIVIL ENGINEER IV   \n",
        "...                         ...                                ...   \n",
        "28206          TAKATA,  NANCY M        EXEC ADMINISTRATIVE ASST II   \n",
        "30552         WEINERT,  DARCI N             SENIOR HEARING OFFICER   \n",
        "30907        WILKINS,  THADDEUS  SENIOR ADMINISTRATIVE LAW OFFICER   \n",
        "31532    WOODRIDGE,  RACHENETTE             ADMINISTRATIVE ASST II   \n",
        "\n",
        "         Department Employee Annual Salary  \n",
        "0       WATER MGMNT              $88968.00  \n",
        "4       WATER MGMNT             $104736.00  \n",
        "18      WATER MGMNT             $106104.00  \n",
        "29      WATER MGMNT             $104736.00  \n",
        "...             ...                    ...  \n",
        "28206  ADMIN HEARNG              $67224.00  \n",
        "30552  ADMIN HEARNG              $66696.00  \n",
        "30907  ADMIN HEARNG              $91980.00  \n",
        "31532  ADMIN HEARNG              $58020.00  \n",
        "\n",
        "[32181 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Objects: Attributes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In addition to functions, objects have (data) attributes:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "(32181, 4)"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "Index([u'Name', u'Position Title', u'Department', u'Employee Annual Salary'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## DataFrames"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "DataFrames are tables. They have rows and columns."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The columns are indexed by their names. We can list the columns using the attribute `columns`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(df.columns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'Name', u'Position Title', u'Department', u'Employee Annual Salary'], dtype='object')\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The rows are indexed by the attribute `index`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(df.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,\n",
        "                9,\n",
        "            ...\n",
        "            32171, 32172, 32173, 32174, 32175, 32176, 32177, 32178, 32179,\n",
        "            32180],\n",
        "           dtype='int64', length=32181)\n"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Python Indexing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We saw last class that we can select a column from our tables as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "departments = df['Department']\n",
      "print(departments)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0             WATER MGMNT\n",
        "1                  POLICE\n",
        "2                  POLICE\n",
        "3        GENERAL SERVICES\n",
        "               ...       \n",
        "32177              POLICE\n",
        "32178              POLICE\n",
        "32179              POLICE\n",
        "32180                DoIT\n",
        "Name: Department, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A single column has its own type, called `Series`, which is like a column."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(type(departments))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.series.Series'>\n"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Pandas Indexing\n",
      "This is called *indexing* and is a generalization of the simpler indexing that we use with lists. All indexing in python is denoted using square brackets `[]`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = ['apple','banana', 'orange']\n",
      "l[2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "'orange'"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For lists the index is an integer, between `0` and `len(list)-1`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = {'a': 1, 'b': 2, 'c': 3}\n",
      "d['a']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Pandas Indexing: Columns"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also select *multiple* columns by indexing with a list of column names:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['Name', 'Department']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Department</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AARON,  KIMBERLEI R</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32177</th>\n",
        "      <td>ZYGOWICZ,  PETER J</td>\n",
        "      <td>POLICE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32180</th>\n",
        "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
        "      <td>DoIT</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32181 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "                      Name        Department\n",
        "0          AARON,  ELVIA J       WATER MGMNT\n",
        "1        AARON,  JEFFERY M            POLICE\n",
        "2           AARON,  KARINA            POLICE\n",
        "3      AARON,  KIMBERLEI R  GENERAL SERVICES\n",
        "...                    ...               ...\n",
        "32177   ZYGOWICZ,  PETER J            POLICE\n",
        "32178    ZYMANTAS,  MARK E            POLICE\n",
        "32179  ZYRKOWSKI,  CARLO E            POLICE\n",
        "32180  ZYSKOWSKI,  DARIUSZ              DoIT\n",
        "\n",
        "[32181 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Pandas Indexing: Rows\n",
      "We also saw that indexes can select a subset of *rows* as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.Department == 'POLICE']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>ABBATE,  TERRY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>ABBOTT,  LYNISE M</td>\n",
        "      <td>CLERK III</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$43920.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32174</th>\n",
        "      <td>ZYDEK,  BRYAN</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32177</th>\n",
        "      <td>ZYGOWICZ,  PETER J</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$83616.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>13570 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "                      Name  Position Title Department Employee Annual Salary\n",
        "1        AARON,  JEFFERY M  POLICE OFFICER     POLICE              $80778.00\n",
        "2           AARON,  KARINA  POLICE OFFICER     POLICE              $80778.00\n",
        "9         ABBATE,  TERRY M  POLICE OFFICER     POLICE              $86520.00\n",
        "11       ABBOTT,  LYNISE M       CLERK III     POLICE              $43920.00\n",
        "...                    ...             ...        ...                    ...\n",
        "32174        ZYDEK,  BRYAN  POLICE OFFICER     POLICE              $80778.00\n",
        "32177   ZYGOWICZ,  PETER J  POLICE OFFICER     POLICE              $86520.00\n",
        "32178    ZYMANTAS,  MARK E  POLICE OFFICER     POLICE              $83616.00\n",
        "32179  ZYRKOWSKI,  CARLO E  POLICE OFFICER     POLICE              $86520.00\n",
        "\n",
        "[13570 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Pandas Indexing: Rows\n",
      "How does this work? What is the index in the expression `df[df.Department == 'POLICE']`?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Department'] == 'POLICE'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": [
        "0        False\n",
        "1         True\n",
        "2         True\n",
        "3        False\n",
        "         ...  \n",
        "32177     True\n",
        "32178     True\n",
        "32179     True\n",
        "32180    False\n",
        "Name: Department, dtype: bool"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is called a *boolean series*.\n",
      "\n",
      "- It is a series of the same index (rows) as `df`. \n",
      "- For each (row) index value (in this case 0 through 32180) the value of the series is a boolean (`True` or `False`)\n",
      "\n",
      "in this case the booleans indicate whether the `Department` column is equal to `'Police'`. When we pass it as an index we are selecting the rows for which the series is `True`."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Applying functions to a Series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salaries = df['Employee Annual Salary'].copy()\n",
      "salaries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "0         $88968.00\n",
        "1         $80778.00\n",
        "2         $80778.00\n",
        "3         $84780.00\n",
        "            ...    \n",
        "32177     $86520.00\n",
        "32178     $83616.00\n",
        "32179     $86520.00\n",
        "32180    $110352.00\n",
        "Name: Employee Annual Salary, dtype: object"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## String manipulation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salary = salaries[0]\n",
      "print_type(salary)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "$88968.00 is type<type 'str'>\n"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How to make this a number? Remove the first character and cast to float"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t = float(salary[1:])\n",
      "print_type(t)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "88968.0 is type<type 'float'>\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Manipulating a Series"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Want to do this for every salary? Could just loop over and assign:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(len(salaries)):\n",
      "    salaries[i] = float(salaries[i][1:])\n",
      "salaries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "0         88968\n",
        "1         80778\n",
        "2         80778\n",
        "3         84780\n",
        "          ...  \n",
        "32177     86520\n",
        "32178     83616\n",
        "32179     86520\n",
        "32180    110352\n",
        "Name: Employee Annual Salary, dtype: object"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Manipulating a Series\n",
      "Alternatively define a function to do the conversion and rewrite the loop:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = 'Eric'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s[1:3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "'ri'"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def salary_to_float(salary):\n",
      "    return float(salary[1:])\n",
      "\n",
      "salaries = df['Employee Annual Salary'].copy()\n",
      "for i in range(len(salaries)):\n",
      "    salaries[i] = salary_to_float(salaries[i])\n",
      "salaries"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "0         88968\n",
        "1         80778\n",
        "2         80778\n",
        "3         84780\n",
        "          ...  \n",
        "32177     86520\n",
        "32178     83616\n",
        "32179     86520\n",
        "32180    110352\n",
        "Name: Employee Annual Salary, dtype: object"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Applying a function to a Series\n",
      "Better yet, we could use the `Series.apply` function, which applies a given function to every element in a Series (every entry in a given column):"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1+1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Employee Annual Salary'].apply(salary_to_float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "0         88968\n",
        "1         80778\n",
        "2         80778\n",
        "3         84780\n",
        "          ...  \n",
        "32177     86520\n",
        "32178     83616\n",
        "32179     86520\n",
        "32180    110352\n",
        "Name: Employee Annual Salary, dtype: float64"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally we can reassign the original column to our converted column:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Employee Annual Salary Numeric'] = df['Employee Annual Salary'].apply(salary_to_float)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "      <th>Employee Annual Salary Numeric</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER RATE TAKER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$88968.00</td>\n",
        "      <td>88968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AARON,  KIMBERLEI R</td>\n",
        "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "      <td>$84780.00</td>\n",
        "      <td>84780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32177</th>\n",
        "      <td>ZYGOWICZ,  PETER J</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$83616.00</td>\n",
        "      <td>83616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32180</th>\n",
        "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
        "      <td>CHIEF DATA BASE ANALYST</td>\n",
        "      <td>DoIT</td>\n",
        "      <td>$110352.00</td>\n",
        "      <td>110352</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32181 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "                      Name            Position Title        Department  \\\n",
        "0          AARON,  ELVIA J          WATER RATE TAKER       WATER MGMNT   \n",
        "1        AARON,  JEFFERY M            POLICE OFFICER            POLICE   \n",
        "2           AARON,  KARINA            POLICE OFFICER            POLICE   \n",
        "3      AARON,  KIMBERLEI R  CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
        "...                    ...                       ...               ...   \n",
        "32177   ZYGOWICZ,  PETER J            POLICE OFFICER            POLICE   \n",
        "32178    ZYMANTAS,  MARK E            POLICE OFFICER            POLICE   \n",
        "32179  ZYRKOWSKI,  CARLO E            POLICE OFFICER            POLICE   \n",
        "32180  ZYSKOWSKI,  DARIUSZ   CHIEF DATA BASE ANALYST              DoIT   \n",
        "\n",
        "      Employee Annual Salary  Employee Annual Salary Numeric  \n",
        "0                  $88968.00                           88968  \n",
        "1                  $80778.00                           80778  \n",
        "2                  $80778.00                           80778  \n",
        "3                  $84780.00                           84780  \n",
        "...                      ...                             ...  \n",
        "32177              $86520.00                           86520  \n",
        "32178              $83616.00                           83616  \n",
        "32179              $86520.00                           86520  \n",
        "32180             $110352.00                          110352  \n",
        "\n",
        "[32181 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salaries_numeric = df['Employee Annual Salary Numeric']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 126
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Datetimes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dates and times are important for data analysis. Let's look again at the Chicago crime dataset."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crimes = pd.read_csv('Crimes_-_2001_to_present.csv', \n",
      "                        parse_dates=['Date'])\n",
      "df_crimes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ID</th>\n",
        "      <th>Case Number</th>\n",
        "      <th>Date</th>\n",
        "      <th>Block</th>\n",
        "      <th>IUCR</th>\n",
        "      <th>Primary Type</th>\n",
        "      <th>Description</th>\n",
        "      <th>Location Description</th>\n",
        "      <th>Arrest</th>\n",
        "      <th>Domestic</th>\n",
        "      <th>...</th>\n",
        "      <th>Ward</th>\n",
        "      <th>Community Area</th>\n",
        "      <th>FBI Code</th>\n",
        "      <th>X Coordinate</th>\n",
        "      <th>Y Coordinate</th>\n",
        "      <th>Year</th>\n",
        "      <th>Updated On</th>\n",
        "      <th>Latitude</th>\n",
        "      <th>Longitude</th>\n",
        "      <th>Location</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>10367002</td>\n",
        "      <td>HZ103159</td>\n",
        "      <td>2016-01-03 23:57:00</td>\n",
        "      <td>016XX W 77TH ST</td>\n",
        "      <td>0486</td>\n",
        "      <td>BATTERY</td>\n",
        "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
        "      <td>RESIDENCE</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
        "      <td>...</td>\n",
        "      <td>17</td>\n",
        "      <td>71</td>\n",
        "      <td>08B</td>\n",
        "      <td>1166811</td>\n",
        "      <td>1853653</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.753985</td>\n",
        "      <td>-87.664263</td>\n",
        "      <td>(41.753985135, -87.664263469)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>10366954</td>\n",
        "      <td>HZ103177</td>\n",
        "      <td>2016-01-03 23:45:00</td>\n",
        "      <td>003XX W 42ND ST</td>\n",
        "      <td>0930</td>\n",
        "      <td>MOTOR VEHICLE THEFT</td>\n",
        "      <td>THEFT/RECOVERY: AUTOMOBILE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>3</td>\n",
        "      <td>37</td>\n",
        "      <td>07</td>\n",
        "      <td>1174795</td>\n",
        "      <td>1877110</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.818180</td>\n",
        "      <td>-87.634306</td>\n",
        "      <td>(41.818179533, -87.634305993)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>10366945</td>\n",
        "      <td>HZ103160</td>\n",
        "      <td>2016-01-03 23:44:00</td>\n",
        "      <td>055XX N LINCOLN AVE</td>\n",
        "      <td>502R</td>\n",
        "      <td>OTHER OFFENSE</td>\n",
        "      <td>VEHICLE TITLE/REG OFFENSE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>40</td>\n",
        "      <td>4</td>\n",
        "      <td>26</td>\n",
        "      <td>1157929</td>\n",
        "      <td>1936966</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.982789</td>\n",
        "      <td>-87.694541</td>\n",
        "      <td>(41.982789367, -87.69454123)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>10367534</td>\n",
        "      <td>HZ103498</td>\n",
        "      <td>2016-01-03 23:36:00</td>\n",
        "      <td>081XX S ESSEX AVE</td>\n",
        "      <td>1320</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO VEHICLE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>7</td>\n",
        "      <td>46</td>\n",
        "      <td>14</td>\n",
        "      <td>1194260</td>\n",
        "      <td>1851569</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.747637</td>\n",
        "      <td>-87.563742</td>\n",
        "      <td>(41.747636668, -87.563741805)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1726</th>\n",
        "      <td>10365595</td>\n",
        "      <td>HZ100414</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>020XX N NEWLAND AVE</td>\n",
        "      <td>1310</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO PROPERTY</td>\n",
        "      <td>RESIDENCE</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>36</td>\n",
        "      <td>25</td>\n",
        "      <td>14</td>\n",
        "      <td>1129760</td>\n",
        "      <td>1912773</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.916931</td>\n",
        "      <td>-87.798697</td>\n",
        "      <td>(41.916930792, -87.798697185)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1727</th>\n",
        "      <td>10368855</td>\n",
        "      <td>HZ104913</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>085XX S SANGAMON ST</td>\n",
        "      <td>2820</td>\n",
        "      <td>OTHER OFFENSE</td>\n",
        "      <td>TELEPHONE THREAT</td>\n",
        "      <td>RESIDENCE</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>21</td>\n",
        "      <td>71</td>\n",
        "      <td>26</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1728</th>\n",
        "      <td>10365598</td>\n",
        "      <td>HZ101287</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>056XX S NARRAGANSETT AVE</td>\n",
        "      <td>1320</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO VEHICLE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>23</td>\n",
        "      <td>56</td>\n",
        "      <td>14</td>\n",
        "      <td>1134682</td>\n",
        "      <td>1866432</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.789678</td>\n",
        "      <td>-87.781707</td>\n",
        "      <td>(41.789678467, -87.781707212)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1729</th>\n",
        "      <td>10366666</td>\n",
        "      <td>HZ102516</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>043XX N SPAULDING AVE</td>\n",
        "      <td>1320</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO VEHICLE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>33</td>\n",
        "      <td>16</td>\n",
        "      <td>14</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>1730 rows \u00d7 22 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 129,
       "text": [
        "            ID Case Number                Date                     Block  \\\n",
        "0     10367002    HZ103159 2016-01-03 23:57:00           016XX W 77TH ST   \n",
        "1     10366954    HZ103177 2016-01-03 23:45:00           003XX W 42ND ST   \n",
        "2     10366945    HZ103160 2016-01-03 23:44:00       055XX N LINCOLN AVE   \n",
        "3     10367534    HZ103498 2016-01-03 23:36:00         081XX S ESSEX AVE   \n",
        "...        ...         ...                 ...                       ...   \n",
        "1726  10365595    HZ100414 2016-01-01 00:01:00       020XX N NEWLAND AVE   \n",
        "1727  10368855    HZ104913 2016-01-01 00:01:00       085XX S SANGAMON ST   \n",
        "1728  10365598    HZ101287 2016-01-01 00:01:00  056XX S NARRAGANSETT AVE   \n",
        "1729  10366666    HZ102516 2016-01-01 00:01:00     043XX N SPAULDING AVE   \n",
        "\n",
        "      IUCR         Primary Type                 Description  \\\n",
        "0     0486              BATTERY     DOMESTIC BATTERY SIMPLE   \n",
        "1     0930  MOTOR VEHICLE THEFT  THEFT/RECOVERY: AUTOMOBILE   \n",
        "2     502R        OTHER OFFENSE   VEHICLE TITLE/REG OFFENSE   \n",
        "3     1320      CRIMINAL DAMAGE                  TO VEHICLE   \n",
        "...    ...                  ...                         ...   \n",
        "1726  1310      CRIMINAL DAMAGE                 TO PROPERTY   \n",
        "1727  2820        OTHER OFFENSE            TELEPHONE THREAT   \n",
        "1728  1320      CRIMINAL DAMAGE                  TO VEHICLE   \n",
        "1729  1320      CRIMINAL DAMAGE                  TO VEHICLE   \n",
        "\n",
        "     Location Description Arrest Domestic              ...                \\\n",
        "0               RESIDENCE  False     True              ...                 \n",
        "1                  STREET  False    False              ...                 \n",
        "2                  STREET   True    False              ...                 \n",
        "3                  STREET  False    False              ...                 \n",
        "...                   ...    ...      ...              ...                 \n",
        "1726            RESIDENCE  False    False              ...                 \n",
        "1727            RESIDENCE  False    False              ...                 \n",
        "1728               STREET  False    False              ...                 \n",
        "1729               STREET  False    False              ...                 \n",
        "\n",
        "      Ward  Community Area  FBI Code  X Coordinate Y Coordinate  Year  \\\n",
        "0       17              71       08B       1166811      1853653  2016   \n",
        "1        3              37        07       1174795      1877110  2016   \n",
        "2       40               4        26       1157929      1936966  2016   \n",
        "3        7              46        14       1194260      1851569  2016   \n",
        "...    ...             ...       ...           ...          ...   ...   \n",
        "1726    36              25        14       1129760      1912773  2016   \n",
        "1727    21              71        26           NaN          NaN  2016   \n",
        "1728    23              56        14       1134682      1866432  2016   \n",
        "1729    33              16        14           NaN          NaN  2016   \n",
        "\n",
        "                  Updated On   Latitude  Longitude  \\\n",
        "0     01/10/2016 08:46:55 AM  41.753985 -87.664263   \n",
        "1     01/10/2016 08:46:55 AM  41.818180 -87.634306   \n",
        "2     01/10/2016 08:46:55 AM  41.982789 -87.694541   \n",
        "3     01/10/2016 08:46:55 AM  41.747637 -87.563742   \n",
        "...                      ...        ...        ...   \n",
        "1726  01/10/2016 08:46:55 AM  41.916931 -87.798697   \n",
        "1727  01/10/2016 08:46:55 AM        NaN        NaN   \n",
        "1728  01/10/2016 08:46:55 AM  41.789678 -87.781707   \n",
        "1729  01/10/2016 08:46:55 AM        NaN        NaN   \n",
        "\n",
        "                           Location  \n",
        "0     (41.753985135, -87.664263469)  \n",
        "1     (41.818179533, -87.634305993)  \n",
        "2      (41.982789367, -87.69454123)  \n",
        "3     (41.747636668, -87.563741805)  \n",
        "...                             ...  \n",
        "1726  (41.916930792, -87.798697185)  \n",
        "1727                            NaN  \n",
        "1728  (41.789678467, -87.781707212)  \n",
        "1729                            NaN  \n",
        "\n",
        "[1730 rows x 22 columns]"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Datetime series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crimes['Date']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "0      2016-01-03 23:57:00\n",
        "1      2016-01-03 23:45:00\n",
        "2      2016-01-03 23:44:00\n",
        "3      2016-01-03 23:36:00\n",
        "               ...        \n",
        "1726   2016-01-01 00:01:00\n",
        "1727   2016-01-01 00:01:00\n",
        "1728   2016-01-01 00:01:00\n",
        "1729   2016-01-01 00:01:00\n",
        "Name: Date, dtype: datetime64[ns]"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Timestamp object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "date = df_crimes['Date'][0]\n",
      "print_type(date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2016-01-03 23:57:00 is type<class 'pandas.tslib.Timestamp'>\n"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(date.year)\n",
      "print(date.month)\n",
      "print(date.day)\n",
      "print(date.dayofweek)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2016\n",
        "1\n",
        "3\n",
        "6\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Applying dayofweek"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dayofweek(date):\n",
      "    return date.dayofweek"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crimes['Date'].apply(dayofweek)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "0       6\n",
        "1       6\n",
        "2       6\n",
        "3       6\n",
        "       ..\n",
        "1726    4\n",
        "1727    4\n",
        "1728    4\n",
        "1729    4\n",
        "Name: Date, dtype: int64"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Applying dayofweek"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crimes['DayOfWeek'] = df_crimes['Date'].apply(dayofweek)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crimes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ID</th>\n",
        "      <th>Case Number</th>\n",
        "      <th>Date</th>\n",
        "      <th>Block</th>\n",
        "      <th>IUCR</th>\n",
        "      <th>Primary Type</th>\n",
        "      <th>Description</th>\n",
        "      <th>Location Description</th>\n",
        "      <th>Arrest</th>\n",
        "      <th>Domestic</th>\n",
        "      <th>...</th>\n",
        "      <th>Community Area</th>\n",
        "      <th>FBI Code</th>\n",
        "      <th>X Coordinate</th>\n",
        "      <th>Y Coordinate</th>\n",
        "      <th>Year</th>\n",
        "      <th>Updated On</th>\n",
        "      <th>Latitude</th>\n",
        "      <th>Longitude</th>\n",
        "      <th>Location</th>\n",
        "      <th>DayOfWeek</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>10367002</td>\n",
        "      <td>HZ103159</td>\n",
        "      <td>2016-01-03 23:57:00</td>\n",
        "      <td>016XX W 77TH ST</td>\n",
        "      <td>0486</td>\n",
        "      <td>BATTERY</td>\n",
        "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
        "      <td>RESIDENCE</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
        "      <td>...</td>\n",
        "      <td>71</td>\n",
        "      <td>08B</td>\n",
        "      <td>1166811</td>\n",
        "      <td>1853653</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.753985</td>\n",
        "      <td>-87.664263</td>\n",
        "      <td>(41.753985135, -87.664263469)</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>10366954</td>\n",
        "      <td>HZ103177</td>\n",
        "      <td>2016-01-03 23:45:00</td>\n",
        "      <td>003XX W 42ND ST</td>\n",
        "      <td>0930</td>\n",
        "      <td>MOTOR VEHICLE THEFT</td>\n",
        "      <td>THEFT/RECOVERY: AUTOMOBILE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>37</td>\n",
        "      <td>07</td>\n",
        "      <td>1174795</td>\n",
        "      <td>1877110</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.818180</td>\n",
        "      <td>-87.634306</td>\n",
        "      <td>(41.818179533, -87.634305993)</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>10366945</td>\n",
        "      <td>HZ103160</td>\n",
        "      <td>2016-01-03 23:44:00</td>\n",
        "      <td>055XX N LINCOLN AVE</td>\n",
        "      <td>502R</td>\n",
        "      <td>OTHER OFFENSE</td>\n",
        "      <td>VEHICLE TITLE/REG OFFENSE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>4</td>\n",
        "      <td>26</td>\n",
        "      <td>1157929</td>\n",
        "      <td>1936966</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.982789</td>\n",
        "      <td>-87.694541</td>\n",
        "      <td>(41.982789367, -87.69454123)</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>10367534</td>\n",
        "      <td>HZ103498</td>\n",
        "      <td>2016-01-03 23:36:00</td>\n",
        "      <td>081XX S ESSEX AVE</td>\n",
        "      <td>1320</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO VEHICLE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>46</td>\n",
        "      <td>14</td>\n",
        "      <td>1194260</td>\n",
        "      <td>1851569</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.747637</td>\n",
        "      <td>-87.563742</td>\n",
        "      <td>(41.747636668, -87.563741805)</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1726</th>\n",
        "      <td>10365595</td>\n",
        "      <td>HZ100414</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>020XX N NEWLAND AVE</td>\n",
        "      <td>1310</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO PROPERTY</td>\n",
        "      <td>RESIDENCE</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>25</td>\n",
        "      <td>14</td>\n",
        "      <td>1129760</td>\n",
        "      <td>1912773</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.916931</td>\n",
        "      <td>-87.798697</td>\n",
        "      <td>(41.916930792, -87.798697185)</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1727</th>\n",
        "      <td>10368855</td>\n",
        "      <td>HZ104913</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>085XX S SANGAMON ST</td>\n",
        "      <td>2820</td>\n",
        "      <td>OTHER OFFENSE</td>\n",
        "      <td>TELEPHONE THREAT</td>\n",
        "      <td>RESIDENCE</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>71</td>\n",
        "      <td>26</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1728</th>\n",
        "      <td>10365598</td>\n",
        "      <td>HZ101287</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>056XX S NARRAGANSETT AVE</td>\n",
        "      <td>1320</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO VEHICLE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>56</td>\n",
        "      <td>14</td>\n",
        "      <td>1134682</td>\n",
        "      <td>1866432</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>41.789678</td>\n",
        "      <td>-87.781707</td>\n",
        "      <td>(41.789678467, -87.781707212)</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1729</th>\n",
        "      <td>10366666</td>\n",
        "      <td>HZ102516</td>\n",
        "      <td>2016-01-01 00:01:00</td>\n",
        "      <td>043XX N SPAULDING AVE</td>\n",
        "      <td>1320</td>\n",
        "      <td>CRIMINAL DAMAGE</td>\n",
        "      <td>TO VEHICLE</td>\n",
        "      <td>STREET</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>...</td>\n",
        "      <td>16</td>\n",
        "      <td>14</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2016</td>\n",
        "      <td>01/10/2016 08:46:55 AM</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>1730 rows \u00d7 23 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "            ID Case Number                Date                     Block  \\\n",
        "0     10367002    HZ103159 2016-01-03 23:57:00           016XX W 77TH ST   \n",
        "1     10366954    HZ103177 2016-01-03 23:45:00           003XX W 42ND ST   \n",
        "2     10366945    HZ103160 2016-01-03 23:44:00       055XX N LINCOLN AVE   \n",
        "3     10367534    HZ103498 2016-01-03 23:36:00         081XX S ESSEX AVE   \n",
        "...        ...         ...                 ...                       ...   \n",
        "1726  10365595    HZ100414 2016-01-01 00:01:00       020XX N NEWLAND AVE   \n",
        "1727  10368855    HZ104913 2016-01-01 00:01:00       085XX S SANGAMON ST   \n",
        "1728  10365598    HZ101287 2016-01-01 00:01:00  056XX S NARRAGANSETT AVE   \n",
        "1729  10366666    HZ102516 2016-01-01 00:01:00     043XX N SPAULDING AVE   \n",
        "\n",
        "      IUCR         Primary Type                 Description  \\\n",
        "0     0486              BATTERY     DOMESTIC BATTERY SIMPLE   \n",
        "1     0930  MOTOR VEHICLE THEFT  THEFT/RECOVERY: AUTOMOBILE   \n",
        "2     502R        OTHER OFFENSE   VEHICLE TITLE/REG OFFENSE   \n",
        "3     1320      CRIMINAL DAMAGE                  TO VEHICLE   \n",
        "...    ...                  ...                         ...   \n",
        "1726  1310      CRIMINAL DAMAGE                 TO PROPERTY   \n",
        "1727  2820        OTHER OFFENSE            TELEPHONE THREAT   \n",
        "1728  1320      CRIMINAL DAMAGE                  TO VEHICLE   \n",
        "1729  1320      CRIMINAL DAMAGE                  TO VEHICLE   \n",
        "\n",
        "     Location Description Arrest Domestic    ...      Community Area  \\\n",
        "0               RESIDENCE  False     True    ...                  71   \n",
        "1                  STREET  False    False    ...                  37   \n",
        "2                  STREET   True    False    ...                   4   \n",
        "3                  STREET  False    False    ...                  46   \n",
        "...                   ...    ...      ...    ...                 ...   \n",
        "1726            RESIDENCE  False    False    ...                  25   \n",
        "1727            RESIDENCE  False    False    ...                  71   \n",
        "1728               STREET  False    False    ...                  56   \n",
        "1729               STREET  False    False    ...                  16   \n",
        "\n",
        "      FBI Code  X Coordinate  Y Coordinate  Year              Updated On  \\\n",
        "0          08B       1166811       1853653  2016  01/10/2016 08:46:55 AM   \n",
        "1           07       1174795       1877110  2016  01/10/2016 08:46:55 AM   \n",
        "2           26       1157929       1936966  2016  01/10/2016 08:46:55 AM   \n",
        "3           14       1194260       1851569  2016  01/10/2016 08:46:55 AM   \n",
        "...        ...           ...           ...   ...                     ...   \n",
        "1726        14       1129760       1912773  2016  01/10/2016 08:46:55 AM   \n",
        "1727        26           NaN           NaN  2016  01/10/2016 08:46:55 AM   \n",
        "1728        14       1134682       1866432  2016  01/10/2016 08:46:55 AM   \n",
        "1729        14           NaN           NaN  2016  01/10/2016 08:46:55 AM   \n",
        "\n",
        "       Latitude  Longitude                       Location  DayOfWeek  \n",
        "0     41.753985 -87.664263  (41.753985135, -87.664263469)          6  \n",
        "1     41.818180 -87.634306  (41.818179533, -87.634305993)          6  \n",
        "2     41.982789 -87.694541   (41.982789367, -87.69454123)          6  \n",
        "3     41.747637 -87.563742  (41.747636668, -87.563741805)          6  \n",
        "...         ...        ...                            ...        ...  \n",
        "1726  41.916931 -87.798697  (41.916930792, -87.798697185)          4  \n",
        "1727        NaN        NaN                            NaN          4  \n",
        "1728  41.789678 -87.781707  (41.789678467, -87.781707212)          4  \n",
        "1729        NaN        NaN                            NaN          4  \n",
        "\n",
        "[1730 rows x 23 columns]"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Complex Queries: Logical Operations on Series"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Given boolean series there are three basic logical operations: and (`&`), or (`|`) and not (`~`). We can use these on boolean series to do more complex queries of our table. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Employee Annual Salary Numeric'] > 100000"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "0        False\n",
        "1        False\n",
        "2        False\n",
        "3        False\n",
        "         ...  \n",
        "32177    False\n",
        "32178    False\n",
        "32179    False\n",
        "32180     True\n",
        "Name: Employee Annual Salary Numeric, dtype: bool"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Logical operation on series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "~(df['Employee Annual Salary Numeric'] > 100000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "0         True\n",
        "1         True\n",
        "2         True\n",
        "3         True\n",
        "         ...  \n",
        "32177     True\n",
        "32178     True\n",
        "32179     True\n",
        "32180    False\n",
        "Name: Employee Annual Salary Numeric, dtype: bool"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Row indexing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[~(df['Employee Annual Salary Numeric'] > 100000)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "      <th>Employee Annual Salary Numeric</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER RATE TAKER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$88968.00</td>\n",
        "      <td>88968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AARON,  KIMBERLEI R</td>\n",
        "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "      <td>$84780.00</td>\n",
        "      <td>84780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32176</th>\n",
        "      <td>ZYGADLO,  MICHAEL J</td>\n",
        "      <td>FRM OF MACHINISTS - AUTOMOTIVE</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "      <td>$97448.00</td>\n",
        "      <td>97448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32177</th>\n",
        "      <td>ZYGOWICZ,  PETER J</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$83616.00</td>\n",
        "      <td>83616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>28262 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "                      Name                  Position Title        Department  \\\n",
        "0          AARON,  ELVIA J                WATER RATE TAKER       WATER MGMNT   \n",
        "1        AARON,  JEFFERY M                  POLICE OFFICER            POLICE   \n",
        "2           AARON,  KARINA                  POLICE OFFICER            POLICE   \n",
        "3      AARON,  KIMBERLEI R        CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
        "...                    ...                             ...               ...   \n",
        "32176  ZYGADLO,  MICHAEL J  FRM OF MACHINISTS - AUTOMOTIVE  GENERAL SERVICES   \n",
        "32177   ZYGOWICZ,  PETER J                  POLICE OFFICER            POLICE   \n",
        "32178    ZYMANTAS,  MARK E                  POLICE OFFICER            POLICE   \n",
        "32179  ZYRKOWSKI,  CARLO E                  POLICE OFFICER            POLICE   \n",
        "\n",
        "      Employee Annual Salary  Employee Annual Salary Numeric  \n",
        "0                  $88968.00                           88968  \n",
        "1                  $80778.00                           80778  \n",
        "2                  $80778.00                           80778  \n",
        "3                  $84780.00                           84780  \n",
        "...                      ...                             ...  \n",
        "32176              $97448.00                           97448  \n",
        "32177              $86520.00                           86520  \n",
        "32178              $83616.00                           83616  \n",
        "32179              $86520.00                           86520  \n",
        "\n",
        "[28262 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Logical operations on series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Parentheses are essential for logical operations like below!\n",
      "over100k_and_police = (df['Employee Annual Salary Numeric'] > 100000) & \n",
      "                      (df['Department'] == 'POLICE')\n",
      "print(over100k_and_police)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0        False\n",
        "1        False\n",
        "2        False\n",
        "3        False\n",
        "         ...  \n",
        "32177    False\n",
        "32178    False\n",
        "32179    False\n",
        "32180    False\n",
        "dtype: bool\n"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>\n",
      "## Indexing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[over100k_and_police]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "      <th>Employee Annual Salary Numeric</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>152</th>\n",
        "      <td>ADDUCI,  MARK A</td>\n",
        "      <td>LIEUTENANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$120264.00</td>\n",
        "      <td>120264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>156</th>\n",
        "      <td>ADE,  JAMES P</td>\n",
        "      <td>SERGEANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$110370.00</td>\n",
        "      <td>110370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>178</th>\n",
        "      <td>ADRIAN,  CHERI A</td>\n",
        "      <td>SERGEANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$106920.00</td>\n",
        "      <td>106920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>190</th>\n",
        "      <td>AGRON,  EDUARDO</td>\n",
        "      <td>SERGEANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$106920.00</td>\n",
        "      <td>106920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32034</th>\n",
        "      <td>ZELITZKY,  ADAM R</td>\n",
        "      <td>SERGEANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$110370.00</td>\n",
        "      <td>110370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32037</th>\n",
        "      <td>ZEMEK,  HELEN O</td>\n",
        "      <td>LIEUTENANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$120264.00</td>\n",
        "      <td>120264</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32065</th>\n",
        "      <td>ZIC SCHLOMAS,  VIRGINIA M</td>\n",
        "      <td>SERGEANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$110370.00</td>\n",
        "      <td>110370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32115</th>\n",
        "      <td>ZODO,  NICOLA E</td>\n",
        "      <td>SERGEANT</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$103590.00</td>\n",
        "      <td>103590</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>1493 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 131,
       "text": [
        "                            Name Position Title Department  \\\n",
        "152              ADDUCI,  MARK A     LIEUTENANT     POLICE   \n",
        "156                ADE,  JAMES P       SERGEANT     POLICE   \n",
        "178             ADRIAN,  CHERI A       SERGEANT     POLICE   \n",
        "190              AGRON,  EDUARDO       SERGEANT     POLICE   \n",
        "...                          ...            ...        ...   \n",
        "32034          ZELITZKY,  ADAM R       SERGEANT     POLICE   \n",
        "32037            ZEMEK,  HELEN O     LIEUTENANT     POLICE   \n",
        "32065  ZIC SCHLOMAS,  VIRGINIA M       SERGEANT     POLICE   \n",
        "32115            ZODO,  NICOLA E       SERGEANT     POLICE   \n",
        "\n",
        "      Employee Annual Salary  Employee Annual Salary Numeric  \n",
        "152               $120264.00                          120264  \n",
        "156               $110370.00                          110370  \n",
        "178               $106920.00                          106920  \n",
        "190               $106920.00                          106920  \n",
        "...                      ...                             ...  \n",
        "32034             $110370.00                          110370  \n",
        "32037             $120264.00                          120264  \n",
        "32065             $110370.00                          110370  \n",
        "32115             $103590.00                          103590  \n",
        "\n",
        "[1493 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 131
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "      <th>Employee Annual Salary Numeric</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER RATE TAKER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>$88968.00</td>\n",
        "      <td>88968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AARON,  KIMBERLEI R</td>\n",
        "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "      <td>$84780.00</td>\n",
        "      <td>84780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32177</th>\n",
        "      <td>ZYGOWICZ,  PETER J</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$83616.00</td>\n",
        "      <td>83616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32180</th>\n",
        "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
        "      <td>CHIEF DATA BASE ANALYST</td>\n",
        "      <td>DoIT</td>\n",
        "      <td>$110352.00</td>\n",
        "      <td>110352</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32181 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 132,
       "text": [
        "                      Name            Position Title        Department  \\\n",
        "0          AARON,  ELVIA J          WATER RATE TAKER       WATER MGMNT   \n",
        "1        AARON,  JEFFERY M            POLICE OFFICER            POLICE   \n",
        "2           AARON,  KARINA            POLICE OFFICER            POLICE   \n",
        "3      AARON,  KIMBERLEI R  CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
        "...                    ...                       ...               ...   \n",
        "32177   ZYGOWICZ,  PETER J            POLICE OFFICER            POLICE   \n",
        "32178    ZYMANTAS,  MARK E            POLICE OFFICER            POLICE   \n",
        "32179  ZYRKOWSKI,  CARLO E            POLICE OFFICER            POLICE   \n",
        "32180  ZYSKOWSKI,  DARIUSZ   CHIEF DATA BASE ANALYST              DoIT   \n",
        "\n",
        "      Employee Annual Salary  Employee Annual Salary Numeric  \n",
        "0                  $88968.00                           88968  \n",
        "1                  $80778.00                           80778  \n",
        "2                  $80778.00                           80778  \n",
        "3                  $84780.00                           84780  \n",
        "...                      ...                             ...  \n",
        "32177              $86520.00                           86520  \n",
        "32178              $83616.00                           83616  \n",
        "32179              $86520.00                           86520  \n",
        "32180             $110352.00                          110352  \n",
        "\n",
        "[32181 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salaries = df['Employee Annual Salary'].copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "salaries[1] = 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name</th>\n",
        "      <th>Position Title</th>\n",
        "      <th>Department</th>\n",
        "      <th>Employee Annual Salary</th>\n",
        "      <th>Employee Annual Salary Numeric</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>AARON,  ELVIA J</td>\n",
        "      <td>WATER RATE TAKER</td>\n",
        "      <td>WATER MGMNT</td>\n",
        "      <td>1</td>\n",
        "      <td>88968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>AARON,  JEFFERY M</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>AARON,  KARINA</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$80778.00</td>\n",
        "      <td>80778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>AARON,  KIMBERLEI R</td>\n",
        "      <td>CHIEF CONTRACT EXPEDITER</td>\n",
        "      <td>GENERAL SERVICES</td>\n",
        "      <td>$84780.00</td>\n",
        "      <td>84780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
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        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
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        "    <tr>\n",
        "      <th>32177</th>\n",
        "      <td>ZYGOWICZ,  PETER J</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32178</th>\n",
        "      <td>ZYMANTAS,  MARK E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$83616.00</td>\n",
        "      <td>83616</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32179</th>\n",
        "      <td>ZYRKOWSKI,  CARLO E</td>\n",
        "      <td>POLICE OFFICER</td>\n",
        "      <td>POLICE</td>\n",
        "      <td>$86520.00</td>\n",
        "      <td>86520</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32180</th>\n",
        "      <td>ZYSKOWSKI,  DARIUSZ</td>\n",
        "      <td>CHIEF DATA BASE ANALYST</td>\n",
        "      <td>DoIT</td>\n",
        "      <td>$110352.00</td>\n",
        "      <td>110352</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>32181 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "                      Name            Position Title        Department  \\\n",
        "0          AARON,  ELVIA J          WATER RATE TAKER       WATER MGMNT   \n",
        "1        AARON,  JEFFERY M            POLICE OFFICER            POLICE   \n",
        "2           AARON,  KARINA            POLICE OFFICER            POLICE   \n",
        "3      AARON,  KIMBERLEI R  CHIEF CONTRACT EXPEDITER  GENERAL SERVICES   \n",
        "...                    ...                       ...               ...   \n",
        "32177   ZYGOWICZ,  PETER J            POLICE OFFICER            POLICE   \n",
        "32178    ZYMANTAS,  MARK E            POLICE OFFICER            POLICE   \n",
        "32179  ZYRKOWSKI,  CARLO E            POLICE OFFICER            POLICE   \n",
        "32180  ZYSKOWSKI,  DARIUSZ   CHIEF DATA BASE ANALYST              DoIT   \n",
        "\n",
        "      Employee Annual Salary  Employee Annual Salary Numeric  \n",
        "0                          1                           88968  \n",
        "1                  $80778.00                           80778  \n",
        "2                  $80778.00                           80778  \n",
        "3                  $84780.00                           84780  \n",
        "...                      ...                             ...  \n",
        "32177              $86520.00                           86520  \n",
        "32178              $83616.00                           83616  \n",
        "32179              $86520.00                           86520  \n",
        "32180             $110352.00                          110352  \n",
        "\n",
        "[32181 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "isalaries=[]\n",
      "salaries=df[']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "0                 1\n",
        "1                10\n",
        "2         $80778.00\n",
        "3         $84780.00\n",
        "            ...    \n",
        "32177     $86520.00\n",
        "32178     $83616.00\n",
        "32179     $86520.00\n",
        "32180    $110352.00\n",
        "Name: Employee Annual Salary, dtype: object"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "d\t date\t dayofweek\t departments\t df\t df_crimes\t i\t l\t over100k_and_police\t \n",
        "pd\t print_type\t return_max\t s\t salaries\t salaries_numeric\t salary\t salary_to_float\t t\t \n",
        "var\t x\t y\t z\t \n"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}