datascience to pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping from `datascience` to Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome! This notebook serves as an introduction to working with Python's widely used Pandas library. The functions introduced will be analogous to those in Berkeley's `datascience` module, with examples provided for each.\n",
    "\n",
    "We will cover the following topics in this notebook:\n",
    "1. [Basics of Pandas](#basics)\n",
    "    - [Importing and Loading Packages](#import)\n",
    "<br>\n",
    "<br>\n",
    "2. [Dataframes: Working with Tabular Data](#dataframes)\n",
    "    - [Creating a Dataframe](#creating)\n",
    "    - [Accessing Values in Dataframe](#accessing)\n",
    "    - [Manipulating Data](#manipulating)\n",
    "<br>\n",
    "<br>\n",
    "3. [Visualizing Data](#visualizing)\n",
    "    - [Histograms](#histograms)\n",
    "    - [Line Plots](#line)\n",
    "    - [Scatter Plots](#scatter)\n",
    "    - [Bar Plots](#bar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Basics <a id='basics'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook assumes familiarity with Python concepts, syntax and data structures at the level of Data 8. For a brief refresher on some Python concepts, refer to this [Python Basics Guide on Github](https://github.com/TiesdeKok/LearnPythonforResearch/blob/master/0_python_basics.ipynb)\n",
    "\n",
    "Python has a great ecosystem of data-centric packages which makes it excellent for data analysis. Pandas is one of those packages, and makes importing and analyzing data much easier. Pandas builds on packages like NumPy and matplotlib to give us a single, convenient, place to do most of our data analysis and visualization work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Importing and Loading Packages <a id='import'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is useful to import certain packages in our workspace for analysis and data visualization. But first, we may need to install these package if they are not present already. We do this via the command line as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datascience in /anaconda3/envs/sohamds/lib/python3.6/site-packages\n",
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      "\u001b[33mYou are using pip version 9.0.1, however version 10.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: pandas in /anaconda3/envs/sohamds/lib/python3.6/site-packages\n",
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      "\u001b[33mYou are using pip version 9.0.1, however version 10.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "Requirement already satisfied: numpy in /anaconda3/envs/sohamds/lib/python3.6/site-packages\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 10.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# run this cell to install packages\n",
    "!pip install datascience\n",
    "!pip install pandas\n",
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have installed the required packages, we do not need to reinstall them again when we start or reopen a Jupyter notebook. We can simply import them using the `import` keyword. Since we import Pandas as `pd`, we need to prefix all functions with `pd`, similar to how we prefix all numpy functions with `np` (such as `np.append()`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run this cell to import the following packages\n",
    "from datascience import * # import the datascience package\n",
    "import pandas as pd # import the pandas library. pd is a common shorthand for pandas\n",
    "import numpy as np # import numpy for working with numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Dataframes: Working with Tabular Data <a id='dataframes'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python's `datascience` module, we used `Table` to build our dataframes and used commands such as `select()`, `where()`, `group()`, `column()` etc. In this section, we will go over some basic commands to work with tabular data in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Creating a dataframe <a id='creating'> </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas introduces a data structure (i.e. dataframe) that represents data as a table with columns and rows. \n",
    "\n",
    "In Python's `datascience` module that is used in Data 8, this is how we created tables from scratch by extending an empty table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>count</th> <th>points</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>a     </td> <td>9    </td> <td>1     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>3    </td> <td>2     </td>\n",
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       "    </tbody>\n",
       "        <tr>\n",
       "            <td>c     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>z     </td> <td>1    </td> <td>10    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
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      "text/plain": [
       "letter | count | points\n",
       "a      | 9     | 1\n",
       "b      | 3     | 2\n",
       "c      | 3     | 2\n",
       "z      | 1     | 10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = Table().with_columns([\n",
    "     'letter', ['a', 'b', 'c', 'z'],\n",
    "     'count',  [  9,   3,   3,   1],\n",
    "     'points', [  1,   2,   2,  10],\n",
    " ])\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we can use the function `pd.DataFrame` to initialize a dataframe from a dictionary or a list-like object. Refer to the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) for more information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>letter</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>a</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>z</td>\n",
       "      <td>10</td>\n",
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      "text/plain": [
       "   count letter  points\n",
       "0      9      a       1\n",
       "1      3      b       2\n",
       "2      3      c       2\n",
       "3      1      z      10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example: creating a dataframe from a dictionary\n",
    "df_from_dict = pd.DataFrame({ 'letter' : ['a', 'b', 'c', 'z'],\n",
    "                      'count' : [  9,   3,   3,   1],\n",
    "                      'points' : [  1,   2,   2,  10]\n",
    "                      })\n",
    "df_from_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More often, we will need to create a dataframe by importing data from a .csv file. In `datascience`, this is how we read data from a csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>Birth.Weight</th> <th>Gestational.Days</th> <th>Maternal.Age</th> <th>Maternal.Height</th> <th>Maternal.Pregnancy.Weight</th> <th>Maternal.Smoker</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1         </td> <td>120         </td> <td>284             </td> <td>27          </td> <td>62             </td> <td>100                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2         </td> <td>113         </td> <td>282             </td> <td>33          </td> <td>64             </td> <td>135                      </td> <td>False          </td>\n",
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       "            <td>3         </td> <td>128         </td> <td>279             </td> <td>28          </td> <td>64             </td> <td>115                      </td> <td>True           </td>\n",
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       "            <td>4         </td> <td>108         </td> <td>282             </td> <td>23          </td> <td>67             </td> <td>125                      </td> <td>True           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>5         </td> <td>136         </td> <td>286             </td> <td>25          </td> <td>62             </td> <td>93                       </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>6         </td> <td>138         </td> <td>244             </td> <td>33          </td> <td>62             </td> <td>178                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>7         </td> <td>132         </td> <td>245             </td> <td>23          </td> <td>65             </td> <td>140                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>8         </td> <td>120         </td> <td>289             </td> <td>25          </td> <td>62             </td> <td>125                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>9         </td> <td>143         </td> <td>299             </td> <td>30          </td> <td>66             </td> <td>136                      </td> <td>True           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>10        </td> <td>140         </td> <td>351             </td> <td>27          </td> <td>68             </td> <td>120                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (1164 rows omitted)</p>"
      ],
      "text/plain": [
       "Unnamed: 0 | Birth.Weight | Gestational.Days | Maternal.Age | Maternal.Height | Maternal.Pregnancy.Weight | Maternal.Smoker\n",
       "1          | 120          | 284              | 27           | 62              | 100                       | False\n",
       "2          | 113          | 282              | 33           | 64              | 135                       | False\n",
       "3          | 128          | 279              | 28           | 64              | 115                       | True\n",
       "4          | 108          | 282              | 23           | 67              | 125                       | True\n",
       "5          | 136          | 286              | 25           | 62              | 93                        | False\n",
       "6          | 138          | 244              | 33           | 62              | 178                       | False\n",
       "7          | 132          | 245              | 23           | 65              | 140                       | False\n",
       "8          | 120          | 289              | 25           | 62              | 125                       | False\n",
       "9          | 143          | 299              | 30           | 66              | 136                       | True\n",
       "10         | 140          | 351              | 27           | 68              | 120                       | False\n",
       "... (1164 rows omitted)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datascience_baby = Table.read_table('baby.csv')\n",
    "datascience_baby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use `pd.read.csv()` to read data from a csv file. Sometimes, we may need to specify the parameters `sep`, `header` or `encoding` as well. For a full list of parameters, refer to [this guide](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Maternal.Age</th>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  Birth.Weight  Gestational.Days  Maternal.Age  Maternal.Height  \\\n",
       "0           1           120               284            27               62   \n",
       "1           2           113               282            33               64   \n",
       "2           3           128               279            28               64   \n",
       "3           4           108               282            23               67   \n",
       "4           5           136               286            25               62   \n",
       "\n",
       "   Maternal.Pregnancy.Weight  Maternal.Smoker  \n",
       "0                        100            False  \n",
       "1                        135            False  \n",
       "2                        115             True  \n",
       "3                        125             True  \n",
       "4                         93            False  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading baby.csv (located in current working directory)\n",
    "baby = pd.read_csv('baby.csv')\n",
    "baby.head() # display first few rows of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Birth.Weight</th>\n",
       "      <th>Gestational.Days</th>\n",
       "      <th>Maternal.Age</th>\n",
       "      <th>Maternal.Height</th>\n",
       "      <th>Maternal.Pregnancy.Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1174.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "      <td>1174.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>587.500000</td>\n",
       "      <td>119.462521</td>\n",
       "      <td>279.101363</td>\n",
       "      <td>27.228279</td>\n",
       "      <td>64.049404</td>\n",
       "      <td>128.478705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>339.048915</td>\n",
       "      <td>18.328671</td>\n",
       "      <td>16.010305</td>\n",
       "      <td>5.817839</td>\n",
       "      <td>2.526102</td>\n",
       "      <td>20.734282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>294.250000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>114.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>587.500000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>880.750000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>288.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1174.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Birth.Weight  Gestational.Days  Maternal.Age  \\\n",
       "count  1174.000000   1174.000000       1174.000000   1174.000000   \n",
       "mean    587.500000    119.462521        279.101363     27.228279   \n",
       "std     339.048915     18.328671         16.010305      5.817839   \n",
       "min       1.000000     55.000000        148.000000     15.000000   \n",
       "25%     294.250000    108.000000        272.000000     23.000000   \n",
       "50%     587.500000    120.000000        280.000000     26.000000   \n",
       "75%     880.750000    131.000000        288.000000     31.000000   \n",
       "max    1174.000000    176.000000        353.000000     45.000000   \n",
       "\n",
       "       Maternal.Height  Maternal.Pregnancy.Weight  \n",
       "count      1174.000000                1174.000000  \n",
       "mean         64.049404                 128.478705  \n",
       "std           2.526102                  20.734282  \n",
       "min          53.000000                  87.000000  \n",
       "25%          62.000000                 114.250000  \n",
       "50%          64.000000                 125.000000  \n",
       "75%          66.000000                 139.000000  \n",
       "max          72.000000                 250.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view summary of data\n",
    "baby.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Participation Rate</th>\n",
       "      <th>Critical Reading</th>\n",
       "      <th>Math</th>\n",
       "      <th>Writing</th>\n",
       "      <th>Combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>2.3</td>\n",
       "      <td>612</td>\n",
       "      <td>620</td>\n",
       "      <td>584</td>\n",
       "      <td>1816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>4.6</td>\n",
       "      <td>599</td>\n",
       "      <td>616</td>\n",
       "      <td>587</td>\n",
       "      <td>1802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>3.1</td>\n",
       "      <td>605</td>\n",
       "      <td>611</td>\n",
       "      <td>578</td>\n",
       "      <td>1794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>2.9</td>\n",
       "      <td>604</td>\n",
       "      <td>609</td>\n",
       "      <td>579</td>\n",
       "      <td>1792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>5.9</td>\n",
       "      <td>598</td>\n",
       "      <td>610</td>\n",
       "      <td>578</td>\n",
       "      <td>1786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          State  Participation Rate  Critical Reading  Math  Writing  Combined\n",
       "0  North Dakota                 2.3               612   620      584      1816\n",
       "1      Illinois                 4.6               599   616      587      1802\n",
       "2          Iowa                 3.1               605   611      578      1794\n",
       "3  South Dakota                 2.9               604   609      579      1792\n",
       "4     Minnesota                 5.9               598   610      578      1786"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example: loading csv from URL\n",
    "sat = pd.read_csv('http://data8.org/textbook/notebooks/sat2014.csv')\n",
    "sat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51, 6)\n",
      "['State' 'Participation Rate' 'Critical Reading' 'Math' 'Writing'\n",
      " 'Combined']\n"
     ]
    }
   ],
   "source": [
    "# view information about dataframe\n",
    "print(sat.shape) # view dimensions (rows, cols)\n",
    "print(sat.columns.values) # view column names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Accessing Values in Dataframe <a id='accessing'> </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `datascience`, we can use `column()` to access values in a particular column as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c', 'z'], \n",
       "      dtype='<U1')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access column 'letter'. returns array\n",
    "t.column('letter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, columns are also known as Series. We can access a Pandas series by using the square bracket notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             North Dakota\n",
       "1                 Illinois\n",
       "2                     Iowa\n",
       "3             South Dakota\n",
       "4                Minnesota\n",
       "5                 Michigan\n",
       "6                Wisconsin\n",
       "7                 Missouri\n",
       "8                  Wyoming\n",
       "9                   Kansas\n",
       "10                Kentucky\n",
       "11                Nebraska\n",
       "12                Colorado\n",
       "13             Mississippi\n",
       "14               Tennessee\n",
       "15                Arkansas\n",
       "16                Oklahoma\n",
       "17                    Utah\n",
       "18               Louisiana\n",
       "19                    Ohio\n",
       "20                 Montana\n",
       "21                 Alabama\n",
       "22              New Mexico\n",
       "23           New Hampshire\n",
       "24           Massachusetts\n",
       "25                 Vermont\n",
       "26                 Arizona\n",
       "27                  Oregon\n",
       "28                Virginia\n",
       "29              New Jersey\n",
       "30             Connecticut\n",
       "31           West Virginia\n",
       "32              Washington\n",
       "33              California\n",
       "34                  Alaska\n",
       "35          North Carolina\n",
       "36            Pennsylvania\n",
       "37            Rhode Island\n",
       "38                 Indiana\n",
       "39                Maryland\n",
       "40                New York\n",
       "41                  Hawaii\n",
       "42                  Nevada\n",
       "43                 Florida\n",
       "44                 Georgia\n",
       "45          South Carolina\n",
       "46                   Texas\n",
       "47                   Maine\n",
       "48                   Idaho\n",
       "49                Delaware\n",
       "50    District of Columbia\n",
       "Name: State, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns Series object\n",
    "sat['State']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want a numpy array of column values, we can call the method `values` on a Series object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['North Dakota', 'Illinois', 'Iowa', 'South Dakota', 'Minnesota',\n",
       "       'Michigan', 'Wisconsin', 'Missouri', 'Wyoming', 'Kansas',\n",
       "       'Kentucky', 'Nebraska', 'Colorado', 'Mississippi', 'Tennessee',\n",
       "       'Arkansas', 'Oklahoma', 'Utah', 'Louisiana', 'Ohio', 'Montana',\n",
       "       'Alabama', 'New Mexico', 'New Hampshire', 'Massachusetts',\n",
       "       'Vermont', 'Arizona', 'Oregon', 'Virginia', 'New Jersey',\n",
       "       'Connecticut', 'West Virginia', 'Washington', 'California',\n",
       "       'Alaska', 'North Carolina', 'Pennsylvania', 'Rhode Island',\n",
       "       'Indiana', 'Maryland', 'New York', 'Hawaii', 'Nevada', 'Florida',\n",
       "       'Georgia', 'South Carolina', 'Texas', 'Maine', 'Idaho', 'Delaware',\n",
       "       'District of Columbia'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat['State'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `datascience`, we used `take()` to access a row in the Table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>count</th> <th>points</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>a     </td> <td>9    </td> <td>1     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "letter | count | points\n",
       "a      | 9     | 1\n",
       "b      | 3     | 2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting first two rows using Python's slicing notation\n",
    "t.take[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we can access rows and column by their position using the `iloc` method. We need to specify the rows and columns we want in the following syntax: `df.iloc[<rows>, <columns>]`. For more information on indexing, refer to [this guide](https://pandas.pydata.org/pandas-docs/stable/indexing.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Birth.Weight</th>\n",
       "      <th>Gestational.Days</th>\n",
       "      <th>Maternal.Age</th>\n",
       "      <th>Maternal.Height</th>\n",
       "      <th>Maternal.Pregnancy.Weight</th>\n",
       "      <th>Maternal.Smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>284</td>\n",
       "      <td>27</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113</td>\n",
       "      <td>282</td>\n",
       "      <td>33</td>\n",
       "      <td>64</td>\n",
       "      <td>135</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Birth.Weight  Gestational.Days  Maternal.Age  Maternal.Height  \\\n",
       "0           1           120               284            27               62   \n",
       "1           2           113               282            33               64   \n",
       "\n",
       "   Maternal.Pregnancy.Weight  Maternal.Smoker  \n",
       "0                        100            False  \n",
       "1                        135            False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting first two rows using iloc\n",
    "baby.iloc[0:2, :] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Birth.Weight</th>\n",
       "      <th>Gestational.Days</th>\n",
       "      <th>Maternal.Age</th>\n",
       "      <th>Maternal.Height</th>\n",
       "      <th>Maternal.Pregnancy.Weight</th>\n",
       "      <th>Maternal.Smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113</td>\n",
       "      <td>282</td>\n",
       "      <td>33</td>\n",
       "      <td>64</td>\n",
       "      <td>135</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>136</td>\n",
       "      <td>286</td>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>93</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>132</td>\n",
       "      <td>245</td>\n",
       "      <td>23</td>\n",
       "      <td>65</td>\n",
       "      <td>140</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Birth.Weight  Gestational.Days  Maternal.Age  Maternal.Height  \\\n",
       "1           2           113               282            33               64   \n",
       "4           5           136               286            25               62   \n",
       "6           7           132               245            23               65   \n",
       "\n",
       "   Maternal.Pregnancy.Weight  Maternal.Smoker  \n",
       "1                        135            False  \n",
       "4                         93            False  \n",
       "6                        140            False  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# specifying row indices\n",
    "baby.iloc[[1, 4, 6], :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also access a specific value in the dataframe by passing in the row and column indices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "282"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get value in second row, third column\n",
    "baby.iloc[1, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Manipulating Data <a id='manipulating'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding Columns**\n",
    "\n",
    "Adding a new column in `datascience` is done by the `with_columns()` function as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>count</th> <th>points</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>a     </td> <td>9    </td> <td>1     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>c     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>z     </td> <td>1    </td> <td>10    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "letter | count | points\n",
       "a      | 9     | 1\n",
       "b      | 3     | 2\n",
       "c      | 3     | 2\n",
       "z      | 1     | 10"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.with_column('vowel', ['yes', 'no', 'no', 'no'])\n",
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we can use the bracket notation and assign a list to add to the dataframe as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>letter</th>\n",
       "      <th>points</th>\n",
       "      <th>newcol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>z</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count letter  points  newcol\n",
       "0      9      a       1       5\n",
       "1      3      b       2       6\n",
       "2      3      c       2       7\n",
       "3      1      z      10       8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding a new column\n",
    "df_from_dict['newcol'] = [5, 6, 7, 8]\n",
    "df_from_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also add an existing column to the new dataframe as a new column by performing an operation on it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>letter</th>\n",
       "      <th>points</th>\n",
       "      <th>newcol</th>\n",
       "      <th>doublecount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>z</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count letter  points  newcol  doublecount\n",
       "0      9      a       1       5           18\n",
       "1      3      b       2       6            6\n",
       "2      3      c       2       7            6\n",
       "3      1      z      10       8            2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding count * 2 to the dataframe\n",
    "df_from_dict['doublecount'] = df_from_dict['count'] * 2\n",
    "df_from_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Selecting Columns**\n",
    "\n",
    "In `datascience`, we used `select()` to subset the dataframe by selecting columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>points</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>a     </td> <td>1     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>c     </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>z     </td> <td>10    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "letter | points\n",
       "a      | 1\n",
       "b      | 2\n",
       "c      | 2\n",
       "z      | 10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.select(['letter', 'points'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use a double bracket notation to select columns. This returns a dataframe, unlike a Series object when we only use single bracket notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>doublecount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  doublecount\n",
       "0      9           18\n",
       "1      3            6\n",
       "2      3            6\n",
       "3      1            2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# double bracket notation for new dataframe\n",
    "df_from_dict[['count', 'doublecount']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering Rows Conditionally**\n",
    "\n",
    "In `datascience`, we used `where()` to select rows according to a given condition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>count</th> <th>points</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>c     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "letter | count | points\n",
       "b      | 3     | 2\n",
       "c      | 3     | 2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.where('points', 2) # rows where points == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>count</th> <th>points</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>c     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>z     </td> <td>1    </td> <td>10    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "letter | count | points\n",
       "b      | 3     | 2\n",
       "c      | 3     | 2\n",
       "z      | 1     | 10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.where(t['count'] < 8) # rows where count < 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we can use the bracket notation to subset the dataframe based on a condition. We first specify a condition and then subset using the bracket notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2        True\n",
       "3        True\n",
       "4       False\n",
       "5       False\n",
       "6       False\n",
       "7       False\n",
       "8        True\n",
       "9       False\n",
       "10       True\n",
       "11       True\n",
       "12       True\n",
       "13      False\n",
       "14      False\n",
       "15       True\n",
       "16       True\n",
       "17      False\n",
       "18       True\n",
       "19      False\n",
       "20       True\n",
       "21      False\n",
       "22      False\n",
       "23      False\n",
       "24       True\n",
       "25      False\n",
       "26       True\n",
       "27      False\n",
       "28       True\n",
       "29      False\n",
       "        ...  \n",
       "1144    False\n",
       "1145     True\n",
       "1146     True\n",
       "1147     True\n",
       "1148    False\n",
       "1149     True\n",
       "1150     True\n",
       "1151    False\n",
       "1152    False\n",
       "1153    False\n",
       "1154    False\n",
       "1155    False\n",
       "1156     True\n",
       "1157    False\n",
       "1158    False\n",
       "1159     True\n",
       "1160     True\n",
       "1161     True\n",
       "1162     True\n",
       "1163     True\n",
       "1164     True\n",
       "1165     True\n",
       "1166    False\n",
       "1167    False\n",
       "1168    False\n",
       "1169    False\n",
       "1170    False\n",
       "1171     True\n",
       "1172    False\n",
       "1173    False\n",
       "Name: Maternal.Smoker, Length: 1174, dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# array of booleans\n",
    "baby['Maternal.Smoker'] == True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Birth.Weight</th>\n",
       "      <th>Gestational.Days</th>\n",
       "      <th>Maternal.Age</th>\n",
       "      <th>Maternal.Height</th>\n",
       "      <th>Maternal.Pregnancy.Weight</th>\n",
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       "      <th>10</th>\n",
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       "      <th>11</th>\n",
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       "      <td>True</td>\n",
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       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>288</td>\n",
       "      <td>43</td>\n",
       "      <td>66</td>\n",
       "      <td>142</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>115</td>\n",
       "      <td>274</td>\n",
       "      <td>27</td>\n",
       "      <td>67</td>\n",
       "      <td>175</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>103</td>\n",
       "      <td>261</td>\n",
       "      <td>27</td>\n",
       "      <td>65</td>\n",
       "      <td>112</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>114</td>\n",
       "      <td>266</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>175</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>114</td>\n",
       "      <td>274</td>\n",
       "      <td>28</td>\n",
       "      <td>66</td>\n",
       "      <td>132</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>134</td>\n",
       "      <td>288</td>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "      <td>92</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>122</td>\n",
       "      <td>267</td>\n",
       "      <td>27</td>\n",
       "      <td>65</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>87</td>\n",
       "      <td>248</td>\n",
       "      <td>37</td>\n",
       "      <td>65</td>\n",
       "      <td>130</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>143</td>\n",
       "      <td>274</td>\n",
       "      <td>27</td>\n",
       "      <td>63</td>\n",
       "      <td>110</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>145</td>\n",
       "      <td>291</td>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>119</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>108</td>\n",
       "      <td>283</td>\n",
       "      <td>31</td>\n",
       "      <td>65</td>\n",
       "      <td>148</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>124</td>\n",
       "      <td>278</td>\n",
       "      <td>26</td>\n",
       "      <td>70</td>\n",
       "      <td>145</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>122</td>\n",
       "      <td>280</td>\n",
       "      <td>23</td>\n",
       "      <td>65</td>\n",
       "      <td>125</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>101</td>\n",
       "      <td>245</td>\n",
       "      <td>23</td>\n",
       "      <td>63</td>\n",
       "      <td>130</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>128</td>\n",
       "      <td>283</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>125</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>104</td>\n",
       "      <td>282</td>\n",
       "      <td>36</td>\n",
       "      <td>65</td>\n",
       "      <td>115</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>137</td>\n",
       "      <td>274</td>\n",
       "      <td>26</td>\n",
       "      <td>69</td>\n",
       "      <td>137</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>103</td>\n",
       "      <td>273</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>170</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>133</td>\n",
       "      <td>284</td>\n",
       "      <td>25</td>\n",
       "      <td>66</td>\n",
       "      <td>125</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>91</td>\n",
       "      <td>270</td>\n",
       "      <td>24</td>\n",
       "      <td>60</td>\n",
       "      <td>149</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>153</td>\n",
       "      <td>286</td>\n",
       "      <td>26</td>\n",
       "      <td>63</td>\n",
       "      <td>107</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>99</td>\n",
       "      <td>272</td>\n",
       "      <td>27</td>\n",
       "      <td>62</td>\n",
       "      <td>103</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>1117</td>\n",
       "      <td>124</td>\n",
       "      <td>289</td>\n",
       "      <td>21</td>\n",
       "      <td>67</td>\n",
       "      <td>145</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>1118</td>\n",
       "      <td>138</td>\n",
       "      <td>292</td>\n",
       "      <td>25</td>\n",
       "      <td>65</td>\n",
       "      <td>130</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>1119</td>\n",
       "      <td>88</td>\n",
       "      <td>276</td>\n",
       "      <td>25</td>\n",
       "      <td>63</td>\n",
       "      <td>103</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>1121</td>\n",
       "      <td>82</td>\n",
       "      <td>274</td>\n",
       "      <td>31</td>\n",
       "      <td>64</td>\n",
       "      <td>101</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>1124</td>\n",
       "      <td>97</td>\n",
       "      <td>269</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>137</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>1128</td>\n",
       "      <td>116</td>\n",
       "      <td>274</td>\n",
       "      <td>21</td>\n",
       "      <td>62</td>\n",
       "      <td>110</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>1129</td>\n",
       "      <td>132</td>\n",
       "      <td>302</td>\n",
       "      <td>36</td>\n",
       "      <td>63</td>\n",
       "      <td>145</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>1131</td>\n",
       "      <td>119</td>\n",
       "      <td>277</td>\n",
       "      <td>18</td>\n",
       "      <td>61</td>\n",
       "      <td>89</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>1132</td>\n",
       "      <td>106</td>\n",
       "      <td>312</td>\n",
       "      <td>24</td>\n",
       "      <td>62</td>\n",
       "      <td>135</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>1134</td>\n",
       "      <td>103</td>\n",
       "      <td>273</td>\n",
       "      <td>36</td>\n",
       "      <td>65</td>\n",
       "      <td>158</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1134</th>\n",
       "      <td>1135</td>\n",
       "      <td>112</td>\n",
       "      <td>299</td>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "      <td>145</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>1136</td>\n",
       "      <td>96</td>\n",
       "      <td>276</td>\n",
       "      <td>33</td>\n",
       "      <td>64</td>\n",
       "      <td>127</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>1137</td>\n",
       "      <td>102</td>\n",
       "      <td>281</td>\n",
       "      <td>19</td>\n",
       "      <td>67</td>\n",
       "      <td>135</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>1138</td>\n",
       "      <td>120</td>\n",
       "      <td>300</td>\n",
       "      <td>34</td>\n",
       "      <td>63</td>\n",
       "      <td>150</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>1140</td>\n",
       "      <td>97</td>\n",
       "      <td>255</td>\n",
       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>107</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1142</th>\n",
       "      <td>1143</td>\n",
       "      <td>97</td>\n",
       "      <td>260</td>\n",
       "      <td>25</td>\n",
       "      <td>63</td>\n",
       "      <td>115</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1145</th>\n",
       "      <td>1146</td>\n",
       "      <td>127</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>61</td>\n",
       "      <td>135</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>1147</td>\n",
       "      <td>87</td>\n",
       "      <td>247</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>125</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>1148</td>\n",
       "      <td>141</td>\n",
       "      <td>281</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "      <td>156</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149</th>\n",
       "      <td>1150</td>\n",
       "      <td>116</td>\n",
       "      <td>273</td>\n",
       "      <td>33</td>\n",
       "      <td>66</td>\n",
       "      <td>130</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>1151</td>\n",
       "      <td>75</td>\n",
       "      <td>265</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "      <td>103</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156</th>\n",
       "      <td>1157</td>\n",
       "      <td>81</td>\n",
       "      <td>285</td>\n",
       "      <td>19</td>\n",
       "      <td>63</td>\n",
       "      <td>150</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>1160</td>\n",
       "      <td>114</td>\n",
       "      <td>290</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "      <td>120</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>1161</td>\n",
       "      <td>124</td>\n",
       "      <td>288</td>\n",
       "      <td>21</td>\n",
       "      <td>64</td>\n",
       "      <td>116</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>1162</td>\n",
       "      <td>115</td>\n",
       "      <td>262</td>\n",
       "      <td>23</td>\n",
       "      <td>64</td>\n",
       "      <td>136</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1162</th>\n",
       "      <td>1163</td>\n",
       "      <td>143</td>\n",
       "      <td>281</td>\n",
       "      <td>28</td>\n",
       "      <td>65</td>\n",
       "      <td>135</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1163</th>\n",
       "      <td>1164</td>\n",
       "      <td>113</td>\n",
       "      <td>287</td>\n",
       "      <td>29</td>\n",
       "      <td>70</td>\n",
       "      <td>145</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>1165</td>\n",
       "      <td>109</td>\n",
       "      <td>244</td>\n",
       "      <td>21</td>\n",
       "      <td>63</td>\n",
       "      <td>102</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>1166</td>\n",
       "      <td>103</td>\n",
       "      <td>278</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>87</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>1172</td>\n",
       "      <td>130</td>\n",
       "      <td>291</td>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>459 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  Birth.Weight  Gestational.Days  Maternal.Age  \\\n",
       "2              3           128               279            28   \n",
       "3              4           108               282            23   \n",
       "8              9           143               299            30   \n",
       "10            11           144               282            32   \n",
       "11            12           141               279            23   \n",
       "12            13           110               281            36   \n",
       "15            16            92               255            25   \n",
       "16            17           115               261            33   \n",
       "18            19           119               288            43   \n",
       "20            21           115               274            27   \n",
       "24            25           103               261            27   \n",
       "26            27           114               266            20   \n",
       "28            29           114               274            28   \n",
       "36            37           134               288            23   \n",
       "37            38           122               267            27   \n",
       "41            42            87               248            37   \n",
       "42            43           143               274            27   \n",
       "46            47           145               291            26   \n",
       "48            49           108               283            31   \n",
       "53            54           124               278            26   \n",
       "59            60           122               280            23   \n",
       "60            61           101               245            23   \n",
       "61            62           128               283            28   \n",
       "62            63           104               282            36   \n",
       "64            65           137               274            26   \n",
       "65            66           103               273            31   \n",
       "69            70           133               284            25   \n",
       "71            72            91               270            24   \n",
       "73            74           153               286            26   \n",
       "76            77            99               272            27   \n",
       "...          ...           ...               ...           ...   \n",
       "1116        1117           124               289            21   \n",
       "1117        1118           138               292            25   \n",
       "1118        1119            88               276            25   \n",
       "1120        1121            82               274            31   \n",
       "1123        1124            97               269            20   \n",
       "1127        1128           116               274            21   \n",
       "1128        1129           132               302            36   \n",
       "1130        1131           119               277            18   \n",
       "1131        1132           106               312            24   \n",
       "1133        1134           103               273            36   \n",
       "1134        1135           112               299            24   \n",
       "1135        1136            96               276            33   \n",
       "1136        1137           102               281            19   \n",
       "1137        1138           120               300            34   \n",
       "1139        1140            97               255            22   \n",
       "1142        1143            97               260            25   \n",
       "1145        1146           127               242            17   \n",
       "1146        1147            87               247            18   \n",
       "1147        1148           141               281            29   \n",
       "1149        1150           116               273            33   \n",
       "1150        1151            75               265            21   \n",
       "1156        1157            81               285            19   \n",
       "1159        1160           114               290            21   \n",
       "1160        1161           124               288            21   \n",
       "1161        1162           115               262            23   \n",
       "1162        1163           143               281            28   \n",
       "1163        1164           113               287            29   \n",
       "1164        1165           109               244            21   \n",
       "1165        1166           103               278            30   \n",
       "1171        1172           130               291            30   \n",
       "\n",
       "      Maternal.Height  Maternal.Pregnancy.Weight  Maternal.Smoker  \n",
       "2                  64                        115             True  \n",
       "3                  67                        125             True  \n",
       "8                  66                        136             True  \n",
       "10                 64                        124             True  \n",
       "11                 63                        128             True  \n",
       "12                 61                         99             True  \n",
       "15                 65                        125             True  \n",
       "16                 60                        125             True  \n",
       "18                 66                        142             True  \n",
       "20                 67                        175             True  \n",
       "24                 65                        112             True  \n",
       "26                 65                        175             True  \n",
       "28                 66                        132             True  \n",
       "36                 63                         92             True  \n",
       "37                 65                        101             True  \n",
       "41                 65                        130             True  \n",
       "42                 63                        110             True  \n",
       "46                 63                        119             True  \n",
       "48                 65                        148             True  \n",
       "53                 70                        145             True  \n",
       "59                 65                        125             True  \n",
       "60                 63                        130             True  \n",
       "61                 63                        125             True  \n",
       "62                 65                        115             True  \n",
       "64                 69                        137             True  \n",
       "65                 63                        170             True  \n",
       "69                 66                        125             True  \n",
       "71                 60                        149             True  \n",
       "73                 63                        107             True  \n",
       "76                 62                        103             True  \n",
       "...               ...                        ...              ...  \n",
       "1116               67                        145             True  \n",
       "1117               65                        130             True  \n",
       "1118               63                        103             True  \n",
       "1120               64                        101             True  \n",
       "1123               65                        137             True  \n",
       "1127               62                        110             True  \n",
       "1128               63                        145             True  \n",
       "1130               61                         89             True  \n",
       "1131               62                        135             True  \n",
       "1133               65                        158             True  \n",
       "1134               67                        145             True  \n",
       "1135               64                        127             True  \n",
       "1136               67                        135             True  \n",
       "1137               63                        150             True  \n",
       "1139               63                        107             True  \n",
       "1142               63                        115             True  \n",
       "1145               61                        135             True  \n",
       "1146               66                        125             True  \n",
       "1147               54                        156             True  \n",
       "1149               66                        130             True  \n",
       "1150               65                        103             True  \n",
       "1156               63                        150             True  \n",
       "1159               65                        120             True  \n",
       "1160               64                        116             True  \n",
       "1161               64                        136             True  \n",
       "1162               65                        135             True  \n",
       "1163               70                        145             True  \n",
       "1164               63                        102             True  \n",
       "1165               60                         87             True  \n",
       "1171               65                        150             True  \n",
       "\n",
       "[459 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter rows by condition Maternal.Smoker == True\n",
    "baby[baby['Maternal.Smoker'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>letter</th>\n",
       "      <th>points</th>\n",
       "      <th>newcol</th>\n",
       "      <th>doublecount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>z</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count letter  points  newcol  doublecount\n",
       "3      1      z      10       8            2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering with multiple conditions\n",
    "df_from_dict[(df_from_dict['count'] < 8) & (df_from_dict['points'] > 5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Renaming Columns**\n",
    "\n",
    "In `datascience`, we used `relabeled()` to rename columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>count</th> <th>other name</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>a     </td> <td>9    </td> <td>1         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>3    </td> <td>2         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>c     </td> <td>3    </td> <td>2         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>z     </td> <td>1    </td> <td>10        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "letter | count | other name\n",
       "a      | 9     | 1\n",
       "b      | 3     | 2\n",
       "c      | 3     | 2\n",
       "z      | 1     | 10"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename 'points' to 'other name'\n",
    "t.relabeled('points', 'other name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas uses `rename()`, which has an `index` parameter that needs to be set to `str` and a `columns` parameter that needs to be set to a dictionary of the names to be replaced with their replacements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>letter</th>\n",
       "      <th>other name</th>\n",
       "      <th>newcol</th>\n",
       "      <th>doublecount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>z</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count letter  other name  newcol  doublecount\n",
       "0      9      a           1       5           18\n",
       "1      3      b           2       6            6\n",
       "2      3      c           2       7            6\n",
       "3      1      z          10       8            2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename 'points' to 'other name'\n",
    "df_from_dict.rename(index = str, columns = {\"points\" : \"other name\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sorting Dataframe by Column**\n",
    "\n",
    "In `datascience` we used `sort()` to sort a Table according to the values in a column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>letter</th> <th>count</th> <th>points</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>z     </td> <td>1    </td> <td>10    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>b     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>c     </td> <td>3    </td> <td>2     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>a     </td> <td>9    </td> <td>1     </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "letter | count | points\n",
       "z      | 1     | 10\n",
       "b      | 3     | 2\n",
       "c      | 3     | 2\n",
       "a      | 9     | 1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by count\n",
    "t.sort('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use the `sort_values()` to sort by column. We need the `by` parameter to specify the row we want to sort by and the optional parameter `ascending = False` if we want to sort in descending order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>letter</th>\n",
       "      <th>points</th>\n",
       "      <th>newcol</th>\n",
       "      <th>doublecount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>z</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count letter  points  newcol  doublecount\n",
       "0      9      a       1       5           18\n",
       "1      3      b       2       6            6\n",
       "2      3      c       2       7            6\n",
       "3      1      z      10       8            2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort by count, descending\n",
    "df_from_dict.sort_values(by = ['count'], ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Grouping and Aggregating**\n",
    "\n",
    "In `datascience`, we used `group()` and the `collect` argument to group a Table by a column and aggregrate values in another column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>count</th> <th>points sum</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1    </td> <td>10        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>3    </td> <td>4         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>9    </td> <td>1         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "count | points sum\n",
       "1     | 10\n",
       "3     | 4\n",
       "9     | 1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by count and aggregate by sum\n",
    "t.select(['count', 'points']).group('count', collect=sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use `groupby()` to group the dataframe. This function returns a groupby object, on which we can then call an aggregation function to return a dataframe with aggregated values for other columns. For more information, refer to the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  points\n",
       "0      9       1\n",
       "1      3       2\n",
       "2      3       2\n",
       "3      1      10"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# selecting two columns for brevity\n",
    "df_subset = df_from_dict[['count', 'points']]\n",
    "df_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       points\n",
       "count        \n",
       "1          10\n",
       "3           4\n",
       "9           1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_sums_df = df_subset.groupby(['count']).sum()\n",
    "count_sums_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pivot Tables**\n",
    "\n",
    "In `datascience`, we used the `pivot()` function to build contingency tables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Flavor</th> <th>Color</th> <th>Price</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>strawberry</td> <td>pink       </td> <td>3.55 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chocolate </td> <td>light brown</td> <td>4.75 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chocolate </td> <td>dark brown </td> <td>5.25 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>strawberry</td> <td>pink       </td> <td>5.25 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chocolate </td> <td>dark brown </td> <td>5.25 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>bubblegum </td> <td>pink       </td> <td>4.75 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Flavor     | Color       | Price\n",
       "strawberry | pink        | 3.55\n",
       "chocolate  | light brown | 4.75\n",
       "chocolate  | dark brown  | 5.25\n",
       "strawberry | pink        | 5.25\n",
       "chocolate  | dark brown  | 5.25\n",
       "bubblegum  | pink        | 4.75"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating new Table\n",
    "cones_tbl = Table().with_columns(\n",
    "    'Flavor', make_array('strawberry', 'chocolate', 'chocolate', 'strawberry', 'chocolate', 'bubblegum'),\n",
    "    'Color', make_array('pink', 'light brown', 'dark brown', 'pink', 'dark brown', 'pink'),\n",
    "    'Price', make_array(3.55, 4.75, 5.25, 5.25, 5.25, 4.75)\n",
    ")\n",
    "\n",
    "cones_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Color</th> <th>bubblegum</th> <th>chocolate</th> <th>strawberry</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>dark brown </td> <td>0        </td> <td>2        </td> <td>0         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>light brown</td> <td>0        </td> <td>1        </td> <td>0         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>pink       </td> <td>1        </td> <td>0        </td> <td>2         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Color       | bubblegum | chocolate | strawberry\n",
       "dark brown  | 0         | 2         | 0\n",
       "light brown | 0         | 1         | 0\n",
       "pink        | 1         | 0         | 2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivoting on color and flavor\n",
    "cones_tbl.pivot(\"Flavor\", \"Color\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also pass in the parameters `values` to specify the values in the table and `collect` to specify the aggregration function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Color</th> <th>bubblegum</th> <th>chocolate</th> <th>strawberry</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>dark brown </td> <td>0        </td> <td>10.5     </td> <td>0         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>light brown</td> <td>0        </td> <td>4.75     </td> <td>0         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>pink       </td> <td>4.75     </td> <td>0        </td> <td>8.8       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Color       | bubblegum | chocolate | strawberry\n",
       "dark brown  | 0         | 10.5      | 0\n",
       "light brown | 0         | 4.75      | 0\n",
       "pink        | 4.75      | 0         | 8.8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setting parameters values and collect\n",
    "cones_tbl.pivot(\"Flavor\", \"Color\", values = \"Price\", collect = np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use `pd.pivot_table()` to create a contingency table. The argument `columns` is similar to the first argument in `datascience`'s `pivot` function and sets the column names of the pivot table. The argument `index` is similar to the second argument in `datascience`'s `pivot` function and sets the first column of the pivot table or the keys to group on. For more information, refer to the [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.pivot_table.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Color</th>\n",
       "      <th>Flavor</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pink</td>\n",
       "      <td>strawberry</td>\n",
       "      <td>3.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>light brown</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dark brown</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pink</td>\n",
       "      <td>strawberry</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dark brown</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pink</td>\n",
       "      <td>bubblegum</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Color      Flavor  Price\n",
       "0         pink  strawberry   3.55\n",
       "1  light brown   chocolate   4.75\n",
       "2   dark brown   chocolate   5.25\n",
       "3         pink  strawberry   5.25\n",
       "4   dark brown   chocolate   5.25\n",
       "5         pink   bubblegum   4.75"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating new dataframe\n",
    "cones_df = pd.DataFrame({\"Flavor\" : ['strawberry', 'chocolate', 'chocolate', 'strawberry', 'chocolate', 'bubblegum'],\n",
    "                         \"Color\" : ['pink', 'light brown', 'dark brown', 'pink', 'dark brown', 'pink'],\n",
    "                         \"Price\" : [3.55, 4.75, 5.25, 5.25, 5.25, 4.75]})\n",
    "cones_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flavor</th>\n",
       "      <th>bubblegum</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>strawberry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dark brown</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>light brown</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pink</th>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Price                     \n",
       "Flavor      bubblegum chocolate strawberry\n",
       "Color                                     \n",
       "dark brown        NaN      5.25        NaN\n",
       "light brown       NaN      4.75        NaN\n",
       "pink             4.75       NaN        4.4"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating the pivot table\n",
    "pd.pivot_table(cones_df, columns = [\"Flavor\"], index = [\"Color\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there is no data in the groups, then Pandas will output `NaN` values. \n",
    "\n",
    "We can also specify the parameters like `values` (equivalent to `values` in `datascience`'s `pivot`) and `aggfunc` (equivalent to `collect` in `datascience`'s `pivot`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Flavor</th>\n",
       "      <th>bubblegum</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>strawberry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dark brown</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>light brown</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pink</th>\n",
       "      <td>4.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Flavor       bubblegum  chocolate  strawberry\n",
       "Color                                        \n",
       "dark brown         NaN      10.50         NaN\n",
       "light brown        NaN       4.75         NaN\n",
       "pink              4.75        NaN         8.8"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# additional arguments\n",
    "pd.pivot_table(cones_df, columns = [\"Flavor\"], index = [\"Color\"], values = \"Price\", aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Joining/Merging**\n",
    "\n",
    "In `datascience`, we used `join()` to join two tables based on shared values in columns. We specify the column name in the first table to match on, the name of the second table and the column name in the second table to match on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Kind</th> <th>Stars</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>strawberry</td> <td>2.5  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chocolate </td> <td>3.5  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>vanilla   </td> <td>4    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Kind       | Stars\n",
       "strawberry | 2.5\n",
       "chocolate  | 3.5\n",
       "vanilla    | 4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating new table\n",
    "ratings_tbl = Table().with_columns(\n",
    "    'Kind', make_array('strawberry', 'chocolate', 'vanilla'),\n",
    "    'Stars', make_array(2.5, 3.5, 4)\n",
    ")\n",
    "ratings_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Flavor</th> <th>Color</th> <th>Price</th> <th>Stars</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>chocolate </td> <td>light brown</td> <td>4.75 </td> <td>3.5  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chocolate </td> <td>dark brown </td> <td>5.25 </td> <td>3.5  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>chocolate </td> <td>dark brown </td> <td>5.25 </td> <td>3.5  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>strawberry</td> <td>pink       </td> <td>3.55 </td> <td>2.5  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>strawberry</td> <td>pink       </td> <td>5.25 </td> <td>2.5  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Flavor     | Color       | Price | Stars\n",
       "chocolate  | light brown | 4.75  | 3.5\n",
       "chocolate  | dark brown  | 5.25  | 3.5\n",
       "chocolate  | dark brown  | 5.25  | 3.5\n",
       "strawberry | pink        | 3.55  | 2.5\n",
       "strawberry | pink        | 5.25  | 2.5"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# joining cones and ratings\n",
    "cones_tbl.join(\"Flavor\", ratings_tbl, \"Kind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we can use the `merge()` function to join two tables together. The first parameter is the name of the second table to join on. The parameters `left_on` and `right_on` specify the columns to use in the left and right tables respectively. There are more parameters such as `how` which specify what kind of join to perform (Inner (Default), Outer, Left, Right). For more information, refer to this [Kaggle Tutorial](https://www.kaggle.com/crawford/python-merge-tutorial/notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kind</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>strawberry</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chocolate</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>vanilla</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Kind  Stars\n",
       "0  strawberry    2.5\n",
       "1   chocolate    3.5\n",
       "2     vanilla    4.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating new ratings df\n",
    "ratings_df = pd.DataFrame({\"Kind\" : ['strawberry', 'chocolate', 'vanilla'],\n",
    "                           \"Stars\" : [2.5, 3.5, 4]})\n",
    "ratings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Color</th>\n",
       "      <th>Flavor</th>\n",
       "      <th>Price</th>\n",
       "      <th>Kind</th>\n",
       "      <th>Stars</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pink</td>\n",
       "      <td>strawberry</td>\n",
       "      <td>3.55</td>\n",
       "      <td>strawberry</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pink</td>\n",
       "      <td>strawberry</td>\n",
       "      <td>5.25</td>\n",
       "      <td>strawberry</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>light brown</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>4.75</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dark brown</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>5.25</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dark brown</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>5.25</td>\n",
       "      <td>chocolate</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Color      Flavor  Price        Kind  Stars\n",
       "0         pink  strawberry   3.55  strawberry    2.5\n",
       "1         pink  strawberry   5.25  strawberry    2.5\n",
       "2  light brown   chocolate   4.75   chocolate    3.5\n",
       "3   dark brown   chocolate   5.25   chocolate    3.5\n",
       "4   dark brown   chocolate   5.25   chocolate    3.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging cones and ratings\n",
    "cones_df.merge(ratings_df, left_on = \"Flavor\", right_on = \"Kind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Visualizing Data <a id='visualizing'> </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In `datascience`, we learned to plot data using histograms, line plots, scatter plots and histograms. The corresponding functions were `hist()`, `plot()`, `scatter()` and `barh()`. Plotting methods in Pandas are nearly identical to `datascience` since both build on the library `matplotlib`\n",
    "\n",
    "In this section we will go through examples of such plots in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='histograms'></a>**3.1 Histograms**\n",
    "\n",
    "In `datascience`, we used `hist()` to create a histogram. In this example, we will be using data from `baby.csv`. Recall that the baby data set contains data on a random sample of 1,174 mothers and their newborn babies. The column `birthwt` contains the birth weight of the baby, in ounces; `gest_days` is the number of gestational days, that is, the number of days the baby was in the womb. There is also data on maternal age, maternal height, maternal pregnancy weight, and whether or not the mother was a smoker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing matplotlib for plotting\n",
    "import matplotlib\n",
    "matplotlib.use('Agg', warn=False)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Unnamed: 0</th> <th>Birth.Weight</th> <th>Gestational.Days</th> <th>Maternal.Age</th> <th>Maternal.Height</th> <th>Maternal.Pregnancy.Weight</th> <th>Maternal.Smoker</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1         </td> <td>120         </td> <td>284             </td> <td>27          </td> <td>62             </td> <td>100                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>2         </td> <td>113         </td> <td>282             </td> <td>33          </td> <td>64             </td> <td>135                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>3         </td> <td>128         </td> <td>279             </td> <td>28          </td> <td>64             </td> <td>115                      </td> <td>True           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>4         </td> <td>108         </td> <td>282             </td> <td>23          </td> <td>67             </td> <td>125                      </td> <td>True           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>5         </td> <td>136         </td> <td>286             </td> <td>25          </td> <td>62             </td> <td>93                       </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>6         </td> <td>138         </td> <td>244             </td> <td>33          </td> <td>62             </td> <td>178                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>7         </td> <td>132         </td> <td>245             </td> <td>23          </td> <td>65             </td> <td>140                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>8         </td> <td>120         </td> <td>289             </td> <td>25          </td> <td>62             </td> <td>125                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>9         </td> <td>143         </td> <td>299             </td> <td>30          </td> <td>66             </td> <td>136                      </td> <td>True           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>10        </td> <td>140         </td> <td>351             </td> <td>27          </td> <td>68             </td> <td>120                      </td> <td>False          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (1164 rows omitted)</p>"
      ],
      "text/plain": [
       "Unnamed: 0 | Birth.Weight | Gestational.Days | Maternal.Age | Maternal.Height | Maternal.Pregnancy.Weight | Maternal.Smoker\n",
       "1          | 120          | 284              | 27           | 62              | 100                       | False\n",
       "2          | 113          | 282              | 33           | 64              | 135                       | False\n",
       "3          | 128          | 279              | 28           | 64              | 115                       | True\n",
       "4          | 108          | 282              | 23           | 67              | 125                       | True\n",
       "5          | 136          | 286              | 25           | 62              | 93                        | False\n",
       "6          | 138          | 244              | 33           | 62              | 178                       | False\n",
       "7          | 132          | 245              | 23           | 65              | 140                       | False\n",
       "8          | 120          | 289              | 25           | 62              | 125                       | False\n",
       "9          | 143          | 299              | 30           | 66              | 136                       | True\n",
       "10         | 140          | 351              | 27           | 68              | 120                       | False\n",
       "... (1164 rows omitted)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading in the data\n",
    "datascience_baby = Table.read_table('baby.csv')\n",
    "datascience_baby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116a57dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a histogram\n",
    "datascience_baby.hist('Birth.Weight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use `hist()` to create histograms, just like `datascience`. Refer to the [documentation](https://pandas.pydata.org/pandas-docs/version/0.21/generated/pandas.DataFrame.hist.html) for a full list of parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x110a37c88>]], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a0a0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a histogram\n",
    "baby.hist('Birth.Weight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='line'></a>**3.2 Line Plots**\n",
    "\n",
    "In `datascience`, we used `plot()` to create a line plot of numerical values. In this example, we will be using census data and plot variables such as Age in a line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b806a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot in datascience\n",
    "census_tbl = Table.read_table(\"https://raw.githubusercontent.com/data-8/materials-x18/master/lec/x18/1/census.csv\").select(['SEX', 'AGE', 'POPESTIMATE2014'])\n",
    "children_tbl = census_tbl.where('SEX', are.equal_to(0)).where('AGE', are.below(19)).drop('SEX')\n",
    "children_tbl.plot('AGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we can use `plot.line()` to create line plots. For a full list of parameters, refer to the [documentation](http://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.DataFrame.plot.line.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11cce10f0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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obIbo3CPzSDEvf5HOxKHdGHOW992wsimp7W9AF+AbEUkVkd875UuA3UA68HfgTgBVzQP+CKx1Ho87ZQB34BrcTwd2AR875U8CV4jITuAK5/Up92GM8U8bM/PZdqCw2eYznzCkG0EB4lMD8X9ashUReORq75xiulGXTKrqF8AXzvM613XOyLrrFMtmA7PrKE8BBtdRngtc1ph9GGP8z7yUfYQFBzApsXkuzusYHsLYhGgWbcjigSvPbtTFj56wKv0wSzYd4L4r+tG9fRtPh1Mn7+p0M8aYkxSXVbAwNYurB3cjKiy42babnBjL/qPHWbf3SLNtsyVUVFbx2KIt9OjYhv+88CxPh3NKlkyMMV5tyaYDFJVWcH0TB95PdsXALoQFB3j9/PBvffs9Ow4W8d8TBhIW7L1zsVgyMcZ4tflr9xHfqS2je3ds1u2GhwZxxcCu/GtjNuWVP7qEzivkFpXyzLIdjE2IZtzAM7+2xh0smRhjvNbunCLW7Mnj+nN6tMj9p5KHdedIcTlf7fS+SbMqq5Qn/rWV42WVPHrN6WeT9AaWTIwxXmt+SiaBAcKUEY27qWNDXdgvhvZtg72uq+tQQQk3vbaaD9bv546L+9C3c6SnQ6pXq54D3hjjvSoqq3jvu0wuOTuGzlFhLbKPkKAArh7SjQ/X76e4rIK2IZ7/Svz3jhzum59KUWkFT/3HkGY7HbqlWcvEGOOVVmzPIaewtMW/TJOHdae4rJJlaQdbdD/1Ka+s4qlPtjF99ho6hoewcOYFTD2np9d3b1XzfBo2xpg6zFu7j+iIUC7p37n+yk1wTnxHurULY2FqFsmJnrlfbOaRYu5+Zz3f7T3KDaN68PuJg2gT4r1nbtXFkokxxuscKihhxfZD/GJsb4Jb+B5UAQGuSbNe+yqDI8fK6ODmO/Eu3XKA3y7YQJXCCzcM94pZE8+EdXMZY7zOe9/tp7JK3TZeMCmxOxVVypLN7rvFX0l5JY9+tJnb31pHr07hLP7VBT6bSMCSiTHGy6gqC1L2cU58B/rERLhlnwO7RdG3c4Tb7tW1O6eIn7y8ijnffM+MC3rz3h3nER8d7pZ9txRLJsYYr7J2zxF2Hz7m1rOYRITkYd1Zk5FH1tHjLbqvD9Zncs1fvyIr/ziv3pzE7yYOJCTI97+Kff8dGGP8yry1+4gIDWLCUPfOxF19E8lFLTRpVnFZBb9dsIHfzNvAwO5RLLl7LJd7+VXtjWHJxBjjNQpLylmyKZtrhnVz+zUfvTqFk9ijfYt0dW07UMCkF7/m3e8y+dWlfXnnP8d47d1/z5QlE2OM1/jXxmyOl1d67EK95MTupGUXsPNg80yapaq8vfp7kl/8mvzj5fxjxmjuG3e2182S2Bz87x0ZY3zWv3fkENu+DYk92ntk/xOHdidAmmd++Pzj5cx8Zz3/9cFmRvXuyJK7x3J+3+hmiNI72XUmxhivoKqsycjjorNjPHbVd0xkKOf3jeaj1CzuvaJfvXGoKoeLytiVU8TunGPsyimqeb7vSDEBIjw4vj+3X3iW10/A1VSWTIwxXmFXzjFyj5U1+63mGys5MZb7F2xg/b6jjOjZAYCyiir25h0j/dCxExLH7pwiCkoqatYNCw7grOgIhvVoz+ThsVw+oAtD4tp56q24lSUTY4xXWJORB8Co3p08GseVg7rwyAcBPL4ojeiIEHblHGNvXjGVVVpTp0tUKH1iIkhOjOWsmHD6xETQp3ME3aLC/L4FciqWTIwxXmFNRi4xkaHEd2rr0Tgiw4KZnBjLB6n76d0pnAHdIpk4tFtN0ugdHU5kM04f7C8smRhjvMLaPUcY1bujV9wl96kpQ/l/PxnSalsZZ8LO5jLGeFzmkWL2Hz3u8fGS2iyRNI4lE2OMx/0wXuI9ycQ0jiUTY4zHrcnIo12bYPr5wPS0pm6WTIwxHrcmI49z4jta15IPs2RiWqW9ucX87NXVLEjZ5+lQWr1DhSXsPnzMq8ZLTOM1OJmISKCIrBeRxc7rmSKSLiIqItG16l0sIvkikuo8fl9r2XgR2e6s91Ct8t4islpEdorIPBEJccpDndfpzvL4Wus87JRvF5Erm3YYTGvy7x05XPPiV3yVfpj/+nAz2w80z32YzJlZm3EEsPESX9eYlsk9wNZar78GLge+r6Pul6qa6DweB1cyAl4CrgIGAjeIyECn/lPAs6qaABwBZjjlM4AjqtoXeNaph7PeNGAQMB542dm+MaekqrzyxS5+/voaurUL4/07zyMqLIh75q6ntKLS0+G1WmsycmkbEsig7lGeDsU0QYOSiYjEAROAV6vLVHW9qu5pxL5GAemqultVy4C5QLK4Tiq/FHjXqTcHuNZ5nuy8xll+mVM/GZirqqWqmgGkO9s3pk7HSiuY+c/1PPXJNq4a0o337zyPET078Ocpw9h2oJA/f7Ld0yG2Wqsz8hjZq4Nf3km3NWnob+854AGgqoH1zxWRDSLysYgMcspigdod1JlOWSfgqKpWnFR+wjrO8nyn/qm2ZcyP7Dl8jJ+8vIqPN2fz8FX9efGG4TVzZVzSvzM3jenFq19l8NXOwx6OtPU5WlzG9oOFNl7iB+pNJiIyETikqusauM3vgF6qOgz4K/Bh9abqqKunKT/TdWqIyG0ikiIiKTk5OfUGbvzPiu2HmPTiVxwsLGHOraO4/aI+P7rC+pGrB9AnJpz7FqRytLjMQ5G2Til7jqDq+ftxmaZrSMvkfGCSiOzB1TV1qYj841SVVbVAVYuc50uAYGeAPhOoPeNNHJAFHAbai0jQSeXUXsdZ3g7IO822To5llqomqWpSTExMA96q8Reqyksr0rn1jbXEdmjLopkXMDah7s9Am5BAnp82nLxjZTzywSZUf/R/iWkha/bkERIYwNBWcmddf1ZvMlHVh1U1TlXjcQ16L1fVn52qvoh0dcY1EJFRzj5ygbVAgnPmVoizrYXq+stdAUxxNjEd+Mh5vtB5jbN8uVN/ITDNOdurN5AArGnE+zZ+rKi0gjvf/o4/L93ONUO78/4d59Gj4+lvHjg4th33XnE2SzYd4N11mW6K1KzOyCOxR3vCgu38GV93xiNeInK3iGTiahVsFJHqwfkpwGYR2QC8AExTlwpgJrAU11lh81V1i7POg8C9IpKOa0zkNaf8NaCTU34v8BCAs958IA34BLhLVe10HEPG4WNMfulrlm45wH9PGMDz0xJpE9KwL6rbLjyL0b078tjCLXyfe6yFIzXHSivYvD/fTgn2E9JamvRJSUmakpLi6TBMC1qx7RB3z11PUIDw4o0jzmiK1P1HjzP+uZUkdI5g/u3n2hlGLejLnTnc9Noa3rx1FBf2s25obyUi61Q1qb569pdifF5VlfLi8p3cOmctPTq0ZeHMC854ru3Y9m144trBfLf3KC+t2NXMkZra1mTkERggjOjVwdOhmGZg85kYn1ZUWsF981NZuuUg1yZ25//9ZGiDu7VOJTkxlhXbDvHC8p2M7RddM3WraV6rM/IY3D2KiFD7GvIH1jIxPmt3ThHXvvQ1n209xO8mDuTZqQ0fH6nPH5IH0zUqjN/MS+VYaUX9K5hGKSmvJHXfURsv8SOWTIxP+mL7IZJf/Jq8Y2W8NWMUMy7o3awz9LVrE8wz1w9jb14xjy9Ka7btGpeNmfmUVVTZ9SV+xJKJ8Tmqym/f3Uj39m1YOPN8zutzZuMj9Rl9VifuuKgP81L28cnmAy2yj9ZqTUYuAOfEWxeiv7BkYnzOocJScgpLuXF0T+I6nP76kab69eX9GBLbjofe38jBgpIW3Vdrsjojj/5dI2nfNsTToZhmYsnE+JwtWfkADHTDXWZDggJ4dmoiJeWV3L9gA1VVreNU+pZUUVnFuu+P2HiJn7FkYnxOWlYBAP27umeK176dI/jvCQP5cudh3li1xy379GdbsgooLqu0ZOJnLJkYn5OWXUCvTm2JDAt22z5/Oronl/XvzJOfbGPbgQK37dcfrcnIA2BUvCUTf2LJxPictKwCt0+kJCI8NWUoUWFB/HpuKiXldveeM7U6I4/e0eF0jgrzdCimGVkyMT6lsKScPbnFDOzm/ln5oiNCaybTenqpTaZ1JqqqlLV78qxV4ocsmRifss2Zr90dg+91scm0mmbHoULyj5fbeIkfsmRifEr14PvAbp6b/6L2ZFpHjtlkWo1RM15iycTvWDIxPiUtq4CO4SF0iQr1WAw2mdaZW52RR/d2YcR1aOPpUEwzs2RifEpatmvwvTlvnXImqifT+njzAR58byPZ+cc9Go8vUFXWZOQxqndHj//+TPOzZGJ8RnllFdsPFHpk8L0ut114Free35sP1u/noj9/wR8WbSGnsNTTYXmtPbnF5BSW2v24/JQlE+MzduUUUVZZ5bHB95MFBgi/v2YgK+6/mMmJsbz5zfdc+L8reOqTbRwtdt9Yiqr6RBKrvh+XjZf4J0smxmf8MPjuHcmkWlyHtjw1ZSjLfnMh4wZ14W//3sXYp1bw3Gc7KCwpb7H9Zhw+xrPLdnDpX/7NOf/zGf/49vsW21dzWJ2RR6fwEPrEhHs6FNMCbFYa4zPSsgoIDQqgd7R3fhmdFRPB89OGc+fFfXlm2Xae+2wnb6zawy8v6sPN5/aibUjT/9wOFZSwaGM2H6XuZ2NmPiIwpncnoiNCeGzhFvrERHBuH+/sRrLxEv9mycT4jLTsAvp3i/L6ednP7hrJ/92UxKbMfP6ybDtPfryNV7/M4K5L+nDj6J6EBjVuAq+CknI+2XyAhalZrNp1mCqFQd2j+K+rBzBxWDe6tWtDYUk5k19exZ1vr+Ojuy6gZ6eWvZtyY+0/epzMI8eZcUFvT4diWoglE+MTVJUtWQVcPaSbp0NpsCFx7Xjj56NI2ZPH059u5w+L0vj7yt386rIEpoyMI/g0SbG0opIV23L4KHU/n287RFlFFT07tuWuS/qSnNidvp1PvMllZFgwr96cRPJLX/Ofb6bw3p3nedV0uGvt+hK/5z2fNmNOIyu/hPzj5V4z+N4YSfEdeec/x7BqVy5/Xrqdh9/fxCtf7OLXlyeQnBhLYICr26eySlmdkctH67NYsjmbwpIKoiNCuHFUTyYldmd4j/an7SKKjw7n5Z+O4ObZa/j13FRm3TSSgADv6FJanZFHZFgQ/bv63u/PNIwlE+MTvHXwvaFEhPP7RnNen06s2H6Ip5fu4N75G3j5i13cNvYsdh4qZOGGLA4WlBIeEsiVg7qSPDyW8/t0alS33vl9o/ndhAE8tiiNvyzbzm+v7N+C76rh1mTkck58x5rEafyPJRPjE9KyChCBAd3cM4dJSxERLu3fhYv7deaTLQd4ZtkOHnhvI8GBwkX9OvPfE7pz+YAutAlp3LhKbdPPi2f7wUJeWrGLfl0iSU6MbcZ30HiHi0rZlXOM65J6eDQO07IsmRifkJadT+/o8GY5I8obBAQIVw/pxpWDuvLd3iP0jYmgQ3jzTGErIvxh0mB2HTrGA+9upHd0OEPj2jfLts+EjZe0Dt59Wowxji1ZBT7bxXU6gQHCOfEdmy2RVAsJCuCVn40gOiKU295cxyEPzl+/OiOPNsGBDO7uuZtzmpbX4GQiIoEisl5EFjuvZ4pIuoioiETXqici8oKzbKOIjKi1bLqI7HQe02uVjxSRTc46L4gzyigiHUVkmVN/mYh0qG8fxv/kHy8n88hxnxx896ROEaG8Oj2JgpJybntrnccm9FqTkceIXu0JCbL/Xf1ZY3679wBba73+GrgcOPmy26uABOdxG/AKuBID8CgwGhgFPFqdHJw6t9Vab7xT/hDwuaomAJ87r0+5D+Oftmb79uC7Jw3oFsUz1yeSuu8oj7zv/jsc5x8vZ+uBAkbFe+eFlKb5NCiZiEgcMAF4tbpMVder6p46qicDb6rLt0B7EekGXAksU9U8VT0CLAPGO8uiVPUbdX3S3wSurbWtOc7zOSeV17UP44eqz+QaZN0kZ2T84K7ce0U/3l+/n1krd7t13+u+z0PVxktag4a2TJ4DHgCqGlA3FthX63WmU3a68sw6ygG6qGo2gPOzcz37OIGI3CYiKSKSkpOT04DQjTfaklVATGQoMZGem8PE1/3q0r5MGNKNJz/Zxopth9y239UZeQQHCsNs/A/KAAAZlUlEQVR7eu4EAOMe9SYTEZkIHFLVdQ3cZl0nkusZlJ/JPk4sUJ2lqkmqmhQTE1PPJo23Ssv2z8F3dxIRnr5uGAO7RXH3O+tJP1Tolv2uychjWFx7woLP/FRn4xsa0jI5H5gkInuAucClIvKP09TPBGqfUB4HZNVTHldHOcDB6u4r52f1v1Sn2pbxM2UVVaQfKrTB92bQJiSQv9+cRGhwAL+Yk9Lit8kvLqtgU2a+dXG1EvUmE1V9WFXjVDUemAYsV9WfnWaVhcDNzhlXY4B8p4tqKTBORDo4A+/jgKXOskIRGeOcxXUz8FGtbVWf9TX9pPK69mH8zM5DhZRXqrVMmkn39m34v5tGknW0hJn/XE9FZUN6rs/M+r1HqahSSyatxBmfqycid4tIdatio4hUD84vAXYD6cDfgTsBVDUP+COw1nk87pQB3IFrcD8d2AV87JQ/CVwhIjuBK5zXp9yH8T8/DL5bMmkuI3t15InJg/kq/TBP/Gtr/SucodUZeQQIjOzVof7Kxuc16nJiVf0C+MJ5/gLwQh11FLjrFOvPBmbXUZ4CDK6jPBe4rDH7MP5lS1YBbUMC6dXJO+cw8VXXJ/Vg+4FCXvsqg/5dI5k2qmez72NNRi6DurcjMiy42bdtvI9dRWS8Wlp2Af27RtoNAlvAw1f158J+Mfzuo82sycirf4VGKK2oZP3eo9bF1YpYMjFeS1XZmlVgg+8tJCgwgL/eMJweHdryy3+sI/NIcbNte1NmPqUVVZZMWhFLJsZrZR45TmFphV2s2ILatQnm79OTKK+s4hdzUprtHl6rnZbOOfGWTFoLSybGa23x8TlMfEWfmAheunEEe3KPMe65lSze2PSz7Ndk5NGvSwQdm/kGlsZ7WTIxXistK58Acc2pblrWhf1i+NfdY+nVKZyZ/1zP3e+sP+PrUCoqq1j3/RHr4mplLJkYr5WWXUCfmAi7etpN+sRE8N4vz+XeK/qxZFM2Vz63kpU7Gn8boq3ZhRSVVjCqt93csTWxZGK8VpoNvrtdUGAAd1+WwAd3nk9kWDA3z17D7z7cTHFZRYO3sTojF4BRNl7SqlgyMV7pyLEysvJL7GJFDxkS147Fv7qAX1zQm3+s/p6rn/+Sdd8fadC6azLy6NWpLV3bhbVwlMabWDIxXimtZg4TO5PLU8KCA/nviQP55y/GUF6pXPe3Vfx56TbKKk59C5aqKmXtnjxrlbRClkyMV6q+jcqAbjb47mnn9unEJ78ey3+MiOOlFbu49qWv2X6g7rsOp+cUcaS43AbfWyFLJsYrpWUX0DUqjE4RNoeJN4gMC+bP1w1j1k0jOVhQwjV//YpZK3dRWXXizA/V15eMtsH3VseSifFKNvjuncYN6srS31zIxWfH8Kcl27hh1rfsy/vhyvk1GXl0jQqjR8c2HozSeIIlE+N1SsorSc8pssF3LxUdEcr/3TSSp68bxtbsAsY/t5K5a/aiqqzJyGVU7464ZpMwrUmj7hpsjDvsOFhIZZXNYeLNRIQpI+MYc1ZHfrtgIw+9v4kP1u/nYEGpjZe0UtYyMV6nevDdurm8X1yHtrz9i9H8fuJAUvcdBbBk0kpZy8R4nbTsAiJCg+jRoa2nQzENEBAg3HpBby7sF8OGfUdJ6Bzh6ZCMB1gyMV4nLauAgd2iCLA5THxK384R9LVE0mpZN5fxKlVVytZsO5PLGF9jycR4le/zijlWVmmD78b4GEsmxqvY4LsxvsmSifEqadn5BAUICV2s790YX2LJxHiVtKwC+naOIDTI5jAxxpdYMjFeZYvdRsUYn2TJxHiNnMJSDhWW2uC7MT7IkonxGluzbfDdGF9lycR4jR8mxLJkYoyvaXAyEZFAEVkvIoud171FZLWI7BSReSIS4pTfIiI5IpLqPH5RaxvTnfo7RWR6rfKRIrJJRNJF5AVxbjkqIh1FZJlTf5mIdHDKxamXLiIbRWREcx0Q4zlpWQXEtm9D+7Yhng7FGNNIjWmZ3ANsrfX6KeBZVU0AjgAzai2bp6qJzuNVcCUG4FFgNDAKeLQ6OQCvALcBCc5jvFP+EPC5s4/PndcAV9Wqe5uzvvFxW7LyrYvLGB/VoGQiInHABKA6MQhwKfCuU2UOcG09m7kSWKaqeap6BFgGjBeRbkCUqn6jqgq8WWtbyc62T95HMvCmunwLtHe2Y3xUcVkFuw8fsy4uY3xUQ1smzwEPAFXO607AUVWtcF5nArG16v+H0/30roj0cMpigX216lSvE+s8P7kcoIuqZgM4PzvXs60TiMhtIpIiIik5OTkNfKvGE7YfKETVBt+N8VX1JhMRmQgcUtV1tYvrqFo9GfQiIF5VhwKf8UPL4lTrnG5bpwyrIeuo6ixVTVLVpJiYmHo2aTypevDdZlc0xjc1pGVyPjBJRPYAc3F1bz2Hq2up+hb2cUAWgKrmqmqpU/53YKTzPBOobqXUXifTeX5yOcDB6u4r5+eherZlfFRaVgFRYUHEtre5w43xRfUmE1V9WFXjVDUemAYsV9WfAiuAKU616cBHUPOlX20SPwzaLwXGiUgHZ+B9HLDU6b4qFJExzljMzdXbAhY62z5hH075zc5ZXWOA/OruMOObqq98t7nDjfFNTZkc60Fgrog8AawHXnPK7xaRSUAFkAfcAqCqeSLyR2CtU+9xVc1znt8BvAG0AT52HgBPAvNFZAawF7jOKV8CXA2kA8XAz5vwPoyHVVYp2w4UcOOoXp4OxRhzhhqVTFT1C+AL5/luXKf4nlznYeDhU6w/G5hdR3kKMLiO8lzgsjrKFbirMbEb75Vx+Bgl5VU2+G6MD7Mr4I3H2eC7Mb7PkonxuC1Z+YQEBtAnxuYwMcZXWTIxHpeWVUBClwhCguzjaIyvsr9e41GqSlpWgV35boyPs2RiPCqnsJTcY2U2XmKMj7NkYjxqS80cJu08HIkxpiksmRiPSstyJZP+3SI9HIkxpiksmRiPSssqoGfHtkSFBXs6FGNME1gyMR6Vlm2D78b4A0smxmOKSivYk3vMBt+N8QOWTIzHbD9QYHOYGOMnLJkYj9mSVX0mlyUTY3ydJRPjMWlZBXRoG0zXqDBPh2KMaSJLJsZj0rJtDhNj/IUlE+MRFZVVbDtQyCC7WNEYv2DJxHjErpxjlFVU2WnBxvgJSybGI9Ky8wEbfDfGX1gyMR6RllVAaFAAZ0WHezoUY0wzsGRiPCItu4D+XSMJCrSPoDH+wP6SjdvVzGFiXVzG+A1LJsbtsvNLOFJcboPvxvgRSybG7dLsyndj/I4lE+N2adkFiED/rpZMjPEXQZ4OwLjPsrSDzE/ZR0RoEJFhQUSEBhERFkRkWDCRoUE/lIcFERkaXPM8uJ5BclWltKKK4rJKjpVWcKysgmOlrufFZRUUlVY6PysoLq1k6ZYD9O4UTnioffyM8Rf219xKHC+r5JEPNlFRWUVEWBBFJRUUllRQUaX1rhsaFOBKOE4CCggQV6IodRJEWWWDtgMQIBAeGsQt58U38R0ZY7xJg5OJiAQCKcB+VZ0oIr2BuUBH4DvgJlUtE5FQ4E1gJJALTFXVPc42HgZmAJXA3aq61CkfDzwPBAKvquqTTnmj92Hq9o9vvyensJT5t5/LqN4dgR9aFIUlFRSWlFNUWkFRSQUFJRXO83IKnee1yyoVYtuH0TbElVzahgQSHhpEePXP6kdNeRDhoa7noUEBdi8uY/xQY1om9wBbgeqO7qeAZ1V1roj8DVeSeMX5eURV+4rINKfeVBEZCEwDBgHdgc9EpJ+zrZeAK4BMYK2ILFTVtMbu4wyPgd87VlrBK//exdiE6JpEAiAihAUHEhYcSExkqAcjNMb4ugYNwItIHDABeNV5LcClwLtOlTnAtc7zZOc1zvLLnPrJwFxVLVXVDCAdGOU80lV1t6qW4WqJJJ/hPkwd3li1h7xjZdx7Rb/6KxtjzBlo6NlczwEPAFXO607AUVWtcF5nArHO81hgH4CzPN+pX1N+0jqnKj+TfZiTFJSUM2vlbi7t35nhPTt4OhxjjJ+qN5mIyETgkKquq11cR1WtZ1lzlde3/xoicpuIpIhISk5OTh2r+L/Xvswg/3i5tUqMMS2qIS2T84FJIrIHVxfUpbhaKu1FpHrMJQ7Icp5nAj0AnOXtgLza5Setc6ryw2ewjxOo6ixVTVLVpJiYmAa8Vf9ytLiM2V9lMH5QVwbH2rwhxpiWU28yUdWHVTVOVeNxDaAvV9WfAiuAKU616cBHzvOFzmuc5ctVVZ3yaSIS6pyllQCsAdYCCSLSW0RCnH0sdNZp7D5MLbNW7qaorILfWKvEGNPCmnKdyYPAXBF5AlgPvOaUvwa8JSLpuFoL0wBUdYuIzAfSgArgLlWtBBCRmcBSXKcGz1bVLWeyD/ODw0WlvLFqDxOHdufsrpGeDscY4+ektfxDn5SUpCkpKZ4Ow22eWJzG7K8zWHbvRfSJifB0OMYYHyUi61Q1qb56dm8uP3SwoIS3vv2eycPjLJEYY9zCkokfenlFOpVVyj2XJXg6FGNMK2HJxM/sP3qcd9bs47qkOHp2auvpcIwxrYQlEz/z4vJ0AGZeaq0SY4z7WDLxI3tzi1mQso8bRvUgtn0bT4djjGlFLJn4kec/30lggHDXJX09HYoxppWxZOInduUU8cH6TG4a04vOUWGeDscY08pYMvETz3+2k7DgQH55cR9Ph2KMaYUsmfiB7QcKWbQxi+nnxRMdYfOSGGPcz5KJH3h22Q4iQoK4/cKzPB2KMaaVsmTi4zbvz+eTLQe49YLetG8b4ulwjDGtlCUTH/fssh20axPMjLG9PR2KMaYVs2Tiw9bvPcLn2w5x24VnERUW7OlwjDGtmCUTH/bMsh10DA/hlvPiPR2KMaaVs2Tio9Zk5PHlzsPccVEfwkObMi2NMcY0nSUTH6Sq/OXT7cREhvKzMb08HY4xxlgy8UWrduWyOiOPuy7uQ5uQQE+HY4wxlkx8TXWrpFu7MKaN6unpcIwxBrBk4nO+2JHDd3uPMvPSvoQFW6vEGOMdLJn4EFXlmU930KNjG64b2cPT4RhjTA1LJj7k07SDbNqfz92XJhASZL86Y4z3sG8kH1FVpTy7bAdnRYczeXisp8MxxpgT2AUKPuBQYQmvfpnBtgOFPD8tkaBA+x/AGONdLJl4KVXlm125vL16L0u3HKCiShk/qCsTh3b3dGjGGPMjlky8zJFjZbz3XSb/XL2X3YeP0a5NMLecF88No3vSJybC0+EZY0ydLJl4AVXlu71HeXv19yzemE1ZRRUje3XgmUv7cvWQbnYKsDHG69WbTEQkDFgJhDr131XVR0XkUuBpIARYB8xQ1QoRuRj4CMhwNvG+qj7ubGs88DwQCLyqqk865b2BuUBH4DvgJlUtE5FQ4E1gJJALTFXVPc46DwMzgErgblVd2sRj4XaFJeV8mJrF299+z7YDhUSEBjE1qQc3ju7JgG5Rng7PGGMarCEtk1LgUlUtEpFg4CsRWQrMAS5T1R0i8jgwHXjNWedLVZ1YeyMiEgi8BFwBZAJrRWShqqYBTwHPqupcEfkbriTxivPziKr2FZFpTr2pIjIQmAYMAroDn4lIP1WtbMrBcJfN+/N5e/VePkrdT3FZJQO7RfGnyUOYlNidCLtpozHGB9X7zaWqChQ5L4OdRyVQqqo7nPJlwMP8kEzqMgpIV9XdACIyF0gWka3ApcCNTr05wGO4kkmy8xzgXeBFERGnfK6qlgIZIpLubP+b+t6Ppxwvq2TxxizeXr2X1H1HCQsO4Jqh3fnpmF4Mi2uH620ZY4xvatC/wU6rYh3QF1frYg0QLCJJqpoCTAFqX5J9rohsALKA+1V1CxAL7KtVJxMYDXQCjqpqRa3y6gspatZxutDynfqxwLcnbatFLr74944c/rg4rcnbOZhfQmFpBX07R/DoNQP5yfA42rW1Ca2MMf6hQcnE6T5KFJH2wAe4upemAc864xqfAtXJ4Dugl9MtdjXwIZAA1PWvt56mnDNcp4aI3AbcBtCz55ndFDEiNIizu0Se0bq1JfXqwLXDYxndu6O1QowxfqdRHfSqelREvgDGq+rTwFgAERkH9HPqFNSqv0REXhaRaFyth9qtlzhcLZfDQHsRCXJaJ9Xl1FonU0SCgHZA3mm2dXK8s4BZAElJST9KNg0xslcHRvbqcCarGmNMq1HvpdQiEuO0SBCRNsDlwDYR6eyUhQIPAn9zXnd1xjUQkVHOPnKBtUCCiPQWkRBcLZuFzpjMClxdZeAayP/Ieb7QeY2zfLlTfyEwTURCnTPBEnB1vRljjPGAhrRMugFznHGTAGC+qi4WkT+LyESn7BVVXe7UnwLcISIVwHFgmpMAKkRkJrAU16nBs52xFHAlo7ki8gSwnh8G8l8D3nIG2PNwJSBUdYuIzAfScHWv3eUrZ3IZY4w/Etf3vP9LSkrSlJQUT4dhjDE+RUTWqWpSffXsjoHGGGOazJKJMcaYJrNkYowxpsksmRhjjGkySybGGGOarNWczSUiOcD3Z7h6NK6LK32Br8RqcTY/X4nV4mxeLR1nL1WNqa9Sq0kmTSEiKQ05Nc4b+EqsFmfz85VYLc7m5S1xWjeXMcaYJrNkYowxpsksmTTMLE8H0Ai+EqvF2fx8JVaLs3l5RZw2ZmKMMabJrGVijDGmySyZ1CIi40Vku4iki8hDdSwPFZF5zvLVIhLvgRh7iMgKEdkqIltE5J466lwsIvkikuo8fu/uOGvFskdENjlx/OhOm+LygnNMN4rICA/EeHatY5UqIgUi8uuT6njsmIrIbBE5JCKba5V1FJFlIrLT+VnnpDsiMt2ps1NEptdVp4Xj/LOIbHN+tx9UT2dRx7qn/Zy4Ic7HRGR/rd/v1adY97TfEW6Ic16tGPeISOop1nXb8ayhqvZwdfUFAruAs4AQYAMw8KQ6dwJ/c55PA+Z5IM5uwAjneSSwo444LwYWe/qYOrHsAaJPs/xq4GNcs2eOAVZ7wefgAK5z673imAIXAiOAzbXK/hd4yHn+EPBUHet1BHY7Pzs4zzu4Oc5xQJDz/Km64mzI58QNcT6Ga4rx+j4bp/2OaOk4T1r+F+D3nj6e1Q9rmfxgFJCuqrtVtQyYCySfVCcZmOM8fxe4rHoiMHdR1WxV/c55XghsBWLdGUMzSwbeVJdvcc262c2D8VwG7FLVM73Atdmp6kpc8/nUVvuzOAe4to5VrwSWqWqeqh4BlgHj3Rmnqn6qrhlUAb7FNSuqR53ieDZEQ74jms3p4nS+d64H3mmp/TeWJZMfxAL7ar3O5Mdf0jV1nD+QfKCTW6Krg9PNNhxYXcfic0Vkg4h8LCKD3BrYiRT4VETWichtdSxvyHF3p2mc+g/UW44pQBdVzQbXPxhA5zrqeNuxvRVXK7Qu9X1O3GGm0x03+xTdht50PMcCB1V15ymWu/14WjL5QV0tjJNPdWtIHbcQkQjgPeDXqlpw0uLvcHXTDAP+Cnzo7vhqOV9VRwBXAXeJyIUnLfemYxoCTAIW1LHYm45pQ3nTsf0vXLOivn2KKvV9TlraK0AfIBHIxtWFdDKvOZ7ADZy+VeL242nJ5AeZQI9ar+OArFPVEZEgoB1n1lxuEhEJxpVI3lbV909erqoFqlrkPF8CBItItJvDrI4ly/l5CPgAV1dBbQ057u5yFfCdqh48eYE3HVPHweruQOfnoTrqeMWxdQb+JwI/VadD/2QN+Jy0KFU9qKqVqloF/P0U+/eW4xkE/ASYd6o6njielkx+sBZIEJHezn+o04CFJ9VZCFSfETMFWH6qP46W4vSVvgZsVdVnTlGna/VYjoiMwvV7znVflDVxhItIZPVzXIOxm0+qthC42TmrawyQX9194wGn/G/PW45pLbU/i9OBj+qosxQYJyIdnG6bcU6Z24jIeOBBYJKqFp+iTkM+Jy3qpHG6yafYf0O+I9zhcmCbqmbWtdBjx9Odo/3e/sB1ZtEOXGds/JdT9jiuPwSAMFxdIOnAGuAsD8R4Aa6m9UYg1XlcDfwS+KVTZyawBdfZJt8C53noeJ7lxLDBiaf6mNaOVYCXnGO+CUjyUKxtcSWHdrXKvOKY4kpw2UA5rv+OZ+Aaq/sc2On87OjUTQJerbXurc7nNR34uQfiTMc1zlD9Wa0+G7I7sOR0nxM3x/mW8/nbiCtBdDs5Tuf1j74j3BmnU/5G9eeyVl2PHc/qh10Bb4wxpsmsm8sYY0yTWTIxxhjTZJZMjDHGNJklE2OMMU1mycQYY0yTWTIxpoWJyGQRURHpX6ssQUQWi8gu55YXK6qvUhaRW0QkR068k/FAz70DY+pnycSYlncD8BWui9wQkTDgX8AsVe2jqiOBX+G6PqDaPFVNrPVIc3vUxjSCJRNjWpBzD7XzcV0YN80p/inwjarWXD2tqptV9Q33R2hM8wjydADG+LlrgU9UdYeI5Ilr8q9BuG4ceTpTReSCWq/PVdXjLRalMU1kycSYlnUD8JzzfK7z+gQi8gGQAOxQ1Z84xfNUdaZ7QjSm6SyZGNNCRKQTcCkwWEQU10x9CvwB1yx6AKjqZBFJAp72SKDGNAMbMzGm5UzBNYtkL1WNV9UeQAauGwWeLyKTatVt65EIjWkm1jIxpuXcADx5Utl7wI245vd4RkSeAw4ChcATteqdPGZyp6quaslgjWkKu2uwMcaYJrNuLmOMMU1mycQYY0yTWTIxxhjTZJZMjDHGNJklE2OMMU1mycQYY0yTWTIxxhjTZJZMjDHGNNn/BzODOTFNcUJPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b80780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pandas\n",
    "census_df = pd.read_csv(\"https://raw.githubusercontent.com/data-8/materials-x18/master/lec/x18/1/census.csv\")[[\"SEX\", \"AGE\", \"POPESTIMATE2014\"]]\n",
    "children_df = census_df[(census_df.SEX == 0) & (census_df.AGE < 19)].drop(\"SEX\", axis=1)\n",
    "children_df.plot.line(x=\"AGE\", y=\"POPESTIMATE2014\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='scatter'></a>**3.3 Scatter Plots**\n",
    "\n",
    "In `datascience`, we used `scatter()` to create a scatter plot of two numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Team</th> <th>Blakeman</th> <th>Prioleau</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>11.5    </td> <td>11.8    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>10.85   </td> <td>11.2    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>11.15   </td> <td>11.5    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>10.7    </td> <td>11      </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>11.1    </td> <td>11.45   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>11.6    </td> <td>11.95   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>11.85   </td> <td>12.3    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>11.1    </td> <td>11.55   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>10.95   </td> <td>11.35   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Patriots</td> <td>10.5    </td> <td>10.9    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (5 rows omitted)</p>"
      ],
      "text/plain": [
       "Team     | Blakeman | Prioleau\n",
       "Patriots | 11.5     | 11.8\n",
       "Patriots | 10.85    | 11.2\n",
       "Patriots | 11.15    | 11.5\n",
       "Patriots | 10.7     | 11\n",
       "Patriots | 11.1     | 11.45\n",
       "Patriots | 11.6     | 11.95\n",
       "Patriots | 11.85    | 12.3\n",
       "Patriots | 11.1     | 11.55\n",
       "Patriots | 10.95    | 11.35\n",
       "Patriots | 10.5     | 10.9\n",
       "... (5 rows omitted)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "football_tbl = Table.read_table('https://raw.githubusercontent.com/data-8/materials-sp18/master/lec/deflategate.csv')\n",
    "football_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ccc9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "football_tbl.scatter('Blakeman', 'Prioleau')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use `plot.scatter()` to create a scatter plot. For a full list of parameters, refer to the [documentation](http://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.DataFrame.plot.scatter.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ce93048>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cef13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "football_df = pd.read_csv('https://raw.githubusercontent.com/data-8/materials-sp18/master/lec/deflategate.csv')\n",
    "football_df.plot.scatter(x=\"Blakeman\", y=\"Prioleau\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='bar'></a>**3.4 Bar Plots**\n",
    "\n",
    "In `datascience`, we used `barh()` to create a horizontal bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cf99630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.barh(\"letter\", \"points\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Pandas, we use `plot.barh()` to create a bar chart. For a full list of parameters, refer to the [documentation](http://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.DataFrame.plot.barh.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11d109198>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zu4j4X0m7bJ9d2/QRSU9WOFKzPSdpnu3Jtb8HH1GiB6iHWS+pt/Zxr6R/LbGTyi+EFhEHbH9a0n9o8NH4r0bE9orHaqb5kn5f0lbbj9e2fT4ivl3hTGiez0j6Wu1E56eSPlHxPE0TET+0vVbSjzX4rLTNGuevpLX9gKRLJE23PSDpC5KWS/q67T/U4D+AVxfZN6+gBYDxrxWWcQAAhRF7AEiA2ANAAsQeABIg9gCQALEHgASIPQAkQOwBIIH/B61qaDlnNy+tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cf01048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_from_dict.plot.barh(x='letter', y='points')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading\n",
    "\n",
    "Here is a list of useful Pandas resources:\n",
    "\n",
    "- [Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/)\n",
    "- [Dataquest Pandas Tutorial](https://www.dataquest.io/blog/pandas-python-tutorial/)\n",
    "- [Pandas Cookbook](http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/tree/master/cookbook/)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}