06_Pandas_Reviewed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:3.75em;color:purple; font-style:bold\"><br>\n",
    "Pandas</p><br>\n",
    "\n",
    "*pandas* is a Python library for data analysis. It offers a number of data exploration, cleaning and transformation operations that are critical in working with data in Python. \n",
    "\n",
    "*pandas* build upon *numpy* and *scipy* providing easy-to-use data structures and data manipulation functions with integrated indexing.\n",
    "\n",
    "The main data structures *pandas* provides are *Series* and *DataFrames*. After a brief introduction to these two data structures and data ingestion, the key features of *pandas* this notebook covers are:\n",
    "* Generating descriptive statistics on data\n",
    "* Data cleaning using built in pandas functions\n",
    "* Frequent data operations for subsetting, filtering, insertion, deletion and aggregation of data\n",
    "* Merging multiple datasets using dataframes\n",
    "* Working with timestamps and time-series data\n",
    "\n",
    "**Additional Recommended Resources:**\n",
    "* *pandas* Documentation: http://pandas.pydata.org/pandas-docs/stable/\n",
    "* *Python for Data Analysis* by Wes McKinney\n",
    "* *Python Data Science Handbook* by Jake VanderPlas\n",
    "\n",
    "Let's get started with our first *pandas* notebook!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Import Libraries\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "Introduction to Pandas Data Structures: Series</p>\n",
    "<br>\n",
    "*pandas* has two main data structures it uses, namely, *Series* and *DataFrames*. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser = pd.Series([100, 200, 300, 400, 500], index=['tom', 'bob', 'nancy', 'dan', 'eric'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tom      100\n",
       "bob      200\n",
       "nancy    300\n",
       "dan      400\n",
       "eric     500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tom', 'bob', 'nancy', 'dan', 'eric'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eric    500\n",
       "dan     400\n",
       "bob     200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[[4, 3, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser['nancy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'bob' in ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tom       200\n",
       "bob       400\n",
       "nancy     600\n",
       "dan       800\n",
       "eric     1000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tom       10000\n",
       "bob       40000\n",
       "nancy     90000\n",
       "dan      160000\n",
       "eric     250000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "Introduction to Pandas Data Structures: DataFrames</p>\n",
    "<br>\n",
    "*pandas* has two main data structures it uses, namely, *Series* and *DataFrames*. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold\">\n",
    "Create DataFrame from dictionary of Python Series</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = {'one' : pd.Series([100., 200., 300.], index=['apple', 'ball', 'clock']),\n",
    "     'two' : pd.Series([111., 222., 333., 4444.], index=['apple', 'ball', 'cerill', 'dancy'])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two\n",
       "apple   100.0   111.0\n",
       "ball    200.0   222.0\n",
       "cerill    NaN   333.0\n",
       "clock   300.0     NaN\n",
       "dancy     NaN  4444.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         one     two\n",
       "dancy    NaN  4444.0\n",
       "ball   200.0   222.0\n",
       "apple  100.0   111.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['dancy', 'ball', 'apple'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>4444.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>222.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          two five\n",
       "dancy  4444.0  NaN\n",
       "ball    222.0  NaN\n",
       "apple   111.0  NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['dancy', 'ball', 'apple'], columns=['two', 'five'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'ball', 'cerill', 'clock', 'dancy'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['one', 'two'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold\">\n",
    "Create DataFrame from list of Python dictionaries</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = [{'alex': 1, 'joe': 2}, {'ema': 5, 'dora': 10, 'alice': 20}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alex</th>\n",
       "      <th>alice</th>\n",
       "      <th>dora</th>\n",
       "      <th>ema</th>\n",
       "      <th>joe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alex  alice  dora  ema  joe\n",
       "0   1.0    NaN   NaN  NaN  2.0\n",
       "1   NaN   20.0  10.0  5.0  NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alex</th>\n",
       "      <th>alice</th>\n",
       "      <th>dora</th>\n",
       "      <th>ema</th>\n",
       "      <th>joe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        alex  alice  dora  ema  joe\n",
       "orange   1.0    NaN   NaN  NaN  2.0\n",
       "red      NaN   20.0  10.0  5.0  NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, index=['orange', 'red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>joe</th>\n",
       "      <th>dora</th>\n",
       "      <th>alice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   joe  dora  alice\n",
       "0  2.0   NaN    NaN\n",
       "1  NaN  10.0   20.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns=['joe', 'dora','alice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold\">\n",
    "Basic DataFrame operations</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two\n",
       "apple   100.0   111.0\n",
       "ball    200.0   222.0\n",
       "cerill    NaN   333.0\n",
       "clock   300.0     NaN\n",
       "dancy     NaN  4444.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     100.0\n",
       "ball      200.0\n",
       "cerill      NaN\n",
       "clock     300.0\n",
       "dancy       NaN\n",
       "Name: one, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>11100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>44400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two    three\n",
       "apple   100.0   111.0  11100.0\n",
       "ball    200.0   222.0  44400.0\n",
       "cerill    NaN   333.0      NaN\n",
       "clock   300.0     NaN      NaN\n",
       "dancy     NaN  4444.0      NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['three'] = df['one'] * df['two']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>11100.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>44400.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two    three   flag\n",
       "apple   100.0   111.0  11100.0  False\n",
       "ball    200.0   222.0  44400.0  False\n",
       "cerill    NaN   333.0      NaN  False\n",
       "clock   300.0     NaN      NaN   True\n",
       "dancy     NaN  4444.0      NaN  False"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flag'] = df['one'] > 250\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "three = df.pop('three')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     11100.0\n",
       "ball      44400.0\n",
       "cerill        NaN\n",
       "clock         NaN\n",
       "dancy         NaN\n",
       "Name: three, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two   flag\n",
       "apple   100.0   111.0  False\n",
       "ball    200.0   222.0  False\n",
       "cerill    NaN   333.0  False\n",
       "clock   300.0     NaN   True\n",
       "dancy     NaN  4444.0  False"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one   flag\n",
       "apple   100.0  False\n",
       "ball    200.0  False\n",
       "cerill    NaN  False\n",
       "clock   300.0   True\n",
       "dancy     NaN  False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>flag</th>\n",
       "      <th>copy_of_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>False</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>True</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one   flag  copy_of_one\n",
       "apple   100.0  False        100.0\n",
       "ball    200.0  False        200.0\n",
       "cerill    NaN  False          NaN\n",
       "clock   300.0   True        300.0\n",
       "dancy     NaN  False          NaN"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(2, 'copy_of_one', df['one'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>flag</th>\n",
       "      <th>copy_of_one</th>\n",
       "      <th>one_upper_half</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>False</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>200.0</td>\n",
       "      <td>False</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cerill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clock</th>\n",
       "      <td>300.0</td>\n",
       "      <td>True</td>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dancy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one   flag  copy_of_one  one_upper_half\n",
       "apple   100.0  False        100.0           100.0\n",
       "ball    200.0  False        200.0           200.0\n",
       "cerill    NaN  False          NaN             NaN\n",
       "clock   300.0   True        300.0             NaN\n",
       "dancy     NaN  False          NaN             NaN"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one_upper_half'] = df['one'][:2]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "Case Study: Movie Data Analysis</p>\n",
    "<br>This notebook uses a dataset from the MovieLens website. We will describe the dataset further as we explore with it using *pandas*. Please note that it is already available in the folder for Week-4 so you don't need to download the dataset, but here are the links to the data source and location. \n",
    "* **Data Source:** MovieLens web site (filename: ml-20m.zip)\n",
    "* **Location:** https://grouplens.org/datasets/movielens/\n",
    "\n",
    "Let us look at the files in this dataset using the UNIX command ls.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "README.txt\t   genome-tags.csv  ml-20m.zip\tratings.csv\r\n",
      "genome-scores.csv  links.csv\t    movies.csv\ttags.csv\r\n"
     ]
    }
   ],
   "source": [
    "# Note: Adjust the name of the folder to match your local directory\n",
    "\n",
    "!ls ./movielens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "Use Pandas to Read the Dataset<br>\n",
    "</p>\n",
    "<br>\n",
    "In this notebook, we will be using three CSV files:\n",
    "* **ratings.csv :** *userId*,*movieId*,*rating*, *timestamp*\n",
    "* **tags.csv :** *userId*,*movieId*, *tag*, *timestamp*\n",
    "* **movies.csv :** *movieId*, *title*, *genres* <br>\n",
    "\n",
    "Using the *read_csv* function in pandas, we will ingest these three files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('./movielens/movies.csv', sep=',')\n",
    "print(type(movies))\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "      <td>1240597180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>1368150078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>1368150079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "      <td>1368149983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>1368150078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag   timestamp\n",
       "0      18     4141    Mark Waters  1240597180\n",
       "1      65      208      dark hero  1368150078\n",
       "2      65      353      dark hero  1368150079\n",
       "3      65      521  noir thriller  1368149983\n",
       "4      65      592      dark hero  1368150078"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970\n",
    "\n",
    "tags = pd.read_csv('./movielens/tags.csv', sep=',')\n",
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112486027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1        2     3.5  1112486027\n",
       "1       1       29     3.5  1112484676\n",
       "2       1       32     3.5  1112484819\n",
       "3       1       47     3.5  1112484727\n",
       "4       1       50     3.5  1112484580"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv('./movielens/ratings.csv', sep=',', parse_dates=['timestamp'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For current analysis, we will remove timestamp (we will come back to it!)\n",
    "\n",
    "del ratings['timestamp']\n",
    "del tags['timestamp']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <h1 style=\"font-size:2em;color:#2467C0\">Data Structures </h1><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:1.5em;color:#2467C0\">Series </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract 0th row: notice that it is infact a Series\n",
    "\n",
    "row_0 = tags.iloc[0]\n",
    "type(row_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId              18\n",
      "movieId           4141\n",
      "tag        Mark Waters\n",
      "Name: 0, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(row_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0['userId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'rating' in row_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'first_row'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0 = row_0.rename('first_row')\n",
    "row_0.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:1.5em;color:#2467C0\">DataFrames </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag\n",
       "0      18     4141    Mark Waters\n",
       "1      65      208      dark hero\n",
       "2      65      353      dark hero\n",
       "3      65      521  noir thriller\n",
       "4      65      592      dark hero"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=465564, step=1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>65</td>\n",
       "      <td>1783</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>910</td>\n",
       "      <td>68554</td>\n",
       "      <td>conspiracy theory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId                tag\n",
       "0         18     4141        Mark Waters\n",
       "11        65     1783      noir thriller\n",
       "2000     910    68554  conspiracy theory"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract row 0, 11, 2000 from DataFrame\n",
    "\n",
    "tags.iloc[ [0,11,2000] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <h1 style=\"font-size:2em;color:#2467C0\">Statistics: Let us look how the ratings are distributed </h1><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.000026e+07\n",
       "mean     3.525529e+00\n",
       "std      1.051989e+00\n",
       "min      5.000000e-01\n",
       "25%      3.000000e+00\n",
       "50%      3.500000e+00\n",
       "75%      4.000000e+00\n",
       "max      5.000000e+00\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5255285642993797"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0519889192942424"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000850</td>\n",
       "      <td>0.001175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <td>-0.000850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>0.001175</td>\n",
       "      <td>0.002606</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           userId   movieId    rating\n",
       "userId   1.000000 -0.000850  0.001175\n",
       "movieId -0.000850  1.000000  0.002606\n",
       "rating   0.001175  0.002606  1.000000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_1 = ratings['rating'] > 5\n",
    "filter_1.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_2 = ratings['rating'] > 0\n",
    "filter_2.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Data Cleaning: Handling Missing Data</h1><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27278, 3)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    False\n",
       "title      False\n",
       "genres     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#is any row NULL ?\n",
    "\n",
    "movies.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Thats nice ! No NULL values !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000263, 3)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     False\n",
       "movieId    False\n",
       "rating     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#is any row NULL ?\n",
    "\n",
    "ratings.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Thats nice ! No NULL values !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465564, 3)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     False\n",
       "movieId    False\n",
       "tag         True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#is any row NULL ?\n",
    "\n",
    "tags.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We have some tags which are NULL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tags = tags.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     False\n",
       "movieId    False\n",
       "tag        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check again: is any row NULL ?\n",
    "\n",
    "tags.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465548, 3)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Thats nice ! No NULL values ! Notice the number of lines have reduced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Data Visualization</h1><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd1b24ca1d0>]], dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AP8UcAAAAAAygmAMAAACAARRzAAAAADCAYg7osn379tEjwHByAHIAEzlg3pwLoJ9iDuiy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd18e6d0c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "ratings.hist(column='rating', figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd13b1e78d0>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd13b1f8080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Adjust y-axis label to go from -1 to 10\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_ylim((-1, 10))\n",
    "\n",
    "#Plot using Pandas boxplot() function\n",
    "ratings.boxplot(column='rating', figsize=(15,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Slicing out columns</h1><br><br><br><br><br>\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Mark Waters\n",
       "1        dark hero\n",
       "2        dark hero\n",
       "3    noir thriller\n",
       "4        dark hero\n",
       "Name: tag, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags['tag'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                title  \\\n",
       "0                    Toy Story (1995)   \n",
       "1                      Jumanji (1995)   \n",
       "2             Grumpier Old Men (1995)   \n",
       "3            Waiting to Exhale (1995)   \n",
       "4  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['title','genres']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>11</td>\n",
       "      <td>527</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>11</td>\n",
       "      <td>531</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>11</td>\n",
       "      <td>541</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>11</td>\n",
       "      <td>546</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>11</td>\n",
       "      <td>551</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>11</td>\n",
       "      <td>586</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>11</td>\n",
       "      <td>587</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>11</td>\n",
       "      <td>588</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>11</td>\n",
       "      <td>589</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>11</td>\n",
       "      <td>592</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId  rating\n",
       "1000      11      527     4.5\n",
       "1001      11      531     4.5\n",
       "1002      11      541     4.5\n",
       "1003      11      546     5.0\n",
       "1004      11      551     5.0\n",
       "1005      11      586     4.0\n",
       "1006      11      587     4.5\n",
       "1007      11      588     5.0\n",
       "1008      11      589     4.5\n",
       "1009      11      592     4.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[1000:1010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sci-fi             3384\n",
       "based on a book    3281\n",
       "atmospheric        2917\n",
       "comedy             2779\n",
       "action             2657\n",
       "surreal            2427\n",
       "BD-R               2334\n",
       "twist ending       2323\n",
       "funny              2072\n",
       "dystopia           1991\n",
       "Name: tag, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_counts = tags['tag'].value_counts()\n",
    "tag_counts[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7e843ae80>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p8xndfcN03NOTvCrJIUneNj0AAADYpL2Gu+5+T5KbWo/uO27imNOTnL6H8R1J7rueAgEA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7e8431400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag_counts[:10].plot(kind='bar', figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Filters for selecting rows</h1><br><br><br><br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20000256</th>\n",
       "      <td>138493</td>\n",
       "      <td>66762</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000257</th>\n",
       "      <td>138493</td>\n",
       "      <td>68319</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000258</th>\n",
       "      <td>138493</td>\n",
       "      <td>68954</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000259</th>\n",
       "      <td>138493</td>\n",
       "      <td>69526</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000261</th>\n",
       "      <td>138493</td>\n",
       "      <td>70286</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating\n",
       "20000256  138493    66762     4.5\n",
       "20000257  138493    68319     4.5\n",
       "20000258  138493    68954     4.5\n",
       "20000259  138493    69526     4.5\n",
       "20000261  138493    70286     5.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_highly_rated = ratings['rating'] >= 4.0\n",
    "\n",
    "ratings[is_highly_rated][-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27241</th>\n",
       "      <td>131132</td>\n",
       "      <td>Kleines Arschloch - Der Film (1997)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27247</th>\n",
       "      <td>131144</td>\n",
       "      <td>Werner - Das muss kesseln!!! (1996)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27248</th>\n",
       "      <td>131146</td>\n",
       "      <td>Werner - Volles Rooäää (1999)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27269</th>\n",
       "      <td>131243</td>\n",
       "      <td>Werner - Gekotzt wird später (2003)</td>\n",
       "      <td>Animation|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27270</th>\n",
       "      <td>131248</td>\n",
       "      <td>Brother Bear 2 (2006)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                title  \\\n",
       "27241   131132  Kleines Arschloch - Der Film (1997)   \n",
       "27247   131144  Werner - Das muss kesseln!!! (1996)   \n",
       "27248   131146        Werner - Volles Rooäää (1999)   \n",
       "27269   131243  Werner - Gekotzt wird später (2003)   \n",
       "27270   131248                Brother Bear 2 (2006)   \n",
       "\n",
       "                                            genres  \n",
       "27241                             Animation|Comedy  \n",
       "27247                             Animation|Comedy  \n",
       "27248                             Animation|Comedy  \n",
       "27269                             Animation|Comedy  \n",
       "27270  Adventure|Animation|Children|Comedy|Fantasy  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_animation = movies['genres'].str.contains('Animation')\n",
    "\n",
    "movies[is_animation][-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Groupby/Aggregate </h1><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>239125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>680732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>279252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>1430997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>883398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>4291193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>2200156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>5561926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.5</th>\n",
       "      <td>1534824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>2898660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        movieId\n",
       "rating         \n",
       "0.5      239125\n",
       "1.0      680732\n",
       "1.5      279252\n",
       "2.0     1430997\n",
       "2.5      883398\n",
       "3.0     4291193\n",
       "3.5     2200156\n",
       "4.0     5561926\n",
       "4.5     1534824\n",
       "5.0     2898660"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_count = ratings[['movieId','rating']].groupby('rating').count()\n",
    "ratings_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131254</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131256</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131258</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131260</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131262</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating\n",
       "movieId        \n",
       "131254      4.0\n",
       "131256      4.0\n",
       "131258      2.5\n",
       "131260      3.0\n",
       "131262      4.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_rating = ratings[['movieId','rating']].groupby('movieId').mean()\n",
    "average_rating.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131254</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131256</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131258</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131260</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131262</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating\n",
       "movieId        \n",
       "131254        1\n",
       "131256        1\n",
       "131258        1\n",
       "131260        1\n",
       "131262        1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_count = ratings[['movieId','rating']].groupby('movieId').count()\n",
    "movie_count.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Merge Dataframes</h1><br><br><br><br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag\n",
       "0      18     4141    Mark Waters\n",
       "1      65      208      dark hero\n",
       "2      65      353      dark hero\n",
       "3      65      521  noir thriller\n",
       "4      65      592      dark hero"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userId</th>\n",
       "      <th>tag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1644</td>\n",
       "      <td>Watched</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>computer animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>Disney animated feature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>Pixar animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1741</td>\n",
       "      <td>TÃ©a Leoni does not star in this movie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres  \\\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "2        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "3        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "4        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "\n",
       "   userId                                     tag  \n",
       "0    1644                                 Watched  \n",
       "1    1741                      computer animation  \n",
       "2    1741                 Disney animated feature  \n",
       "3    1741                         Pixar animation  \n",
       "4    1741  TÃ©a Leoni does not star in this movie  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = movies.merge(tags, on='movieId', how='inner')\n",
    "t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More examples: http://pandas.pydata.org/pandas-docs/stable/merging.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "\n",
    "Combine aggregation, merging, and filters to get useful analytics\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.921240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.211977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.151040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.861393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.064592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId    rating\n",
       "0        1  3.921240\n",
       "1        2  3.211977\n",
       "2        3  3.151040\n",
       "3        4  2.861393\n",
       "4        5  3.064592"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_ratings = ratings.groupby('movieId', as_index=False).mean()\n",
    "del avg_ratings['userId']\n",
    "avg_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26739</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26740</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26741</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26742</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26743</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                          title                    genres  \\\n",
       "26739   131254   Kein Bund für's Leben (2007)                    Comedy   \n",
       "26740   131256  Feuer, Eis & Dosenbier (2002)                    Comedy   \n",
       "26741   131258             The Pirates (2014)                 Adventure   \n",
       "26742   131260            Rentun Ruusu (2001)        (no genres listed)   \n",
       "26743   131262               Innocence (2014)  Adventure|Fantasy|Horror   \n",
       "\n",
       "       rating  \n",
       "26739     4.0  \n",
       "26740     4.0  \n",
       "26741     2.5  \n",
       "26742     3.0  \n",
       "26743     4.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office = movies.merge(avg_ratings, on='movieId', how='inner')\n",
    "box_office.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26737</th>\n",
       "      <td>131250</td>\n",
       "      <td>No More School (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26738</th>\n",
       "      <td>131252</td>\n",
       "      <td>Forklift Driver Klaus: The First Day on the Jo...</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26739</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26740</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26743</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                              title  \\\n",
       "26737   131250                              No More School (2000)   \n",
       "26738   131252  Forklift Driver Klaus: The First Day on the Jo...   \n",
       "26739   131254                       Kein Bund für's Leben (2007)   \n",
       "26740   131256                      Feuer, Eis & Dosenbier (2002)   \n",
       "26743   131262                                   Innocence (2014)   \n",
       "\n",
       "                         genres  rating  \n",
       "26737                    Comedy     4.0  \n",
       "26738             Comedy|Horror     4.0  \n",
       "26739                    Comedy     4.0  \n",
       "26740                    Comedy     4.0  \n",
       "26743  Adventure|Fantasy|Horror     4.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_highly_rated = box_office['rating'] >= 4.0\n",
    "\n",
    "box_office[is_highly_rated][-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>3.921240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>3.151040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>2.861393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>3.064592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>3.366484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "6        7                      Sabrina (1995)   \n",
       "\n",
       "                                        genres    rating  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  3.921240  \n",
       "2                               Comedy|Romance  3.151040  \n",
       "3                         Comedy|Drama|Romance  2.861393  \n",
       "4                                       Comedy  3.064592  \n",
       "6                               Comedy|Romance  3.366484  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_comedy = box_office['genres'].str.contains('Comedy')\n",
    "\n",
    "box_office[is_comedy][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26736</th>\n",
       "      <td>131248</td>\n",
       "      <td>Brother Bear 2 (2006)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26737</th>\n",
       "      <td>131250</td>\n",
       "      <td>No More School (2000)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26738</th>\n",
       "      <td>131252</td>\n",
       "      <td>Forklift Driver Klaus: The First Day on the Jo...</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26739</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26740</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                              title  \\\n",
       "26736   131248                              Brother Bear 2 (2006)   \n",
       "26737   131250                              No More School (2000)   \n",
       "26738   131252  Forklift Driver Klaus: The First Day on the Jo...   \n",
       "26739   131254                       Kein Bund für's Leben (2007)   \n",
       "26740   131256                      Feuer, Eis & Dosenbier (2002)   \n",
       "\n",
       "                                            genres  rating  \n",
       "26736  Adventure|Animation|Children|Comedy|Fantasy     4.0  \n",
       "26737                                       Comedy     4.0  \n",
       "26738                                Comedy|Horror     4.0  \n",
       "26739                                       Comedy     4.0  \n",
       "26740                                       Comedy     4.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box_office[is_comedy & is_highly_rated][-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <h1 style=\"font-size:2em;color:#2467C0\"><br><br>Vectorized String Operations</h1><br><br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "More here: http://pandas.pydata.org/pandas-docs/stable/text.html#text-string-methods\n",
    "<br> </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Split 'genres' into multiple columns\n",
    "\n",
    "<br> </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_genres = movies['genres'].str.split('|', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Children</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Action</td>\n",
       "      <td>Crime</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Children</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Action</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1         2       3        4     5     6     7     8  \\\n",
       "0  Adventure  Animation  Children  Comedy  Fantasy  None  None  None  None   \n",
       "1  Adventure   Children   Fantasy    None     None  None  None  None  None   \n",
       "2     Comedy    Romance      None    None     None  None  None  None  None   \n",
       "3     Comedy      Drama   Romance    None     None  None  None  None  None   \n",
       "4     Comedy       None      None    None     None  None  None  None  None   \n",
       "5     Action      Crime  Thriller    None     None  None  None  None  None   \n",
       "6     Comedy    Romance      None    None     None  None  None  None  None   \n",
       "7  Adventure   Children      None    None     None  None  None  None  None   \n",
       "8     Action       None      None    None     None  None  None  None  None   \n",
       "9     Action  Adventure  Thriller    None     None  None  None  None  None   \n",
       "\n",
       "      9  \n",
       "0  None  \n",
       "1  None  \n",
       "2  None  \n",
       "3  None  \n",
       "4  None  \n",
       "5  None  \n",
       "6  None  \n",
       "7  None  \n",
       "8  None  \n",
       "9  None  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_genres[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "You can also add a new column to detect each genre: e.g. isComedy <br><br>\n",
    "This will be handy when creating features in your Machine Learning course\n",
    "<br> </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_genres['isComedy'] = movies['genres'].str.contains('Comedy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>isComedy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Animation</td>\n",
       "      <td>Children</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Children</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Action</td>\n",
       "      <td>Crime</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Children</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Action</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Action</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0          1         2       3        4     5     6     7     8  \\\n",
       "0  Adventure  Animation  Children  Comedy  Fantasy  None  None  None  None   \n",
       "1  Adventure   Children   Fantasy    None     None  None  None  None  None   \n",
       "2     Comedy    Romance      None    None     None  None  None  None  None   \n",
       "3     Comedy      Drama   Romance    None     None  None  None  None  None   \n",
       "4     Comedy       None      None    None     None  None  None  None  None   \n",
       "5     Action      Crime  Thriller    None     None  None  None  None  None   \n",
       "6     Comedy    Romance      None    None     None  None  None  None  None   \n",
       "7  Adventure   Children      None    None     None  None  None  None  None   \n",
       "8     Action       None      None    None     None  None  None  None  None   \n",
       "9     Action  Adventure  Thriller    None     None  None  None  None  None   \n",
       "\n",
       "      9 isComedy  \n",
       "0  None     True  \n",
       "1  None    False  \n",
       "2  None     True  \n",
       "3  None     True  \n",
       "4  None     True  \n",
       "5  None    False  \n",
       "6  None     True  \n",
       "7  None    False  \n",
       "8  None    False  \n",
       "9  None    False  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_genres[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Extract year from title e.g. (1995)\n",
    "\n",
    "<br> </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['year'] = movies['title'].str.extract('.*\\((.*)\\).*', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27273</th>\n",
       "      <td>131254</td>\n",
       "      <td>Kein Bund für's Leben (2007)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27274</th>\n",
       "      <td>131256</td>\n",
       "      <td>Feuer, Eis &amp; Dosenbier (2002)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27275</th>\n",
       "      <td>131258</td>\n",
       "      <td>The Pirates (2014)</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27276</th>\n",
       "      <td>131260</td>\n",
       "      <td>Rentun Ruusu (2001)</td>\n",
       "      <td>(no genres listed)</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27277</th>\n",
       "      <td>131262</td>\n",
       "      <td>Innocence (2014)</td>\n",
       "      <td>Adventure|Fantasy|Horror</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                          title                    genres  year\n",
       "27273   131254   Kein Bund für's Leben (2007)                    Comedy  2007\n",
       "27274   131256  Feuer, Eis & Dosenbier (2002)                    Comedy  2002\n",
       "27275   131258             The Pirates (2014)                 Adventure  2014\n",
       "27276   131260            Rentun Ruusu (2001)        (no genres listed)  2001\n",
       "27277   131262               Innocence (2014)  Adventure|Fantasy|Horror  2014"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Parsing Timestamps</h1><br><br><br><br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\">\n",
    "\n",
    "Timestamps are common in sensor data or other time series datasets <br><br>\n",
    "Let us revisit the tags.csv dataset and read the timestamps !\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = pd.read_csv('./movielens/tags.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId        int64\n",
       "movieId       int64\n",
       "tag          object\n",
       "timestamp     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\">\n",
    "\n",
    "Unix time / POSIX time / epoch time records \n",
    "time in seconds <br> since midnight Coordinated Universal Time (UTC) of January 1, 1970\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "      <td>1240597180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>1368150078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>353</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>1368150079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>521</td>\n",
       "      <td>noir thriller</td>\n",
       "      <td>1368149983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>65</td>\n",
       "      <td>592</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>1368150078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId            tag   timestamp\n",
       "0      18     4141    Mark Waters  1240597180\n",
       "1      65      208      dark hero  1368150078\n",
       "2      65      353      dark hero  1368150079\n",
       "3      65      521  noir thriller  1368149983\n",
       "4      65      592      dark hero  1368150078"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tags['parsed_time'] = pd.to_datetime(tags['timestamp'], unit='s')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\">\n",
    "\n",
    "Data Type datetime64[ns] maps to either <M8[ns] or >M8[ns] depending on the hardware\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tags['parsed_time'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>parsed_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>4141</td>\n",
       "      <td>Mark Waters</td>\n",
       "      <td>1240597180</td>\n",
       "      <td>2009-04-24 18:19:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>208</td>\n",
       "      <td>dark hero</td>\n",
       "      <td>1368150078</td>\n",
       "      <td>2013-05-10 01:41:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId          tag   timestamp         parsed_time\n",
       "0      18     4141  Mark Waters  1240597180 2009-04-24 18:19:40\n",
       "1      65      208    dark hero  1368150078 2013-05-10 01:41:18"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\">\n",
    "\n",
    "Selecting rows based on timestamps\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((465564, 5), (12130, 5))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greater_than_t = tags['parsed_time'] > '2015-02-01'\n",
    "\n",
    "selected_rows = tags[greater_than_t]\n",
    "\n",
    "tags.shape, selected_rows.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\">\n",
    "\n",
    "Sorting the table using the timestamps\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>parsed_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>333932</th>\n",
       "      <td>100371</td>\n",
       "      <td>2788</td>\n",
       "      <td>monty python</td>\n",
       "      <td>1135429210</td>\n",
       "      <td>2005-12-24 13:00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333927</th>\n",
       "      <td>100371</td>\n",
       "      <td>1732</td>\n",
       "      <td>coen brothers</td>\n",
       "      <td>1135429236</td>\n",
       "      <td>2005-12-24 13:00:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333924</th>\n",
       "      <td>100371</td>\n",
       "      <td>1206</td>\n",
       "      <td>stanley kubrick</td>\n",
       "      <td>1135429248</td>\n",
       "      <td>2005-12-24 13:00:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333923</th>\n",
       "      <td>100371</td>\n",
       "      <td>1193</td>\n",
       "      <td>jack nicholson</td>\n",
       "      <td>1135429371</td>\n",
       "      <td>2005-12-24 13:02:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333939</th>\n",
       "      <td>100371</td>\n",
       "      <td>5004</td>\n",
       "      <td>peter sellers</td>\n",
       "      <td>1135429399</td>\n",
       "      <td>2005-12-24 13:03:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333922</th>\n",
       "      <td>100371</td>\n",
       "      <td>47</td>\n",
       "      <td>morgan freeman</td>\n",
       "      <td>1135429412</td>\n",
       "      <td>2005-12-24 13:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333921</th>\n",
       "      <td>100371</td>\n",
       "      <td>47</td>\n",
       "      <td>brad pitt</td>\n",
       "      <td>1135429412</td>\n",
       "      <td>2005-12-24 13:03:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333936</th>\n",
       "      <td>100371</td>\n",
       "      <td>4011</td>\n",
       "      <td>brad pitt</td>\n",
       "      <td>1135429431</td>\n",
       "      <td>2005-12-24 13:03:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333937</th>\n",
       "      <td>100371</td>\n",
       "      <td>4011</td>\n",
       "      <td>guy ritchie</td>\n",
       "      <td>1135429431</td>\n",
       "      <td>2005-12-24 13:03:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333920</th>\n",
       "      <td>100371</td>\n",
       "      <td>32</td>\n",
       "      <td>bruce willis</td>\n",
       "      <td>1135429442</td>\n",
       "      <td>2005-12-24 13:04:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId              tag   timestamp         parsed_time\n",
       "333932  100371     2788     monty python  1135429210 2005-12-24 13:00:10\n",
       "333927  100371     1732    coen brothers  1135429236 2005-12-24 13:00:36\n",
       "333924  100371     1206  stanley kubrick  1135429248 2005-12-24 13:00:48\n",
       "333923  100371     1193   jack nicholson  1135429371 2005-12-24 13:02:51\n",
       "333939  100371     5004    peter sellers  1135429399 2005-12-24 13:03:19\n",
       "333922  100371       47   morgan freeman  1135429412 2005-12-24 13:03:32\n",
       "333921  100371       47        brad pitt  1135429412 2005-12-24 13:03:32\n",
       "333936  100371     4011        brad pitt  1135429431 2005-12-24 13:03:51\n",
       "333937  100371     4011      guy ritchie  1135429431 2005-12-24 13:03:51\n",
       "333920  100371       32     bruce willis  1135429442 2005-12-24 13:04:02"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.sort_values(by='parsed_time', ascending=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <br><br><h1 style=\"font-size:2em;color:#2467C0\">Average Movie Ratings over time<br> - Are Movie ratings related to the year of launch? </h1><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26739</th>\n",
       "      <td>131254</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26740</th>\n",
       "      <td>131256</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26741</th>\n",
       "      <td>131258</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26742</th>\n",
       "      <td>131260</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26743</th>\n",
       "      <td>131262</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId  rating\n",
       "26739   131254     4.0\n",
       "26740   131256     4.0\n",
       "26741   131258     2.5\n",
       "26742   131260     3.0\n",
       "26743   131262     4.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_rating = ratings[['movieId','rating']].groupby('movieId', as_index=False).mean()\n",
    "average_rating.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.921240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.211977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.151040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1995</td>\n",
       "      <td>2.861393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.064592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  year    rating  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  1995  3.921240  \n",
       "1                   Adventure|Children|Fantasy  1995  3.211977  \n",
       "2                               Comedy|Romance  1995  3.151040  \n",
       "3                         Comedy|Drama|Romance  1995  2.861393  \n",
       "4                                       Comedy  1995  3.064592  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined = movies.merge(average_rating, on='movieId', how='inner')\n",
    "joined.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1891</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1893</td>\n",
       "      <td>3.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1894</td>\n",
       "      <td>3.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1895</td>\n",
       "      <td>3.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1896</td>\n",
       "      <td>3.183036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1898</td>\n",
       "      <td>3.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1899</td>\n",
       "      <td>3.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1900</td>\n",
       "      <td>3.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1901</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1902</td>\n",
       "      <td>3.738189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    rating\n",
       "0  1891  3.000000\n",
       "1  1893  3.375000\n",
       "2  1894  3.071429\n",
       "3  1895  3.125000\n",
       "4  1896  3.183036\n",
       "5  1898  3.850000\n",
       "6  1899  3.625000\n",
       "7  1900  3.166667\n",
       "8  1901  5.000000\n",
       "9  1902  3.738189"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_average = joined[['year','rating']].groupby('year', as_index=False).mean()\n",
    "yearly_average[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7f39f8160>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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3AKD7CG0AAPQgqw072jz5XIr2SADwEaENAIAe5LUzLrU9UpLy2SSDSADAR4Q2AAB6kBey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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc7f39f1358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_average[-20:].plot(x='year', y='rating', figsize=(15,10), grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.35em;color:#2462C0; font-style:bold\">\n",
    "\n",
    "Do some years look better for the boxoffice movies than others? <br><br>\n",
    "\n",
    "Does any data point seem like an outlier in some sense?\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}