RefToPandas.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</p>\n",
    "<br>\n",
    "*pandas* has two main data structures it uses, namely, *Series* and *DataFrames*. \n",
    "\n",
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "pandas Series</p>\n",
    "\n",
    "*pandas Series* one-dimensional labeled array. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser = pd.Series([100, 'foo', 300, 'bar', 500], ['tom', 'bob', 'nancy', 'dan', 'eric'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tom      100\n",
       "bob      foo\n",
       "nancy    300\n",
       "dan      bar\n",
       "eric     500\n",
       "dtype: object"
      ]
     },
     "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": [
       "nancy    300\n",
       "bob      foo\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.loc[['nancy','bob']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eric    500\n",
       "dan     bar\n",
       "bob     foo\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[[4, 3, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'bob' in ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tom      100\n",
       "bob      foo\n",
       "nancy    300\n",
       "dan      bar\n",
       "eric     500\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tom         200\n",
       "bob      foofoo\n",
       "nancy       600\n",
       "dan      barbar\n",
       "eric       1000\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nancy     90000\n",
       "eric     250000\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[['nancy', 'eric']] ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "pandas DataFrame</p>\n",
    "\n",
    "*pandas DataFrame* is a 2-dimensional labeled data structure."
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          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\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(d)\n",
    "print(df)"
   ]
  },
  {
   "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": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "    </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",
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      "text/plain": [
       "         one     two\n",
       "dancy    NaN  4444.0\n",
       "ball   200.0   222.0\n",
       "apple  100.0   111.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['dancy', 'ball', 'apple'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "          two five\n",
       "dancy  4444.0  NaN\n",
       "ball    222.0  NaN\n",
       "apple   111.0  NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['dancy', 'ball', 'apple'], columns=['two', 'five'])"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [{'alex': 1, 'joe': 2}, {'ema': 5, 'dora': 10, 'alice': 20}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>alex</th>\n",
       "      <th>alice</th>\n",
       "      <th>dora</th>\n",
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       "      <th>joe</th>\n",
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       "      <td>2.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
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      "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <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",
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      "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, index=['orange', 'red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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      "text/plain": [
       "   joe  dora  alice\n",
       "0  2.0   NaN    NaN\n",
       "1  NaN  10.0   20.0"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </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",
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      "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['three'] = df['one'] * df['two']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flag'] = df['one'] > 250\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "three = df.pop('three')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>flag</th>\n",
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       "  </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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<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",
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       "      <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",
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       "</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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(2, 'copy_of_one', df['one'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\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": 32,
     "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*. \n",
    "\n",
    "## Download the Dataset\n",
    "\n",
    "Please note that **you will need to download the dataset**. Although the video for this notebook says that the data is in your folder, the folder turned out to be too large to fit on the edX platform due to size constraints.\n",
    "\n",
    "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",
    "Once the download completes, please make sure the data files are in a directory called *movielens* in your *Week-3-pandas* folder. \n",
    "\n",
    "Let us look at the files in this dataset using the UNIX command ls.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'ls' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "# Note: Adjust the name of the folder to match your local directory\n",
    "\n",
    "!ls ./movielens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'cat' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!cat ./movielens/movies.csv | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!head -5 ./movielens/ratings.csv"
   ]
  },
  {
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
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       "  <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Heat (1995)</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Sabrina (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Tom and Huck (1995)</td>\n",
       "      <td>Adventure|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Sudden Death (1995)</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>American President, The (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Dracula: Dead and Loving It (1995)</td>\n",
       "      <td>Comedy|Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Balto (1995)</td>\n",
       "      <td>Adventure|Animation|Children</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Nixon (1995)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Cutthroat Island (1995)</td>\n",
       "      <td>Action|Adventure|Romance</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",
       "5         6                         Heat (1995)   \n",
       "6         7                      Sabrina (1995)   \n",
       "7         8                 Tom and Huck (1995)   \n",
       "8         9                 Sudden Death (1995)   \n",
       "9        10                    GoldenEye (1995)   \n",
       "10       11      American President, The (1995)   \n",
       "11       12  Dracula: Dead and Loving It (1995)   \n",
       "12       13                        Balto (1995)   \n",
       "13       14                        Nixon (1995)   \n",
       "14       15             Cutthroat Island (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  \n",
       "5                         Action|Crime|Thriller  \n",
       "6                                Comedy|Romance  \n",
       "7                            Adventure|Children  \n",
       "8                                        Action  \n",
       "9                     Action|Adventure|Thriller  \n",
       "10                         Comedy|Drama|Romance  \n",
       "11                                Comedy|Horror  \n",
       "12                 Adventure|Animation|Children  \n",
       "13                                        Drama  \n",
       "14                     Action|Adventure|Romance  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv('C:\\\\Users\\\\user\\\\PyDaSc\\\\Week-4-Pandas\\\\movies.csv', sep=',')\n",
    "print(type(movies))\n",
    "movies.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 37,
     "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('C:\\\\Users\\\\user\\\\PyDaSc\\\\Week-4-Pandas\\\\tags.csv', sep=',')\n",
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv('C:\\\\Users\\\\user\\\\PyDaSc\\\\Week-4-Pandas\\\\ratings.csv', sep=',', parse_dates=['timestamp'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:1.5em;color:#2467C0\">Series</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 40,
     "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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0['userId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'rating' in row_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_0.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'first_row'"
      ]
     },
     "execution_count": 46,
     "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": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=465564, step=1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['userId', 'movieId', 'tag'], dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract row 0, 11, 2000 from DataFrame\n",
    "\n",
    "tags.iloc[ [0,11,2000] ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Descriptive Statistics</h1>\n",
    "\n",
    "Let's look how the ratings are distributed! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000026e+07</td>\n",
       "      <td>2.000026e+07</td>\n",
       "      <td>2.000026e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.904587e+04</td>\n",
       "      <td>9.041567e+03</td>\n",
       "      <td>3.525529e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.003863e+04</td>\n",
       "      <td>1.978948e+04</td>\n",
       "      <td>1.051989e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.439500e+04</td>\n",
       "      <td>9.020000e+02</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.914100e+04</td>\n",
       "      <td>2.167000e+03</td>\n",
       "      <td>3.500000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.036370e+05</td>\n",
       "      <td>4.770000e+03</td>\n",
       "      <td>4.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.384930e+05</td>\n",
       "      <td>1.312620e+05</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             userId       movieId        rating\n",
       "count  2.000026e+07  2.000026e+07  2.000026e+07\n",
       "mean   6.904587e+04  9.041567e+03  3.525529e+00\n",
       "std    4.003863e+04  1.978948e+04  1.051989e+00\n",
       "min    1.000000e+00  1.000000e+00  5.000000e-01\n",
       "25%    3.439500e+04  9.020000e+02  3.000000e+00\n",
       "50%    6.914100e+04  2.167000e+03  3.500000e+00\n",
       "75%    1.036370e+05  4.770000e+03  4.000000e+00\n",
       "max    1.384930e+05  1.312620e+05  5.000000e+00"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5255285642993797"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     69045.872583\n",
       "movieId     9041.567330\n",
       "rating         3.525529\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.051988919275684"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings['rating'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0           False\n",
      "1           False\n",
      "2           False\n",
      "3           False\n",
      "4           False\n",
      "5           False\n",
      "6           False\n",
      "7           False\n",
      "8           False\n",
      "9           False\n",
      "10          False\n",
      "11          False\n",
      "12          False\n",
      "13          False\n",
      "14          False\n",
      "15          False\n",
      "16          False\n",
      "17          False\n",
      "18          False\n",
      "19          False\n",
      "20          False\n",
      "21          False\n",
      "22          False\n",
      "23          False\n",
      "24          False\n",
      "25          False\n",
      "26          False\n",
      "27          False\n",
      "28          False\n",
      "29          False\n",
      "            ...  \n",
      "20000233    False\n",
      "20000234    False\n",
      "20000235    False\n",
      "20000236    False\n",
      "20000237    False\n",
      "20000238    False\n",
      "20000239    False\n",
      "20000240    False\n",
      "20000241    False\n",
      "20000242    False\n",
      "20000243    False\n",
      "20000244    False\n",
      "20000245    False\n",
      "20000246    False\n",
      "20000247    False\n",
      "20000248    False\n",
      "20000249    False\n",
      "20000250    False\n",
      "20000251    False\n",
      "20000252    False\n",
      "20000253    False\n",
      "20000254    False\n",
      "20000255    False\n",
      "20000256    False\n",
      "20000257    False\n",
      "20000258    False\n",
      "20000259    False\n",
      "20000260    False\n",
      "20000261    False\n",
      "20000262    False\n",
      "Name: rating, Length: 20000263, dtype: bool\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_1 = ratings['rating'] > 5\n",
    "print(filter_1)\n",
    "filter_1.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_2 = ratings['rating'] > 0\n",
    "filter_2.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Data Cleaning: Handling Missing Data</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27278, 3)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    False\n",
       "title      False\n",
       "genres     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 64,
     "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": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000263, 3)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     False\n",
       "movieId    False\n",
       "rating     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 66,
     "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465564, 3)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     False\n",
       "movieId    False\n",
       "tag         True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 68,
     "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": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = tags.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId     False\n",
       "movieId    False\n",
       "tag        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check again: is any row NULL ?\n",
    "\n",
    "tags.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(465548, 3)"
      ]
     },
     "execution_count": 73,
     "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": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Data Visualization</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000023402657208>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "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 0x2340270b9e8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings.boxplot(column='rating', figsize=(15,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Slicing Out Columns</h1>\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags['tag'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['title','genres']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20000253</th>\n",
       "      <td>138493</td>\n",
       "      <td>60816</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000254</th>\n",
       "      <td>138493</td>\n",
       "      <td>61160</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20000255</th>\n",
       "      <td>138493</td>\n",
       "      <td>65682</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\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>20000260</th>\n",
       "      <td>138493</td>\n",
       "      <td>69644</td>\n",
       "      <td>3.0</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",
       "    <tr>\n",
       "      <th>20000262</th>\n",
       "      <td>138493</td>\n",
       "      <td>71619</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating\n",
       "20000253  138493    60816     4.5\n",
       "20000254  138493    61160     4.0\n",
       "20000255  138493    65682     4.5\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",
       "20000260  138493    69644     3.0\n",
       "20000261  138493    70286     5.0\n",
       "20000262  138493    71619     2.5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a bit boring - I've seen better documentaries    1\n",
       "Laurie Mitchell                                  1\n",
       "filmmakers                                       1\n",
       "young girls                                      1\n",
       "Franz Liszt                                      1\n",
       "you've seen them all.                            1\n",
       "Cautionary fable                                 1\n",
       "first cgi film                                   1\n",
       "Charles Shyer                                    1\n",
       "outsource                                        1\n",
       "Name: tag, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_counts = tags['tag'].value_counts()\n",
    "tag_counts[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x234027cfcc0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag_counts[:10].plot(kind='bar', figsize=(15,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Filters for Selecting Rows</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1</td>\n",
       "      <td>2100</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1</td>\n",
       "      <td>2118</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1</td>\n",
       "      <td>2138</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1</td>\n",
       "      <td>2140</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1</td>\n",
       "      <td>2143</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1</td>\n",
       "      <td>2173</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1</td>\n",
       "      <td>2174</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1</td>\n",
       "      <td>2193</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1</td>\n",
       "      <td>2288</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "      <td>2291</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>2542</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>2628</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1</td>\n",
       "      <td>2762</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "      <td>2872</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>2944</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1</td>\n",
       "      <td>2959</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>2968</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1</td>\n",
       "      <td>3081</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>3153</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userId  movieId  rating\n",
       "68        1     2021     4.0\n",
       "69        1     2100     4.0\n",
       "70        1     2118     4.0\n",
       "71        1     2138     4.0\n",
       "72        1     2140     4.0\n",
       "73        1     2143     4.0\n",
       "74        1     2173     4.0\n",
       "75        1     2174     4.0\n",
       "76        1     2193     4.0\n",
       "79        1     2288     4.0\n",
       "80        1     2291     4.0\n",
       "81        1     2542     4.0\n",
       "82        1     2628     4.0\n",
       "90        1     2762     4.0\n",
       "92        1     2872     4.0\n",
       "94        1     2944     4.0\n",
       "96        1     2959     4.0\n",
       "97        1     2968     4.0\n",
       "101       1     3081     4.0\n",
       "102       1     3153     4.0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_highly_rated = ratings['rating'] >= 4.0\n",
    "\n",
    "ratings[is_highly_rated][30:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>310</th>\n",
       "      <td>313</td>\n",
       "      <td>Swan Princess, The (1994)</td>\n",
       "      <td>Animation|Children</td>\n",
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       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>364</td>\n",
       "      <td>Lion King, The (1994)</td>\n",
       "      <td>Adventure|Animation|Children|Drama|Musical|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>392</td>\n",
       "      <td>Secret Adventures of Tom Thumb, The (1993)</td>\n",
       "      <td>Adventure|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>551</td>\n",
       "      <td>Nightmare Before Christmas, The (1993)</td>\n",
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       "    <tr>\n",
       "      <th>553</th>\n",
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       "      <td>Action|Adventure|Animation|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>588</td>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>594</td>\n",
       "      <td>Snow White and the Seven Dwarfs (1937)</td>\n",
       "      <td>Animation|Children|Drama|Fantasy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>595</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical|Romance|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>596</td>\n",
       "      <td>Pinocchio (1940)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>610</td>\n",
       "      <td>Heavy Metal (1981)</td>\n",
       "      <td>Action|Adventure|Animation|Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                                       title  \\\n",
       "310      313                   Swan Princess, The (1994)   \n",
       "360      364                       Lion King, The (1994)   \n",
       "388      392  Secret Adventures of Tom Thumb, The (1993)   \n",
       "547      551      Nightmare Before Christmas, The (1993)   \n",
       "553      558                      Pagemaster, The (1994)   \n",
       "582      588                              Aladdin (1992)   \n",
       "588      594      Snow White and the Seven Dwarfs (1937)   \n",
       "589      595                 Beauty and the Beast (1991)   \n",
       "590      596                            Pinocchio (1940)   \n",
       "604      610                          Heavy Metal (1981)   \n",
       "\n",
       "                                              genres  \n",
       "310                               Animation|Children  \n",
       "360  Adventure|Animation|Children|Drama|Musical|IMAX  \n",
       "388                              Adventure|Animation  \n",
       "547               Animation|Children|Fantasy|Musical  \n",
       "553      Action|Adventure|Animation|Children|Fantasy  \n",
       "582      Adventure|Animation|Children|Comedy|Musical  \n",
       "588         Animation|Children|Drama|Fantasy|Musical  \n",
       "589  Animation|Children|Fantasy|Musical|Romance|IMAX  \n",
       "590               Animation|Children|Fantasy|Musical  \n",
       "604         Action|Adventure|Animation|Horror|Sci-Fi  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_animation = movies['genres'].str.contains('Animation')\n",
    "\n",
    "movies[is_animation][5:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Toy Story (1995)</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Balto (1995)</td>\n",
       "      <td>Adventure|Animation|Children</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children|Drama|Musical|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>239</td>\n",
       "      <td>Goofy Movie, A (1995)</td>\n",
       "      <td>Animation|Children|Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>244</td>\n",
       "      <td>Gumby: The Movie (1995)</td>\n",
       "      <td>Animation|Children</td>\n",
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       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>313</td>\n",
       "      <td>Swan Princess, The (1994)</td>\n",
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       "      <th>360</th>\n",
       "      <td>364</td>\n",
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       "    <tr>\n",
       "      <th>388</th>\n",
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       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>551</td>\n",
       "      <td>Nightmare Before Christmas, The (1993)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>558</td>\n",
       "      <td>Pagemaster, The (1994)</td>\n",
       "      <td>Action|Adventure|Animation|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>588</td>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>594</td>\n",
       "      <td>Snow White and the Seven Dwarfs (1937)</td>\n",
       "      <td>Animation|Children|Drama|Fantasy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>595</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical|Romance|IMAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>596</td>\n",
       "      <td>Pinocchio (1940)</td>\n",
       "      <td>Animation|Children|Fantasy|Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>610</td>\n",
       "      <td>Heavy Metal (1981)</td>\n",
       "      <td>Action|Adventure|Animation|Horror|Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movieId                                       title  \\\n",
       "0          1                            Toy Story (1995)   \n",
       "12        13                                Balto (1995)   \n",
       "47        48                           Pocahontas (1995)   \n",
       "236      239                       Goofy Movie, A (1995)   \n",
       "241      244                     Gumby: The Movie (1995)   \n",
       "310      313                   Swan Princess, The (1994)   \n",
       "360      364                       Lion King, The (1994)   \n",
       "388      392  Secret Adventures of Tom Thumb, The (1993)   \n",
       "547      551      Nightmare Before Christmas, The (1993)   \n",
       "553      558                      Pagemaster, The (1994)   \n",
       "582      588                              Aladdin (1992)   \n",
       "588      594      Snow White and the Seven Dwarfs (1937)   \n",
       "589      595                 Beauty and the Beast (1991)   \n",
       "590      596                            Pinocchio (1940)   \n",
       "604      610                          Heavy Metal (1981)   \n",
       "\n",
       "                                              genres  \n",
       "0        Adventure|Animation|Children|Comedy|Fantasy  \n",
       "12                      Adventure|Animation|Children  \n",
       "47          Animation|Children|Drama|Musical|Romance  \n",
       "236                Animation|Children|Comedy|Romance  \n",
       "241                               Animation|Children  \n",
       "310                               Animation|Children  \n",
       "360  Adventure|Animation|Children|Drama|Musical|IMAX  \n",
       "388                              Adventure|Animation  \n",
       "547               Animation|Children|Fantasy|Musical  \n",
       "553      Action|Adventure|Animation|Children|Fantasy  \n",
       "582      Adventure|Animation|Children|Comedy|Musical  \n",
       "588         Animation|Children|Drama|Fantasy|Musical  \n",
       "589  Animation|Children|Fantasy|Musical|Romance|IMAX  \n",
       "590               Animation|Children|Fantasy|Musical  \n",
       "604         Action|Adventure|Animation|Horror|Sci-Fi  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[is_animation].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Group By and Aggregate </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0.5</th>\n",
       "      <td>239125</td>\n",
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       "      <th>1.0</th>\n",
       "      <td>680732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>279252</td>\n",
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       "      <th>2.0</th>\n",
       "      <td>1430997</td>\n",
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       "      <th>2.5</th>\n",
       "      <td>883398</td>\n",
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       "      <th>3.0</th>\n",
       "      <td>4291193</td>\n",
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       "      <th>3.5</th>\n",
       "      <td>2200156</td>\n",
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       "    <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": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_count = ratings[['movieId','rating']].groupby('rating').count()\n",
    "ratings_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>rating</th>\n",
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       "      <th>1</th>\n",
       "      <td>3.921240</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.211977</td>\n",
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       "      <th>3</th>\n",
       "      <td>3.151040</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.861393</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.064592</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "           rating\n",
       "movieId          \n",
       "1        3.921240\n",
       "2        3.211977\n",
       "3        3.151040\n",
       "4        2.861393\n",
       "5        3.064592"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_rating = ratings[['movieId','rating']].groupby('movieId').mean()\n",
    "average_rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>rating</th>\n",
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       "    <tr>\n",
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       "      <th>1</th>\n",
       "      <td>49695</td>\n",
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       "      <th>2</th>\n",
       "      <td>22243</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12735</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12161</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rating\n",
       "movieId        \n",
       "1         49695\n",
       "2         22243\n",
       "3         12735\n",
       "4          2756\n",
       "5         12161"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_count = ratings[['movieId','rating']].groupby('movieId').count()\n",
    "movie_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_count = ratings[['movieId','rating']].groupby('movieId').count()\n",
    "movie_count.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Merge Dataframes</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 90,
     "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 aggreagation, merging, and filters to get useful analytics\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 91,
     "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": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 92,
     "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": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 93,
     "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": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\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": 94,
     "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": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
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       "      <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": 95,
     "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\">Vectorized String Operations</h1>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
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       "      <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",
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       "    </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": 96,
     "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": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_genres = movies['genres'].str.split('|', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>Children</td>\n",
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       "      <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",
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       "    <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": 98,
     "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",
    "Add a new column for comedy genre flag\n",
    "\n",
    "<br> </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_genres['isComedy'] = movies['genres'].str.contains('Comedy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>Romance</td>\n",
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       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
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       "      <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": 100,
     "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": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['year'] = movies['title'].str.extract('.*\\((.*)\\).*', expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.tail()"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Parsing Timestamps</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Timestamps are common in sensor data or other time series datasets.\n",
    "Let us revisit the *tags.csv* dataset and read the timestamps!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = pd.read_csv('C:\\\\Users\\\\user\\\\PyDaSc\\\\Week-4-Pandas\\\\tags.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId        int64\n",
       "movieId       int64\n",
       "tag          object\n",
       "timestamp     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "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": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "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": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "tags['parsed_time'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 109,
     "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": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((465564, 5), (12130, 5))"
      ]
     },
     "execution_count": 110,
     "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": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags.sort_values(by='parsed_time', ascending=True)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:2em;color:#2467C0\">Average Movie Ratings over Time </h1>\n",
    "## Are Movie ratings related to the year of launch?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 112,
     "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": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.090369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>-0.090369</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          movieId    rating\n",
       "movieId  1.000000 -0.090369\n",
       "rating  -0.090369  1.000000"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined = movies.merge(average_rating, on='movieId', how='inner')\n",
    "joined.head()\n",
    "joined.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 114,
     "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": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23407ad3940>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}