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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Lecture 4\n",
"\n",
"### Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"In the previous lecture we saw the package `numpy`. `numpy` is fantastic for dealing with numerical data.\n",
"\n",
"`pandas` is a package for dealing with *structured* data of mixed type. A common form of *structured data* is tabular, or spreadsheet data, where each column stores a different type of data (e.g. strings, dates, numerical values).\n",
"\n",
"Rule of thumb: Whenever you reach for an Excel spreadsheet, you should strongly consider using `pandas` instead!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's get started with some data!\n",
"\n",
"This example is adapted from Wes McKinney's excellent book \"Python for Data Analysis\"\n",
"\n",
"MovieLens Latest Data - 100,000 ratings and 3,600 tag applications applied to 9,000 movies by 600 users. \n",
"\n",
"https://grouplens.org/datasets/movielens/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/opt/notebooks\n"
]
},
{
"data": {
"text/plain": [
"('ml-1m.zip', <http.client.HTTPMessage at 0x7f422449e780>)"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%cd /opt/notebooks\n",
"import urllib\n",
"urllib.request.urlretrieve(\"http://files.grouplens.org/datasets/movielens/ml-1m.zip\", \n",
" \"ml-1m.zip\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import zipfile\n",
"with zipfile.ZipFile(\"ml-1m.zip\",\"r\") as f:\n",
" f.extractall(\".\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"01_getting_up_and_running.ipynb LICENSE \u001b[0m\u001b[01;34mml-1m\u001b[0m/ \u001b[01;32mstart.sh\u001b[0m*\r\n",
"02_some_basics.ipynb README.md ml-1m.zip\r\n",
"03_numpy.ipynb \u001b[01;32mbuild.sh\u001b[0m* \u001b[01;34mml-latest-small\u001b[0m/\r\n",
"04_pandas.ipynb \u001b[01;34mdocker\u001b[0m/ ml-latest-small.zip\r\n"
]
}
],
"source": [
"%ls"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/opt/notebooks/ml-1m\n"
]
}
],
"source": [
"%cd ml-1m/"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"README movies.dat ratings.dat users.dat\r\n"
]
}
],
"source": [
"%ls"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SUMMARY\r\n",
"================================================================================\r\n",
"\r\n",
"These files contain 1,000,209 anonymous ratings of approximately 3,900 movies \r\n",
"made by 6,040 MovieLens users who joined MovieLens in 2000.\r\n",
"\r\n",
"USAGE LICENSE\r\n",
"================================================================================\r\n",
"\r\n",
"Neither the University of Minnesota nor any of the researchers\r\n",
"involved can guarantee the correctness of the data, its suitability\r\n",
"for any particular purpose, or the validity of results based on the\r\n",
"use of the data set. The data set may be used for any research\r\n",
"purposes under the following conditions:\r\n",
"\r\n",
" * The user may not state or imply any endorsement from the\r\n",
" University of Minnesota or the GroupLens Research Group.\r\n",
"\r\n",
" * The user must acknowledge the use of the data set in\r\n",
" publications resulting from the use of the data set\r\n",
" (see below for citation information).\r\n",
"\r\n",
" * The user may not redistribute the data without separate\r\n",
" permission.\r\n",
"\r\n",
" * The user may not use this information for any commercial or\r\n",
" revenue-bearing purposes without first obtaining permission\r\n",
" from a faculty member of the GroupLens Research Project at the\r\n",
" University of Minnesota.\r\n",
"\r\n",
"If you have any further questions or comments, please contact GroupLens\r\n",
"<grouplens-info@cs.umn.edu>. \r\n",
"\r\n",
"CITATION\r\n",
"================================================================================\r\n",
"\r\n",
"To acknowledge use of the dataset in publications, please cite the following\r\n",
"paper:\r\n",
"\r\n",
"F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History\r\n",
"and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4,\r\n",
"Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872\r\n",
"\r\n",
"\r\n",
"ACKNOWLEDGEMENTS\r\n",
"================================================================================\r\n",
"\r\n",
"Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data\r\n",
"set.\r\n",
"\r\n",
"FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT\r\n",
"================================================================================\r\n",
"\r\n",
"The GroupLens Research Project is a research group in the Department of \r\n",
"Computer Science and Engineering at the University of Minnesota. Members of \r\n",
"the GroupLens Research Project are involved in many research projects related \r\n",
"to the fields of information filtering, collaborative filtering, and \r\n",
"recommender systems. The project is lead by professors John Riedl and Joseph \r\n",
"Konstan. The project began to explore automated collaborative filtering in \r\n",
"1992, but is most well known for its world wide trial of an automated \r\n",
"collaborative filtering system for Usenet news in 1996. Since then the project \r\n",
"has expanded its scope to research overall information filtering solutions, \r\n",
"integrating in content-based methods as well as improving current collaborative \r\n",
"filtering technology.\r\n",
"\r\n",
"Further information on the GroupLens Research project, including research \r\n",
"publications, can be found at the following web site:\r\n",
" \r\n",
" http://www.grouplens.org/\r\n",
"\r\n",
"GroupLens Research currently operates a movie recommender based on \r\n",
"collaborative filtering:\r\n",
"\r\n",
" http://www.movielens.org/\r\n",
"\r\n",
"RATINGS FILE DESCRIPTION\r\n",
"================================================================================\r\n",
"\r\n",
"All ratings are contained in the file \"ratings.dat\" and are in the\r\n",
"following format:\r\n",
"\r\n",
"UserID::MovieID::Rating::Timestamp\r\n",
"\r\n",
"- UserIDs range between 1 and 6040 \r\n",
"- MovieIDs range between 1 and 3952\r\n",
"- Ratings are made on a 5-star scale (whole-star ratings only)\r\n",
"- Timestamp is represented in seconds since the epoch as returned by time(2)\r\n",
"- Each user has at least 20 ratings\r\n",
"\r\n",
"USERS FILE DESCRIPTION\r\n",
"================================================================================\r\n",
"\r\n",
"User information is in the file \"users.dat\" and is in the following\r\n",
"format:\r\n",
"\r\n",
"UserID::Gender::Age::Occupation::Zip-code\r\n",
"\r\n",
"All demographic information is provided voluntarily by the users and is\r\n",
"not checked for accuracy. Only users who have provided some demographic\r\n",
"information are included in this data set.\r\n",
"\r\n",
"- Gender is denoted by a \"M\" for male and \"F\" for female\r\n",
"- Age is chosen from the following ranges:\r\n",
"\r\n",
"\t* 1: \"Under 18\"\r\n",
"\t* 18: \"18-24\"\r\n",
"\t* 25: \"25-34\"\r\n",
"\t* 35: \"35-44\"\r\n",
"\t* 45: \"45-49\"\r\n",
"\t* 50: \"50-55\"\r\n",
"\t* 56: \"56+\"\r\n",
"\r\n",
"- Occupation is chosen from the following choices:\r\n",
"\r\n",
"\t* 0: \"other\" or not specified\r\n",
"\t* 1: \"academic/educator\"\r\n",
"\t* 2: \"artist\"\r\n",
"\t* 3: \"clerical/admin\"\r\n",
"\t* 4: \"college/grad student\"\r\n",
"\t* 5: \"customer service\"\r\n",
"\t* 6: \"doctor/health care\"\r\n",
"\t* 7: \"executive/managerial\"\r\n",
"\t* 8: \"farmer\"\r\n",
"\t* 9: \"homemaker\"\r\n",
"\t* 10: \"K-12 student\"\r\n",
"\t* 11: \"lawyer\"\r\n",
"\t* 12: \"programmer\"\r\n",
"\t* 13: \"retired\"\r\n",
"\t* 14: \"sales/marketing\"\r\n",
"\t* 15: \"scientist\"\r\n",
"\t* 16: \"self-employed\"\r\n",
"\t* 17: \"technician/engineer\"\r\n",
"\t* 18: \"tradesman/craftsman\"\r\n",
"\t* 19: \"unemployed\"\r\n",
"\t* 20: \"writer\"\r\n",
"\r\n",
"MOVIES FILE DESCRIPTION\r\n",
"================================================================================\r\n",
"\r\n",
"Movie information is in the file \"movies.dat\" and is in the following\r\n",
"format:\r\n",
"\r\n",
"MovieID::Title::Genres\r\n",
"\r\n",
"- Titles are identical to titles provided by the IMDB (including\r\n",
"year of release)\r\n",
"- Genres are pipe-separated and are selected from the following genres:\r\n",
"\r\n",
"\t* Action\r\n",
"\t* Adventure\r\n",
"\t* Animation\r\n",
"\t* Children's\r\n",
"\t* Comedy\r\n",
"\t* Crime\r\n",
"\t* Documentary\r\n",
"\t* Drama\r\n",
"\t* Fantasy\r\n",
"\t* Film-Noir\r\n",
"\t* Horror\r\n",
"\t* Musical\r\n",
"\t* Mystery\r\n",
"\t* Romance\r\n",
"\t* Sci-Fi\r\n",
"\t* Thriller\r\n",
"\t* War\r\n",
"\t* Western\r\n",
"\r\n",
"- Some MovieIDs do not correspond to a movie due to accidental duplicate\r\n",
"entries and/or test entries\r\n",
"- Movies are mostly entered by hand, so errors and inconsistencies may exist\r\n"
]
}
],
"source": [
"%cat README"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/opt/notebooks\n"
]
}
],
"source": [
"%cd ../"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's read in the data from the `ratings.csv` file. pandas has tools that make reading data from CSV (comma-seperated value) files very easy."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"ratings_names = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
"ratings = pd.read_csv(\"ml-1m/ratings.dat\", sep='::', header=None, engine='python', names=ratings_names)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"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",
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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>user_id</th>\n",
" <th>movie_id</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>1193</td>\n",
" <td>5</td>\n",
" <td>978300760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>661</td>\n",
" <td>3</td>\n",
" <td>978302109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>914</td>\n",
" <td>3</td>\n",
" <td>978301968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>3408</td>\n",
" <td>4</td>\n",
" <td>978300275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>2355</td>\n",
" <td>5</td>\n",
" <td>978824291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id movie_id rating timestamp\n",
"0 1 1193 5 978300760\n",
"1 1 661 3 978302109\n",
"2 1 914 3 978301968\n",
"3 1 3408 4 978300275\n",
"4 1 2355 5 978824291"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ratings[:5]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"users_names = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
"users = pd.read_csv(\"ml-1m/users.dat\", sep='::', header=None, engine='python', names=users_names)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .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>user_id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>F</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>48067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>M</td>\n",
" <td>56</td>\n",
" <td>16</td>\n",
" <td>70072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>15</td>\n",
" <td>55117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>M</td>\n",
" <td>45</td>\n",
" <td>7</td>\n",
" <td>02460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>20</td>\n",
" <td>55455</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id gender age occupation zip\n",
"0 1 F 1 10 48067\n",
"1 2 M 56 16 70072\n",
"2 3 M 25 15 55117\n",
"3 4 M 45 7 02460\n",
"4 5 M 25 20 55455"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* *Exercise*. Look in the `README` file. Understand the columns in the `movies.dat` file. Read in the file into a variables `movies`."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"movies_names = ['movie_id', 'title', 'genres']\n",
"movies = pd.read_csv(\"ml-1m/movies.dat\", sep='::', header=None, engine='python', names=movies_names)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/html": [
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"<style scoped>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>movie_id</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>Animation|Children's|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Jumanji (1995)</td>\n",
" <td>Adventure|Children's|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</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": [
" movie_id title genres\n",
"0 1 Toy Story (1995) Animation|Children's|Comedy\n",
"1 2 Jumanji (1995) Adventure|Children's|Fantasy\n",
"2 3 Grumpier Old Men (1995) Comedy|Romance\n",
"3 4 Waiting to Exhale (1995) Comedy|Drama\n",
"4 5 Father of the Bride Part II (1995) Comedy"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movies[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Things to note:\n",
"\n",
"* Each rating is tied to a single user by the `user_id`.\n",
"* Each rating is tied to a single movie by the `movie_id`.\n",
"\n",
"We can use the `pandas.merge` function to join these seperate tables together."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"data = pd.merge(pd.merge(ratings, users), movies)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>movie_id</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</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>1193</td>\n",
" <td>5</td>\n",
" <td>978300760</td>\n",
" <td>F</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>48067</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>978298413</td>\n",
" <td>M</td>\n",
" <td>56</td>\n",
" <td>16</td>\n",
" <td>70072</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12</td>\n",
" <td>1193</td>\n",
" <td>4</td>\n",
" <td>978220179</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>12</td>\n",
" <td>32793</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>15</td>\n",
" <td>1193</td>\n",
" <td>4</td>\n",
" <td>978199279</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>7</td>\n",
" <td>22903</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>17</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>978158471</td>\n",
" <td>M</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>95350</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>18</td>\n",
" <td>1193</td>\n",
" <td>4</td>\n",
" <td>978156168</td>\n",
" <td>F</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>95825</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>19</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>982730936</td>\n",
" <td>M</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>48073</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>24</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>978136709</td>\n",
" <td>F</td>\n",
" <td>25</td>\n",
" <td>7</td>\n",
" <td>10023</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>28</td>\n",
" <td>1193</td>\n",
" <td>3</td>\n",
" <td>978125194</td>\n",
" <td>F</td>\n",
" <td>25</td>\n",
" <td>1</td>\n",
" <td>14607</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>33</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>978557765</td>\n",
" <td>M</td>\n",
" <td>45</td>\n",
" <td>3</td>\n",
" <td>55421</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id movie_id rating timestamp gender age occupation zip \\\n",
"0 1 1193 5 978300760 F 1 10 48067 \n",
"1 2 1193 5 978298413 M 56 16 70072 \n",
"2 12 1193 4 978220179 M 25 12 32793 \n",
"3 15 1193 4 978199279 M 25 7 22903 \n",
"4 17 1193 5 978158471 M 50 1 95350 \n",
"5 18 1193 4 978156168 F 18 3 95825 \n",
"6 19 1193 5 982730936 M 1 10 48073 \n",
"7 24 1193 5 978136709 F 25 7 10023 \n",
"8 28 1193 3 978125194 F 25 1 14607 \n",
"9 33 1193 5 978557765 M 45 3 55421 \n",
"\n",
" title genres \n",
"0 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"1 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"2 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"3 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"4 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"5 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"6 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"7 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"8 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"9 One Flew Over the Cuckoo's Nest (1975) Drama "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Slicing and selecting data\n",
"\n",
"pandas supports similar slicing and boolean operations on data as numpy."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Select the first 5 rows of the `gender` column."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 F\n",
"1 M\n",
"2 M\n",
"3 M\n",
"4 M\n",
"Name: gender, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[\"gender\"][:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Select all ratings from user number 3663."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>214995</th>\n",
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" <td>17055</td>\n",
" <td>Alien (1979)</td>\n",
" <td>Action|Horror|Sci-Fi|Thriller</td>\n",
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" <tr>\n",
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" <td>966411178</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Mad Max (1979)</td>\n",
" <td>Action|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552669</th>\n",
" <td>3663</td>\n",
" <td>208</td>\n",
" <td>1</td>\n",
" <td>966411327</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Waterworld (1995)</td>\n",
" <td>Action|Adventure</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558770</th>\n",
" <td>3663</td>\n",
" <td>3448</td>\n",
" <td>4</td>\n",
" <td>966410583</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Good Morning, Vietnam (1987)</td>\n",
" <td>Comedy|Drama|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>604070</th>\n",
" <td>3663</td>\n",
" <td>1608</td>\n",
" <td>5</td>\n",
" <td>966410942</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Air Force One (1997)</td>\n",
" <td>Action|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>608274</th>\n",
" <td>3663</td>\n",
" <td>2273</td>\n",
" <td>4</td>\n",
" <td>966411120</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Rush Hour (1998)</td>\n",
" <td>Action|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>645370</th>\n",
" <td>3663</td>\n",
" <td>2527</td>\n",
" <td>1</td>\n",
" <td>966411120</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Westworld (1973)</td>\n",
" <td>Action|Sci-Fi|Thriller|Western</td>\n",
" </tr>\n",
" <tr>\n",
" <th>647534</th>\n",
" <td>3663</td>\n",
" <td>2533</td>\n",
" <td>2</td>\n",
" <td>966411375</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Escape from the Planet of the Apes (1971)</td>\n",
" <td>Action|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>652154</th>\n",
" <td>3663</td>\n",
" <td>2985</td>\n",
" <td>4</td>\n",
" <td>966411433</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Robocop (1987)</td>\n",
" <td>Action|Crime|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>652874</th>\n",
" <td>3663</td>\n",
" <td>2989</td>\n",
" <td>2</td>\n",
" <td>966411433</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>For Your Eyes Only (1981)</td>\n",
" <td>Action</td>\n",
" </tr>\n",
" <tr>\n",
" <th>657760</th>\n",
" <td>3663</td>\n",
" <td>168</td>\n",
" <td>4</td>\n",
" <td>966411307</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>First Knight (1995)</td>\n",
" <td>Action|Adventure|Drama|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>665455</th>\n",
" <td>3663</td>\n",
" <td>1876</td>\n",
" <td>3</td>\n",
" <td>966411056</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Deep Impact (1998)</td>\n",
" <td>Action|Drama|Sci-Fi|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>669978</th>\n",
" <td>3663</td>\n",
" <td>3635</td>\n",
" <td>3</td>\n",
" <td>966410916</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Spy Who Loved Me, The (1977)</td>\n",
" <td>Action</td>\n",
" </tr>\n",
" <tr>\n",
" <th>671550</th>\n",
" <td>3663</td>\n",
" <td>2402</td>\n",
" <td>4</td>\n",
" <td>966411402</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Rambo: First Blood Part II (1985)</td>\n",
" <td>Action|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>674355</th>\n",
" <td>3663</td>\n",
" <td>940</td>\n",
" <td>4</td>\n",
" <td>966410970</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Adventures of Robin Hood, The (1938)</td>\n",
" <td>Action|Adventure</td>\n",
" </tr>\n",
" <tr>\n",
" <th>682209</th>\n",
" <td>3663</td>\n",
" <td>3698</td>\n",
" <td>2</td>\n",
" <td>966411120</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Running Man, The (1987)</td>\n",
" <td>Action|Adventure|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688402</th>\n",
" <td>3663</td>\n",
" <td>2991</td>\n",
" <td>3</td>\n",
" <td>966410801</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Live and Let Die (1973)</td>\n",
" <td>Action</td>\n",
" </tr>\n",
" <tr>\n",
" <th>745639</th>\n",
" <td>3663</td>\n",
" <td>2409</td>\n",
" <td>4</td>\n",
" <td>966411453</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Rocky II (1979)</td>\n",
" <td>Action|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765986</th>\n",
" <td>3663</td>\n",
" <td>1833</td>\n",
" <td>5</td>\n",
" <td>966411028</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Mercury Rising (1998)</td>\n",
" <td>Action|Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779562</th>\n",
" <td>3663</td>\n",
" <td>1100</td>\n",
" <td>2</td>\n",
" <td>966411209</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Days of Thunder (1990)</td>\n",
" <td>Action|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>801737</th>\n",
" <td>3663</td>\n",
" <td>3366</td>\n",
" <td>3</td>\n",
" <td>966411261</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Where Eagles Dare (1969)</td>\n",
" <td>Action|Adventure|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>803507</th>\n",
" <td>3663</td>\n",
" <td>2944</td>\n",
" <td>3</td>\n",
" <td>966410801</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Dirty Dozen, The (1967)</td>\n",
" <td>Action|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>804609</th>\n",
" <td>3663</td>\n",
" <td>3405</td>\n",
" <td>2</td>\n",
" <td>966410745</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Night to Remember, A (1958)</td>\n",
" <td>Action|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>847219</th>\n",
" <td>3663</td>\n",
" <td>2334</td>\n",
" <td>3</td>\n",
" <td>966411150</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Siege, The (1998)</td>\n",
" <td>Action|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>847935</th>\n",
" <td>3663</td>\n",
" <td>507</td>\n",
" <td>4</td>\n",
" <td>966410618</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Perfect World, A (1993)</td>\n",
" <td>Action|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>855029</th>\n",
" <td>3663</td>\n",
" <td>2524</td>\n",
" <td>3</td>\n",
" <td>966411178</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Towering Inferno, The (1974)</td>\n",
" <td>Action|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>867102</th>\n",
" <td>3663</td>\n",
" <td>909</td>\n",
" <td>3</td>\n",
" <td>966410618</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Apartment, The (1960)</td>\n",
" <td>Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>870595</th>\n",
" <td>3663</td>\n",
" <td>2468</td>\n",
" <td>3</td>\n",
" <td>966411433</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Jumpin' Jack Flash (1986)</td>\n",
" <td>Action|Comedy|Romance|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>875803</th>\n",
" <td>3663</td>\n",
" <td>3404</td>\n",
" <td>1</td>\n",
" <td>966411003</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Titanic (1953)</td>\n",
" <td>Action|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>877812</th>\n",
" <td>3663</td>\n",
" <td>170</td>\n",
" <td>4</td>\n",
" <td>966411096</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Hackers (1995)</td>\n",
" <td>Action|Crime|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>916857</th>\n",
" <td>3663</td>\n",
" <td>2457</td>\n",
" <td>5</td>\n",
" <td>966410898</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>Running Scared (1986)</td>\n",
" <td>Action|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>983399</th>\n",
" <td>3663</td>\n",
" <td>2307</td>\n",
" <td>3</td>\n",
" <td>966411278</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>17055</td>\n",
" <td>One Tough Cop (1998)</td>\n",
" <td>Action|Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>91 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" user_id movie_id rating timestamp gender age occupation zip \\\n",
"8641 3663 1287 4 966410744 F 35 9 17055 \n",
"32102 3663 1721 5 966410583 F 35 9 17055 \n",
"48559 3663 260 3 966410744 F 35 9 17055 \n",
"64046 3663 648 5 966411209 F 35 9 17055 \n",
"69483 3663 2916 4 966410942 F 35 9 17055 \n",
"72461 3663 1210 3 966410898 F 35 9 17055 \n",
"81028 3663 3107 4 966411028 F 35 9 17055 \n",
"84287 3663 1610 5 966410898 F 35 9 17055 \n",
"91561 3663 1544 4 966411307 F 35 9 17055 \n",
"95833 3663 3257 2 966411178 F 35 9 17055 \n",
"103743 3663 589 3 966410847 F 35 9 17055 \n",
"118241 3663 480 5 966410822 F 35 9 17055 \n",
"122818 3663 1370 5 966411028 F 35 9 17055 \n",
"125225 3663 2353 3 966410898 F 35 9 17055 \n",
"130137 3663 1196 3 966410744 F 35 9 17055 \n",
"132941 3663 736 4 966411178 F 35 9 17055 \n",
"134897 3663 1198 5 966410801 F 35 9 17055 \n",
"139601 3663 95 5 966411351 F 35 9 17055 \n",
"143667 3663 1917 5 966411307 F 35 9 17055 \n",
"154348 3663 21 2 966410970 F 35 9 17055 \n",
"159469 3663 349 4 966410942 F 35 9 17055 \n",
"166620 3663 1385 4 966410898 F 35 9 17055 \n",
"168674 3663 780 5 966411003 F 35 9 17055 \n",
"170339 3663 2002 4 966410847 F 35 9 17055 \n",
"187493 3663 2735 2 966411402 F 35 9 17055 \n",
"197049 3663 1580 4 966410898 F 35 9 17055 \n",
"210181 3663 2115 5 966411150 F 35 9 17055 \n",
"214995 3663 1214 4 966410583 F 35 9 17055 \n",
"216805 3663 1036 5 966410822 F 35 9 17055 \n",
"218116 3663 3702 2 966411178 F 35 9 17055 \n",
"... ... ... ... ... ... ... ... ... \n",
"552669 3663 208 1 966411327 F 35 9 17055 \n",
"558770 3663 3448 4 966410583 F 35 9 17055 \n",
"604070 3663 1608 5 966410942 F 35 9 17055 \n",
"608274 3663 2273 4 966411120 F 35 9 17055 \n",
"645370 3663 2527 1 966411120 F 35 9 17055 \n",
"647534 3663 2533 2 966411375 F 35 9 17055 \n",
"652154 3663 2985 4 966411433 F 35 9 17055 \n",
"652874 3663 2989 2 966411433 F 35 9 17055 \n",
"657760 3663 168 4 966411307 F 35 9 17055 \n",
"665455 3663 1876 3 966411056 F 35 9 17055 \n",
"669978 3663 3635 3 966410916 F 35 9 17055 \n",
"671550 3663 2402 4 966411402 F 35 9 17055 \n",
"674355 3663 940 4 966410970 F 35 9 17055 \n",
"682209 3663 3698 2 966411120 F 35 9 17055 \n",
"688402 3663 2991 3 966410801 F 35 9 17055 \n",
"745639 3663 2409 4 966411453 F 35 9 17055 \n",
"765986 3663 1833 5 966411028 F 35 9 17055 \n",
"779562 3663 1100 2 966411209 F 35 9 17055 \n",
"801737 3663 3366 3 966411261 F 35 9 17055 \n",
"803507 3663 2944 3 966410801 F 35 9 17055 \n",
"804609 3663 3405 2 966410745 F 35 9 17055 \n",
"847219 3663 2334 3 966411150 F 35 9 17055 \n",
"847935 3663 507 4 966410618 F 35 9 17055 \n",
"855029 3663 2524 3 966411178 F 35 9 17055 \n",
"867102 3663 909 3 966410618 F 35 9 17055 \n",
"870595 3663 2468 3 966411433 F 35 9 17055 \n",
"875803 3663 3404 1 966411003 F 35 9 17055 \n",
"877812 3663 170 4 966411096 F 35 9 17055 \n",
"916857 3663 2457 5 966410898 F 35 9 17055 \n",
"983399 3663 2307 3 966411278 F 35 9 17055 \n",
"\n",
" title \\\n",
"8641 Ben-Hur (1959) \n",
"32102 Titanic (1997) \n",
"48559 Star Wars: Episode IV - A New Hope (1977) \n",
"64046 Mission: Impossible (1996) \n",
"69483 Total Recall (1990) \n",
"72461 Star Wars: Episode VI - Return of the Jedi (1983) \n",
"81028 Backdraft (1991) \n",
"84287 Hunt for Red October, The (1990) \n",
"91561 Lost World: Jurassic Park, The (1997) \n",
"95833 Bodyguard, The (1992) \n",
"103743 Terminator 2: Judgment Day (1991) \n",
"118241 Jurassic Park (1993) \n",
"122818 Die Hard 2 (1990) \n",
"125225 Enemy of the State (1998) \n",
"130137 Star Wars: Episode V - The Empire Strikes Back... \n",
"132941 Twister (1996) \n",
"134897 Raiders of the Lost Ark (1981) \n",
"139601 Broken Arrow (1996) \n",
"143667 Armageddon (1998) \n",
"154348 Get Shorty (1995) \n",
"159469 Clear and Present Danger (1994) \n",
"166620 Under Siege (1992) \n",
"168674 Independence Day (ID4) (1996) \n",
"170339 Lethal Weapon 3 (1992) \n",
"187493 Golden Child, The (1986) \n",
"197049 Men in Black (1997) \n",
"210181 Indiana Jones and the Temple of Doom (1984) \n",
"214995 Alien (1979) \n",
"216805 Die Hard (1988) \n",
"218116 Mad Max (1979) \n",
"... ... \n",
"552669 Waterworld (1995) \n",
"558770 Good Morning, Vietnam (1987) \n",
"604070 Air Force One (1997) \n",
"608274 Rush Hour (1998) \n",
"645370 Westworld (1973) \n",
"647534 Escape from the Planet of the Apes (1971) \n",
"652154 Robocop (1987) \n",
"652874 For Your Eyes Only (1981) \n",
"657760 First Knight (1995) \n",
"665455 Deep Impact (1998) \n",
"669978 Spy Who Loved Me, The (1977) \n",
"671550 Rambo: First Blood Part II (1985) \n",
"674355 Adventures of Robin Hood, The (1938) \n",
"682209 Running Man, The (1987) \n",
"688402 Live and Let Die (1973) \n",
"745639 Rocky II (1979) \n",
"765986 Mercury Rising (1998) \n",
"779562 Days of Thunder (1990) \n",
"801737 Where Eagles Dare (1969) \n",
"803507 Dirty Dozen, The (1967) \n",
"804609 Night to Remember, A (1958) \n",
"847219 Siege, The (1998) \n",
"847935 Perfect World, A (1993) \n",
"855029 Towering Inferno, The (1974) \n",
"867102 Apartment, The (1960) \n",
"870595 Jumpin' Jack Flash (1986) \n",
"875803 Titanic (1953) \n",
"877812 Hackers (1995) \n",
"916857 Running Scared (1986) \n",
"983399 One Tough Cop (1998) \n",
"\n",
" genres \n",
"8641 Action|Adventure|Drama \n",
"32102 Drama|Romance \n",
"48559 Action|Adventure|Fantasy|Sci-Fi \n",
"64046 Action|Adventure|Mystery \n",
"69483 Action|Adventure|Sci-Fi|Thriller \n",
"72461 Action|Adventure|Romance|Sci-Fi|War \n",
"81028 Action|Drama \n",
"84287 Action|Thriller \n",
"91561 Action|Adventure|Sci-Fi|Thriller \n",
"95833 Action|Drama|Romance|Thriller \n",
"103743 Action|Sci-Fi|Thriller \n",
"118241 Action|Adventure|Sci-Fi \n",
"122818 Action|Thriller \n",
"125225 Action|Thriller \n",
"130137 Action|Adventure|Drama|Sci-Fi|War \n",
"132941 Action|Adventure|Romance|Thriller \n",
"134897 Action|Adventure \n",
"139601 Action|Thriller \n",
"143667 Action|Adventure|Sci-Fi|Thriller \n",
"154348 Action|Comedy|Drama \n",
"159469 Action|Adventure|Thriller \n",
"166620 Action \n",
"168674 Action|Sci-Fi|War \n",
"170339 Action|Comedy|Crime|Drama \n",
"187493 Action|Adventure|Comedy \n",
"197049 Action|Adventure|Comedy|Sci-Fi \n",
"210181 Action|Adventure \n",
"214995 Action|Horror|Sci-Fi|Thriller \n",
"216805 Action|Thriller \n",
"218116 Action|Sci-Fi \n",
"... ... \n",
"552669 Action|Adventure \n",
"558770 Comedy|Drama|War \n",
"604070 Action|Thriller \n",
"608274 Action|Thriller \n",
"645370 Action|Sci-Fi|Thriller|Western \n",
"647534 Action|Sci-Fi \n",
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"652874 Action \n",
"657760 Action|Adventure|Drama|Romance \n",
"665455 Action|Drama|Sci-Fi|Thriller \n",
"669978 Action \n",
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"674355 Action|Adventure \n",
"682209 Action|Adventure|Sci-Fi \n",
"688402 Action \n",
"745639 Action|Drama \n",
"765986 Action|Drama|Thriller \n",
"779562 Action|Romance \n",
"801737 Action|Adventure|War \n",
"803507 Action|War \n",
"804609 Action|Drama \n",
"847219 Action|Thriller \n",
"847935 Action|Drama \n",
"855029 Action|Drama \n",
"867102 Comedy|Drama \n",
"870595 Action|Comedy|Romance|Thriller \n",
"875803 Action|Drama \n",
"877812 Action|Crime|Thriller \n",
"916857 Action|Comedy \n",
"983399 Action|Drama \n",
"\n",
"[91 rows x 10 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data[\"user_id\"] == 3663]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* *Exercise*: Select all ratings made by women."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
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" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000170</th>\n",
" <td>4651</td>\n",
" <td>134</td>\n",
" <td>4</td>\n",
" <td>963953346</td>\n",
" <td>F</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>10036</td>\n",
" <td>Sonic Outlaws (1995)</td>\n",
" <td>Documentary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000175</th>\n",
" <td>5878</td>\n",
" <td>1842</td>\n",
" <td>4</td>\n",
" <td>1043783975</td>\n",
" <td>F</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>60640</td>\n",
" <td>Illtown (1996)</td>\n",
" <td>Crime|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000176</th>\n",
" <td>4790</td>\n",
" <td>2309</td>\n",
" <td>5</td>\n",
" <td>963105678</td>\n",
" <td>F</td>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>94133</td>\n",
" <td>Inheritors, The (Die Siebtelbauern) (1998)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000177</th>\n",
" <td>4816</td>\n",
" <td>2309</td>\n",
" <td>4</td>\n",
" <td>965869069</td>\n",
" <td>F</td>\n",
" <td>45</td>\n",
" <td>6</td>\n",
" <td>04240</td>\n",
" <td>Inheritors, The (Die Siebtelbauern) (1998)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000182</th>\n",
" <td>4874</td>\n",
" <td>624</td>\n",
" <td>4</td>\n",
" <td>962781918</td>\n",
" <td>F</td>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" <td>70808</td>\n",
" <td>Condition Red (1995)</td>\n",
" <td>Action|Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000184</th>\n",
" <td>5947</td>\n",
" <td>1434</td>\n",
" <td>4</td>\n",
" <td>957190428</td>\n",
" <td>F</td>\n",
" <td>45</td>\n",
" <td>16</td>\n",
" <td>97215</td>\n",
" <td>Stranger, The (1994)</td>\n",
" <td>Action</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000186</th>\n",
" <td>5944</td>\n",
" <td>1868</td>\n",
" <td>1</td>\n",
" <td>957197520</td>\n",
" <td>F</td>\n",
" <td>18</td>\n",
" <td>10</td>\n",
" <td>27606</td>\n",
" <td>Truce, The (1996)</td>\n",
" <td>Drama|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000188</th>\n",
" <td>5185</td>\n",
" <td>404</td>\n",
" <td>4</td>\n",
" <td>963402617</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>4</td>\n",
" <td>44485</td>\n",
" <td>Brother Minister: The Assassination of Malcolm...</td>\n",
" <td>Documentary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000192</th>\n",
" <td>5754</td>\n",
" <td>2543</td>\n",
" <td>4</td>\n",
" <td>958272316</td>\n",
" <td>F</td>\n",
" <td>18</td>\n",
" <td>1</td>\n",
" <td>60640</td>\n",
" <td>Six Ways to Sunday (1997)</td>\n",
" <td>Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000196</th>\n",
" <td>5328</td>\n",
" <td>2438</td>\n",
" <td>4</td>\n",
" <td>960838075</td>\n",
" <td>F</td>\n",
" <td>25</td>\n",
" <td>4</td>\n",
" <td>91740</td>\n",
" <td>Outside Ozona (1998)</td>\n",
" <td>Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000197</th>\n",
" <td>5334</td>\n",
" <td>3323</td>\n",
" <td>3</td>\n",
" <td>960796159</td>\n",
" <td>F</td>\n",
" <td>56</td>\n",
" <td>13</td>\n",
" <td>46140</td>\n",
" <td>Chain of Fools (2000)</td>\n",
" <td>Comedy|Crime</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000198</th>\n",
" <td>5334</td>\n",
" <td>127</td>\n",
" <td>1</td>\n",
" <td>960795494</td>\n",
" <td>F</td>\n",
" <td>56</td>\n",
" <td>13</td>\n",
" <td>46140</td>\n",
" <td>Silence of the Palace, The (Saimt el Qusur) (1...</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000199</th>\n",
" <td>5334</td>\n",
" <td>3382</td>\n",
" <td>5</td>\n",
" <td>960796159</td>\n",
" <td>F</td>\n",
" <td>56</td>\n",
" <td>13</td>\n",
" <td>46140</td>\n",
" <td>Song of Freedom (1936)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000200</th>\n",
" <td>5420</td>\n",
" <td>1843</td>\n",
" <td>3</td>\n",
" <td>960156505</td>\n",
" <td>F</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>14850</td>\n",
" <td>Slappy and the Stinkers (1998)</td>\n",
" <td>Children's|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000201</th>\n",
" <td>5433</td>\n",
" <td>286</td>\n",
" <td>3</td>\n",
" <td>960240881</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>17</td>\n",
" <td>45014</td>\n",
" <td>Nemesis 2: Nebula (1995)</td>\n",
" <td>Action|Sci-Fi|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000202</th>\n",
" <td>5494</td>\n",
" <td>3530</td>\n",
" <td>4</td>\n",
" <td>959816296</td>\n",
" <td>F</td>\n",
" <td>35</td>\n",
" <td>17</td>\n",
" <td>94306</td>\n",
" <td>Smoking/No Smoking (1993)</td>\n",
" <td>Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1000207</th>\n",
" <td>5851</td>\n",
" <td>3607</td>\n",
" <td>5</td>\n",
" <td>957756608</td>\n",
" <td>F</td>\n",
" <td>18</td>\n",
" <td>20</td>\n",
" <td>55410</td>\n",
" <td>One Little Indian (1973)</td>\n",
" <td>Comedy|Drama|Western</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>246440 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" user_id movie_id rating timestamp gender age occupation zip \\\n",
"0 1 1193 5 978300760 F 1 10 48067 \n",
"5 18 1193 4 978156168 F 18 3 95825 \n",
"7 24 1193 5 978136709 F 25 7 10023 \n",
"8 28 1193 3 978125194 F 25 1 14607 \n",
"19 59 1193 4 977934292 F 50 1 55413 \n",
"20 62 1193 4 977968584 F 35 3 98105 \n",
"22 81 1193 5 977785864 F 25 0 60640 \n",
"23 88 1193 5 977694161 F 45 1 02476 \n",
"24 89 1193 5 977683596 F 56 9 85749 \n",
"26 96 1193 3 977621789 F 25 16 78028 \n",
"27 99 1193 2 982791053 F 1 10 19390 \n",
"36 139 1193 4 977359402 F 25 20 45409 \n",
"37 146 1193 4 979940868 F 35 20 10954 \n",
"39 151 1193 4 993121122 F 25 20 85013 \n",
"43 175 1193 5 978051929 F 25 2 95123 \n",
"50 196 1193 4 977004875 F 35 9 94587 \n",
"55 224 1193 4 976833559 F 18 4 14850 \n",
"62 242 1193 4 977267440 F 18 4 53706 \n",
"67 265 1193 5 976649008 F 35 7 55116 \n",
"71 281 1193 5 976571257 F 35 0 94117 \n",
"72 285 1193 4 976579214 F 35 0 94109 \n",
"77 294 1193 4 976541860 F 45 9 43147 \n",
"87 312 1193 4 976475038 F 50 2 22207 \n",
"88 314 1193 3 976751458 F 56 9 46911 \n",
"89 315 1193 5 976465623 F 56 1 55105 \n",
"91 318 1193 4 976426473 F 56 13 55104 \n",
"92 319 1193 5 976418958 F 50 6 33436 \n",
"99 334 1193 5 976378531 F 56 2 55113 \n",
"102 340 1193 3 976341657 F 25 3 28001 \n",
"105 353 1193 4 976323254 F 35 7 92625 \n",
"... ... ... ... ... ... ... ... ... \n",
"1000118 3790 2623 4 966019247 F 25 17 94618 \n",
"1000124 3842 396 4 979988590 F 35 16 92054 \n",
"1000126 3945 3413 4 965690592 F 45 1 96778 \n",
"1000128 3952 1316 4 974595248 F 45 1 12449 \n",
"1000145 4888 820 4 962739636 F 56 0 08055 \n",
"1000150 6001 138 1 956807238 F 25 7 94117 \n",
"1000160 5334 3123 1 960795763 F 56 13 46140 \n",
"1000163 4553 3290 3 964551353 F 35 16 48237 \n",
"1000164 4553 1709 2 964551353 F 35 16 48237 \n",
"1000165 4572 3164 4 964460301 F 1 10 17036 \n",
"1000166 4579 3315 2 1033002064 F 18 4 63332 \n",
"1000167 4628 1832 4 964050337 F 56 6 23225 \n",
"1000169 4888 439 4 962737163 F 56 0 08055 \n",
"1000170 4651 134 4 963953346 F 45 0 10036 \n",
"1000175 5878 1842 4 1043783975 F 25 0 60640 \n",
"1000176 4790 2309 5 963105678 F 25 2 94133 \n",
"1000177 4816 2309 4 965869069 F 45 6 04240 \n",
"1000182 4874 624 4 962781918 F 25 4 70808 \n",
"1000184 5947 1434 4 957190428 F 45 16 97215 \n",
"1000186 5944 1868 1 957197520 F 18 10 27606 \n",
"1000188 5185 404 4 963402617 F 35 4 44485 \n",
"1000192 5754 2543 4 958272316 F 18 1 60640 \n",
"1000196 5328 2438 4 960838075 F 25 4 91740 \n",
"1000197 5334 3323 3 960796159 F 56 13 46140 \n",
"1000198 5334 127 1 960795494 F 56 13 46140 \n",
"1000199 5334 3382 5 960796159 F 56 13 46140 \n",
"1000200 5420 1843 3 960156505 F 1 19 14850 \n",
"1000201 5433 286 3 960240881 F 35 17 45014 \n",
"1000202 5494 3530 4 959816296 F 35 17 94306 \n",
"1000207 5851 3607 5 957756608 F 18 20 55410 \n",
"\n",
" title \\\n",
"0 One Flew Over the Cuckoo's Nest (1975) \n",
"5 One Flew Over the Cuckoo's Nest (1975) \n",
"7 One Flew Over the Cuckoo's Nest (1975) \n",
"8 One Flew Over the Cuckoo's Nest (1975) \n",
"19 One Flew Over the Cuckoo's Nest (1975) \n",
"20 One Flew Over the Cuckoo's Nest (1975) \n",
"22 One Flew Over the Cuckoo's Nest (1975) \n",
"23 One Flew Over the Cuckoo's Nest (1975) \n",
"24 One Flew Over the Cuckoo's Nest (1975) \n",
"26 One Flew Over the Cuckoo's Nest (1975) \n",
"27 One Flew Over the Cuckoo's Nest (1975) \n",
"36 One Flew Over the Cuckoo's Nest (1975) \n",
"37 One Flew Over the Cuckoo's Nest (1975) \n",
"39 One Flew Over the Cuckoo's Nest (1975) \n",
"43 One Flew Over the Cuckoo's Nest (1975) \n",
"50 One Flew Over the Cuckoo's Nest (1975) \n",
"55 One Flew Over the Cuckoo's Nest (1975) \n",
"62 One Flew Over the Cuckoo's Nest (1975) \n",
"67 One Flew Over the Cuckoo's Nest (1975) \n",
"71 One Flew Over the Cuckoo's Nest (1975) \n",
"72 One Flew Over the Cuckoo's Nest (1975) \n",
"77 One Flew Over the Cuckoo's Nest (1975) \n",
"87 One Flew Over the Cuckoo's Nest (1975) \n",
"88 One Flew Over the Cuckoo's Nest (1975) \n",
"89 One Flew Over the Cuckoo's Nest (1975) \n",
"91 One Flew Over the Cuckoo's Nest (1975) \n",
"92 One Flew Over the Cuckoo's Nest (1975) \n",
"99 One Flew Over the Cuckoo's Nest (1975) \n",
"102 One Flew Over the Cuckoo's Nest (1975) \n",
"105 One Flew Over the Cuckoo's Nest (1975) \n",
"... ... \n",
"1000118 Trippin' (1999) \n",
"1000124 Fall Time (1995) \n",
"1000126 Impact (1949) \n",
"1000128 Anna (1996) \n",
"1000145 Death in the Garden (Mort en ce jardin, La) (1... \n",
"1000150 Neon Bible, The (1995) \n",
"1000160 Spring Fever USA (a.k.a. Lauderdale) (1989) \n",
"1000163 Soft Toilet Seats (1999) \n",
"1000164 Legal Deceit (1997) \n",
"1000165 Alley Cats, The (1968) \n",
"1000166 Happy Go Lovely (1951) \n",
"1000167 Heaven's Burning (1997) \n",
"1000169 Dangerous Game (1993) \n",
"1000170 Sonic Outlaws (1995) \n",
"1000175 Illtown (1996) \n",
"1000176 Inheritors, The (Die Siebtelbauern) (1998) \n",
"1000177 Inheritors, The (Die Siebtelbauern) (1998) \n",
"1000182 Condition Red (1995) \n",
"1000184 Stranger, The (1994) \n",
"1000186 Truce, The (1996) \n",
"1000188 Brother Minister: The Assassination of Malcolm... \n",
"1000192 Six Ways to Sunday (1997) \n",
"1000196 Outside Ozona (1998) \n",
"1000197 Chain of Fools (2000) \n",
"1000198 Silence of the Palace, The (Saimt el Qusur) (1... \n",
"1000199 Song of Freedom (1936) \n",
"1000200 Slappy and the Stinkers (1998) \n",
"1000201 Nemesis 2: Nebula (1995) \n",
"1000202 Smoking/No Smoking (1993) \n",
"1000207 One Little Indian (1973) \n",
"\n",
" genres \n",
"0 Drama \n",
"5 Drama \n",
"7 Drama \n",
"8 Drama \n",
"19 Drama \n",
"20 Drama \n",
"22 Drama \n",
"23 Drama \n",
"24 Drama \n",
"26 Drama \n",
"27 Drama \n",
"36 Drama \n",
"37 Drama \n",
"39 Drama \n",
"43 Drama \n",
"50 Drama \n",
"55 Drama \n",
"62 Drama \n",
"67 Drama \n",
"71 Drama \n",
"72 Drama \n",
"77 Drama \n",
"87 Drama \n",
"88 Drama \n",
"89 Drama \n",
"91 Drama \n",
"92 Drama \n",
"99 Drama \n",
"102 Drama \n",
"105 Drama \n",
"... ... \n",
"1000118 Comedy \n",
"1000124 Drama \n",
"1000126 Crime|Drama \n",
"1000128 Drama \n",
"1000145 Drama \n",
"1000150 Drama \n",
"1000160 Comedy \n",
"1000163 Comedy \n",
"1000164 Thriller \n",
"1000165 Drama \n",
"1000166 Musical \n",
"1000167 Action|Drama \n",
"1000169 Drama \n",
"1000170 Documentary \n",
"1000175 Crime|Drama \n",
"1000176 Drama \n",
"1000177 Drama \n",
"1000182 Action|Drama|Thriller \n",
"1000184 Action \n",
"1000186 Drama|War \n",
"1000188 Documentary \n",
"1000192 Comedy \n",
"1000196 Drama|Thriller \n",
"1000197 Comedy|Crime \n",
"1000198 Drama \n",
"1000199 Drama \n",
"1000200 Children's|Comedy \n",
"1000201 Action|Sci-Fi|Thriller \n",
"1000202 Comedy \n",
"1000207 Comedy|Drama|Western \n",
"\n",
"[246440 rows x 10 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[data[\"gender\"] == \"F\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* *Exercise*: Select all *movies* that are of genres *Drama*.\n",
" * Hint: Try the `.str.contains()` method."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"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>movie_id</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Waiting to Exhale (1995)</td>\n",
" <td>Comedy|Drama</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>13</th>\n",
" <td>14</td>\n",
" <td>Nixon (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>Casino (1995)</td>\n",
" <td>Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>Sense and Sensibility (1995)</td>\n",
" <td>Drama|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>Get Shorty (1995)</td>\n",
" <td>Action|Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>Copycat (1995)</td>\n",
" <td>Crime|Drama|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>Powder (1995)</td>\n",
" <td>Drama|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>Leaving Las Vegas (1995)</td>\n",
" <td>Drama|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>Othello (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>Now and Then (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>Shanghai Triad (Yao a yao yao dao waipo qiao) ...</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>31</td>\n",
" <td>Dangerous Minds (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32</td>\n",
" <td>Twelve Monkeys (1995)</td>\n",
" <td>Drama|Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>34</td>\n",
" <td>Babe (1995)</td>\n",
" <td>Children's|Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>35</td>\n",
" <td>Carrington (1995)</td>\n",
" <td>Drama|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>36</td>\n",
" <td>Dead Man Walking (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>Cry, the Beloved Country (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>41</td>\n",
" <td>Richard III (1995)</td>\n",
" <td>Drama|War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>42</td>\n",
" <td>Dead Presidents (1995)</td>\n",
" <td>Action|Crime|Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" movie_id title \\\n",
"3 4 Waiting to Exhale (1995) \n",
"10 11 American President, The (1995) \n",
"13 14 Nixon (1995) \n",
"15 16 Casino (1995) \n",
"16 17 Sense and Sensibility (1995) \n",
"20 21 Get Shorty (1995) \n",
"21 22 Copycat (1995) \n",
"23 24 Powder (1995) \n",
"24 25 Leaving Las Vegas (1995) \n",
"25 26 Othello (1995) \n",
"26 27 Now and Then (1995) \n",
"29 30 Shanghai Triad (Yao a yao yao dao waipo qiao) ... \n",
"30 31 Dangerous Minds (1995) \n",
"31 32 Twelve Monkeys (1995) \n",
"33 34 Babe (1995) \n",
"34 35 Carrington (1995) \n",
"35 36 Dead Man Walking (1995) \n",
"39 40 Cry, the Beloved Country (1995) \n",
"40 41 Richard III (1995) \n",
"41 42 Dead Presidents (1995) \n",
"\n",
" genres \n",
"3 Comedy|Drama \n",
"10 Comedy|Drama|Romance \n",
"13 Drama \n",
"15 Drama|Thriller \n",
"16 Drama|Romance \n",
"20 Action|Comedy|Drama \n",
"21 Crime|Drama|Thriller \n",
"23 Drama|Sci-Fi \n",
"24 Drama|Romance \n",
"25 Drama \n",
"26 Drama \n",
"29 Drama \n",
"30 Drama \n",
"31 Drama|Sci-Fi \n",
"33 Children's|Comedy|Drama \n",
"34 Drama|Romance \n",
"35 Drama \n",
"39 Drama \n",
"40 Drama|War \n",
"41 Action|Crime|Drama "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movies[movies['genres'].str.contains('Drama')][:20]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* *Exercise*: Select all *movies* that are of *only* of genres *Drama*.\n",
" * Hint: Use boolean selection."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"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>movie_id</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\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>25</th>\n",
" <td>26</td>\n",
" <td>Othello (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>Now and Then (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>Shanghai Triad (Yao a yao yao dao waipo qiao) ...</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>31</td>\n",
" <td>Dangerous Minds (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>36</td>\n",
" <td>Dead Man Walking (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>Cry, the Beloved Country (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>Restoration (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>53</td>\n",
" <td>Lamerica (1994)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>55</td>\n",
" <td>Georgia (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>57</td>\n",
" <td>Home for the Holidays (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>62</td>\n",
" <td>Mr. Holland's Opus (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>67</td>\n",
" <td>Two Bits (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>78</td>\n",
" <td>Crossing Guard, The (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>80</td>\n",
" <td>White Balloon, The (Badkonake Sefid ) (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>82</td>\n",
" <td>Antonia's Line (Antonia) (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>83</td>\n",
" <td>Once Upon a Time... When We Were Colored (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>90</td>\n",
" <td>Journey of August King, The (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>94</td>\n",
" <td>Beautiful Girls (1996)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95</th>\n",
" <td>97</td>\n",
" <td>Hate (Haine, La) (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>114</td>\n",
" <td>Margaret's Museum (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>120</td>\n",
" <td>Race the Sun (1996)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>121</td>\n",
" <td>Boys of St. Vincent, The (1993)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>124</td>\n",
" <td>Star Maker, The (Uomo delle stelle, L') (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>127</td>\n",
" <td>Silence of the Palace, The (Saimt el Qusur) (1...</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td>130</td>\n",
" <td>Angela (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>138</td>\n",
" <td>Neon Bible, The (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>142</td>\n",
" <td>Shadows (Cienie) (1988)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>143</td>\n",
" <td>Gospa (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>147</td>\n",
" <td>Basketball Diaries, The (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3720</th>\n",
" <td>3789</td>\n",
" <td>Pawnbroker, The (1965)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3721</th>\n",
" <td>3790</td>\n",
" <td>Groove (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3722</th>\n",
" <td>3791</td>\n",
" <td>Footloose (1984)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3726</th>\n",
" <td>3795</td>\n",
" <td>Five Senses, The (1999)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3734</th>\n",
" <td>3803</td>\n",
" <td>Greaser's Palace (1972)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3744</th>\n",
" <td>3813</td>\n",
" <td>Interiors (1978)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3746</th>\n",
" <td>3816</td>\n",
" <td>Official Story, The (La Historia Oficial) (1985)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3753</th>\n",
" <td>3823</td>\n",
" <td>Wonderland (1999)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3755</th>\n",
" <td>3825</td>\n",
" <td>Coyote Ugly (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3774</th>\n",
" <td>3844</td>\n",
" <td>Steel Magnolias (1989)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3775</th>\n",
" <td>3845</td>\n",
" <td>And God Created Woman (Et Dieu&amp;#8230;Cr�a la F...</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3783</th>\n",
" <td>3853</td>\n",
" <td>Tic Code, The (1998)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3786</th>\n",
" <td>3856</td>\n",
" <td>Autumn Heart (1999)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3797</th>\n",
" <td>3867</td>\n",
" <td>All the Rage (a.k.a. It's the Rage) (1999)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3800</th>\n",
" <td>3870</td>\n",
" <td>Our Town (1940)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3802</th>\n",
" <td>3872</td>\n",
" <td>Suddenly, Last Summer (1959)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3806</th>\n",
" <td>3876</td>\n",
" <td>Jerry &amp; Tom (1998)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3816</th>\n",
" <td>3886</td>\n",
" <td>Steal This Movie! (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3817</th>\n",
" <td>3887</td>\n",
" <td>Went to Coney Island on a Mission From God... ...</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3824</th>\n",
" <td>3894</td>\n",
" <td>Solas (1999)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3832</th>\n",
" <td>3902</td>\n",
" <td>Goya in Bordeaux (Goya en Bodeos) (1999)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3833</th>\n",
" <td>3903</td>\n",
" <td>Urbania (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3834</th>\n",
" <td>3904</td>\n",
" <td>Uninvited Guest, An (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3837</th>\n",
" <td>3907</td>\n",
" <td>Prince of Central Park, The (1999)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3844</th>\n",
" <td>3914</td>\n",
" <td>Broken Hearts Club, The (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3845</th>\n",
" <td>3915</td>\n",
" <td>Girlfight (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3846</th>\n",
" <td>3916</td>\n",
" <td>Remember the Titans (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3879</th>\n",
" <td>3949</td>\n",
" <td>Requiem for a Dream (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3880</th>\n",
" <td>3950</td>\n",
" <td>Tigerland (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3881</th>\n",
" <td>3951</td>\n",
" <td>Two Family House (2000)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>843 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" movie_id title genres\n",
"13 14 Nixon (1995) Drama\n",
"25 26 Othello (1995) Drama\n",
"26 27 Now and Then (1995) Drama\n",
"29 30 Shanghai Triad (Yao a yao yao dao waipo qiao) ... Drama\n",
"30 31 Dangerous Minds (1995) Drama\n",
"35 36 Dead Man Walking (1995) Drama\n",
"39 40 Cry, the Beloved Country (1995) Drama\n",
"42 43 Restoration (1995) Drama\n",
"52 53 Lamerica (1994) Drama\n",
"54 55 Georgia (1995) Drama\n",
"56 57 Home for the Holidays (1995) Drama\n",
"61 62 Mr. Holland's Opus (1995) Drama\n",
"66 67 Two Bits (1995) Drama\n",
"77 78 Crossing Guard, The (1995) Drama\n",
"79 80 White Balloon, The (Badkonake Sefid ) (1995) Drama\n",
"81 82 Antonia's Line (Antonia) (1995) Drama\n",
"82 83 Once Upon a Time... When We Were Colored (1995) Drama\n",
"89 90 Journey of August King, The (1995) Drama\n",
"92 94 Beautiful Girls (1996) Drama\n",
"95 97 Hate (Haine, La) (1995) Drama\n",
"112 114 Margaret's Museum (1995) Drama\n",
"118 120 Race the Sun (1996) Drama\n",
"119 121 Boys of St. Vincent, The (1993) Drama\n",
"122 124 Star Maker, The (Uomo delle stelle, L') (1995) Drama\n",
"125 127 Silence of the Palace, The (Saimt el Qusur) (1... Drama\n",
"128 130 Angela (1995) Drama\n",
"136 138 Neon Bible, The (1995) Drama\n",
"140 142 Shadows (Cienie) (1988) Drama\n",
"141 143 Gospa (1995) Drama\n",
"145 147 Basketball Diaries, The (1995) Drama\n",
"... ... ... ...\n",
"3720 3789 Pawnbroker, The (1965) Drama\n",
"3721 3790 Groove (2000) Drama\n",
"3722 3791 Footloose (1984) Drama\n",
"3726 3795 Five Senses, The (1999) Drama\n",
"3734 3803 Greaser's Palace (1972) Drama\n",
"3744 3813 Interiors (1978) Drama\n",
"3746 3816 Official Story, The (La Historia Oficial) (1985) Drama\n",
"3753 3823 Wonderland (1999) Drama\n",
"3755 3825 Coyote Ugly (2000) Drama\n",
"3774 3844 Steel Magnolias (1989) Drama\n",
"3775 3845 And God Created Woman (Et Dieu&#8230;Cr�a la F... Drama\n",
"3783 3853 Tic Code, The (1998) Drama\n",
"3786 3856 Autumn Heart (1999) Drama\n",
"3797 3867 All the Rage (a.k.a. It's the Rage) (1999) Drama\n",
"3800 3870 Our Town (1940) Drama\n",
"3802 3872 Suddenly, Last Summer (1959) Drama\n",
"3806 3876 Jerry & Tom (1998) Drama\n",
"3816 3886 Steal This Movie! (2000) Drama\n",
"3817 3887 Went to Coney Island on a Mission From God... ... Drama\n",
"3824 3894 Solas (1999) Drama\n",
"3832 3902 Goya in Bordeaux (Goya en Bodeos) (1999) Drama\n",
"3833 3903 Urbania (2000) Drama\n",
"3834 3904 Uninvited Guest, An (2000) Drama\n",
"3837 3907 Prince of Central Park, The (1999) Drama\n",
"3844 3914 Broken Hearts Club, The (2000) Drama\n",
"3845 3915 Girlfight (2000) Drama\n",
"3846 3916 Remember the Titans (2000) Drama\n",
"3879 3949 Requiem for a Dream (2000) Drama\n",
"3880 3950 Tigerland (2000) Drama\n",
"3881 3951 Two Family House (2000) Drama\n",
"\n",
"[843 rows x 3 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"movies[movies['genres'] == \"Drama\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Grouping data\n",
"\n",
"Sometimes it is useful to *group* data together by some common element. For example, say we want to group all of the ratings by title and count how many ratings there are for each title."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"num_ratings_by_title = data.groupby('title').size()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"title\n",
"American Beauty (1999) 3428\n",
"Star Wars: Episode IV - A New Hope (1977) 2991\n",
"Star Wars: Episode V - The Empire Strikes Back (1980) 2990\n",
"Star Wars: Episode VI - Return of the Jedi (1983) 2883\n",
"Jurassic Park (1993) 2672\n",
"Saving Private Ryan (1998) 2653\n",
"Terminator 2: Judgment Day (1991) 2649\n",
"Matrix, The (1999) 2590\n",
"Back to the Future (1985) 2583\n",
"Silence of the Lambs, The (1991) 2578\n",
"dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_ratings_by_title.sort_values(ascending=False)[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"*Exercise*: Show the user ids of the top 100 users who rated the most movies. Assign the result to `users_with_time`.\n",
"\n",
"Tips: Use `groupby`, `sort_values`, `size`, slicing on `data`. Try writing it in one line!"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"user_id\n",
"4169 2314\n",
"1680 1850\n",
"4277 1743\n",
"1941 1595\n",
"1181 1521\n",
"dtype: int64\n"
]
}
],
"source": [
"users_ids_with_time = data.groupby('user_id').size().sort_values(ascending=False)[:100]\n",
"print(users_ids_with_time[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's find out about these people who can watch so many movies! What jobs do they have?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"First of all, the original table only contains numbers which *map* to occupations. It would be nice to have the occupations rather than the numbers."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"number_to_occupation = {0: \"other or not specified\",\n",
"1: \"academic/educator\",\n",
"2: \"artist\",\n",
"3: \"clerical/admin\",\n",
"4: \"college/grad student\",\n",
"5: \"customer service\",\n",
"6: \"doctor/health care\",\n",
"7: \"executive/managerial\",\n",
"8: \"farmer\",\n",
"9: \"homemaker\",\n",
"10: \"K-12 student\",\n",
"11: \"lawyer\",\n",
"12: \"programmer\",\n",
"13: \"retired\",\n",
"14: \"sales/marketing\",\n",
"15: \"scientist\",\n",
"16: \"self-employed\",\n",
"17: \"technician/engineer\",\n",
"18: \"tradesman/craftsman\",\n",
"19: \"unemployed\",\n",
"20: \"writer\"}"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"users['occupation'] = users['occupation'].map(number_to_occupation)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"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>user_id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>F</td>\n",
" <td>1</td>\n",
" <td>K-12 student</td>\n",
" <td>48067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>M</td>\n",
" <td>56</td>\n",
" <td>self-employed</td>\n",
" <td>70072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>scientist</td>\n",
" <td>55117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>M</td>\n",
" <td>45</td>\n",
" <td>executive/managerial</td>\n",
" <td>02460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>writer</td>\n",
" <td>55455</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id gender age occupation zip\n",
"0 1 F 1 K-12 student 48067\n",
"1 2 M 56 self-employed 70072\n",
"2 3 M 25 scientist 55117\n",
"3 4 M 45 executive/managerial 02460\n",
"4 5 M 25 writer 55455"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f41f57d67f0>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users_with_time = users[users[\"user_id\"].isin(users_ids_with_time.index)]\n",
"(users_with_time['occupation'].value_counts()).plot(kind='bar', title=\"Occupations of top 100 users with most ratings\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* *Exercise*: Perform a similar analysis, but look at whether there are more male or female users in the top 100. Genera\n",
" * Hint: Start with `users_with_time`. Use `value_counts`. Use the `.plot()` method."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"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>user_id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>352</td>\n",
" <td>M</td>\n",
" <td>18</td>\n",
" <td>college/grad student</td>\n",
" <td>60115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>423</th>\n",
" <td>424</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>technician/engineer</td>\n",
" <td>55112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>481</th>\n",
" <td>482</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>sales/marketing</td>\n",
" <td>55305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>523</th>\n",
" <td>524</td>\n",
" <td>M</td>\n",
" <td>18</td>\n",
" <td>other or not specified</td>\n",
" <td>91320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>530</th>\n",
" <td>531</td>\n",
" <td>F</td>\n",
" <td>18</td>\n",
" <td>sales/marketing</td>\n",
" <td>22206</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id gender age occupation zip\n",
"351 352 M 18 college/grad student 60115\n",
"423 424 M 25 technician/engineer 55112\n",
"481 482 M 25 sales/marketing 55305\n",
"523 524 M 18 other or not specified 91320\n",
"530 531 F 18 sales/marketing 22206"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"users_with_time.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"M 80\n",
"F 20\n",
"Name: gender, dtype: int64\n"
]
}
],
"source": [
"count_gender_of_users_with_time = users_with_time[\"gender\"].value_counts()\n",
"print(count_gender_of_users_with_time)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f41f57f9358>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"count_gender_of_users_with_time.plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Pivot tables\n",
"\n",
"'Pivot tables' are a concept popularised by Microsoft Excel. \n",
"They are a tool for:\n",
"* first *aggregating* data by one or more *keys*, \n",
"* and then arranging the data with some of the *keys* along the rows, and some along the columns."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Question: What is the mean movie rating for each film grouped by gender?"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"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>user_id</th>\n",
" <th>movie_id</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</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>1193</td>\n",
" <td>5</td>\n",
" <td>978300760</td>\n",
" <td>F</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>48067</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>978298413</td>\n",
" <td>M</td>\n",
" <td>56</td>\n",
" <td>16</td>\n",
" <td>70072</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12</td>\n",
" <td>1193</td>\n",
" <td>4</td>\n",
" <td>978220179</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>12</td>\n",
" <td>32793</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>15</td>\n",
" <td>1193</td>\n",
" <td>4</td>\n",
" <td>978199279</td>\n",
" <td>M</td>\n",
" <td>25</td>\n",
" <td>7</td>\n",
" <td>22903</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>17</td>\n",
" <td>1193</td>\n",
" <td>5</td>\n",
" <td>978158471</td>\n",
" <td>M</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>95350</td>\n",
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id movie_id rating timestamp gender age occupation zip \\\n",
"0 1 1193 5 978300760 F 1 10 48067 \n",
"1 2 1193 5 978298413 M 56 16 70072 \n",
"2 12 1193 4 978220179 M 25 12 32793 \n",
"3 15 1193 4 978199279 M 25 7 22903 \n",
"4 17 1193 5 978158471 M 50 1 95350 \n",
"\n",
" title genres \n",
"0 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"1 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"2 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"3 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"4 One Flew Over the Cuckoo's Nest (1975) Drama "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" <tr>\n",
" <th>title</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>$1,000,000 Duck (1971)</th>\n",
" <td>3.375000</td>\n",
" <td>2.761905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Night Mother (1986)</th>\n",
" <td>3.388889</td>\n",
" <td>3.352941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Til There Was You (1997)</th>\n",
" <td>2.675676</td>\n",
" <td>2.733333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'burbs, The (1989)</th>\n",
" <td>2.793478</td>\n",
" <td>2.962085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...And Justice for All (1979)</th>\n",
" <td>3.828571</td>\n",
" <td>3.689024</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"gender F M\n",
"title \n",
"$1,000,000 Duck (1971) 3.375000 2.761905\n",
"'Night Mother (1986) 3.388889 3.352941\n",
"'Til There Was You (1997) 2.675676 2.733333\n",
"'burbs, The (1989) 2.793478 2.962085\n",
"...And Justice for All (1979) 3.828571 3.689024"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_rating = data.pivot_table('rating', index='title', columns='gender', aggfunc=np.mean)\n",
"mean_rating.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* Exercise: Using the `.pivot_table()` method, also compute the standard deviation of the aggregated data.\n",
" * Hint: Look at the documentation for the `aggfunc` argument to `pd.pivot_table` using the `?` command.\n",
" * Hint: Find functions in `numpy` (`np`) that compute the mean and standard deviation.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"pd.pivot_table?"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/pandas/core/reshape/pivot.py:45: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n",
" return concat(pieces, keys=keys, axis=1)\n"
]
},
{
"data": {
"text/html": [
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"</style>\n",
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" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">mean</th>\n",
" <th colspan=\"2\" halign=\"left\">std</th>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>$1,000,000 Duck (1971)</th>\n",
" <td>3.375000</td>\n",
" <td>2.761905</td>\n",
" <td>0.957427</td>\n",
" <td>1.135991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Night Mother (1986)</th>\n",
" <td>3.388889</td>\n",
" <td>3.352941</td>\n",
" <td>1.021981</td>\n",
" <td>1.228015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Til There Was You (1997)</th>\n",
" <td>2.675676</td>\n",
" <td>2.733333</td>\n",
" <td>0.973369</td>\n",
" <td>1.162919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'burbs, The (1989)</th>\n",
" <td>2.793478</td>\n",
" <td>2.962085</td>\n",
" <td>1.124532</td>\n",
" <td>1.099127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...And Justice for All (1979)</th>\n",
" <td>3.828571</td>\n",
" <td>3.689024</td>\n",
" <td>0.954424</td>\n",
" <td>0.862086</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std \n",
"gender F M F M\n",
"$1,000,000 Duck (1971) 3.375000 2.761905 0.957427 1.135991\n",
"'Night Mother (1986) 3.388889 3.352941 1.021981 1.228015\n",
"'Til There Was You (1997) 2.675676 2.733333 0.973369 1.162919\n",
"'burbs, The (1989) 2.793478 2.962085 1.124532 1.099127\n",
"...And Justice for All (1979) 3.828571 3.689024 0.954424 0.862086"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_rating = data.pivot_table('rating', index='title', \n",
" columns='gender', \n",
" aggfunc=[np.mean, np.std])\n",
"mean_rating.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Analysis - Getting answers to questions\n",
"\n",
"Question:\n",
"\n",
"1. Which films were most divisive between male and female viewers?"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">mean</th>\n",
" <th colspan=\"2\" halign=\"left\">std</th>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>$1,000,000 Duck (1971)</th>\n",
" <td>3.375000</td>\n",
" <td>2.761905</td>\n",
" <td>0.957427</td>\n",
" <td>1.135991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Night Mother (1986)</th>\n",
" <td>3.388889</td>\n",
" <td>3.352941</td>\n",
" <td>1.021981</td>\n",
" <td>1.228015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Til There Was You (1997)</th>\n",
" <td>2.675676</td>\n",
" <td>2.733333</td>\n",
" <td>0.973369</td>\n",
" <td>1.162919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'burbs, The (1989)</th>\n",
" <td>2.793478</td>\n",
" <td>2.962085</td>\n",
" <td>1.124532</td>\n",
" <td>1.099127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...And Justice for All (1979)</th>\n",
" <td>3.828571</td>\n",
" <td>3.689024</td>\n",
" <td>0.954424</td>\n",
" <td>0.862086</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std \n",
"gender F M F M\n",
"$1,000,000 Duck (1971) 3.375000 2.761905 0.957427 1.135991\n",
"'Night Mother (1986) 3.388889 3.352941 1.021981 1.228015\n",
"'Til There Was You (1997) 2.675676 2.733333 0.973369 1.162919\n",
"'burbs, The (1989) 2.793478 2.962085 1.124532 1.099127\n",
"...And Justice for All (1979) 3.828571 3.689024 0.954424 0.862086"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_rating.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"mean_rating[\"mean_diff\"] = mean_rating[\"mean\"][\"M\"] - mean_rating[\"mean\"][\"F\"]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"sorted_by_diff = mean_rating.sort_values(by=\"mean_diff\")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">mean</th>\n",
" <th colspan=\"2\" halign=\"left\">std</th>\n",
" <th>mean_diff</th>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>James Dean Story, The (1957)</th>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>-3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Country Life (1994)</th>\n",
" <td>5.000000</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>-3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Spiders, The (Die Spinnen, 1. Teil: Der Goldene See) (1919)</th>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.414214</td>\n",
" <td>0.000000</td>\n",
" <td>-3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Babyfever (1994)</th>\n",
" <td>3.666667</td>\n",
" <td>1.000000</td>\n",
" <td>0.577350</td>\n",
" <td>NaN</td>\n",
" <td>-2.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Woman of Paris, A (1923)</th>\n",
" <td>5.000000</td>\n",
" <td>2.428571</td>\n",
" <td>NaN</td>\n",
" <td>1.272418</td>\n",
" <td>-2.571429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cobra (1925)</th>\n",
" <td>4.000000</td>\n",
" <td>1.500000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>-2.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other Side of Sunday, The (S�ndagsengler) (1996)</th>\n",
" <td>5.000000</td>\n",
" <td>2.928571</td>\n",
" <td>0.000000</td>\n",
" <td>1.542440</td>\n",
" <td>-2.071429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>To Have, or Not (1995)</th>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>For the Moment (1994)</th>\n",
" <td>5.000000</td>\n",
" <td>3.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.816497</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Phat Beach (1996)</th>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Crude Oasis, The (1995)</th>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coming Apart (1969)</th>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Separation, The (La S�paration) (1994)</th>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.414214</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mad Dog Time (1996)</th>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.279204</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Theodore Rex (1995)</th>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>-2.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean \\\n",
"gender F M \n",
"James Dean Story, The (1957) 4.000000 1.000000 \n",
"Country Life (1994) 5.000000 2.000000 \n",
"Spiders, The (Die Spinnen, 1. Teil: Der Goldene... 4.000000 1.000000 \n",
"Babyfever (1994) 3.666667 1.000000 \n",
"Woman of Paris, A (1923) 5.000000 2.428571 \n",
"Cobra (1925) 4.000000 1.500000 \n",
"Other Side of Sunday, The (S�ndagsengler) (1996) 5.000000 2.928571 \n",
"To Have, or Not (1995) 4.000000 2.000000 \n",
"For the Moment (1994) 5.000000 3.000000 \n",
"Phat Beach (1996) 3.000000 1.000000 \n",
"Crude Oasis, The (1995) 3.000000 1.000000 \n",
"Coming Apart (1969) 4.000000 2.000000 \n",
"Separation, The (La S�paration) (1994) 4.000000 2.000000 \n",
"Mad Dog Time (1996) 4.000000 2.000000 \n",
"Theodore Rex (1995) 3.000000 1.000000 \n",
"\n",
" std \\\n",
"gender F M \n",
"James Dean Story, The (1957) 0.000000 NaN \n",
"Country Life (1994) NaN 0.000000 \n",
"Spiders, The (Die Spinnen, 1. Teil: Der Goldene... 1.414214 0.000000 \n",
"Babyfever (1994) 0.577350 NaN \n",
"Woman of Paris, A (1923) NaN 1.272418 \n",
"Cobra (1925) NaN 1.000000 \n",
"Other Side of Sunday, The (S�ndagsengler) (1996) 0.000000 1.542440 \n",
"To Have, or Not (1995) NaN NaN \n",
"For the Moment (1994) NaN 0.816497 \n",
"Phat Beach (1996) 0.000000 0.000000 \n",
"Crude Oasis, The (1995) NaN NaN \n",
"Coming Apart (1969) NaN NaN \n",
"Separation, The (La S�paration) (1994) NaN 1.414214 \n",
"Mad Dog Time (1996) NaN 1.279204 \n",
"Theodore Rex (1995) NaN 0.000000 \n",
"\n",
" mean_diff \n",
"gender \n",
"James Dean Story, The (1957) -3.000000 \n",
"Country Life (1994) -3.000000 \n",
"Spiders, The (Die Spinnen, 1. Teil: Der Goldene... -3.000000 \n",
"Babyfever (1994) -2.666667 \n",
"Woman of Paris, A (1923) -2.571429 \n",
"Cobra (1925) -2.500000 \n",
"Other Side of Sunday, The (S�ndagsengler) (1996) -2.071429 \n",
"To Have, or Not (1995) -2.000000 \n",
"For the Moment (1994) -2.000000 \n",
"Phat Beach (1996) -2.000000 \n",
"Crude Oasis, The (1995) -2.000000 \n",
"Coming Apart (1969) -2.000000 \n",
"Separation, The (La S�paration) (1994) -2.000000 \n",
"Mad Dog Time (1996) -2.000000 \n",
"Theodore Rex (1995) -2.000000 "
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_by_diff[:15]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"*Exercise:* Take a look at the data in the above table. What is the problem with what we have done?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Answer: Some movies only have one or two ratings! They are not statistically significant. Let's remove movies with only a few ratings."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index([''burbs, The (1989)', '10 Things I Hate About You (1999)',\n",
" '101 Dalmatians (1961)', '101 Dalmatians (1996)', '12 Angry Men (1957)',\n",
" '13th Warrior, The (1999)', '2 Days in the Valley (1996)',\n",
" '20,000 Leagues Under the Sea (1954)', '2001: A Space Odyssey (1968)',\n",
" '2010 (1984)',\n",
" ...\n",
" 'X-Men (2000)', 'Year of Living Dangerously (1982)',\n",
" 'Yellow Submarine (1968)', 'You've Got Mail (1998)',\n",
" 'Young Frankenstein (1974)', 'Young Guns (1988)',\n",
" 'Young Guns II (1990)', 'Young Sherlock Holmes (1985)',\n",
" 'Zero Effect (1998)', 'eXistenZ (1999)'],\n",
" dtype='object', name='title', length=1216)\n"
]
}
],
"source": [
"ratings_by_title = data.groupby('title').size()\n",
"ratings_by_title\n",
"active_titles = ratings_by_title.index[ratings_by_title >= 250]\n",
"print(active_titles)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"mean_rating = mean_rating.loc[active_titles]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">mean</th>\n",
" <th colspan=\"2\" halign=\"left\">std</th>\n",
" <th>mean_diff</th>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th></th>\n",
" </tr>\n",
" <tr>\n",
" <th>title</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>'burbs, The (1989)</th>\n",
" <td>2.793478</td>\n",
" <td>2.962085</td>\n",
" <td>1.124532</td>\n",
" <td>1.099127</td>\n",
" <td>0.168607</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 Things I Hate About You (1999)</th>\n",
" <td>3.646552</td>\n",
" <td>3.311966</td>\n",
" <td>0.986965</td>\n",
" <td>0.973364</td>\n",
" <td>-0.334586</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101 Dalmatians (1961)</th>\n",
" <td>3.791444</td>\n",
" <td>3.500000</td>\n",
" <td>0.882525</td>\n",
" <td>1.015137</td>\n",
" <td>-0.291444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101 Dalmatians (1996)</th>\n",
" <td>3.240000</td>\n",
" <td>2.911215</td>\n",
" <td>1.072412</td>\n",
" <td>1.099110</td>\n",
" <td>-0.328785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 Angry Men (1957)</th>\n",
" <td>4.184397</td>\n",
" <td>4.328421</td>\n",
" <td>0.883199</td>\n",
" <td>0.788587</td>\n",
" <td>0.144024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13th Warrior, The (1999)</th>\n",
" <td>3.112000</td>\n",
" <td>3.168000</td>\n",
" <td>1.108734</td>\n",
" <td>1.147293</td>\n",
" <td>0.056000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 Days in the Valley (1996)</th>\n",
" <td>3.488889</td>\n",
" <td>3.244813</td>\n",
" <td>1.100046</td>\n",
" <td>0.881656</td>\n",
" <td>-0.244076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20,000 Leagues Under the Sea (1954)</th>\n",
" <td>3.670103</td>\n",
" <td>3.709205</td>\n",
" <td>0.932310</td>\n",
" <td>0.857302</td>\n",
" <td>0.039102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2001: A Space Odyssey (1968)</th>\n",
" <td>3.825581</td>\n",
" <td>4.129738</td>\n",
" <td>1.197099</td>\n",
" <td>0.991174</td>\n",
" <td>0.304156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2010 (1984)</th>\n",
" <td>3.446809</td>\n",
" <td>3.413712</td>\n",
" <td>0.973754</td>\n",
" <td>0.944679</td>\n",
" <td>-0.033097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 Days (2000)</th>\n",
" <td>3.209424</td>\n",
" <td>2.977707</td>\n",
" <td>0.950410</td>\n",
" <td>0.891644</td>\n",
" <td>-0.231717</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39 Steps, The (1935)</th>\n",
" <td>3.965517</td>\n",
" <td>4.107692</td>\n",
" <td>0.935940</td>\n",
" <td>0.827175</td>\n",
" <td>0.142175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54 (1998)</th>\n",
" <td>2.701754</td>\n",
" <td>2.782178</td>\n",
" <td>1.179663</td>\n",
" <td>0.993551</td>\n",
" <td>0.080424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7th Voyage of Sinbad, The (1958)</th>\n",
" <td>3.409091</td>\n",
" <td>3.658879</td>\n",
" <td>1.063522</td>\n",
" <td>0.898873</td>\n",
" <td>0.249788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8MM (1999)</th>\n",
" <td>2.906250</td>\n",
" <td>2.850962</td>\n",
" <td>1.094267</td>\n",
" <td>1.121550</td>\n",
" <td>-0.055288</td>\n",
" </tr>\n",
" <tr>\n",
" <th>About Last Night... (1986)</th>\n",
" <td>3.188679</td>\n",
" <td>3.140909</td>\n",
" <td>0.863142</td>\n",
" <td>0.980708</td>\n",
" <td>-0.047770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Absent Minded Professor, The (1961)</th>\n",
" <td>3.469388</td>\n",
" <td>3.446809</td>\n",
" <td>0.910467</td>\n",
" <td>0.950187</td>\n",
" <td>-0.022579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Absolute Power (1997)</th>\n",
" <td>3.469136</td>\n",
" <td>3.327759</td>\n",
" <td>0.923125</td>\n",
" <td>0.882111</td>\n",
" <td>-0.141377</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Abyss, The (1989)</th>\n",
" <td>3.659236</td>\n",
" <td>3.689507</td>\n",
" <td>0.983146</td>\n",
" <td>0.940340</td>\n",
" <td>0.030272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ace Ventura: Pet Detective (1994)</th>\n",
" <td>3.000000</td>\n",
" <td>3.197917</td>\n",
" <td>1.195229</td>\n",
" <td>1.118569</td>\n",
" <td>0.197917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ace Ventura: When Nature Calls (1995)</th>\n",
" <td>2.269663</td>\n",
" <td>2.543333</td>\n",
" <td>1.145783</td>\n",
" <td>1.182985</td>\n",
" <td>0.273670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Addams Family Values (1993)</th>\n",
" <td>3.000000</td>\n",
" <td>2.878531</td>\n",
" <td>1.000000</td>\n",
" <td>1.017937</td>\n",
" <td>-0.121469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Addams Family, The (1991)</th>\n",
" <td>3.186170</td>\n",
" <td>3.163498</td>\n",
" <td>1.030249</td>\n",
" <td>0.954128</td>\n",
" <td>-0.022672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adventures in Babysitting (1987)</th>\n",
" <td>3.455782</td>\n",
" <td>3.208122</td>\n",
" <td>1.105598</td>\n",
" <td>0.953011</td>\n",
" <td>-0.247660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adventures of Buckaroo Bonzai Across the 8th Dimension, The (1984)</th>\n",
" <td>3.308511</td>\n",
" <td>3.402321</td>\n",
" <td>1.218377</td>\n",
" <td>1.151606</td>\n",
" <td>0.093810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adventures of Priscilla, Queen of the Desert, The (1994)</th>\n",
" <td>3.989071</td>\n",
" <td>3.688811</td>\n",
" <td>0.864368</td>\n",
" <td>1.078179</td>\n",
" <td>-0.300260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adventures of Robin Hood, The (1938)</th>\n",
" <td>4.166667</td>\n",
" <td>3.918367</td>\n",
" <td>0.691312</td>\n",
" <td>0.960021</td>\n",
" <td>-0.248299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>African Queen, The (1951)</th>\n",
" <td>4.324232</td>\n",
" <td>4.223822</td>\n",
" <td>0.767932</td>\n",
" <td>0.838715</td>\n",
" <td>-0.100410</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age of Innocence, The (1993)</th>\n",
" <td>3.827068</td>\n",
" <td>3.339506</td>\n",
" <td>0.908806</td>\n",
" <td>0.871663</td>\n",
" <td>-0.487561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Agnes of God (1985)</th>\n",
" <td>3.534884</td>\n",
" <td>3.244898</td>\n",
" <td>0.893086</td>\n",
" <td>0.955100</td>\n",
" <td>-0.289986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>White Men Can't Jump (1992)</th>\n",
" <td>3.028777</td>\n",
" <td>3.231061</td>\n",
" <td>0.908434</td>\n",
" <td>0.942171</td>\n",
" <td>0.202284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Who Framed Roger Rabbit? (1988)</th>\n",
" <td>3.569378</td>\n",
" <td>3.713251</td>\n",
" <td>1.004174</td>\n",
" <td>0.950380</td>\n",
" <td>0.143873</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Who's Afraid of Virginia Woolf? (1966)</th>\n",
" <td>4.029703</td>\n",
" <td>4.096939</td>\n",
" <td>1.043604</td>\n",
" <td>0.914718</td>\n",
" <td>0.067236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Whole Nine Yards, The (2000)</th>\n",
" <td>3.296552</td>\n",
" <td>3.404814</td>\n",
" <td>0.979825</td>\n",
" <td>0.957552</td>\n",
" <td>0.108262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wild Bunch, The (1969)</th>\n",
" <td>3.636364</td>\n",
" <td>4.128099</td>\n",
" <td>1.176980</td>\n",
" <td>0.899582</td>\n",
" <td>0.491736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wild Things (1998)</th>\n",
" <td>3.392000</td>\n",
" <td>3.459082</td>\n",
" <td>1.076853</td>\n",
" <td>1.022166</td>\n",
" <td>0.067082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wild Wild West (1999)</th>\n",
" <td>2.275449</td>\n",
" <td>2.131973</td>\n",
" <td>1.073586</td>\n",
" <td>1.062232</td>\n",
" <td>-0.143476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Shakespeare's Romeo and Juliet (1996)</th>\n",
" <td>3.532609</td>\n",
" <td>3.318644</td>\n",
" <td>1.120608</td>\n",
" <td>1.189591</td>\n",
" <td>-0.213965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Willow (1988)</th>\n",
" <td>3.658683</td>\n",
" <td>3.453543</td>\n",
" <td>1.028348</td>\n",
" <td>0.991589</td>\n",
" <td>-0.205139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Willy Wonka and the Chocolate Factory (1971)</th>\n",
" <td>4.063953</td>\n",
" <td>3.789474</td>\n",
" <td>0.960735</td>\n",
" <td>1.026031</td>\n",
" <td>-0.274480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Witness (1985)</th>\n",
" <td>4.115854</td>\n",
" <td>3.941504</td>\n",
" <td>0.777351</td>\n",
" <td>0.798834</td>\n",
" <td>-0.174349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wizard of Oz, The (1939)</th>\n",
" <td>4.355030</td>\n",
" <td>4.203138</td>\n",
" <td>0.868981</td>\n",
" <td>0.903633</td>\n",
" <td>-0.151892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wolf (1994)</th>\n",
" <td>3.074074</td>\n",
" <td>2.899083</td>\n",
" <td>1.179252</td>\n",
" <td>1.055553</td>\n",
" <td>-0.174992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Women on the Verge of a Nervous Breakdown (1988)</th>\n",
" <td>3.934307</td>\n",
" <td>3.865741</td>\n",
" <td>0.901016</td>\n",
" <td>0.870996</td>\n",
" <td>-0.068566</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wonder Boys (2000)</th>\n",
" <td>4.043796</td>\n",
" <td>3.913649</td>\n",
" <td>0.922501</td>\n",
" <td>0.862508</td>\n",
" <td>-0.130147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Working Girl (1988)</th>\n",
" <td>3.606742</td>\n",
" <td>3.312500</td>\n",
" <td>0.865118</td>\n",
" <td>0.871073</td>\n",
" <td>-0.294242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>World Is Not Enough, The (1999)</th>\n",
" <td>3.337500</td>\n",
" <td>3.388889</td>\n",
" <td>1.002434</td>\n",
" <td>0.966510</td>\n",
" <td>0.051389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wrong Trousers, The (1993)</th>\n",
" <td>4.588235</td>\n",
" <td>4.478261</td>\n",
" <td>0.635275</td>\n",
" <td>0.732171</td>\n",
" <td>-0.109974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wyatt Earp (1994)</th>\n",
" <td>3.147059</td>\n",
" <td>3.283898</td>\n",
" <td>1.076818</td>\n",
" <td>0.899122</td>\n",
" <td>0.136839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>X-Files: Fight the Future, The (1998)</th>\n",
" <td>3.489474</td>\n",
" <td>3.493797</td>\n",
" <td>0.974487</td>\n",
" <td>0.878642</td>\n",
" <td>0.004323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>X-Men (2000)</th>\n",
" <td>3.682310</td>\n",
" <td>3.851702</td>\n",
" <td>1.039167</td>\n",
" <td>0.882313</td>\n",
" <td>0.169391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Year of Living Dangerously (1982)</th>\n",
" <td>3.951220</td>\n",
" <td>3.869403</td>\n",
" <td>0.876409</td>\n",
" <td>0.883535</td>\n",
" <td>-0.081817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yellow Submarine (1968)</th>\n",
" <td>3.714286</td>\n",
" <td>3.689286</td>\n",
" <td>1.113422</td>\n",
" <td>1.060817</td>\n",
" <td>-0.025000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>You've Got Mail (1998)</th>\n",
" <td>3.542424</td>\n",
" <td>3.275591</td>\n",
" <td>1.013445</td>\n",
" <td>1.040992</td>\n",
" <td>-0.266834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Young Frankenstein (1974)</th>\n",
" <td>4.289963</td>\n",
" <td>4.239177</td>\n",
" <td>0.912972</td>\n",
" <td>0.860939</td>\n",
" <td>-0.050785</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Young Guns (1988)</th>\n",
" <td>3.371795</td>\n",
" <td>3.425620</td>\n",
" <td>0.884337</td>\n",
" <td>1.037917</td>\n",
" <td>0.053825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Young Guns II (1990)</th>\n",
" <td>2.934783</td>\n",
" <td>2.904025</td>\n",
" <td>1.062537</td>\n",
" <td>1.074875</td>\n",
" <td>-0.030758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Young Sherlock Holmes (1985)</th>\n",
" <td>3.514706</td>\n",
" <td>3.363344</td>\n",
" <td>0.889284</td>\n",
" <td>0.890709</td>\n",
" <td>-0.151362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zero Effect (1998)</th>\n",
" <td>3.864407</td>\n",
" <td>3.723140</td>\n",
" <td>1.166451</td>\n",
" <td>1.011245</td>\n",
" <td>-0.141266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eXistenZ (1999)</th>\n",
" <td>3.098592</td>\n",
" <td>3.289086</td>\n",
" <td>1.185085</td>\n",
" <td>1.176281</td>\n",
" <td>0.190494</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1216 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" mean \\\n",
"gender F M \n",
"title \n",
"'burbs, The (1989) 2.793478 2.962085 \n",
"10 Things I Hate About You (1999) 3.646552 3.311966 \n",
"101 Dalmatians (1961) 3.791444 3.500000 \n",
"101 Dalmatians (1996) 3.240000 2.911215 \n",
"12 Angry Men (1957) 4.184397 4.328421 \n",
"13th Warrior, The (1999) 3.112000 3.168000 \n",
"2 Days in the Valley (1996) 3.488889 3.244813 \n",
"20,000 Leagues Under the Sea (1954) 3.670103 3.709205 \n",
"2001: A Space Odyssey (1968) 3.825581 4.129738 \n",
"2010 (1984) 3.446809 3.413712 \n",
"28 Days (2000) 3.209424 2.977707 \n",
"39 Steps, The (1935) 3.965517 4.107692 \n",
"54 (1998) 2.701754 2.782178 \n",
"7th Voyage of Sinbad, The (1958) 3.409091 3.658879 \n",
"8MM (1999) 2.906250 2.850962 \n",
"About Last Night... (1986) 3.188679 3.140909 \n",
"Absent Minded Professor, The (1961) 3.469388 3.446809 \n",
"Absolute Power (1997) 3.469136 3.327759 \n",
"Abyss, The (1989) 3.659236 3.689507 \n",
"Ace Ventura: Pet Detective (1994) 3.000000 3.197917 \n",
"Ace Ventura: When Nature Calls (1995) 2.269663 2.543333 \n",
"Addams Family Values (1993) 3.000000 2.878531 \n",
"Addams Family, The (1991) 3.186170 3.163498 \n",
"Adventures in Babysitting (1987) 3.455782 3.208122 \n",
"Adventures of Buckaroo Bonzai Across the 8th Di... 3.308511 3.402321 \n",
"Adventures of Priscilla, Queen of the Desert, T... 3.989071 3.688811 \n",
"Adventures of Robin Hood, The (1938) 4.166667 3.918367 \n",
"African Queen, The (1951) 4.324232 4.223822 \n",
"Age of Innocence, The (1993) 3.827068 3.339506 \n",
"Agnes of God (1985) 3.534884 3.244898 \n",
"... ... ... \n",
"White Men Can't Jump (1992) 3.028777 3.231061 \n",
"Who Framed Roger Rabbit? (1988) 3.569378 3.713251 \n",
"Who's Afraid of Virginia Woolf? (1966) 4.029703 4.096939 \n",
"Whole Nine Yards, The (2000) 3.296552 3.404814 \n",
"Wild Bunch, The (1969) 3.636364 4.128099 \n",
"Wild Things (1998) 3.392000 3.459082 \n",
"Wild Wild West (1999) 2.275449 2.131973 \n",
"William Shakespeare's Romeo and Juliet (1996) 3.532609 3.318644 \n",
"Willow (1988) 3.658683 3.453543 \n",
"Willy Wonka and the Chocolate Factory (1971) 4.063953 3.789474 \n",
"Witness (1985) 4.115854 3.941504 \n",
"Wizard of Oz, The (1939) 4.355030 4.203138 \n",
"Wolf (1994) 3.074074 2.899083 \n",
"Women on the Verge of a Nervous Breakdown (1988) 3.934307 3.865741 \n",
"Wonder Boys (2000) 4.043796 3.913649 \n",
"Working Girl (1988) 3.606742 3.312500 \n",
"World Is Not Enough, The (1999) 3.337500 3.388889 \n",
"Wrong Trousers, The (1993) 4.588235 4.478261 \n",
"Wyatt Earp (1994) 3.147059 3.283898 \n",
"X-Files: Fight the Future, The (1998) 3.489474 3.493797 \n",
"X-Men (2000) 3.682310 3.851702 \n",
"Year of Living Dangerously (1982) 3.951220 3.869403 \n",
"Yellow Submarine (1968) 3.714286 3.689286 \n",
"You've Got Mail (1998) 3.542424 3.275591 \n",
"Young Frankenstein (1974) 4.289963 4.239177 \n",
"Young Guns (1988) 3.371795 3.425620 \n",
"Young Guns II (1990) 2.934783 2.904025 \n",
"Young Sherlock Holmes (1985) 3.514706 3.363344 \n",
"Zero Effect (1998) 3.864407 3.723140 \n",
"eXistenZ (1999) 3.098592 3.289086 \n",
"\n",
" std \\\n",
"gender F M \n",
"title \n",
"'burbs, The (1989) 1.124532 1.099127 \n",
"10 Things I Hate About You (1999) 0.986965 0.973364 \n",
"101 Dalmatians (1961) 0.882525 1.015137 \n",
"101 Dalmatians (1996) 1.072412 1.099110 \n",
"12 Angry Men (1957) 0.883199 0.788587 \n",
"13th Warrior, The (1999) 1.108734 1.147293 \n",
"2 Days in the Valley (1996) 1.100046 0.881656 \n",
"20,000 Leagues Under the Sea (1954) 0.932310 0.857302 \n",
"2001: A Space Odyssey (1968) 1.197099 0.991174 \n",
"2010 (1984) 0.973754 0.944679 \n",
"28 Days (2000) 0.950410 0.891644 \n",
"39 Steps, The (1935) 0.935940 0.827175 \n",
"54 (1998) 1.179663 0.993551 \n",
"7th Voyage of Sinbad, The (1958) 1.063522 0.898873 \n",
"8MM (1999) 1.094267 1.121550 \n",
"About Last Night... (1986) 0.863142 0.980708 \n",
"Absent Minded Professor, The (1961) 0.910467 0.950187 \n",
"Absolute Power (1997) 0.923125 0.882111 \n",
"Abyss, The (1989) 0.983146 0.940340 \n",
"Ace Ventura: Pet Detective (1994) 1.195229 1.118569 \n",
"Ace Ventura: When Nature Calls (1995) 1.145783 1.182985 \n",
"Addams Family Values (1993) 1.000000 1.017937 \n",
"Addams Family, The (1991) 1.030249 0.954128 \n",
"Adventures in Babysitting (1987) 1.105598 0.953011 \n",
"Adventures of Buckaroo Bonzai Across the 8th Di... 1.218377 1.151606 \n",
"Adventures of Priscilla, Queen of the Desert, T... 0.864368 1.078179 \n",
"Adventures of Robin Hood, The (1938) 0.691312 0.960021 \n",
"African Queen, The (1951) 0.767932 0.838715 \n",
"Age of Innocence, The (1993) 0.908806 0.871663 \n",
"Agnes of God (1985) 0.893086 0.955100 \n",
"... ... ... \n",
"White Men Can't Jump (1992) 0.908434 0.942171 \n",
"Who Framed Roger Rabbit? (1988) 1.004174 0.950380 \n",
"Who's Afraid of Virginia Woolf? (1966) 1.043604 0.914718 \n",
"Whole Nine Yards, The (2000) 0.979825 0.957552 \n",
"Wild Bunch, The (1969) 1.176980 0.899582 \n",
"Wild Things (1998) 1.076853 1.022166 \n",
"Wild Wild West (1999) 1.073586 1.062232 \n",
"William Shakespeare's Romeo and Juliet (1996) 1.120608 1.189591 \n",
"Willow (1988) 1.028348 0.991589 \n",
"Willy Wonka and the Chocolate Factory (1971) 0.960735 1.026031 \n",
"Witness (1985) 0.777351 0.798834 \n",
"Wizard of Oz, The (1939) 0.868981 0.903633 \n",
"Wolf (1994) 1.179252 1.055553 \n",
"Women on the Verge of a Nervous Breakdown (1988) 0.901016 0.870996 \n",
"Wonder Boys (2000) 0.922501 0.862508 \n",
"Working Girl (1988) 0.865118 0.871073 \n",
"World Is Not Enough, The (1999) 1.002434 0.966510 \n",
"Wrong Trousers, The (1993) 0.635275 0.732171 \n",
"Wyatt Earp (1994) 1.076818 0.899122 \n",
"X-Files: Fight the Future, The (1998) 0.974487 0.878642 \n",
"X-Men (2000) 1.039167 0.882313 \n",
"Year of Living Dangerously (1982) 0.876409 0.883535 \n",
"Yellow Submarine (1968) 1.113422 1.060817 \n",
"You've Got Mail (1998) 1.013445 1.040992 \n",
"Young Frankenstein (1974) 0.912972 0.860939 \n",
"Young Guns (1988) 0.884337 1.037917 \n",
"Young Guns II (1990) 1.062537 1.074875 \n",
"Young Sherlock Holmes (1985) 0.889284 0.890709 \n",
"Zero Effect (1998) 1.166451 1.011245 \n",
"eXistenZ (1999) 1.185085 1.176281 \n",
"\n",
" mean_diff \n",
"gender \n",
"title \n",
"'burbs, The (1989) 0.168607 \n",
"10 Things I Hate About You (1999) -0.334586 \n",
"101 Dalmatians (1961) -0.291444 \n",
"101 Dalmatians (1996) -0.328785 \n",
"12 Angry Men (1957) 0.144024 \n",
"13th Warrior, The (1999) 0.056000 \n",
"2 Days in the Valley (1996) -0.244076 \n",
"20,000 Leagues Under the Sea (1954) 0.039102 \n",
"2001: A Space Odyssey (1968) 0.304156 \n",
"2010 (1984) -0.033097 \n",
"28 Days (2000) -0.231717 \n",
"39 Steps, The (1935) 0.142175 \n",
"54 (1998) 0.080424 \n",
"7th Voyage of Sinbad, The (1958) 0.249788 \n",
"8MM (1999) -0.055288 \n",
"About Last Night... (1986) -0.047770 \n",
"Absent Minded Professor, The (1961) -0.022579 \n",
"Absolute Power (1997) -0.141377 \n",
"Abyss, The (1989) 0.030272 \n",
"Ace Ventura: Pet Detective (1994) 0.197917 \n",
"Ace Ventura: When Nature Calls (1995) 0.273670 \n",
"Addams Family Values (1993) -0.121469 \n",
"Addams Family, The (1991) -0.022672 \n",
"Adventures in Babysitting (1987) -0.247660 \n",
"Adventures of Buckaroo Bonzai Across the 8th Di... 0.093810 \n",
"Adventures of Priscilla, Queen of the Desert, T... -0.300260 \n",
"Adventures of Robin Hood, The (1938) -0.248299 \n",
"African Queen, The (1951) -0.100410 \n",
"Age of Innocence, The (1993) -0.487561 \n",
"Agnes of God (1985) -0.289986 \n",
"... ... \n",
"White Men Can't Jump (1992) 0.202284 \n",
"Who Framed Roger Rabbit? (1988) 0.143873 \n",
"Who's Afraid of Virginia Woolf? (1966) 0.067236 \n",
"Whole Nine Yards, The (2000) 0.108262 \n",
"Wild Bunch, The (1969) 0.491736 \n",
"Wild Things (1998) 0.067082 \n",
"Wild Wild West (1999) -0.143476 \n",
"William Shakespeare's Romeo and Juliet (1996) -0.213965 \n",
"Willow (1988) -0.205139 \n",
"Willy Wonka and the Chocolate Factory (1971) -0.274480 \n",
"Witness (1985) -0.174349 \n",
"Wizard of Oz, The (1939) -0.151892 \n",
"Wolf (1994) -0.174992 \n",
"Women on the Verge of a Nervous Breakdown (1988) -0.068566 \n",
"Wonder Boys (2000) -0.130147 \n",
"Working Girl (1988) -0.294242 \n",
"World Is Not Enough, The (1999) 0.051389 \n",
"Wrong Trousers, The (1993) -0.109974 \n",
"Wyatt Earp (1994) 0.136839 \n",
"X-Files: Fight the Future, The (1998) 0.004323 \n",
"X-Men (2000) 0.169391 \n",
"Year of Living Dangerously (1982) -0.081817 \n",
"Yellow Submarine (1968) -0.025000 \n",
"You've Got Mail (1998) -0.266834 \n",
"Young Frankenstein (1974) -0.050785 \n",
"Young Guns (1988) 0.053825 \n",
"Young Guns II (1990) -0.030758 \n",
"Young Sherlock Holmes (1985) -0.151362 \n",
"Zero Effect (1998) -0.141266 \n",
"eXistenZ (1999) 0.190494 \n",
"\n",
"[1216 rows x 5 columns]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_rating"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">mean</th>\n",
" <th colspan=\"2\" halign=\"left\">std</th>\n",
" <th>mean_diff</th>\n",
" </tr>\n",
" <tr>\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" <th></th>\n",
" </tr>\n",
" <tr>\n",
" <th>title</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Dirty Dancing (1987)</th>\n",
" <td>3.790378</td>\n",
" <td>2.959596</td>\n",
" <td>1.105181</td>\n",
" <td>1.087738</td>\n",
" <td>-0.830782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jumpin' Jack Flash (1986)</th>\n",
" <td>3.254717</td>\n",
" <td>2.578358</td>\n",
" <td>1.078459</td>\n",
" <td>1.134007</td>\n",
" <td>-0.676359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Grease (1978)</th>\n",
" <td>3.975265</td>\n",
" <td>3.367041</td>\n",
" <td>1.022490</td>\n",
" <td>1.102666</td>\n",
" <td>-0.608224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Little Women (1994)</th>\n",
" <td>3.870588</td>\n",
" <td>3.321739</td>\n",
" <td>0.887630</td>\n",
" <td>0.903675</td>\n",
" <td>-0.548849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Steel Magnolias (1989)</th>\n",
" <td>3.901734</td>\n",
" <td>3.365957</td>\n",
" <td>0.906467</td>\n",
" <td>1.106561</td>\n",
" <td>-0.535777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anastasia (1997)</th>\n",
" <td>3.800000</td>\n",
" <td>3.281609</td>\n",
" <td>1.029789</td>\n",
" <td>0.897012</td>\n",
" <td>-0.518391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rocky Horror Picture Show, The (1975)</th>\n",
" <td>3.673016</td>\n",
" <td>3.160131</td>\n",
" <td>1.190890</td>\n",
" <td>1.257241</td>\n",
" <td>-0.512885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Color Purple, The (1985)</th>\n",
" <td>4.158192</td>\n",
" <td>3.659341</td>\n",
" <td>0.824244</td>\n",
" <td>1.120200</td>\n",
" <td>-0.498851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age of Innocence, The (1993)</th>\n",
" <td>3.827068</td>\n",
" <td>3.339506</td>\n",
" <td>0.908806</td>\n",
" <td>0.871663</td>\n",
" <td>-0.487561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Free Willy (1993)</th>\n",
" <td>2.921348</td>\n",
" <td>2.438776</td>\n",
" <td>1.140422</td>\n",
" <td>0.966796</td>\n",
" <td>-0.482573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>French Kiss (1995)</th>\n",
" <td>3.535714</td>\n",
" <td>3.056962</td>\n",
" <td>0.914899</td>\n",
" <td>0.972512</td>\n",
" <td>-0.478752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Little Shop of Horrors, The (1960)</th>\n",
" <td>3.650000</td>\n",
" <td>3.179688</td>\n",
" <td>1.067187</td>\n",
" <td>1.032291</td>\n",
" <td>-0.470312</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Guys and Dolls (1955)</th>\n",
" <td>4.051724</td>\n",
" <td>3.583333</td>\n",
" <td>0.842820</td>\n",
" <td>1.112548</td>\n",
" <td>-0.468391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mary Poppins (1964)</th>\n",
" <td>4.197740</td>\n",
" <td>3.730594</td>\n",
" <td>0.884487</td>\n",
" <td>1.035435</td>\n",
" <td>-0.467147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Patch Adams (1998)</th>\n",
" <td>3.473282</td>\n",
" <td>3.008746</td>\n",
" <td>1.198257</td>\n",
" <td>1.195721</td>\n",
" <td>-0.464536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Grease 2 (1982)</th>\n",
" <td>2.243478</td>\n",
" <td>1.792553</td>\n",
" <td>1.151592</td>\n",
" <td>0.972646</td>\n",
" <td>-0.450925</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sound of Music, The (1965)</th>\n",
" <td>4.233677</td>\n",
" <td>3.783418</td>\n",
" <td>1.024039</td>\n",
" <td>1.154415</td>\n",
" <td>-0.450259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Never Been Kissed (1999)</th>\n",
" <td>3.452174</td>\n",
" <td>3.002538</td>\n",
" <td>0.959835</td>\n",
" <td>0.968981</td>\n",
" <td>-0.449636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Casper (1995)</th>\n",
" <td>3.291139</td>\n",
" <td>2.844444</td>\n",
" <td>0.907969</td>\n",
" <td>1.029304</td>\n",
" <td>-0.446695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jewel of the Nile, The (1985)</th>\n",
" <td>3.494253</td>\n",
" <td>3.048096</td>\n",
" <td>0.865171</td>\n",
" <td>0.945127</td>\n",
" <td>-0.446157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Pacific (1958)</th>\n",
" <td>3.887850</td>\n",
" <td>3.445087</td>\n",
" <td>0.924786</td>\n",
" <td>0.917302</td>\n",
" <td>-0.442764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gone with the Wind (1939)</th>\n",
" <td>4.269841</td>\n",
" <td>3.829371</td>\n",
" <td>0.956899</td>\n",
" <td>1.076346</td>\n",
" <td>-0.440471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sommersby (1993)</th>\n",
" <td>3.482270</td>\n",
" <td>3.046667</td>\n",
" <td>0.997159</td>\n",
" <td>1.057645</td>\n",
" <td>-0.435603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Skulls, The (2000)</th>\n",
" <td>3.053333</td>\n",
" <td>2.621145</td>\n",
" <td>1.172815</td>\n",
" <td>1.139435</td>\n",
" <td>-0.432188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Corrina, Corrina (1994)</th>\n",
" <td>3.523490</td>\n",
" <td>3.092593</td>\n",
" <td>0.934214</td>\n",
" <td>1.106793</td>\n",
" <td>-0.430897</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iron Eagle (1986)</th>\n",
" <td>2.968750</td>\n",
" <td>2.543933</td>\n",
" <td>1.121185</td>\n",
" <td>0.959886</td>\n",
" <td>-0.424817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hope Floats (1998)</th>\n",
" <td>3.206186</td>\n",
" <td>2.782383</td>\n",
" <td>1.105222</td>\n",
" <td>1.105997</td>\n",
" <td>-0.423802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beverly Hills Cop III (1994)</th>\n",
" <td>2.865385</td>\n",
" <td>2.443333</td>\n",
" <td>1.048413</td>\n",
" <td>1.014999</td>\n",
" <td>-0.422051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sense and Sensibility (1995)</th>\n",
" <td>4.233333</td>\n",
" <td>3.819277</td>\n",
" <td>0.830696</td>\n",
" <td>0.929191</td>\n",
" <td>-0.414056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parent Trap, The (1961)</th>\n",
" <td>3.805556</td>\n",
" <td>3.393333</td>\n",
" <td>0.980733</td>\n",
" <td>1.035586</td>\n",
" <td>-0.412222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tommy Boy (1995)</th>\n",
" <td>3.170732</td>\n",
" <td>3.585366</td>\n",
" <td>1.215181</td>\n",
" <td>1.110982</td>\n",
" <td>0.414634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eyes Wide Shut (1999)</th>\n",
" <td>2.891213</td>\n",
" <td>3.305949</td>\n",
" <td>1.242070</td>\n",
" <td>1.249065</td>\n",
" <td>0.414736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Park: Bigger, Longer and Uncut (1999)</th>\n",
" <td>3.422481</td>\n",
" <td>3.846686</td>\n",
" <td>1.373668</td>\n",
" <td>1.182896</td>\n",
" <td>0.424206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Godzilla (Gojira) (1954)</th>\n",
" <td>2.948718</td>\n",
" <td>3.374046</td>\n",
" <td>1.316869</td>\n",
" <td>1.176630</td>\n",
" <td>0.425328</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Raging Bull (1980)</th>\n",
" <td>3.831933</td>\n",
" <td>4.263441</td>\n",
" <td>0.985654</td>\n",
" <td>0.819723</td>\n",
" <td>0.431508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Reservoir Dogs (1992)</th>\n",
" <td>3.769231</td>\n",
" <td>4.213873</td>\n",
" <td>1.193221</td>\n",
" <td>0.924602</td>\n",
" <td>0.444642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Where Eagles Dare (1969)</th>\n",
" <td>3.384615</td>\n",
" <td>3.833333</td>\n",
" <td>1.192928</td>\n",
" <td>0.754799</td>\n",
" <td>0.448718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kingpin (1996)</th>\n",
" <td>3.092308</td>\n",
" <td>3.557480</td>\n",
" <td>1.210075</td>\n",
" <td>1.085212</td>\n",
" <td>0.465173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Me, Myself and Irene (2000)</th>\n",
" <td>2.629310</td>\n",
" <td>3.096847</td>\n",
" <td>1.183438</td>\n",
" <td>1.160715</td>\n",
" <td>0.467537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Akira (1988)</th>\n",
" <td>3.511111</td>\n",
" <td>3.980344</td>\n",
" <td>1.254487</td>\n",
" <td>0.949258</td>\n",
" <td>0.469233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wild Bunch, The (1969)</th>\n",
" <td>3.636364</td>\n",
" <td>4.128099</td>\n",
" <td>1.176980</td>\n",
" <td>0.899582</td>\n",
" <td>0.491736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>From Dusk Till Dawn (1996)</th>\n",
" <td>2.735714</td>\n",
" <td>3.232558</td>\n",
" <td>1.255839</td>\n",
" <td>1.148788</td>\n",
" <td>0.496844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Big Trouble in Little China (1986)</th>\n",
" <td>2.987952</td>\n",
" <td>3.485030</td>\n",
" <td>1.041738</td>\n",
" <td>1.005125</td>\n",
" <td>0.497078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beavis and Butt-head Do America (1996)</th>\n",
" <td>2.637363</td>\n",
" <td>3.135417</td>\n",
" <td>1.312474</td>\n",
" <td>1.141387</td>\n",
" <td>0.498054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rocky II (1979)</th>\n",
" <td>2.741379</td>\n",
" <td>3.242894</td>\n",
" <td>1.117019</td>\n",
" <td>0.953814</td>\n",
" <td>0.501515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Barb Wire (1996)</th>\n",
" <td>1.585366</td>\n",
" <td>2.100386</td>\n",
" <td>0.921293</td>\n",
" <td>1.098607</td>\n",
" <td>0.515020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fright Night (1985)</th>\n",
" <td>2.973684</td>\n",
" <td>3.500000</td>\n",
" <td>1.052329</td>\n",
" <td>0.981095</td>\n",
" <td>0.526316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exorcist, The (1973)</th>\n",
" <td>3.537634</td>\n",
" <td>4.067239</td>\n",
" <td>1.299140</td>\n",
" <td>1.016934</td>\n",
" <td>0.529605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Animal House (1978)</th>\n",
" <td>3.628906</td>\n",
" <td>4.167192</td>\n",
" <td>1.058571</td>\n",
" <td>0.921336</td>\n",
" <td>0.538286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Porky's (1981)</th>\n",
" <td>2.296875</td>\n",
" <td>2.836364</td>\n",
" <td>1.150289</td>\n",
" <td>1.136065</td>\n",
" <td>0.539489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>For a Few Dollars More (1965)</th>\n",
" <td>3.409091</td>\n",
" <td>3.953795</td>\n",
" <td>1.181568</td>\n",
" <td>0.820582</td>\n",
" <td>0.544704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Caddyshack (1980)</th>\n",
" <td>3.396135</td>\n",
" <td>3.969737</td>\n",
" <td>1.087254</td>\n",
" <td>0.998222</td>\n",
" <td>0.573602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rocky III (1982)</th>\n",
" <td>2.361702</td>\n",
" <td>2.943503</td>\n",
" <td>1.091977</td>\n",
" <td>1.030512</td>\n",
" <td>0.581801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hidden, The (1987)</th>\n",
" <td>3.137931</td>\n",
" <td>3.745098</td>\n",
" <td>1.059789</td>\n",
" <td>0.828696</td>\n",
" <td>0.607167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Evil Dead II (Dead By Dawn) (1987)</th>\n",
" <td>3.297297</td>\n",
" <td>3.909283</td>\n",
" <td>1.331697</td>\n",
" <td>1.061631</td>\n",
" <td>0.611985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cable Guy, The (1996)</th>\n",
" <td>2.250000</td>\n",
" <td>2.863787</td>\n",
" <td>1.051390</td>\n",
" <td>1.100629</td>\n",
" <td>0.613787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Longest Day, The (1962)</th>\n",
" <td>3.411765</td>\n",
" <td>4.031447</td>\n",
" <td>0.957194</td>\n",
" <td>0.847497</td>\n",
" <td>0.619682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dumb &amp; Dumber (1994)</th>\n",
" <td>2.697987</td>\n",
" <td>3.336595</td>\n",
" <td>1.354196</td>\n",
" <td>1.277333</td>\n",
" <td>0.638608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kentucky Fried Movie, The (1977)</th>\n",
" <td>2.878788</td>\n",
" <td>3.555147</td>\n",
" <td>1.023400</td>\n",
" <td>1.007669</td>\n",
" <td>0.676359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Good, The Bad and The Ugly, The (1966)</th>\n",
" <td>3.494949</td>\n",
" <td>4.221300</td>\n",
" <td>1.146189</td>\n",
" <td>0.814168</td>\n",
" <td>0.726351</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1216 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" mean std \\\n",
"gender F M F \n",
"title \n",
"Dirty Dancing (1987) 3.790378 2.959596 1.105181 \n",
"Jumpin' Jack Flash (1986) 3.254717 2.578358 1.078459 \n",
"Grease (1978) 3.975265 3.367041 1.022490 \n",
"Little Women (1994) 3.870588 3.321739 0.887630 \n",
"Steel Magnolias (1989) 3.901734 3.365957 0.906467 \n",
"Anastasia (1997) 3.800000 3.281609 1.029789 \n",
"Rocky Horror Picture Show, The (1975) 3.673016 3.160131 1.190890 \n",
"Color Purple, The (1985) 4.158192 3.659341 0.824244 \n",
"Age of Innocence, The (1993) 3.827068 3.339506 0.908806 \n",
"Free Willy (1993) 2.921348 2.438776 1.140422 \n",
"French Kiss (1995) 3.535714 3.056962 0.914899 \n",
"Little Shop of Horrors, The (1960) 3.650000 3.179688 1.067187 \n",
"Guys and Dolls (1955) 4.051724 3.583333 0.842820 \n",
"Mary Poppins (1964) 4.197740 3.730594 0.884487 \n",
"Patch Adams (1998) 3.473282 3.008746 1.198257 \n",
"Grease 2 (1982) 2.243478 1.792553 1.151592 \n",
"Sound of Music, The (1965) 4.233677 3.783418 1.024039 \n",
"Never Been Kissed (1999) 3.452174 3.002538 0.959835 \n",
"Casper (1995) 3.291139 2.844444 0.907969 \n",
"Jewel of the Nile, The (1985) 3.494253 3.048096 0.865171 \n",
"South Pacific (1958) 3.887850 3.445087 0.924786 \n",
"Gone with the Wind (1939) 4.269841 3.829371 0.956899 \n",
"Sommersby (1993) 3.482270 3.046667 0.997159 \n",
"Skulls, The (2000) 3.053333 2.621145 1.172815 \n",
"Corrina, Corrina (1994) 3.523490 3.092593 0.934214 \n",
"Iron Eagle (1986) 2.968750 2.543933 1.121185 \n",
"Hope Floats (1998) 3.206186 2.782383 1.105222 \n",
"Beverly Hills Cop III (1994) 2.865385 2.443333 1.048413 \n",
"Sense and Sensibility (1995) 4.233333 3.819277 0.830696 \n",
"Parent Trap, The (1961) 3.805556 3.393333 0.980733 \n",
"... ... ... ... \n",
"Tommy Boy (1995) 3.170732 3.585366 1.215181 \n",
"Eyes Wide Shut (1999) 2.891213 3.305949 1.242070 \n",
"South Park: Bigger, Longer and Uncut (1999) 3.422481 3.846686 1.373668 \n",
"Godzilla (Gojira) (1954) 2.948718 3.374046 1.316869 \n",
"Raging Bull (1980) 3.831933 4.263441 0.985654 \n",
"Reservoir Dogs (1992) 3.769231 4.213873 1.193221 \n",
"Where Eagles Dare (1969) 3.384615 3.833333 1.192928 \n",
"Kingpin (1996) 3.092308 3.557480 1.210075 \n",
"Me, Myself and Irene (2000) 2.629310 3.096847 1.183438 \n",
"Akira (1988) 3.511111 3.980344 1.254487 \n",
"Wild Bunch, The (1969) 3.636364 4.128099 1.176980 \n",
"From Dusk Till Dawn (1996) 2.735714 3.232558 1.255839 \n",
"Big Trouble in Little China (1986) 2.987952 3.485030 1.041738 \n",
"Beavis and Butt-head Do America (1996) 2.637363 3.135417 1.312474 \n",
"Rocky II (1979) 2.741379 3.242894 1.117019 \n",
"Barb Wire (1996) 1.585366 2.100386 0.921293 \n",
"Fright Night (1985) 2.973684 3.500000 1.052329 \n",
"Exorcist, The (1973) 3.537634 4.067239 1.299140 \n",
"Animal House (1978) 3.628906 4.167192 1.058571 \n",
"Porky's (1981) 2.296875 2.836364 1.150289 \n",
"For a Few Dollars More (1965) 3.409091 3.953795 1.181568 \n",
"Caddyshack (1980) 3.396135 3.969737 1.087254 \n",
"Rocky III (1982) 2.361702 2.943503 1.091977 \n",
"Hidden, The (1987) 3.137931 3.745098 1.059789 \n",
"Evil Dead II (Dead By Dawn) (1987) 3.297297 3.909283 1.331697 \n",
"Cable Guy, The (1996) 2.250000 2.863787 1.051390 \n",
"Longest Day, The (1962) 3.411765 4.031447 0.957194 \n",
"Dumb & Dumber (1994) 2.697987 3.336595 1.354196 \n",
"Kentucky Fried Movie, The (1977) 2.878788 3.555147 1.023400 \n",
"Good, The Bad and The Ugly, The (1966) 3.494949 4.221300 1.146189 \n",
"\n",
" mean_diff \n",
"gender M \n",
"title \n",
"Dirty Dancing (1987) 1.087738 -0.830782 \n",
"Jumpin' Jack Flash (1986) 1.134007 -0.676359 \n",
"Grease (1978) 1.102666 -0.608224 \n",
"Little Women (1994) 0.903675 -0.548849 \n",
"Steel Magnolias (1989) 1.106561 -0.535777 \n",
"Anastasia (1997) 0.897012 -0.518391 \n",
"Rocky Horror Picture Show, The (1975) 1.257241 -0.512885 \n",
"Color Purple, The (1985) 1.120200 -0.498851 \n",
"Age of Innocence, The (1993) 0.871663 -0.487561 \n",
"Free Willy (1993) 0.966796 -0.482573 \n",
"French Kiss (1995) 0.972512 -0.478752 \n",
"Little Shop of Horrors, The (1960) 1.032291 -0.470312 \n",
"Guys and Dolls (1955) 1.112548 -0.468391 \n",
"Mary Poppins (1964) 1.035435 -0.467147 \n",
"Patch Adams (1998) 1.195721 -0.464536 \n",
"Grease 2 (1982) 0.972646 -0.450925 \n",
"Sound of Music, The (1965) 1.154415 -0.450259 \n",
"Never Been Kissed (1999) 0.968981 -0.449636 \n",
"Casper (1995) 1.029304 -0.446695 \n",
"Jewel of the Nile, The (1985) 0.945127 -0.446157 \n",
"South Pacific (1958) 0.917302 -0.442764 \n",
"Gone with the Wind (1939) 1.076346 -0.440471 \n",
"Sommersby (1993) 1.057645 -0.435603 \n",
"Skulls, The (2000) 1.139435 -0.432188 \n",
"Corrina, Corrina (1994) 1.106793 -0.430897 \n",
"Iron Eagle (1986) 0.959886 -0.424817 \n",
"Hope Floats (1998) 1.105997 -0.423802 \n",
"Beverly Hills Cop III (1994) 1.014999 -0.422051 \n",
"Sense and Sensibility (1995) 0.929191 -0.414056 \n",
"Parent Trap, The (1961) 1.035586 -0.412222 \n",
"... ... ... \n",
"Tommy Boy (1995) 1.110982 0.414634 \n",
"Eyes Wide Shut (1999) 1.249065 0.414736 \n",
"South Park: Bigger, Longer and Uncut (1999) 1.182896 0.424206 \n",
"Godzilla (Gojira) (1954) 1.176630 0.425328 \n",
"Raging Bull (1980) 0.819723 0.431508 \n",
"Reservoir Dogs (1992) 0.924602 0.444642 \n",
"Where Eagles Dare (1969) 0.754799 0.448718 \n",
"Kingpin (1996) 1.085212 0.465173 \n",
"Me, Myself and Irene (2000) 1.160715 0.467537 \n",
"Akira (1988) 0.949258 0.469233 \n",
"Wild Bunch, The (1969) 0.899582 0.491736 \n",
"From Dusk Till Dawn (1996) 1.148788 0.496844 \n",
"Big Trouble in Little China (1986) 1.005125 0.497078 \n",
"Beavis and Butt-head Do America (1996) 1.141387 0.498054 \n",
"Rocky II (1979) 0.953814 0.501515 \n",
"Barb Wire (1996) 1.098607 0.515020 \n",
"Fright Night (1985) 0.981095 0.526316 \n",
"Exorcist, The (1973) 1.016934 0.529605 \n",
"Animal House (1978) 0.921336 0.538286 \n",
"Porky's (1981) 1.136065 0.539489 \n",
"For a Few Dollars More (1965) 0.820582 0.544704 \n",
"Caddyshack (1980) 0.998222 0.573602 \n",
"Rocky III (1982) 1.030512 0.581801 \n",
"Hidden, The (1987) 0.828696 0.607167 \n",
"Evil Dead II (Dead By Dawn) (1987) 1.061631 0.611985 \n",
"Cable Guy, The (1996) 1.100629 0.613787 \n",
"Longest Day, The (1962) 0.847497 0.619682 \n",
"Dumb & Dumber (1994) 1.277333 0.638608 \n",
"Kentucky Fried Movie, The (1977) 1.007669 0.676359 \n",
"Good, The Bad and The Ugly, The (1966) 0.814168 0.726351 \n",
"\n",
"[1216 rows x 5 columns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_by_mean_diff = mean_rating.sort_values(by=\"mean_diff\")\n",
"sorted_by_mean_diff"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"*Exercise*: Which 20 films had the widest difference of opinion?\n",
" * a) amongst males.\n",
" * b) amongst females.\n",
" * c) amongst people.\n",
" \n",
"Tips: Start with `mean_rating`. Select column using `[\"std\"]`. Use `.sort_values`. Reverse using `[start:stop:slice]`."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" <tr>\n",
" <th>title</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Blair Witch Project, The (1999)</th>\n",
" <td>1.313929</td>\n",
" <td>1.317775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Natural Born Killers (1994)</th>\n",
" <td>1.355926</td>\n",
" <td>1.298487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dumb &amp; Dumber (1994)</th>\n",
" <td>1.354196</td>\n",
" <td>1.277333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tank Girl (1995)</th>\n",
" <td>1.300054</td>\n",
" <td>1.264366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Serial Mom (1994)</th>\n",
" <td>1.125951</td>\n",
" <td>1.257872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rocky Horror Picture Show, The (1975)</th>\n",
" <td>1.190890</td>\n",
" <td>1.257241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Billy Madison (1995)</th>\n",
" <td>1.245505</td>\n",
" <td>1.253217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eyes Wide Shut (1999)</th>\n",
" <td>1.242070</td>\n",
" <td>1.249065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hellraiser (1987)</th>\n",
" <td>1.252069</td>\n",
" <td>1.243272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Breaking the Waves (1996)</th>\n",
" <td>1.102092</td>\n",
" <td>1.242802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Evita (1996)</th>\n",
" <td>1.253818</td>\n",
" <td>1.238358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fear and Loathing in Las Vegas (1998)</th>\n",
" <td>1.273826</td>\n",
" <td>1.231933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Babe: Pig in the City (1998)</th>\n",
" <td>1.262133</td>\n",
" <td>1.229537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Deuce Bigalow: Male Gigolo (1999)</th>\n",
" <td>1.229448</td>\n",
" <td>1.223522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wes Craven's New Nightmare (1994)</th>\n",
" <td>1.321668</td>\n",
" <td>1.223053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thin Red Line, The (1998)</th>\n",
" <td>1.166778</td>\n",
" <td>1.222385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bicentennial Man (1999)</th>\n",
" <td>1.333791</td>\n",
" <td>1.219125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Waterboy, The (1998)</th>\n",
" <td>1.212296</td>\n",
" <td>1.217671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Last Temptation of Christ, The (1988)</th>\n",
" <td>1.222954</td>\n",
" <td>1.214983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Toys (1992)</th>\n",
" <td>1.221060</td>\n",
" <td>1.212467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armageddon (1998)</th>\n",
" <td>1.170792</td>\n",
" <td>1.209580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Scary Movie (2000)</th>\n",
" <td>1.261764</td>\n",
" <td>1.206718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>For Love of the Game (1999)</th>\n",
" <td>1.211221</td>\n",
" <td>1.201493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Patch Adams (1998)</th>\n",
" <td>1.198257</td>\n",
" <td>1.195721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Starship Troopers (1997)</th>\n",
" <td>1.214295</td>\n",
" <td>1.194683</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bringing Out the Dead (1999)</th>\n",
" <td>1.205031</td>\n",
" <td>1.194258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Shakespeare's Romeo and Juliet (1996)</th>\n",
" <td>1.120608</td>\n",
" <td>1.189591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Titanic (1997)</th>\n",
" <td>1.107878</td>\n",
" <td>1.185341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Psycho (1998)</th>\n",
" <td>1.188255</td>\n",
" <td>1.183553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ace Ventura: When Nature Calls (1995)</th>\n",
" <td>1.145783</td>\n",
" <td>1.182985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rainmaker, The (1997)</th>\n",
" <td>0.894597</td>\n",
" <td>0.770553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Big Sleep, The (1946)</th>\n",
" <td>0.806986</td>\n",
" <td>0.768648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fugitive, The (1993)</th>\n",
" <td>0.788330</td>\n",
" <td>0.768577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>One Flew Over the Cuckoo's Nest (1975)</th>\n",
" <td>0.846061</td>\n",
" <td>0.767364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Henry V (1989)</th>\n",
" <td>0.825078</td>\n",
" <td>0.767078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Marathon Man (1976)</th>\n",
" <td>0.863731</td>\n",
" <td>0.766480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>In the Line of Fire (1993)</th>\n",
" <td>0.829369</td>\n",
" <td>0.766120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thin Man, The (1934)</th>\n",
" <td>0.784723</td>\n",
" <td>0.760690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cool Hand Luke (1967)</th>\n",
" <td>0.855369</td>\n",
" <td>0.760099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Three Days of the Condor (1975)</th>\n",
" <td>0.797339</td>\n",
" <td>0.759519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</th>\n",
" <td>0.686272</td>\n",
" <td>0.757177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Where Eagles Dare (1969)</th>\n",
" <td>1.192928</td>\n",
" <td>0.754799</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Treasure of the Sierra Madre, The (1948)</th>\n",
" <td>0.940383</td>\n",
" <td>0.754581</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Third Man, The (1949)</th>\n",
" <td>0.777320</td>\n",
" <td>0.753081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954)</th>\n",
" <td>0.706853</td>\n",
" <td>0.750476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>When We Were Kings (1996)</th>\n",
" <td>0.978511</td>\n",
" <td>0.749823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Verdict, The (1982)</th>\n",
" <td>0.792945</td>\n",
" <td>0.749666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Usual Suspects, The (1995)</th>\n",
" <td>0.758730</td>\n",
" <td>0.746085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hustler, The (1961)</th>\n",
" <td>0.684448</td>\n",
" <td>0.743437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bridge on the River Kwai, The (1957)</th>\n",
" <td>0.810143</td>\n",
" <td>0.743074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Godfather, The (1972)</th>\n",
" <td>0.892762</td>\n",
" <td>0.736354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>North by Northwest (1959)</th>\n",
" <td>0.731056</td>\n",
" <td>0.733261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wrong Trousers, The (1993)</th>\n",
" <td>0.635275</td>\n",
" <td>0.732171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sting, The (1973)</th>\n",
" <td>0.861887</td>\n",
" <td>0.720568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Double Indemnity (1944)</th>\n",
" <td>0.801570</td>\n",
" <td>0.709533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Raiders of the Lost Ark (1981)</th>\n",
" <td>0.792796</td>\n",
" <td>0.699111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rear Window (1954)</th>\n",
" <td>0.671284</td>\n",
" <td>0.696009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Shawshank Redemption, The (1994)</th>\n",
" <td>0.719194</td>\n",
" <td>0.693091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Close Shave, A (1995)</th>\n",
" <td>0.584934</td>\n",
" <td>0.690455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Great Escape, The (1963)</th>\n",
" <td>0.802156</td>\n",
" <td>0.669139</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1216 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
"gender F M\n",
"title \n",
"Blair Witch Project, The (1999) 1.313929 1.317775\n",
"Natural Born Killers (1994) 1.355926 1.298487\n",
"Dumb & Dumber (1994) 1.354196 1.277333\n",
"Tank Girl (1995) 1.300054 1.264366\n",
"Serial Mom (1994) 1.125951 1.257872\n",
"Rocky Horror Picture Show, The (1975) 1.190890 1.257241\n",
"Billy Madison (1995) 1.245505 1.253217\n",
"Eyes Wide Shut (1999) 1.242070 1.249065\n",
"Hellraiser (1987) 1.252069 1.243272\n",
"Breaking the Waves (1996) 1.102092 1.242802\n",
"Evita (1996) 1.253818 1.238358\n",
"Fear and Loathing in Las Vegas (1998) 1.273826 1.231933\n",
"Babe: Pig in the City (1998) 1.262133 1.229537\n",
"Deuce Bigalow: Male Gigolo (1999) 1.229448 1.223522\n",
"Wes Craven's New Nightmare (1994) 1.321668 1.223053\n",
"Thin Red Line, The (1998) 1.166778 1.222385\n",
"Bicentennial Man (1999) 1.333791 1.219125\n",
"Waterboy, The (1998) 1.212296 1.217671\n",
"Last Temptation of Christ, The (1988) 1.222954 1.214983\n",
"Toys (1992) 1.221060 1.212467\n",
"Armageddon (1998) 1.170792 1.209580\n",
"Scary Movie (2000) 1.261764 1.206718\n",
"For Love of the Game (1999) 1.211221 1.201493\n",
"Patch Adams (1998) 1.198257 1.195721\n",
"Starship Troopers (1997) 1.214295 1.194683\n",
"Bringing Out the Dead (1999) 1.205031 1.194258\n",
"William Shakespeare's Romeo and Juliet (1996) 1.120608 1.189591\n",
"Titanic (1997) 1.107878 1.185341\n",
"Psycho (1998) 1.188255 1.183553\n",
"Ace Ventura: When Nature Calls (1995) 1.145783 1.182985\n",
"... ... ...\n",
"Rainmaker, The (1997) 0.894597 0.770553\n",
"Big Sleep, The (1946) 0.806986 0.768648\n",
"Fugitive, The (1993) 0.788330 0.768577\n",
"One Flew Over the Cuckoo's Nest (1975) 0.846061 0.767364\n",
"Henry V (1989) 0.825078 0.767078\n",
"Marathon Man (1976) 0.863731 0.766480\n",
"In the Line of Fire (1993) 0.829369 0.766120\n",
"Thin Man, The (1934) 0.784723 0.760690\n",
"Cool Hand Luke (1967) 0.855369 0.760099\n",
"Three Days of the Condor (1975) 0.797339 0.759519\n",
"Sunset Blvd. (a.k.a. Sunset Boulevard) (1950) 0.686272 0.757177\n",
"Where Eagles Dare (1969) 1.192928 0.754799\n",
"Treasure of the Sierra Madre, The (1948) 0.940383 0.754581\n",
"Third Man, The (1949) 0.777320 0.753081\n",
"Seven Samurai (The Magnificent Seven) (Shichini... 0.706853 0.750476\n",
"When We Were Kings (1996) 0.978511 0.749823\n",
"Verdict, The (1982) 0.792945 0.749666\n",
"Usual Suspects, The (1995) 0.758730 0.746085\n",
"Hustler, The (1961) 0.684448 0.743437\n",
"Bridge on the River Kwai, The (1957) 0.810143 0.743074\n",
"Godfather, The (1972) 0.892762 0.736354\n",
"North by Northwest (1959) 0.731056 0.733261\n",
"Wrong Trousers, The (1993) 0.635275 0.732171\n",
"Sting, The (1973) 0.861887 0.720568\n",
"Double Indemnity (1944) 0.801570 0.709533\n",
"Raiders of the Lost Ark (1981) 0.792796 0.699111\n",
"Rear Window (1954) 0.671284 0.696009\n",
"Shawshank Redemption, The (1994) 0.719194 0.693091\n",
"Close Shave, A (1995) 0.584934 0.690455\n",
"Great Escape, The (1963) 0.802156 0.669139\n",
"\n",
"[1216 rows x 2 columns]"
]
},
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}
],
"source": [
"mean_rating[\"std\"].sort_values(by=\"M\")[-1::-1]"
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{
"cell_type": "code",
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{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" <tr>\n",
" <th>title</th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th>Soldier (1998)</th>\n",
" <td>1.374946</td>\n",
" <td>1.048801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Park: Bigger, Longer and Uncut (1999)</th>\n",
" <td>1.373668</td>\n",
" <td>1.182896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nightmare on Elm Street, A (1984)</th>\n",
" <td>1.365397</td>\n",
" <td>1.131927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Natural Born Killers (1994)</th>\n",
" <td>1.355926</td>\n",
" <td>1.298487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dumb &amp; Dumber (1994)</th>\n",
" <td>1.354196</td>\n",
" <td>1.277333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Clockwork Orange, A (1971)</th>\n",
" <td>1.352036</td>\n",
" <td>1.037948</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rocky IV (1985)</th>\n",
" <td>1.342549</td>\n",
" <td>1.135562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bicentennial Man (1999)</th>\n",
" <td>1.333791</td>\n",
" <td>1.219125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Evil Dead II (Dead By Dawn) (1987)</th>\n",
" <td>1.331697</td>\n",
" <td>1.061631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ghost Dog: The Way of the Samurai (1999)</th>\n",
" <td>1.325736</td>\n",
" <td>1.002974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wes Craven's New Nightmare (1994)</th>\n",
" <td>1.321668</td>\n",
" <td>1.223053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Godzilla (Gojira) (1954)</th>\n",
" <td>1.316869</td>\n",
" <td>1.176630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Blair Witch Project, The (1999)</th>\n",
" <td>1.313929</td>\n",
" <td>1.317775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rambo III (1988)</th>\n",
" <td>1.312565</td>\n",
" <td>1.059979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pee-wee's Big Adventure (1985)</th>\n",
" <td>1.312522</td>\n",
" <td>1.136171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beavis and Butt-head Do America (1996)</th>\n",
" <td>1.312474</td>\n",
" <td>1.141387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pet Sematary (1989)</th>\n",
" <td>1.307950</td>\n",
" <td>1.090072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lord of the Rings, The (1978)</th>\n",
" <td>1.300551</td>\n",
" <td>1.112479</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tank Girl (1995)</th>\n",
" <td>1.300054</td>\n",
" <td>1.264366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Happiness (1998)</th>\n",
" <td>1.299933</td>\n",
" <td>1.104618</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"gender F M\n",
"title \n",
"Soldier (1998) 1.374946 1.048801\n",
"South Park: Bigger, Longer and Uncut (1999) 1.373668 1.182896\n",
"Nightmare on Elm Street, A (1984) 1.365397 1.131927\n",
"Natural Born Killers (1994) 1.355926 1.298487\n",
"Dumb & Dumber (1994) 1.354196 1.277333\n",
"Clockwork Orange, A (1971) 1.352036 1.037948\n",
"Rocky IV (1985) 1.342549 1.135562\n",
"Bicentennial Man (1999) 1.333791 1.219125\n",
"Evil Dead II (Dead By Dawn) (1987) 1.331697 1.061631\n",
"Ghost Dog: The Way of the Samurai (1999) 1.325736 1.002974\n",
"Wes Craven's New Nightmare (1994) 1.321668 1.223053\n",
"Godzilla (Gojira) (1954) 1.316869 1.176630\n",
"Blair Witch Project, The (1999) 1.313929 1.317775\n",
"Rambo III (1988) 1.312565 1.059979\n",
"Pee-wee's Big Adventure (1985) 1.312522 1.136171\n",
"Beavis and Butt-head Do America (1996) 1.312474 1.141387\n",
"Pet Sematary (1989) 1.307950 1.090072\n",
"Lord of the Rings, The (1978) 1.300551 1.112479\n",
"Tank Girl (1995) 1.300054 1.264366\n",
"Happiness (1998) 1.299933 1.104618"
]
},
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}
],
"source": [
"mean_rating[\"std\"].sort_values(by=\"F\", ascending=False)[0:20]"
]
},
{
"cell_type": "code",
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{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f41eb5d7630>"
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"(mean_rating[\"std\"].sort_values(by=\"F\", ascending=False)[0:20]).plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Mini-project: Eurostat data\n",
"\n",
"### Performing some simple analysis on Eurostat data\n",
"\n",
"1. Get the data.\n",
"2. Clean the data.\n",
"3. Manipulate the data.\n",
"4. Analyse the data.\n",
"\n",
"https://ec.europa.eu/eurostat"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### How can we get Eurostat data?"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting pandasdmx\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/e7/5d/7e29616b0376d0a15eb3013c909c4e9021b6c6e244ef1b48817b4d0c4d46/pandaSDMX-0.9-py2.py3-none-any.whl (45kB)\n",
"\u001b[K 100% |████████████████████████████████| 51kB 2.7MB/s ta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from pandasdmx) (2.21.0)\n",
"Requirement already satisfied: lxml in /opt/conda/lib/python3.7/site-packages (from pandasdmx) (4.2.5)\n",
"Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from pandasdmx) (40.6.3)\n",
"Collecting jsonpath-rw (from pandasdmx)\n",
" Downloading https://files.pythonhosted.org/packages/71/7c/45001b1f19af8c4478489fbae4fc657b21c4c669d7a5a036a86882581d85/jsonpath-rw-1.4.0.tar.gz\n",
"Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from pandasdmx) (0.23.4)\n",
"Requirement already satisfied: urllib3<1.25,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->pandasdmx) (1.24.1)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->pandasdmx) (2018.11.29)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->pandasdmx) (3.0.4)\n",
"Requirement already satisfied: idna<2.9,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->pandasdmx) (2.8)\n",
"Requirement already satisfied: ply in /opt/conda/lib/python3.7/site-packages (from jsonpath-rw->pandasdmx) (3.11)\n",
"Requirement already satisfied: decorator in /opt/conda/lib/python3.7/site-packages (from jsonpath-rw->pandasdmx) (4.3.0)\n",
"Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from jsonpath-rw->pandasdmx) (1.12.0)\n",
"Requirement already satisfied: python-dateutil>=2.5.0 in /opt/conda/lib/python3.7/site-packages (from pandas->pandasdmx) (2.7.5)\n",
"Requirement already satisfied: pytz>=2011k in /opt/conda/lib/python3.7/site-packages (from pandas->pandasdmx) (2018.7)\n",
"Requirement already satisfied: numpy>=1.9.0 in /opt/conda/lib/python3.7/site-packages (from pandas->pandasdmx) (1.15.4)\n",
"Building wheels for collected packages: jsonpath-rw\n",
" Running setup.py bdist_wheel for jsonpath-rw ... \u001b[?25ldone\n",
"\u001b[?25h Stored in directory: /root/.cache/pip/wheels/5c/00/9a/82822db383c2d96dcebf839786665a185f92d37e5026f9806f\n",
"Successfully built jsonpath-rw\n",
"Installing collected packages: jsonpath-rw, pandasdmx\n",
"Successfully installed jsonpath-rw-1.4.0 pandasdmx-0.9\n"
]
}
],
"source": [
"!pip install pandasdmx"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"from pandasdmx import Request\n",
"estat = Request('ESTAT')\n",
"resp = estat.data('une_rt_a', params={'startPeriod': '2007'})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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