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Intro to Pandas for Data Analysis

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Practice Data Filtering & Sorting with Hollywood Movie Data

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Practice Data Filtering & Sorting with Hollywood Movie Data¶

Explore¶

Let's explore our dataset in our lab

In [1]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

In [2]:

df = pd.read_csv('data/movies_dataset.csv', low_memory=False)

In [3]:

df.head()

Out[3]:

	title	adult	budget	genres	homepage	id	imdb_id	original_language	original_title	overview	...	runtime	spoken_languages	status	tagline	video	vote_average	vote_count	production_companies_name	production_companies_id	production_countries_name
0	GoldenEye	True	58000000	Adventure, Action, Thriller	http://www.mgm.com/view/movie/757/Goldeneye/	710	tt0113189	English	GoldenEye	James Bond must unmask the mysterious head of ...	...	130.0	English, Pусский, Español	Released	No limits. No fears. No substitutes.	False	6.6	1194.0	United Artists, Eon Productions	60, 7576	United Kingdom, United States of America
1	Friday	True	3500000	Comedy	http://www.newline.com/properties/friday.html	10634	tt0113118	English	Friday	Craig and Smokey are two guys in Los Angeles h...	...	91.0	English	Released	A lot can go down between thursday and saturda...	False	7.0	513.0	New Line Cinema	12	United States of America
2	From Dusk Till Dawn	True	19000000	Horror, Action, Thriller, Crime	http://www.miramax.com/movie/from-dusk-till-dawn/	755	tt0116367	English	From Dusk Till Dawn	Seth Gecko and his younger brother Richard are...	...	108.0	English, Español	Released	One night is all that stands between them and ...	False	6.9	1644.0	A Band Apart, Dimension Films, Los Hooligans P...	59, 7405, 11705, 53009	United States of America
3	Blue in the Face	True	2000000	Comedy	http://miramax.com/movie/blue-in-the-face/	5894	tt0112541	English	Blue in the Face	Auggie runs a small tobacco shop in Brooklyn, ...	...	83.0	English	Released	Welcome to the planet Brooklyn.	False	6.8	28.0	Miramax Films, InterAL	14, 30334	United States of America
4	Mighty Morphin Power Rangers: The Movie	True	15000000	Action, Adventure, Science Fiction, Family, Fa...	http://www.powerrangers.com/	9070	tt0113820	English	Mighty Morphin Power Rangers: The Movie	Power up with six incredible teens who out-man...	...	92.0	English	Released	The Power Is On!	False	5.2	153.0	Twentieth Century Fox Film Corporation, Saban ...	306, 2106, 9255	Japan, United States of America

5 rows × 24 columns

In [4]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 693 entries, 0 to 692
Data columns (total 24 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   title                      693 non-null    object 
 1   adult                      693 non-null    bool   
 2   budget                     693 non-null    int64  
 3   genres                     693 non-null    object 
 4   homepage                   693 non-null    object 
 5   id                         693 non-null    int64  
 6   imdb_id                    693 non-null    object 
 7   original_language          693 non-null    object 
 8   original_title             693 non-null    object 
 9   overview                   693 non-null    object 
 10  popularity                 693 non-null    float64
 11  poster_path                693 non-null    object 
 12  release_date               693 non-null    object 
 13  revenue                    693 non-null    float64
 14  runtime                    693 non-null    float64
 15  spoken_languages           691 non-null    object 
 16  status                     693 non-null    object 
 17  tagline                    693 non-null    object 
 18  video                      693 non-null    bool   
 19  vote_average               693 non-null    float64
 20  vote_count                 693 non-null    float64
 21  production_companies_name  662 non-null    object 
 22  production_companies_id    662 non-null    object 
 23  production_countries_name  682 non-null    object 
dtypes: bool(2), float64(5), int64(2), object(15)
memory usage: 120.6+ KB

1) Find the movie with the maximum `runtime`.¶

In [10]:

df['runtime'].max()

Out[10]:

476.0

2) What is the earliest Release Date?¶

In [7]:

df['release_date'].min()

Out[7]:

'1940-11-13'

3) What's the highest value in the `vote_count` column?¶

In [11]:

df['vote_count'].max()

Out[11]:

12269.0

4) Select the first 5 movies¶

In [17]:

first_5_movies = df.iloc[:5]

5) What is the `title` of the 10th movie?¶

In [19]:

df.iloc[9]

Out[19]:

title                                                            Jurassic Park
adult                                                                     True
budget                                                                63000000
genres                                              Adventure, Science Fiction
homepage                                          http://www.jurassicpark.com/
id                                                                         329
imdb_id                                                              tt0107290
original_language                                                      English
original_title                                                   Jurassic Park
overview                     A wealthy entrepreneur secretly creates a them...
popularity                                                            8.863776
poster_path                                   /c414cDeQ9b6qLPLeKmiJuLDUREJ.jpg
release_date                                                        1993-06-11
revenue                                                            920100000.0
runtime                                                                  127.0
spoken_languages                                              English, Español
status                                                                Released
tagline                           An adventure 65 million years in the making.
video                                                                    False
vote_average                                                               7.6
vote_count                                                              4956.0
production_companies_name             Universal Pictures, Amblin Entertainment
production_companies_id                                                 33, 56
production_countries_name                             United States of America
Name: 9, dtype: object

6) Select the `title` and `release_date` of the movies in positions 2nd, 6th and 11th¶

In [22]:

# Select specific rows and columns from the DataFrame 'df'
selected_movies = df.iloc[[1,5,10],[0,12]]

7) Sort the `df` by `release_date` in ascending order.¶

In [24]:

# Sort the DataFrame 'df' by the 'release_date' column in ascending order
df = df.sort_values('release_date')

8) Select movies in English and sort them by Vote Average¶

In [26]:

# Get English movies sorted by highest rating
df_english_movies = df.loc[df['original_language']=='English'].sort_values('vote_average',ascending=False)

9) Select movies or series with long runtime and high rating¶

In [30]:

# Get movies with long runtime and high rating
df_runtime_above_120 = df.loc[(df['runtime'] > 120) & (df['vote_average'] > 7)]

10) Sort by `genres` (asc) and `revenue` (desc)¶

In [32]:

# Sort the df by 'genres' in ascending order and 'revenue' in descending order
df_genres_revenue = df.sort_values(['genres','revenue'],ascending=[True,False])

11) Select movies or series that have the `Action` genre and sort them by voting average and revenue¶

In [36]:

# Filter the DataFrame to include only movies with 'Action' genre
df_action = df.loc[df['genres'].str.contains('Action')].sort_values(['vote_average','revenue'],ascending=[False,True])

12) Select movies or series with a `budget > 50,000,000` and `runtime > 120 minutes`¶

Let's visualize the budget column. We can see the green line, which represents the median (50th percentile) of the data. The individual circles above the top whisker represent outliers, which are data points that lie more than 1.5 times the interquartile range (IQR) from the 75th percentile.

In [38]:

df['budget'].plot(kind='box')

Out[38]:

<Axes: >

No description has been provided for this image

In [39]:

# Create a new DataFrame 'df_bug_run' with filtered rows and selected columns
df_budget_runtime = df.loc[(df['budget'] > 50000000) & (df['runtime'] > 120),('original_title','budget','runtime')]

13) Find movies/series that contain the `Action` and `Adventure` genres with a high vote average, and sort them by vote average (desc) and revenue (asc)¶

In [46]:

# Filter the df with 'Action' or 'Adventure' genre and a vote_average greater than 7 then sort the df by 'vote_average' in descending order and 'revenue' in ascending order
df.loc[df['genres'].str.contains('Action|Adventure')].sort_values(['vote_average','revenue'],ascending=[False,True])

Out[46]:

	title	adult	budget	genres	homepage	id	imdb_id	original_language	original_title	overview	...	runtime	spoken_languages	status	tagline	video	vote_average	vote_count	production_companies_name	production_companies_id	production_countries_name
237	The Dark Knight	True	185000000	Drama, Action, Crime, Thriller	http://thedarkknight.warnerbros.com/dvdsite/	155	tt0468569	English	The Dark Knight	Batman raises the stakes in his war on crime. ...	...	152.0	English, 普通话	Released	Why So Serious?	False	8.3	12269.0	DC Comics, Legendary Pictures, Warner Bros., D...	429, 923, 6194, 9993, 9996	United Kingdom, United States of America
27	The Empire Strikes Back	True	18000000	Adventure, Action, Science Fiction	http://www.starwars.com/films/star-wars-episod...	1891	tt0080684	English	The Empire Strikes Back	The epic saga continues as Luke Skywalker, in ...	...	124.0	English	Released	The Adventure Continues...	False	8.2	5998.0	Lucasfilm, Twentieth Century Fox Film Corporation	1, 306	United States of America
6	Star Wars	True	11000000	Adventure, Action, Science Fiction	http://www.starwars.com/films/star-wars-episod...	11	tt0076759	English	Star Wars	Princess Leia is captured and held hostage by ...	...	121.0	English	Released	A long time ago in a galaxy far, far away...	False	8.1	6778.0	Lucasfilm, Twentieth Century Fox Film Corporation	1, 306	United States of America
132	The Lord of the Rings: The Return of the King	True	94000000	Adventure, Fantasy, Action	http://www.lordoftherings.net	122	tt0167260	English	The Lord of the Rings: The Return of the King	Aragorn is revealed as the heir to the ancient...	...	201.0	English	Released	The eye of the enemy is moving.	False	8.1	8226.0	WingNut Films, New Line Cinema	11, 12	New Zealand, United States of America
33	Back to the Future	True	19000000	Adventure, Comedy, Science Fiction, Family	http://www.backtothefuture.com/movies/backtoth...	105	tt0088763	English	Back to the Future	Eighties teenager Marty McFly is accidentally ...	...	116.0	English	Released	He's the only kid ever to get into trouble bef...	False	8.0	6239.0	Universal Pictures, Amblin Entertainment, U-Dr...	33, 56, 20448	United States of America
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
171	BloodRayne	True	25000000	Action, Adventure, Fantasy, Horror	http://www.bloodrayne-themovie.com/main/index....	168705	tt0383222	English	BloodRayne	In eighteenth century Romania, Rayne, a dhampi...	...	95.0	English, Português	Released	Revenge never tasted so sweet.	False	3.6	120.0	Boll KG	15127	United States of America
655	Birdemic 2: The Resurrection	True	0	Action, Comedy, Horror, Science Fiction	https://chill.com/james_nguyen/birdemic2	188489	tt1674047	English	Birdemic 2: The Resurrection	A platoon of eagles and vultures attack Hollyw...	...	82.0	English	Released	you asked for it	False	3.2	20.0	I Got a Fish Productions, Moviehead Pictures	23389, 23390	United States of America
624	Mega Shark vs. Mecha Shark	True	0	Action, Science Fiction, Thriller	http://www.theasylum.cc/product.php?id=242	246594	tt3152098	English	Mega Shark vs. Mecha Shark	When another Mega Shark returns from the depth...	...	90.0	English	Released	Monster vs Metal	False	3.1	29.0	Asylum, The	3991	United States of America
126	House of the Dead	True	7000000	Horror, Action, Thriller	http://www.house-of-the-dead.com/	11059	tt0317676	English	House of the Dead	Set on an island off the coast, a techno rave ...	...	90.0	English	Released	You won't last the night.	False	2.8	106.0	Boll Kino Beteiligungs GmbH & Co. KG, Brightli...	1062, 1063, 1064, 8068	Canada, Germany, United States of America
417	Mega Shark vs. Crocosaurus	True	0	Action, Horror, Science Fiction	http://www.theasylum.cc/product.php?id=180	52454	tt1705773	English	Mega Shark vs. Crocosaurus	When the prehistoric warm-water beast the Croc...	...	90.0	English, Français, Español	Released	Whoever wins... we lose!	False	2.7	39.0	The Asylum	1311	United States of America

381 rows × 24 columns

14) Select movies or series that have `Star Wars` in the `title` and sort them by `release_date` (asc) and `revenue` (desc).¶

In [47]:

# Filter the df with 'Star Wars' in the original title and sort them by release_date' in ascending order and 'revenue' in descending order
df_star_wars = df.loc[df['title'].str.contains('Star Wars')].sort_values(['release_date','revenue'],ascending=[True,False])

15) Select `original_title`, `budget`, `revenue`, and `vote_average` of the rows with a budget greater than 100M.¶

In [49]:

# Filter DataFrame for high-budget movies and select relevant columns
df_high_budget = df.loc[df['budget'] > 100000000,('original_title','budget','revenue','vote_average')]

16) Find the movies with a runtime greater than 120 and a vote average greater than 7. Sort them in descending order.¶

Run the cell below to see the histogram, which shows a right-skewed distribution of movie runtimes. The highest frequency is around 130 minutes. This indicates a predominance of movies around 2 hours long. Very short or very long movies are relatively rare.

In [51]:

df['runtime'].plot(kind='hist', bins=20, figsize=(10, 6), title='Distribution of Movie Runtimes');

In [52]:

df_run_vote = df.loc[(df['runtime'] > 120) & (df['vote_average'] > 7)].sort_values('runtime',ascending=False)

17) Find the top 10 movies or series with highest voting count and a voting average greater than 7 and select `original_title`, `vote_count`, and `vote_average` columns.¶

In [56]:

df_top_10 = df.loc[(df['vote_count'] > 1000) & (df['vote_average'] > 7),('original_title','vote_count','vote_average')].head(10)

18) Find the movies or series that have a `vote_average` above 8 or have made more than 500,000,000 in `revenue`.¶

In [58]:

# Filter the DataFrame to include movies with vote_average > 8 or revenue > $500,000,000
df_high = df.loc[(df['vote_average'] > 8) | (df['revenue'] > 500000000)]

19) Enter the release date of the movie that is not in `English`.¶

Run the cell below to see the bar plot, there are many movies and series in the English language, but few are non-English, including Hindi, Japanese, Chinese, etc.

In [ ]:

df['original_language'].value_counts().plot(kind='bar', title='Count of Movies by Language', figsize=(10, 5));

In [62]:

df.loc[df['original_language'] != 'English','release_date'].min()

Out[62]:

'1961-01-23'

20) Select movies with `runtime > 150` , `vote_average > 7`, `popularity > 20`, and sort them by descending order.¶

In [63]:

df_long_movies = df.loc[(df['runtime'] > 150) & (df['vote_average'] > 7) & (df['popularity'] > 20)].sort_values('vote_average',ascending=False)

21) Find movies in the Action, Adventure, or Science Fiction genres and sort them by descending order¶

In [65]:

df_action_sci_adv = df.loc[df['genres'].str.contains('Action|Adventure|Science Fiction')].sort_values('runtime',ascending=False)

22) Find movies with a budget under 20M and revenue over 100M and sort them by descending order.¶

In [67]:

df_budget_revenue = df.loc[(df['budget'] < 20000000) & (df['revenue'] > 100000000)].sort_values('revenue',ascending=False)

In [ ]:

Statement of Completion#2b21bc89

Intro to Pandas for Data Analysis

Practice Data Filtering & Sorting with Hollywood Movie Data

Practice Data Filtering & Sorting with Hollywood Movie Data¶

Explore¶

1) Find the movie with the maximum runtime.¶

2) What is the earliest Release Date?¶

3) What's the highest value in the vote_count column?¶

4) Select the first 5 movies¶

5) What is the title of the 10th movie?¶

6) Select the title and release_date of the movies in positions 2nd, 6th and 11th¶

7) Sort the df by release_date in ascending order.¶

8) Select movies in English and sort them by Vote Average¶

9) Select movies or series with long runtime and high rating¶

10) Sort by genres (asc) and revenue (desc)¶

11) Select movies or series that have the Action genre and sort them by voting average and revenue¶

12) Select movies or series with a budget > 50,000,000 and runtime > 120 minutes¶

13) Find movies/series that contain the Action and Adventure genres with a high vote average, and sort them by vote average (desc) and revenue (asc)¶

14) Select movies or series that have Star Wars in the title and sort them by release_date (asc) and revenue (desc).¶

15) Select original_title, budget, revenue, and vote_average of the rows with a budget greater than 100M.¶