regishshrestha / analyzing_netflix_data

Analyzing_netflix_data

This project is done with the datacamp and this is solution of that project: https://app.datacamp.com/workspace/dashboard You can go to the link above and try it yourself

THIS IS THE RAW FORM OF THE REPO, IF YOU WANT TO TRY THE DATA YOURSELF YOU CAN READ BELOW:

Hypothesis: The average duration of movies has declined

1. Loading the data from a CSV

Read in the CSV as a DataFrame

netflix_df = pd.read_csv("datasets/netflix_data.csv")

Print the first five rows of the DataFrame

display(netflix_df.head())

show_id 	type 	title 	director 	cast 	country 	date_added 	release_year 	duration 	description 	genre

0 s1 TV Show 3% NaN João Miguel, Bianca Comparato, Michel Gomes, R... Brazil August 14, 2020 2020 4 In a future where the elite inhabit an island ... International TV 1 s2 Movie 7:19 Jorge Michel Grau Demián Bichir, Héctor Bonilla, Oscar Serrano, ... Mexico December 23, 2016 2016 93 After a devastating earthquake hits Mexico Cit... Dramas 2 s3 Movie 23:59 Gilbert Chan Tedd Chan, Stella Chung, Henley Hii, Lawrence ... Singapore December 20, 2018 2011 78 When an army recruit is found dead, his fellow... Horror Movies 2. Filtering for movies!

Okay, we have our data! Now we can dive in and start looking at movie lengths. Or can we? Looking at the first five rows of our new DataFrame, we notice a column type. Scanning the column, it's clear there are also TV shows in the dataset! Moreover, the duration column we planned to use seems to represent different values depending on whether the row is a movie or a show

Subset the DataFrame for type "Movie"

netflix_df_movies_only = netflix_df[netflix_df.type == 'Movie']

Select only the columns of interest

netflix_movies_col_subset = netflix_df_movies_only[["title", 'country', 'genre', 'release_year','duration']]

Print the first five rows of the new DataFrame

print(netflix_movies_col_subset.head())

title 	country 	genre 	release_year 	duration

1 7:19 Mexico Dramas 2016 93 2 23:59 Singapore Horror Movies 2011 78 3 9 United States Action 2009 80 4 21 United States Dramas 2008 123 6 122 Egypt Horror Movies 2019 95 3. Creating a scatter plot

Okay, now we're getting somewhere. We've read in the raw data, selected rows of movies, and have limited our DataFrame to our columns of interest. Let's try visualizing the data again to inspect the data over a longer range of time.

Create a scatter plot of duration versus year

plt.scatter(netflix_movies_col_subset.release_year, netflix_movies_col_subset.duration)

Create a title

plt.title("Movie Duration by Year of Release")

Show the plot

plt.show()

4. Digging deeper

Upon further inspection, something else is going on. Some of these films are under an hour long! Let's filter our DataFrame for movies with a duration under 60 minutes and look at the genres. This might give us some insight into what is dragging down the average. title country genre release_year duration 35 #Rucker50 United States Documentaries 2016 56 55 100 Things to do Before High School United States Uncategorized 2014 44 67 13TH: A Conversation with Oprah Winfrey & Ava ... NaN Uncategorized 2017 37 101 3 Seconds Divorce Canada Documentaries 2018 53 146 A 3 Minute Hug Mexico Documentaries 2019 28

Interesting! It looks as though many of the films that are under 60 minutes fall into genres such as "Children", "Stand-Up", and "Documentaries". This is a logical result, as these types of films are probably often shorter than 90 minute Hollywood blockbuster 5. Marking non-feature films

We could eliminate these rows from our DataFrame and plot the values again. But another interesting way to explore the effect of these genres on our data would be to plot them, but mark them with a different color.

colors = []

Iterate over rows of netflix_movies_col_subset

for row, ser in netflix_movies_col_subset.iterrows(): if ser['genre'] == 'Children': colors.append('red') elif ser['genre'] == 'Documentaries' : colors.append('blue') elif ser['genre'] == "Stand-Up" : colors.append('green') else: colors.append('black')

Inspect the first 10 values in your list

print(colors[:11])

['black', 'black', 'black', 'black', 'black', 'black', 'black', 'black', 'black', 'blue', 'black'] 6. Plotting with color!

We now have a colors list that we can pass to our scatter plot, which should allow us to visually inspect whether these genres might be responsible for the decline in the average duration of movies.

Set the figure style and initalize a new figure

fig = plt.figure(figsize=(12,8))

Create a scatter plot of duration versus release_year

plt.scatter(netflix_movies_col_subset.release_year, netflix_movies_col_subset.duration, c=colors)

Create a title and axis labels

plt.title("Movie duration by year of release") plt.xlabel("Release year") plt.ylabel("Duration (min)")

Show the plot

plt.show()

Well, as we suspected, non-typical genres such as children's movies and documentaries are all clustered around the bottom half of the plot. But we can't know for certain until we perform additional analyses.

Are we certain that movies are getting shorter?

are_movies_getting_shorter = "Nope"