dark-coder12/analyzing-netflix-data

A Datacamp Project I completed that analyzes the duration of movies throughout the years and visualizes it using Matplotlib and pandas.

analyzing-netflix-data

A Datacamp Project I completed that analyzes the duration of movies throughout the years and visualizes it using Matplotlib and pandas.

1. Loading your friend's data into a dictionary

Create the years and durations lists

years = [ 2011 , 2012 , 2013 , 2014 , 2015 , 2016 , 2017 , 2018 , 2019 , 2020 ] durations =[ 103 , 101 , 99 , 100 , 100 , 95 , 95 , 96 , 93 , 90 ]

Create a dictionary with the two lists

movie_dict = {"years": years, "durations":durations}

Print the dictionary

movie_dict

{'years': [2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020], 'durations': [103, 101, 99, 100, 100, 95, 95, 96, 93, 90]}

2. Creating a DataFrame from a dictionary

Import pandas under its usual alias

import pandas as pd

Create a DataFrame from the dictionary

durations_df = pd.DataFrame(movie_dict)

Print the DataFrame

print(durations_df)

years durations 0 2011 103 1 2012 101 2 2013 99 3 2014 100 4 2015 100 5 2016 95 6 2017 95 7 2018 96 8 2019 93 9 2020 90

3. A visual inspection of our data

Import matplotlib.pyplot under its usual alias and create a figure

import matplotlib.pyplot as plt fig = plt.figure()

Draw a line plot of release_years and durations

plt.plot(durations_df['years'], durations_df["durations"])

Create a title

plt.title("Netflix Movie Durations 2011-2020")

Show the plot

plt.show()

4. Loading the rest of 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

print(netflix_df[: 5 ])

show_id ... genre 0 s1 ... International TV 1 s2 ... Dramas 2 s3 ... Horror Movies 3 s4 ... Action 4 s5 ... Dramas

[5 rows x 11 columns]

5. Filtering for movies!

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[: 5 ])

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

6. Creating a scatter plot

Create a figure and increase the figure size

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

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()

7. Digging deeper

Filter for durations shorter than 60 minutes

short_movies = netflix_movies_col_subset[netflix_movies_col_subset['duration'] < 60 ]

Print the first 20 rows of short_movies

print(short_movies[: 20 ])

title ... duration 35 #Rucker50 ... 56 55 100 Things to do Before High School ... 44 67 13TH: A Conversation with Oprah Winfrey & Ava ... ... 37 101 3 Seconds Divorce ... 53 146 A 3 Minute Hug ... 28 162 A Christmas Special: Miraculous: Tales of Lady... ... 22 171 A Family Reunion Christmas ... 29 177 A Go! Go! Cory Carson Christmas ... 22 178 A Go! Go! Cory Carson Halloween ... 22 179 A Go! Go! Cory Carson Summer Camp ... 21 181 A Grand Night In: The Story of Aardman ... 59 200 A Love Song for Latasha ... 20 220 A Russell Peters Christmas ... 44 233 A StoryBots Christmas ... 26 237 A Tale of Two Kitchens ... 30 242 A Trash Truck Christmas ... 28 247 A Very Murray Christmas ... 57 285 Abominable Christmas ... 44 295 Across Grace Alley ... 24 305 Adam Devine: Best Time of Our Lives ... 59

[20 rows x 5 columns]

8. Marking non-feature films 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']

9. Plotting with color!

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()

10. What next?

Are we certain that movies are getting shorter?

are_movies_getting_shorter = "NO"