EDA Report on Netflix Data Set.ipynb

Kernel: Python 3

EDA on Netflix Data

%%time
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

netflix = pd.read_csv(r"C:\Users\suyashi144893\Documents\data Sets\netflix_data.csv")
netflix.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8807 entries, 0 to 8806
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   show_id       8807 non-null   object
 1   type          8807 non-null   object
 2   title         8807 non-null   object
 3   director      6173 non-null   object
 4   cast          7982 non-null   object
 5   country       7976 non-null   object
 6   date_added    8797 non-null   object
 7   release_year  8807 non-null   int64 
 8   rating        8803 non-null   object
 9   duration      8804 non-null   object
 10  listed_in     8807 non-null   object
 11  description   8807 non-null   object
dtypes: int64(1), object(11)
memory usage: 825.8+ KB
Wall time: 1.56 s

netflix.shape

(8807, 12)

About Data

This dataset contains data collected from Netflix of different TV shows and movies from the year 2008 to 2021.

• type: Gives information about 2 different unique values one is TV Show and another is Movie

• title: Gives information about the title of Movie or TV Show

• director: Gives information about the director who directed the Movie or TV Show

• cast: Gives information about the cast who plays role in Movie or TV Show

• release_year: Gives information about the year when Movie or TV Show was released

• rating: Gives information about the Movie or TV Show are in which category (eg like the movies are only for students, or adults, etc)

• duration: Gives information about the duration of Movie or TV Show

• listed_in: Gives information about the genre of Movie or TV Show

• description: Gives information about the description of Movie or TV Show

netflix.head(2)

Data Prepartion

• Check Missing Values

m=netflix.isnull().sum()
miss= (netflix.isnull().sum()/len(netflix))*100
miss_data=pd.concat([m,miss],axis=1,keys=['Total','%'])
print(miss_data)

              Total          %
show_id           0   0.000000
type              0   0.000000
title             0   0.000000
director       2634  29.908028
cast            825   9.367549
country         831   9.435676
date_added       10   0.113546
release_year      0   0.000000
rating            4   0.045418
duration          3   0.034064
listed_in         0   0.000000
description       0   0.000000

Dropping the cast and director features because we are not going to use those features right now

netflix = netflix.dropna( how='any',subset=['cast', 'director'])

netflix.fillna({ 'country': 'missing', 'rating': 'missing'}, inplace=True)
netflix.isnull().sum()

show_id         0
type            0
title           0
director        0
cast            0
country         0
date_added      0
release_year    0
rating          0
duration        3
listed_in       0
description     0
dtype: int64

netflix['date_added'] = pd.to_datetime(netflix['date_added'])
netflix.head()

## Finding how many unique values are there in the dataset
netflix.nunique()

show_id         5700
type               2
title           5700
director        4152
cast            5512
country          605
date_added      1478
release_year      72
rating            18
duration         205
listed_in        346
description     5677
dtype: int64

EDA

• What different types of show or movie are uploaded on Netflix?

• Correlation between the features

• Most watched shows on the Netflix

• Distribution of Ratings

• Which has the highest rating Tv show or Movies

• Finding the best Month for releasing content

• Highest watched genres on Netflix

• Released movie over the years

netflix.type.value_counts().to_frame('values_count')

pip install matplotlib

%config InlineBackend.figure_format = 'svg'
plt.style.use('seaborn')
fig, ax = plt.subplots()
plot = ax.bar(netflix.type.unique(), netflix.type.value_counts(), edgecolor="black", linewidth=1)
ax.set_ylabel('Value Count')
#ax.bar_label(plot, padding=-15, color='white')
ax.set_title('Type', fontweight='bold');