Exploring Netflix Data with Python: A Developer’s Deep Dive

As developers, we love digging into datasets to uncover interesting stories—and Netflix’s ever-growing catalog is no exception. In this post, I’ll share how I analyzed Netflix’s content data using Python and Pandas, plus tips for you to get started.

The Dataset

I used the popular Netflix Titles dataset from Kaggle (link here). It contains:

6,000+ records of movies and TV shows
Metadata like title, type, director, cast, country, date added, release year, rating, duration, and genres

What I Did

Data Cleaning & Preparation

Using Pandas, I:

Filled missing values in director, cast, and country
Converted date_added to datetime for time-series analysis
Extracted year and month of addition
Split genres into lists for better filtering
Created new columns for movie duration and TV show seasons

Here’s a snippet:

import pandas as pd

df = pd.read_csv('netflix_titles.csv')
df['date_added'] = pd.to_datetime(df['date_added'])
df['year_added'] = df['date_added'].dt.year
df['month_added'] = df['date_added'].dt.month

# Handle missing data
df['director'].fillna('Unknown', inplace=True)
df['cast'].fillna('Various', inplace=True)
df['country'].fillna('Unknown', inplace=True)

Exploratory Data Analysis (EDA)

I focused on:

Content type trends (movies vs TV shows) over years
Top producing countries
Most popular genres
Movie durations and TV show season counts

Example plot using Seaborn:

import seaborn as sns
import matplotlib.pyplot as plt

sns.countplot(data=df, x='type', hue='year_added', palette='muted')
plt.title('Movies vs TV Shows Over Years')
plt.show()

What I Found

A clear shift towards more TV shows added since 2016
The US dominates content production, but countries like India and UK are growing fast
Drama and Comedy lead genre counts, but Documentaries are on the rise
Movies are mostly under 100 minutes, while TV shows average 1–3 seasons