Netflix Data Analysis with Python: Beginner-Friendly Project with Code & Insights
🗂️ Project Overview:
In this project, we’ll explore a real-world Netflix dataset using Python, Netflix Data Analysis . You’ll learn how to clean data, extract insights, and visualize trends using libraries like Pandas, Matplotlib, and Seaborn. This is perfect for Python beginners looking to build data analysis skills with a practical, fun project.
📥 Dataset Link:
We’ll use the Netflix Movies and TV Shows dataset from Kaggle:
🔗 Netflix Titles Dataset on Kaggle
Download the dataset (netflix_titles.csv) directly from Kaggle after logging in.
🧰 Tools Required:
- Python 3.x
- Jupyter Notebook or Google Colab
- Libraries:
pandas,matplotlib,seaborn
Install libraries (if needed):
pip install pandas matplotlib seaborn
🧪 Step 1: Import Libraries and Load the Dataset
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load dataset
df = pd.read_csv("netflix_titles.csv")
# Display first 5 rows
df.head()
🧼 Step 2: Explore and Clean the Dataset
# Check data shape and info
print("Dataset shape:", df.shape)
df.info()
# Check for null values
df.isnull().sum()
Handle missing data:
# Fill missing 'director' and 'cast' with 'Unknown'
df['director'].fillna('Unknown', inplace=True)
df['cast'].fillna('Unknown', inplace=True)
# Drop rows with missing 'date_added' or 'country'
df.dropna(subset=['date_added', 'country'], inplace=True)
# Convert 'date_added' to datetime
df['date_added'] = pd.to_datetime(df['date_added'])
📊 Step 3: Data Questions and Visualizations
Let’s explore insights visually.
🔎 Q1: What type of content is most common on Netflix?
# Count of Movies vs TV Shows
df['type'].value_counts().plot(kind='bar', color=['red', 'blue'])
plt.title('Content Type Distribution')
plt.xlabel('Type')
plt.ylabel('Count')
plt.show()
📆 Q2: How has content changed over the years?
# Extract year from date_added
df['year_added'] = df['date_added'].dt.year
# Group by year
content_per_year = df['year_added'].value_counts().sort_index()
# Plot trend over time
content_per_year.plot(kind='line', marker='o')
plt.title('Content Added Over the Years')
plt.xlabel('Year')
plt.ylabel('Number of Titles')
plt.grid(True)
plt.show()
🌎 Q3: Which countries produce the most Netflix content?
top_countries = df['country'].value_counts().head(10)
sns.barplot(x=top_countries.values, y=top_countries.index, palette='viridis')
plt.title('Top 10 Countries by Number of Titles')
plt.xlabel('Number of Titles')
plt.ylabel('Country')
plt.show()
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⏱️ Q4: What is the distribution of movie durations?
Netflix includes short films, full-length movies, and miniseries. Let’s focus on movies and analyze their duration.
# Filter only Movies
movies_df = df[df['type'] == 'Movie']
# Extract numeric duration (e.g., "90 min" → 90)
movies_df['duration_int'] = movies_df['duration'].str.extract('(\d+)').astype(float)
# Plot distribution
plt.figure(figsize=(10,6))
sns.histplot(movies_df['duration_int'], bins=30, kde=True, color='coral')
plt.title('Distribution of Movie Durations')
plt.xlabel('Duration (minutes)')
plt.ylabel('Number of Movies')
plt.show()
🧪 Insight:
Most Netflix movies are around 90–100 minutes, with a sharp drop-off after 120 minutes.
📺 Q5: What’s the distribution of Netflix content ratings?
Let’s visualize how Netflix categorizes its shows and movies by audience rating.
plt.figure(figsize=(12,6))
sns.countplot(data=df, x='rating', order=df['rating'].value_counts().index[:10], palette='Set2')
plt.title('Top Content Ratings on Netflix')
plt.xlabel('Rating')
plt.ylabel('Number of Titles')
plt.xticks(rotation=45)
plt.show()
🧪 Insight:
TV-MA and TV-14 are the most common ratings, indicating mature and teen content dominates.
🧾 Summary of Findings
Let’s recap the key insights from this Netflix data analysis:
- Movies dominate Netflix’s catalog over TV Shows.
- Content additions peaked around 2018–2019, with a slowdown in recent years.
- The U.S., India, and the U.K. lead in content production.
- Most movies are under 120 minutes, clustered around the 90-minute mark.
- Mature ratings (TV-MA, R) are the most common, suggesting an adult-oriented content focus.
💡 Project Extension Ideas
If you want to take this project further, here are a few ideas:
- Analyze the most frequent directors or actors.
- Track genre popularity over time.
- Cluster content by language or region.
- Create an interactive dashboard using Plotly or Streamlit.
📁 Bonus: Save Notebook for Download
If you want to export the project as a .ipynb notebook:
# In Jupyter, go to File > Download as > Notebook (.ipynb) # Or in Google Colab: File > Download > Download .ipynb
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