Data Analysis · 8,807 Netflix titles · 10 notebooks
Netflix Catalog Analysis
Carry out an end-to-end data analysis of the Netflix catalog. Each notebook starts with a concrete question and teaches you to prepare the data, choose a relevant representation and support an interpretation.
PythonPandasMatplotlibSeaborn
Free · No account·Beginner·~1h30
What you'll build
- ✓A complete catalog analysis, from the raw file to an evidence-based synthesis
- ✓A cleaned and enriched dataset that makes dates, countries, genres and durations usable
- ✓Visualizations selected for the question being asked, not merely to decorate the data
- ✓A critical reading of catalog trends: formats, evolution, origins, genres and content ratings
The 10 notebooks
00
Get to know the data
Understand what a DataFrame is and inspect the catalog structure before analyzing it.
01
Make the dataset usable
Handle missing information, convert dates, derive useful variables and save a clean version.
02
Compare movies and TV shows
Measure the share of each format and contrast two ways of showing the same distribution.
03
Read the catalog's evolution
Group additions over time and compare the trajectories of movies and TV shows.
04
Identify leading countries
Summarize production origins while keeping the comparison readable.
05
Study content age
Observe release-year distribution, then narrow the analysis to recent years.
06
Break down genres
Transform a multi-category column so that each genre can be counted correctly.
07
Reveal seasonality
Cross months and years of addition to expose patterns that a simple list would hide.
08
Compare duration distributions
Study movie duration by content rating while accounting for unusual values.
09
Build a synthesis
Connect observations, match charts to questions and state the limits of the analysis.
Skills covered
- · Methodical dataset exploration
- · Data cleaning and variable creation
- · Data aggregation and transformation
- · Choosing an appropriate visualization
- · Reading distributions and trends
- · Interpreting and synthesizing results
Prerequisites
- · Python basics (variables, loops, functions)
- · No data science experience required
- · No installation : online lab