1. NumPy
The foundation of numerical computing in Python.
It provides fast, memory-efficient arrays and mathematical functions used by almost every other data library.
Key uses: arrays, linear algebra, statistics, performance optimization
2. Pandas
The most important library for data analysis and manipulation.
It makes working with structured data (tables, CSVs, Excel files) simple and intuitive.
Key uses: data cleaning, filtering, grouping, time series analysis
3. Matplotlib
The core plotting library in Python.
It gives full control over visualizations, from simple charts to complex custom plots.
Key uses: line charts, bar charts, histograms, customization
4. Seaborn
Built on top of Matplotlib, Seaborn makes statistical visualizations easier and more attractive.
Key uses: heatmaps, box plots, distribution plots, correlation analysis
5. SciPy
Extends NumPy with advanced scientific and statistical functions.
Key uses: hypothesis testing, optimization, interpolation, signal processing
6. Scikit-learn
The go-to library for machine learning in data analysis.
Key uses: regression, classification, clustering, model evaluation, preprocessing
7. Statsmodels
Focused on statistical modeling and inference rather than prediction.
Key uses: linear regression, time series analysis, statistical tests, econometrics
8. Jupyter Notebook
An interactive environment for writing and running code, visualizing data, and documenting analysis.
Key uses: exploratory data analysis, reporting, prototyping
9. SQLAlchemy
A powerful library for working with SQL databases using Python.
Key uses: database connections, querying data, integrating SQL with Python workflows
10. Plotly
A modern library for creating interactive and web-ready visualizations.
Key uses: dashboards, interactive charts, sharing insights online
Final Tip
If you're starting out, prioritize learning NumPy, Pandas, Matplotlib, Seaborn, and Jupyter first. These form the core toolkit for most data analyst roles.
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