The 10 most important Python libraries to use for exploratory data analysis (EDA) and what each one of them does are:
- Pandas - a library for working with data, including the ability to load, manipulate, and analyse data stored in a variety of formats.
- NumPy - a library for performing mathematical and statistical calculations on data.
- Matplotlib - a library for creating a wide range of graphs and charts to help you explore and understand your data.
- Seaborn - a library for creating more advanced and visually appealing graphs and charts.
- Scikit-learn - a library for performing machine learning algorithms and analysis on data.
- Statsmodels - a library for performing more advanced statistical analysis on data.
- Scipy - a library for performing specialised scientific and mathematical calculations on data.
- Bokeh - a library for creating interactive and dynamic visualisations of data.
- Plotly - a library for creating visually appealing and interactive visualisations of data.
- Altair - a library for creating high-level and declarative visualisations of data.
These libraries provide a range of powerful tools and functions for working with data, and are essential for performing exploratory data analysis. By using these libraries, you can gain valuable insights into the underlying structure and patterns in your data, and make more informed decisions based on your analysis.
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