7 Must-Know Python Libraries for Data Analysts

Manikandan

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1. Requests

Requests is essential for making HTTP requests, enabling data analysts to fetch real-time data and external datasets with ease.

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2. Beautiful Soup

Use Beautiful Soup to parse HTML and XML, helping analysts extract non-API data from websites effectively.

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3. NumPy

NumPy is your go-to library for fast numerical computations and array manipulations, forming the foundation for advanced analysis.

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4. Pandas

Pandas makes data cleaning, transformation, and analysis straightforward, a must-have for every data analyst.

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5. Polars

An excellent alternative to pandas, Polars offers blazing-fast performance for handling large datasets effortlessly.

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6. DuckDB

DuckDB integrates SQL capabilities with Python for efficient exploration and analysis of large-scale datasets.

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7. Statsmodels

Statsmodels allows you to perform hypothesis testing, statistical modeling, and diagnostics with ease.

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