What are some Python libraries used in Data Analysis?

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Some of the vital Python libraries used in Data Analysis include –

  • Bokeh
  • Matplotlib
  • NumPy
  • Pandas
  • SciKit
  • SciPy
  • Seaborn
  • TensorFlow
  • Keras

There are several Python libraries commonly used in data analysis. Some of the most popular ones include:

  1. Pandas: Pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrame and Series, along with functions to efficiently manipulate and analyze structured data.
  2. NumPy: NumPy is fundamental for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
  3. Matplotlib: Matplotlib is a widely-used plotting library in Python. It provides a MATLAB-like interface for creating static, interactive, and animated visualizations in Python.
  4. Seaborn: Seaborn is built on top of Matplotlib and provides a high-level interface for drawing attractive and informative statistical graphics. It simplifies the process of creating complex visualizations such as heatmaps, time series, and categorical plots.
  5. SciPy: SciPy is a library for scientific and technical computing. It builds on NumPy and provides additional functionality for optimization, integration, interpolation, linear algebra, statistics, and more.
  6. Scikit-learn: Scikit-learn is a machine learning library in Python. It provides simple and efficient tools for data mining and data analysis, including various supervised and unsupervised learning algorithms, as well as utilities for model selection and evaluation.
  7. Statsmodels: Statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration.
  8. Plotly: Plotly is a graphing library that makes interactive, publication-quality graphs online. It provides APIs in Python, R, and other programming languages.
  9. TensorFlow and PyTorch: While primarily known for deep learning, these libraries also offer tools for data manipulation and analysis, especially for tasks involving large-scale numerical computation.

These libraries provide a robust ecosystem for data analysis in Python, covering everything from data manipulation and visualization to statistical analysis and machine learning. Depending on the specific requirements of a data analysis task, one or more of these libraries may be used in combination to efficiently process and analyze data.