This repository contains examples and explanations for three essential Python libraries for data analysis: Pandas, NumPy, and Scikit-learn.
Pandas is an open-source data manipulation and analysis library. It offers data structures for effectively handling relational datasets and working with time series data. Pandas provides tools for cleaning, transforming, and analyzing data, and it can handle a variety of data formats, including CSV, Excel, SQL databases, and HTML tables.
You can install Pandas using pip, a package manager for Python: pip install pandas
NumPy is a Python library for numerical computing. It provides tools for working with multi-dimensional arrays, and it offers functions for performing mathematical operations on these arrays. NumPy is used extensively in scientific computing, data analysis, and machine learning.
You can install NumPy using pip: pip install numpy
Scikit-learn is a Python library for machine learning. It provides tools for data preprocessing, feature selection, model selection, and performance evaluation. Scikit-learn supports a variety of machine learning algorithms, including linear and logistic regression, decision trees, and support vector machines.
You can install Scikit-learn using pip: pip install scikit-learn