Python is a powerful programming language for data manipulation, and several libraries are available for working with data in Python. This article will explore some of the most popular Python libraries for data manipulation. How data manipulation in python works? Check this link to know.
NumPy is a virtual library for data manipulation in Python. It provides a robust set of functions for working with multi-dimensional arrays and matrices. NumPy’s array-based approach to data manipulation makes it efficient and fast, even for large datasets. It provides several functions for vectorized operations, such as addition, multiplication, and comparison, making it a popular choice for scientific computing and data analysis.
Pandas are another popular library for data manipulation in Python. It provides a robust set of functions for working with structured data, such as tables and time-series data. Pandas provide several data structures, including Series, DataFrame, and Panel, which help work with two-dimensional, two-dimensional, and three-dimensional data. Pandas also provide data cleaning, aggregation, transformation, and visualization functions.
Matplotlib is a popular library for data visualization in Python. It provides many functions for creating high-quality visualizations, including line plots; scatter plots, bar charts, and histograms. Matplotlib is highly customizable and allows you to control every aspect of your visualizations, from the colors and labels to the fonts and formatting.
Seaborn is another library for data visualization in Python. It provides a set of high-level functions for creating statistical graphics. Seaborn is built on top of matplotlib and offers several functions for creating complex visualizations such as heat maps, pair plots, and categorical plots. Seaborn is designed to work well with Pandas data structures, making it a popular choice for data analysts and data scientists.
Scikit-learn are a popular library for machine learning in Python. It provides a wide range of functions for data preprocessing, feature selection, model selection, and evaluation. Scikit-learn is built on top of NumPy and offers a set of algorithms for classification, regression, clustering, and dimensionality reduction. Scikit-learn are designed to be easy to use and provide a consistent interface for all its algorithms.
TensorFlow is a popular library for deep learning in Python. It provides a set of functions for building and training deep neural networks. TensorFlow is highly scalable and can train models on large datasets. It offers a wide range of model selection and evaluation tools, making it a popular choice for data scientists and machine learning practitioners.