Top 15 Libraries in Python You Must Know In 2025

Python is a prevalent programming language. It’s easy to use, highly interpretable, interactive, and object-oriented. Python libraries contain functions and methods that facilitate specific tasks. Also, it saves developers a significant amount of time and headache!

As a newly hired Product Growth Analyst, having a basic understanding of these libraries has eased the transition into my new role. The Python libraries have helped a lot in manipulating and representing data in a much more understandable manner, whether using Scikit-Learn to build models or Matplotlib to visualize data in a graphic format.

Let us now look at some python libraries:

1. TensorFlow

An open-source library developed by Google to aid in developing and training machine learning models. Data scientists can instantly develop and deploy machine learning models using TensorFlow, developed initially for computing large mathematical operations.

Features

• The visualization of computational graphs is exceptional.
• Google manages libraries
• Parallel Neural Network Training

Uses

• Speech and image recognition
• Text-based applications
• Time-series analysis
• Video detection

2. Scikit-Learn

Scikit-Learn is one of the most popular and valuable python libraries in machine learning. It contains all machine learning algorithms that you might need, like linear and logistic regression, gradient boosting, support vector machines, random forests, etc.,

Features

• It contains several methods for checking the accuracy of a model on unseen data.
• Provides all types of ML models for different types of data
• It is an effective tool for predictive data analysis.

Uses

• Model selection
• Dimensionality reduction

3. PyTorch

It is open-source software used for computer vision and natural language processing. In addition to being fast and inexpensive, PyTorch is the best deep learning framework because it can accelerate the research on deep learning models.

Features

• Production Ready
• Distributed Training
• Robust Ecosystem
• Cloud support

Uses

PyTorch is famous for providing two of the most high-level features:

• Tensor computations with solid GPU acceleration support
• Building deep neural networks on a tape-based auto-grade system

4. Matplotlib

Matplotlib is the most commonly used library for visualization in the Python community. With endless customization in charts and graphs, the developer can use everything from histograms to scatter plots. You can choose from an array of themes and colour schemes. This library is handy for the exploratory analysis of data during machine learning projects.

Features

• It’s free and open source.
• Complete control of axes properties, font properties, line styles, etc.
• Low memory consumption and better runtime behaviour

Uses

• Correlation analysis of variables
• Visualize 95 per cent confidence intervals of the models
• Outlier detection using a scatter plot etc.
• Visualize the distribution of data to gain instant insights

5. Pandas

If you want to get into the data science domain, Pandas is the library you should be mastered in. It is an open-sourced library heavily used for data exploration, manipulation, and analysis. It provides fast, flexible, and inexpensive data structures, making them easy to work with.

Features

• The capability of performing custom operations
• Enhances the ease of data manipulation
• Provides aggregations, concatenations, iteration, reindexing, and visualization capabilities

Uses

• Used as excellent support for loading CSV files into its data frame format
• Time-series-specific functionality includes date range generation, moving window, linear regression, and date shifting

6. Keras

This open-sourced library supports deep learning and neural networks. Model aggregation, graph visualization, and dataset analysis are among the features of Keras. Furthermore, it offers prelabeled datasets that can be imported and loaded directly. Besides being easy to use, it is versatile and suitable for innovative research.

Features

• Its Python-based nature makes debugging and exploring easier.
• Modular by nature
• Combining neural network models can lead to more complex models
• It runs smoothly on both CPU and GPU.

Uses

• Keras can make predictions and extract features in deep learning models with corresponding weights without using a new train model.

7. NLTK

NLTK stands for Natural Language Toolkit. This library helps in processing text data, and it contains text processing libraries such as classification, tokenization, stemming, tagging, parsing, etc. It also includes 50+ corpora.

Features

• It comes with a part-of-speech tagger
• N-gram and collocations
• Named-entity recognition

Uses

• Sentiment analysis
• Topic analysis

8. Gensim

This open-source library is used in unsupervised topic modelling and natural language processing. It was specially developed for handling extensive text collections, or corpora, utilizing data streaming and incremental online algorithms. The most distinguishing feature of Gensim is that, unlike its contemporaries, it doesn’t target only in-memory processing.

Features

• Streamed parallelized implementation of doc2vec, fastText, and word2vec algorithms
• The function can handle latent Dirichlet allocation, latent semantic analysis, non-negative matrix factorization, random projections, and tf-IDF.

9. Statsmodels

Statsmodel is a python library that conducts statistical tests and statistical data exploration. Statsmodels allows users to explore data, estimate statistical models and perform statistical tests.

Features

• Time series hypothesis tests: unit root, cointegration, etc.
• Descriptive statistics and process models for time series analysis

Uses

• Used for statistical testing

10. Selenium

Web browsers can be automated using Selenium, an open-source tool. It supports many browsers such as Firefox, Chrome, IE, and Safari. However, using the Selenium WebDriver, we can only automate testing for web applications.

Features

• Multi-Browser Compatibility
• Multiple Language Support
• Speed and Performance

Uses

• Selenium is an open-source and portable Web testing framework.
• Selenium commands are categorized into classes, making them easier to comprehend and implement.
• Selenium supports parallel test execution that reduces the time to execute similar tests.

11. NumPy

NumPy is a fundamental Python library for scientific computing. It provides support for large, multi-dimensional arrays and matrices, along with a vast collection of high-level mathematical functions to operate on these arrays.

Features

• Powerful N-dimensional array object
• Broadcasting functionality
• Integrates with other Python libraries like SciPy and Matplotlib
• Fast and efficient operations on arrays

Uses

• Linear algebra operations
• Fourier transforms
• Handling large datasets efficiently
• Support for vectorized operations on arrays

12. Eli5

Eli5 is a Python library designed to help explain machine learning models and their predictions in a way that humans can understand. It provides an easy way to debug and interpret models, particularly for non-experts in the field.

Features

• Supports many machine learning frameworks like scikit-learn, XGBoost, and LightGBM
• Generates HTML or text explanations for feature importance, predictions, and other aspects of the model
• Customizable formatting and style options

Uses

• Explaining predictions of complex models
• Understanding feature contributions and interactions
• Debugging and analyzing machine learning models

13. SciPy

SciPy is a Python library that provides many user-friendly and efficient numerical routines, such as numerical integration, interpolation, optimization, linear algebra, and statistics.

Features

• Numerical optimization algorithms
• Signal and image processing routines
• Linear algebra operations beyond NumPy
• Extensive statistical functions

Uses

• Solving differential equations
• Image processing and analysis
• Curve fitting and optimization problems
• Statistical analysis and hypothesis testing

14. LightGBM

LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be highly efficient and perform well on large-scale data.

Features

• Faster training speed and higher efficiency
• Lower memory usage
• Better accuracy than other boosting algorithms
• Support for parallel and GPU learning

Uses

• Large-scale machine learning tasks
• Ranking and classification problems
• Click-through rate prediction
• Computer vision and NLP tasks

15. Theano

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Features

• Tight integration with NumPy
• Transparent use of GPU for computation
• Efficient symbolic differentiation
• Extensive unit-testing and self-verification

Uses

• Building and training deep learning models
• Efficient numerical computations on arrays
• Large-scale computationally intensive applications

Conclusion

There are many helpful Python libraries for data science in addition to these top 10 Python libraries, and which one the user chooses is mainly based on the kind of project they are engaged in. And as a next step, if you are interested in learning and mastering data science with python, head onto Analytics Vidhya Introduction to Python Certification Course. Explore other available courses, and unlock your career as a data scientist!

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