20 Python Libraries for Data Science for 2026

Introduction

According to a report by PWC, the world’s largest private bank, JPMorgan Chase, saves 360,000 review hours each year with a Python‑based AI platform. The Mayo Clinic cut diagnostic time by 30% with models built on the same ecosystem. Outcomes like these have turned boardroom heads and put Python libraries for data science at the center of real operations.

These are some of the reasons Python surpassed JavaScript as the most-used language on GitHub in 2024. And as a result, knowing the main Python libraries for data science is a core business competency. These libraries are the tools used to build real-world value, and mastering them is a requirement for any aspiring data engineer or data scientist.

Did You Know?

45% of data professionals cite data quality and pipeline consistency issues affecting their production environment. (Source: Anaconda)

Why Python for Data Science?

Python's popularity in data science was not an accident. Its simple, readable syntax makes it easy to learn. Its open-source status means a massive, active community supports it. That community builds and maintains a powerful ecosystem of Python libraries for data science. These libraries are simply packages of pre-written code that make complex jobs much simpler.

Instead of writing hundreds of lines of code to run a statistical regression, you import a library and do it in three. This philosophy lets you focus on solving the problem, not on writing code from scratch. Here are some more market signals:

• A 2025 McKinsey report found the talent-to-demand ratio for Python skills is just 0.5x, making it one of the most scarce and valuable skill sets in the market
• The "Python in Excel" integration, now standard for enterprise users, brought Python libraries for data analysis like Pandas and Scikit-learn to millions of finance and business analysts, cementing Python as the new standard in the enterprise

Also Read: Introduction to Python Basics

The Core Four: Foundational Python Libraries

Almost every data science project in Python begins with these four libraries. These Python packages for data science are the building blocks for nearly everything else on this list. For many data scientists, mastering these core Python libraries for data science is the first step.

1. NumPy (Numerical Python)

NumPy is the fundamental package for scientific computing in Python. Its main feature is the N-dimensional array, a data structure that lets Python handle huge arrays of numbers and perform mathematical operations on them very quickly.

Key Features

• N-dimensional arrays: A fast, efficient data structure for vectors and matrices
• Mathematical functions: A large collection of high-level functions to operate on these arrays
• Linear algebra: Tools for matrix multiplication, Fourier transforms, and random number generation

What are the applications of NumPy?

NumPy is the backbone for many other libraries, including Pandas and Scikit-learn. It's used for any task needing numerical computation, like processing sensor data, manipulating images (which are just arrays of pixels), and preparing data for machine learning models.

How do I use NumPy for array manipulation?

NumPy makes complex math simple. You can create arrays from plain Python lists, perform calculations on entire arrays at once (vectorization), and select data with ease.

Example: Basic Array Operations

import numpy as np

# Create an array from a Python list

a = np.array([1, 2, 3, 4, 5])

# Create a 2x3 array (two rows, three columns)

b = np.array([[1, 2, 3], [4, 5, 6]])

# Select a single element (row 1, column 2)

element = b[1, 2] # Result: 6

# Perform math on an entire array

# This multiplies every number in 'a' by 2

doubled = a * 2 # Result: [ 2,  4,  6,  8, 10]

# Calculate the mean of all elements in 'a'

mean_val = np.mean(a) # Result: 3.0

# Select elements greater than 3

c = a[a > 3] # Result: [4, 5]

How to Install NumPy

• pip:

pip install numpy

• conda:

conda install numpy

2. Pandas

If NumPy is the foundation, Pandas is the workhorse. It's the most popular Python library for data manipulation and analysis. It introduces two main data structures: the Series (1-dimensional) and the DataFrame (2-dimensional, like a spreadsheet or SQL table). 77% of data scientists use Pandas for data exploration, according to a 2024 JetBrains survey.

Key Features

• DataFrame object: A flexible table-like structure with labeled rows and columns
• Data I/O: Easily read and write data from CSV files, Excel, SQL databases, and more
• Data cleaning: A complete set of tools for handling missing data, duplicates, and data type conversions
• Analysis tools: Powerful functions for grouping, merging, joining, and reshaping data

What are the applications of Pandas in data science?

Pandas is used in the first and most critical steps of any project. A data scientist spends most of their time cleaning and preparing data, and Pandas is the primary tool for this.

• Data Cleaning: Removing or filling in missing values (.fillna()), dropping duplicates (.drop_duplicates()), and standardizing text
• Exploratory Data Analysis (EDA): Using functions like .describe() for a statistical summary, .groupby() to aggregate sales by region, and .plot() for quick charts
• Data Preparation: Merging data from multiple sources (e.g., combining customer info with sales data) and transforming data to prepare it for machine learning
• Financial Analysis: Handling and manipulating time-series data, a core task in finance

How to Install Pandas

• pip:

pip install pandas

• conda:

conda install pandas

3. Matplotlib

Matplotlib is the original and most fundamental data visualization library in Python. It provides enormous flexibility to create static, publication-quality 2D plots. It can be complex, but its main strength is its total control. If you can imagine a plot, you can build it with Matplotlib.

Key Features

• Wide plot variety: Creates line plots, bar charts, scatter plots, histograms, and more
• Full control: Allows customization of every single element of a plot: labels, colors, titles, ticks
• Ecosystem integration: Works perfectly with NumPy, Pandas, and the entire scientific Python stack

What are the applications of Matplotlib?

Matplotlib is used to visually inspect data. This can be for exploring a new dataset, understanding a variable's distribution, or communicating findings. For example, you could plot company revenue over time or create a scatter plot to see the relationship between ad spending and sales.

How to Install Matplotlib

• pip:

pip install matplotlib

• conda:

conda install matplotlib

4. Scikit-learn (Sklearn)

Scikit-learn is the gold standard for classical machine learning in Python. It provides a uniform API across regression, classification, clustering, feature scaling, model selection, and pipelines. With over 80 million downloads each month, it's a critical piece of data science infrastructure.

Key Features

• Classification: Algorithms like Logistic Regression and Random Forest to predict a category (e.g., "spam" or "not spam")
• Regression: Algorithms like Linear Regression to predict a continuous value (e.g., housing price)
• Clustering: Algorithms like K-Means to find patterns and group unlabeled data (e.g., customer segmentation)
• Model selection: Tools to split data for training and testing (train_test_split) and check model performance
• Preprocessing: Functions for feature scaling, normalization, and encoding categorical data

What are the applications of Scikit-learn?

The Siemens and Mayo Clinic examples mentioned earlier relied on libraries like Scikit-learn. It's used to build models that answer business questions like "Which customers are likely to churn?" or "What will our sales be next quarter?".

How to Install Scikit-learn

• pip:

pip install scikit-learn

• conda:

conda install scikit-learn

Mastering these four libraries is a prerequisite for nearly all practical data analytics with Python. To master these essential Python libraries, explore and enroll in our Data Science Course in collaboration with IBM.

Data Visualization Libraries

While Matplotlib is powerful, other Python libraries for data science make it easier to create specific types of plots. This brings up a common question: Which Python library is best for data visualization? The answer is: It depends on your needs. Each library serves a different purpose.

5. Seaborn

Seaborn is built on top of Matplotlib. It is designed to make creating complex and attractive statistical visualizations much easier. Where Matplotlib gives you total control, Seaborn gives you high-level functions for common statistical plot types.

Key Features

• Statistical plotting: Designed to work directly with Pandas DataFrames for statistical analysis
• Attractive defaults: Creates professional-looking plots with very little code
• Advanced plots: Easily create complex plots like heatmaps, pair plots, violin plots, and facet grids

Applications: Seaborn is best for quickly exploring relationships in your data. An analyst might use sns.pairplot() to see scatter plots for every variable against every other variable in a single line of code.

How to Install Seaborn

• pip: 

pip install seaborn

• conda:

conda install seaborn

6. Plotly

Plotly is the leading library for creating interactive, web-based visualizations. Matplotlib and Seaborn create static images. Plotly generates interactive charts where you can zoom, pan, and hover over data points to see more information.

Key Features

• Interactivity: Creates charts perfect for web dashboards and reports
• Wide range: Supports over forty unique chart types, including 3D plots and maps
• Dash: Plotly is the backend for Dash, a popular Python framework for building analytical web applications

Applications: Plotly is used when you present findings to a non-technical audience. An analyst would use Plotly to build a dashboard where a manager can click and filter data.

How to Install Plotly

• pip:

pip install plotly

• conda:

conda install plotly

7. Bokeh

Bokeh is another excellent library for interactive visualization. It's a close competitor to Plotly, also focusing on charts for web browsers.

Key Features

• Web-native: Designed from the ground up to produce interactive web plots
• Streaming data: Has strong capabilities for handling and visualizing streaming or real-time data
• Flexible: Can produce simple charts quickly or build complex, interactive dashboards

Applications: Bokeh is a great choice for web applications that need to display real-time data, such as a stock market tracker or a dashboard monitoring website traffic.

How to Install Bokeh

• pip:

pip install bokeh

• conda:

conda install bokeh

Deep Learning Libraries

Deep learning is a subfield of machine learning focused on neural networks. These models power everything from chatbots to self-driving cars. For these tasks, you need more specialized libraries.

8. TensorFlow

Developed by Google, TensorFlow is an end-to-end open-source platform for deep learning. It is a complete ecosystem with tools for building, training, and deploying large-scale neural networks. It is known for its scalability and production-readiness.

Key Features

• Scalable: Designed to run on multiple CPUs, GPUs, or TPUs, and on servers, desktops, or mobile devices
• Production-ready: Offers robust tools like TensorFlow Serving for deploying models in real-world applications
• Ecosystem: Includes tools like TensorBoard for visualization and TensorFlow Lite for mobile deployment

Applications: TensorFlow is an industrial-strength tool used by companies like Google, Airbnb, and PayPal. It powers search rankings, ad recommendations, and fraud detection.

How to Install TensorFlow

• pip:

pip install tensorflow

• conda:

conda install tensorflow

9. Keras

Keras is a high-level deep learning API that runs on top of TensorFlow (it's now fully integrated as tf.keras). It is famous for its user-friendliness and simplicity. This makes it the perfect choice for beginners or for rapid prototyping.

Key Features

• Simple API: Lets you build and train complex neural networks in just a few lines of code
• User-friendly: Designed with a focus on a clear and simple developer experience
• Fast prototyping: Makes it easy to experiment with different model architectures

Applications: Keras is ideal for learning deep learning. A student or researcher might use Keras to quickly build and test a new idea for an image classifier before investing time in a more complex implementation.

How to Install Keras

Keras is included with TensorFlow 2.0 and later.

• pip:

pip install tensorflow

(this includes Keras)

• conda:

conda install tensorflow

10. PyTorch

Developed by Meta (Facebook), PyTorch is the other major deep learning library. It is widely loved by the research community for its flexibility and "Pythonic" feel. It uses a dynamic computation graph, which makes debugging and building complex models more intuitive.

Key Features

• Dynamic graph: Allows for more flexible model building and easier debugging
• Researcher favorite: The go-to library for many AI researchers, especially in natural language processing (NLP)
• Easy to learn: Its interface feels very natural to Python developers

Applications: PyTorch is used by companies like Tesla for its Autopilot software and by countless research labs. Its flexibility makes it a top choice for cutting-edge AI research.

How to Install PyTorch

Installation is best done using the official command from the PyTorch website, as it depends on your system (Linux/Mac/Windows) and hardware (CPU/NVIDIA GPU).

• pip (example):

pip3 install torch torchvision torchaudio

• conda (example): 

conda install pytorch torchvision torchaudio -c pytorch

What Are the Differences Between TensorFlow, Keras, and PyTorch?

This is a common question for those starting in deep learning. Here is a simple breakdown to help you choose.

For a startup or a research lab prototyping a new model, PyTorch's flexibility is often preferred. Its code is easier to debug and feels more like standard Python.

In contrast, a large enterprise with established deployment pipelines might choose TensorFlow. Its ecosystem (like TensorFlow Serving and TensorFlow Lite) makes it easier to deploy models reliably at scale, whether on a server or a mobile phone. Keras is the starting point for most people, as it provides a simple interface on top of TensorFlow's powerful engine.

Did You Know?

92% of data science professionals use open-source AI tools and models. (Source: Anaconda)

Specialized Data Science Python Libraries

Beyond the main categories, many Python libraries for data science are built for specific tasks. Here are ten more essential Python libraries for data science. These cover everything from text analysis to big data.

Natural Language Processing (NLP)

11. NLTK (Natural Language Toolkit)

NLTK is the original, academic library for NLP. It's a wonderful learning tool that provides the fundamental building blocks of text processing, from tokenization (splitting text into words) to stemming (reducing words to their root form).

• Applications: Best for teaching and learning NLP concepts
• Install:

pip install nltk

12. spaCy

SpaCy is the modern, industrial-strength NLP library. It's designed to be fast, efficient, and production-ready for real-world text analysis tasks. It comes with pre-trained models for over 60 languages.

• Applications: Used in production systems to extract names, locations, and topics from articles, or to power chatbots
• Install:

pip install -U spacy 

followed by downloading a model, e.g.,

python -m spacy download en_core_web_sm)

13. Hugging Face Transformers

This library has revolutionized NLP. It provides easy access to thousands of state-of-the-art pre-trained models (like BERT and GPT) for tasks like text summarization, translation, and sentiment analysis.

• Applications: Powering generative AI features, summarizing legal documents, or performing sentiment analysis on customer reviews
• Install:

pip install transformers

Web Scraping

14. Scrapy

Scrapy is a powerful, all-in-one framework for large-scale web crawling. It handles everything from sending requests and following links to processing the output data.

• Applications: Building a dataset of product prices from an e-commerce site or gathering news articles from thousands of sources
• Install:

pip install scrapy

15. BeautifulSoup

BeautifulSoup is a library for parsing HTML and XML. It's perfect for smaller scraping jobs. It is often used with the requests library (which fetches the web page).

• Applications: A simple script to pull a daily weather forecast or scrape a single page of stock data
• Install:

pip install beautifulsoup4

Machine Learning and Statistics

16. LightGBM

This is a high-performance gradient boosting framework. It is known for being extremely fast, memory-efficient, and often provides state-of-the-art results on tabular (spreadsheet-like) data.

• Applications: Used in data science competitions and in production for tasks like fraud detection or ad-click prediction
• Install:

pip install lightgbm

17. XGBoost

This is the other dominant gradient boosting library. It is famous for its use in winning Kaggle (data science) competitions. It is known for its accuracy and performance.

• Applications: Very similar to LightGBM. It is a robust and powerful tool for any predictive modeling task on structured data.
• Install:

pip install xgboost

18. Statsmodels

This is a library for rigorous statistical modeling. Where Scikit-learn focuses on prediction, Statsmodels focuses on inference and statistical testing.

• Applications: An economist would use Statsmodels to determine if a policy change had a statistically meaningful effect on employment, complete with p-values and confidence intervals
• Install:

pip install statsmodels

Big Data and Scaling

19. Dask

Dask is a flexible parallel computing library that scales your existing tools. Dask provides parallel versions of NumPy arrays and Pandas DataFrames. This allows you to work with datasets that are larger than your computer's RAM.

• Applications: Analyzing a 100GB log file on your laptop by processing it in chunks, all while using a familiar Pandas-like API
• Install:

pip install "dask[complete]"

20. PySpark

This is the Python API for Apache Spark. This is the industry-standard tool for distributed big data processing. It allows you to run data analysis and machine learning on massive clusters of computers.

• Applications: Processing terabytes of data daily in a large corporation's data pipeline. This is a core tool for data engineers.
• Install:

pip install pyspark

How to Choose the Right Python Library for a Specific Data Science Task?

Here is a quick guide to help you navigate a project and choose from the many Python libraries for data science.

• Start with a Question: You have a business problem. For example, "Why are our customer sales down?"
• Get the Data: You might need to pull data from a database (using Pandas) or scrape it from a website (using BeautifulSoup)
• Clean and Explore: The data is messy. You will use Pandas to handle missing values and NumPy for any custom math. You will use Matplotlib and Seaborn to create plots and understand the data
• Build a Model: You want to predict which customers might leave. This is a classification task, so you start with Scikit-learn. If your data is tabular, you might try XGBoost for better performance
• Handle Advanced Data: If your task involves analyzing customer reviews, you'll use spaCy or Hugging Face. If it involves image data, you'll use PyTorch or TensorFlow
• Present Your Findings: You build an interactive dashboard to show your results to your manager. You use Plotly to create the charts

Did You Know?

48% of Python developers are involved in data exploration and processing. (Source: JetBrains)

Conclusion

Knowing the names of these Python libraries for data science is the first step. The next step is mastering them through practice. A career in data science is rewarding. As reports show, it is a good career choice that requires a specific set of data scientist skills.

If you’re ready to begin your journey, enrolling in the Professional Certificate in Data Science and Generative AI offered by Simplilearn can help you build a strong foundation and advance from beginner to expert level.

FAQs

1. What are the alternatives to Scikit-learn?

While Scikit-learn is the best general-purpose ML library, several alternatives exist for specific needs.

• XGBoost & LightGBM: As mentioned, these are often the best-performing alternatives for gradient boosting, a powerful algorithm for tabular data. You would choose them when you need to squeeze out the highest possible accuracy.
• PyTorch & TensorFlow: For deep learning tasks (like image recognition or advanced NLP), you must use a deep learning framework. Scikit-learn does not support building deep neural networks.

2. Is Python or R better for data science?

This is a classic debate. The simple answer is that both are excellent, but they have different strengths.

• Python: This is a general-purpose language that is strong in all areas. Its key strengths are in production, deep learning, and integrating data science models into larger applications. Because you can use it for many other things, it's a very flexible skill.
• R: This is a language built by statisticians for statisticians. It is exceptionally strong in classical statistical analysis and academic-quality visualization.

3. What are the prerequisites for learning data science with Python libraries?

Before you dive into these Python libraries for data science, you should have a good grasp of the following:

• Basic Python Programming: You should be comfortable with data types (lists, dictionaries), variables, loops, and functions
• Basic Math Concepts: A foundational understanding of basic statistics (mean, median, mode) and linear algebra (vectors, matrices) is very helpful
• Domain Knowledge: Knowing the industry you want to apply data science to (e.g., finance, healthcare, marketing) is a huge advantage

If you are new to the field, it's helpful to first understand what data science is before you move on to more advanced topics.