Most Popular Python Packages in 2021 (2023)

Python packages provide an efficient and beginner-friendly way to solve complex problems in scientific computing, data visualization, data modeling, and many other fields. Let's look at the most popular python packages of 2021 for data analysts and developers.

With the advent of data science and artificial intelligence, Python has become one of the most popular programming languages. That's allpreferred by leading organizations, including Netflix, Uber, IBM, AstraZeneca, NASA and the CIA. And Python isn't limited to data science and AI; that's allused in many industries, including blockchain, physics, astronomy, medicine, game development, and entertainment.

Python has severalKey features that make it so popular: It's beginner-friendly, supports many career paths, and has a welcoming community. However, one of the main reasons to learn Python is the rich and diverse ecosystem of the language. Think of any random task, and there's a good chance that Python has a module or package that can make your work much more efficient.

What is a Python package?

Complex tasks are best approached step by step, subtask by subtask. That's why programmers create and useModule, or related code sets stored in separate files designed to solve specific tasks.

When you have a lot of different modules, you definitely want to group and organize them. TOThe Python package is a directory of a collection of modules.Just as you organize files on your computer into folders and subfolders, you can organize modules into packages and subpackages.

Each package must contain a file This file usually contains the initialization code of the corresponding package.

Here is an example ofmy modelPackage with three sub-packages:Training,Presentation, zmetric.

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To access the code for a Python package, you can import the entire package or its specific modules and subpackages.

For example, to access the code defined, can:

  • It also imports the entire package.import my_template;
  • import thosemetricpartial package withimportar my_model.metrics;
  • import thoseprecision.pyModule with one of these code snippets:
    import my_model.metrics.precision# o desde my_model.metrics import precision

You don't have to create your own Python packages to take advantage of this tool. There are many built-in and third-party packages that you can use in your work. Let's take a look at the most popular Python packages for 2021.

Top 10 Python Packages in 2021

Python packages streamline many important processes, e.g. For example, analyzing and visualizing data, building machine learning models, collecting unstructured data from the web, and processing information from images and text efficiently. Here are some of the best Python packages of 2021:


NumPy is the leading scientific computing tool in Python. It combines the flexibility and simplicity of Python with the speed of languages ​​like C and Fortran.

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NumPy is used to:

  • Advanced matrix operations (for example, add, multiply, intersect, transform, index).
  • Full math functions.
  • Generation of random numbers.
  • Linear algebra routines.
  • Fourier transforms, etc.

With NumPy, you get the computational power of compiled code while using Python's accessible syntax. It's no surprise that there is a vast ecosystem of Python packages and libraries that harness the power of NumPy. These include popular packages like Pandas, Seaborn, SciPy, OpenCV, and others.


If you work with tabular, time series, or matrix data, Pandas is the Python package for you. It is known as a fast, efficient, and easy-to-use tool for analyzing and manipulating data. It works with data frame objects; A data frame is a dedicated structure for two-dimensional data. Data frames have rows and columns like database tables or Excel spreadsheets.

Among other things, pandas can be used to:

  • Read/write data from/to CSV and Excel files and SQL databases.
  • Remodeling and panning of data sets.
  • Segment, index and create subsets of data sets.
  • Data aggregation and transformation.
  • Combine and join data sets.

If you want to learn how to use data frames in pandas and how to calculate descriptive statistics using its basic statistical functions, you should consider this interactive study.Python for data scienceaccompany.


Matplotlib is the most widely used data visualization and exploration library. You can use it to create simple charts like line charts, histograms, scatter charts, bar charts, and pie charts. You can also create animated and interactive visualizations with this library. Matplotlib is the foundation for all other visualization libraries.

The library offers great flexibility when it comes to formatting and designing graphics. You can freely choose how labels, grids, legends, etc. are displayed. However, to create complex and visually appealing charts, you need to write a lot of code.

Suppose we want to draw two line graphs: y = 2x and z = x2, where x is in the interval [0; 100].

We first calculate these variables with NumPy.

importiere numpy als npx = np.arange(0,100)y = x*2z = x**2

We then use matplotlib to create two subplots for two features and customize their formatting and styling:

import matplotlib.pyplot como plt%matplotlib, axis = plt.subplots(nrows=1, ncols=2, figsize=(12,2))axes[0].plot(x,y, color= "verde", lw=3)Achsen[0].set_xlabel('x')Achsen[0].set_ylabel('y')Achsen[1].plot(x,z, color="blue", lw=2 , ls='--')Achsen[1].set_xlabel('x')Achsen[1].set_ylabel('z')

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As you can see, Matplotlib syntax allows you to have multiple subplots in one plot, set arbitrary labels, choose line color, width, style, etc. tedious and time consuming task. Depending on your task, it might be more effective to use a different display package.

Learn the basics of data visualization in PythonIntroduction to Python for data scienceCourse. You will learn how to create simple data visualizations using matplotlib.

4.born of the sea

Seaborn is a high-level interface for drawing attractive statistical graphs with just a few lines of code. Let's see it in action.

Let's use the famousIris Flower Dice Setin our example. For those unfamiliar, this data set contains four features (the length and width of the sepals and petals) for three species of iris (silky iris,iris virgen, zIris versicolor). Let's see how these four traits relate to each other based on the iris species.

Look how Seaborn ispair diagramfunction solves this task. Keep in mind that you can create a complex and visually appealing chart with just three lines of code:

importar seaborn como snsiris = sns.load_dataset('iris') sns.pairplot(iris, hue = 'species', palette = 'pie')

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Notice how all the labels, styles, and a tag have been set up automatically. Similarly, you can easily create complex heatmaps, violin charts, joint charts, multi-chart grids, and many other chart types with this library.

5.learn scikit

Do you want to perform a regression? Or maybe you have a problem with data classification? scikit-learn is an efficient and beginner-friendly tool for predictive data analysis. With scikit-learn you can, among other things:

  • Identify which category an object likely belongs to (used for fraud detection, imaging detection, cancer detection, etc.).
  • Forecast a continuous variable based on available resources (used to predict house prices and inflation).
  • Group similar objects into groups (used in customer segmentation, social media analytics, etc.).

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scikit-learn makes machine learning with Python accessible to people with minimal programming experience. With just a few lines of code, you can model your data using algorithms like random forest, support vector machines (SVMs), k-means, spectral binning, and more.

6.requests for

This library was developed to make HTTP requests with Python more responsive and easy to use. The intuitive JSON method that Requests offers helps you avoid adding query strings to URLs manually. With orders you can:

  • Customize, inspect, authorize, and configure HTTP requests.
  • Add parameters, headers, and multipart files.
  • Unzip the data automatically.
  • Upload multiple files at once.

This package is a real boon for beginners and advanced users alike, making it one of theMost Downloaded Python Packages.


urllib3 is another Python-friendly HTTP client. it's justthe most downloaded PyPi packageand supports requests and some other popular Python packages. urllib3 provides many important features that standard libraries lack:

  • safety wire.
  • connection group.
  • Repeat orders.
  • Handling HTTP redirects.
  • Complete test coverage.


The Natural Language Toolkit (NLTK) is one of the main Python platforms for processing language data. It is a set of libraries and language processors that provide a set of tools for:

  • Classification.
  • tokenization
  • Earring.
  • Brand.
  • Analyze.
  • semantic reasoning.

NLTK is an essential tool for computational linguistics in Python. It is highly valued by linguists, engineers, researchers, and industry users.

If you are new to natural language processing, you may benefit from thisWorking with strings in PythonCourse that is part of our interactivePython for data scienceaccompany.


If you work with images, be sure to check out the Pillow package. It is a fork of PIL (Python Image Library) that has become an efficient and easy-to-use tool for image manipulation in Python.

With the pillows you can:

  • Open and save images of different file types (JPEG, PNG, GIF, PDF, etc.).
  • Create thumbnails for images.
  • Use a collection of image filters (for example, SOFT, BLUR, SHARP).

This is a great photo editing tool for beginners and it has some pretty powerful image processing features.


This package provides a variety of modules for testing new code, including small unit tests and complex functional tests for applications and libraries.

Simple syntax and a rich set of functions make pytest one of the most popular Python packages among programmers. This test automation framework offers:

  • Built-in support for test discovery.
  • Modular accessories for test setup (eg database connection setup, URL, input data).
  • Extensive plugin architecture (315+ external plugins).
  • Integrated unit tests.

pytest is a great tool to improve your programs. And well-tested programs are good programs!

It's 2021: time to learn Python packages!

If you're thinking about learning Python packages, start by learning the language yourself. This gives you a decisive competitive advantage on the job market. Programmers, data analysts, salespeople, office workers, scientists, doctors, and even artists can do this.improve your daily work with Python.

For a deep understanding of Python fundamentals and real-world use case experience, I recommend following's interactive study paths:

  • Python BasicsIt's a mini track for whoever wantsstart programming. It includes three courses with a total of 229 programming challenges. They include variables, if statements, loops, functions, basic data structures, and more.
  • Learn to program with Pythonis an extended version ofPython BasicsWith five interactive courses and 419 coding challenges, you'll go beyond the basics and get hands-on experience with Python's data structures and built-in algorithms.
  • Python for data scienceis for those interested in data analysis and data science. Contains five courses and 329 coding challenges covering pandas and matplotlib packages, working with strings in Python, and processing CSV, Excel, and JSON files. are someIdeas for your first data science projects. Have fun!

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