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10 Essential Python Libraries for Data Professionals


Indispensable additions to your Python toolkit

Over the last few years, Python has seen a huge surge in popularity and is fast becoming the language of choice for many budding data professionals. It is, without doubt, one of the fastest-growing and most in-demand programming languages, which is no surprise given its relatively simple and easy-to-learn syntax, extensive collection of libraries, incredible community support, and all-around versatility.

If you’re looking to step up your game in data analysis with Python, then the following list of 10 libraries is a good place to start. Ranging from data manipulation to data visualization and statistical computation, you’ll find these libraries an essential addition to your Python toolkit.

1. Numpy

Numpy, which stands for “numerical python”, is the fundamental scientific computing package for Python. Numpy offers a comprehensive list of mathematical functions which include linear algebra routines, random number generators, basic statistical calculations, Fourier transforms, and many more. Several commonly used Python libraries, including a few in this list, are built on top of Numpy. At the core of Numpy is the array, which offers 1-dimensional, 2-dimensional, or multi-dimensional vectorization, indexing, and broadcasting. Owing to its speed and flexibility, the Numpy Array has become the de-facto language of multi-dimensional data interchange in Python. You can find the official documentation here. For an introduction to the basics of Numpy, check out this tutorial.

2. Pandas

The name Pandas is derived from the term “panel data” and is the core library for data manipulation and analysis in Python. The primary data structures in Pandas are the series and dataframe objects which simplify operations on data sets by presenting them in a tabular format. It is an indispensable tool during the data preparation and exploration phase. Among its comprehensive list of features are:

  • Tools for reading and writing data in formats such as CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
  • Handling of missing data and easy manipulation of messy data into an orderly form;
  • Reshaping and pivoting of data sets between wide and long formats;
  • Conditional slicing, indexing, and subsetting of large data sets;
  • Aggregating data by performing group by operations or datetime resampling;
  • Merging and joining different data sets;
  • Time series-functionality: date range generation and frequency conversion.
  • Data visualization

To get started with Pandas, you can check out my Medium article on data preparation here. You can also refer to the official Pandas documentation via this link.

3. Matplotlib

Matplotlib is a plotting library for creating static, animated, and interactive visualizations in Python. It offers tons of customizable chart options, visual styles, and layouts. A growing list of third-party packages extend and build on Matplotlib functionality. One of such packages is Seaborn, which is next on our list.

4. Seaborn

Seaborn is a high-level data visualization library that is built on top of Matplotlib and integrates closely with Pandas dataframes. It is used for creating visually appealing and informative statistical charts and graphs by internally performing the necessary semantic mapping and statistical aggregations necessary. Since it’s based on Matplotlib, you can still use Matplotlib functionality to edit or augment your Seaborn plots, while still retaining the option of more beautiful and advanced chart types. To see how Seaborn simplifies the data visualization process, you can check out my article on exploratory data analysis.

5. Scipy

The name SciPy is derived from “Scientific Python” and is built upon Numpy. It offers additional features and functionalities by providing algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, and many other classes of problems. It also provides additional tools for array computing and provides specialized data structures such as sparse matrices and k-dimensional trees. You can find the official documentation here.

6. Statsmodels

The statsmodels library is primarily used for statistical modeling, hypothesis testing, and data exploration. It provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests. You can get started with statsmodels by following the steps in this tutorial. The official documentation is available here.

7. Beautiful Soup

Ever heard the term “web scraping”? This is the means by which data is extracted from websites and if that’s something you’re looking to do, then look no further than Beautiful Soup. It is used for parsing HTML and XML documents by creating a parse tree for parsed pages. To use Beautiful Soup, you’ll also need to get familiar with the Requests library, which is essentially an HTTP library for the Python programming language. To get started with web scraping using the Beautiful Soup library, then check out this tutorial on Youtube. The official documentation can be found on this link.


Natural Language Processing (NLP) is one of the fastest-growing subfields in Data Science owing to the vast amounts of textual data that is continuously being generated. NLTK, which stands for Natural Language Toolkit, is one of the leading NLP platforms for processing and analyzing human language, also known as natural language. NLTK comes with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. The NLTK documentation contains examples and use cases to get you started.

9. Scikit Learn

Scikit Learn is the product of a Google Summer of Code project which has since evolved into a comprehensive machine learning library that supports supervised and unsupervised learning. Apart from its large selection of machine learning algorithms for classification, regression, and clustering, it also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. Scikit Learn is an invaluable resource for any aspiring machine learning engineer or data scientist owing to its ease of use, performance, and overall variety of algorithms available.

10. Tensorflow

TensorFlow is an end-to-end open-source platform for machine learning with a particular focus on the training and inference of deep neural networks. Using Tensorflow, machine learning engineers can easily build and deploy large-scale neural networks with numerous layers. It has vast applications such as computer vision, facial recognition, time series forecasting, sentiment analysis, and voice and sound recognition. It is often used in conjunction with Keras, another Python library that acts as an interface for the TensorFlow library.


While the above list is by no means exhaustive, it is a good starting point for anyone looking to transition into data science or analytics. There are many other amazing libraries in Python for data science and in time you will know which others to include in your toolkit. In the meantime, you’ll be surprised how much you can achieve by mastering these 10 libraries. I wish you the best of luck in your foray into the field and do let me know in the comments section if there are any libraries you feel should have made the cut.


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