10 Must Know Libraries for a Successful Data Scientist in 2023

10 Must Know Libraries for a Successful Data Scientist in 2023

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What are the must-know Python libraries For a successful data scientist in 2023?

Continue reading to find out!

Python libraries have no doubt been embraced by data scientists because of the ease, time-saving capabilities, and more that they provide.

There are over 137,000 Python libraries present today, and they aid in various aspects of data science such as data analysis, data visualization, and machine learning model development.

Must-Know Python Libraries For a Successful Data Scientist

Let’s go over the fundamentals of some Python libraries, their features, and applications in data science and other areas.

You can also learn about these Python libraries further with the help of a full-time data science course.

Here’s a list of 10 libraries in the Python space:

  1. NumPy
  2. Pandas
  3. Matplotlib
  4. Seaborn
  5. Scikit-learn
  6. Statsmodels
  7. TensorFlow
  8. PyTorch
  9. NLTK
  10. XGBoost:

1. NumPy

NumPy (Numerical Python) is an open-source Python library that is essential for numerical computations and array operations. It helps to process arrays that store values of the same data type and makes performing math operations on arrays easier.

It provides a multidimensional array object, (called N-array), various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and much more. (https://numpy.org/doc/stable/)

NumPy adds powerful data structures to Python that guarantee efficient calculations with arrays and matrices. It also supplies an enormous library of high-level mathematical functions that operate on these arrays and matrices, such as elementwise addition and multiplication, the computation of the Kronecker product, etc., which is not supported by Python lists.

The NumPy API is used extensively in Pandas, SciPy, Matplotlib, Scikit-learn, Scikit-image, and most other data science and scientific Python libraries and is a must-know tool for data scientists in 2023.

Features of NumPy

  • A powerful multi-dimensional array object.
  • It has tools for integrating C/C++ and Fortran code.
  • It has arbitrary data type definition capability to seamlessly and quickly integrate with a wide variety of databases.
  • Supports an object-oriented approach.
  • NumPy provides compact and faster computations with vectorization.
  • Contains fast, precompiled functions for numerical routines.

Applications of NumPy

  • Used extensively in data analysis.
  • Performing mathematical operations.
  • Used for scientific calculations.
  • A faster alternative for lists and arrays in Python.
  • It forms the base of other libraries like SciPy and Scikit-learn.
  • When used with Scipy and Matplotlib, NumPy can serve as a replacement for MATLAB.

2. Pandas

Pandas (Python data analysis) is a widely used Python library for data science that is crucial for data manipulation and analysis with data frames. It is built on top of NumPy, another Python library that provides support for multi-dimensional arrays.

It is based on two main data structures – “Series” and “Data Frames”. It allows converting data structures to DataFrame objects, handling missing data, adding/deleting columns from DataFrame, plotting data with a histogram or plot box, and imputing missing files.

Although Pandas provides many statistical methods, it is not enough to do data science in Python. Pandas depend on other Python libraries for data science like NumPy, SciPy, Sci-Kit Learn, and Matplotlib to draw conclusions from large data sets.

Features of Pandas

  • Pandas Python provides the flexibility for reshaping data structures.
  • Provide labeling on series and tabular data to allow automatic data alignment and indexing
  • Has High-level abstraction
  • Missing data floating and non-floating pointing numbers can be identified and mixed quickly using pandas.
  • Powerful capabilities to load and save data from various formats such as JSON, CSV, HDF5, etc.

Applications of Pandas

  • Pandas make it simple to do many of the time-consuming, repetitive tasks associated with working with data, like statistical analysis, data inspection, and data loading and saving.
  • Time series-specific functionality such as date range generation and frequency conversion.
  • Used in a variety of academic and commercial areas, including science, health, statistics, and finance.
  • ETL (extract, transform, load) jobs for data transformation and data storage.

3. Matplotlib

Matplotlib is a comprehensive library that is key for creating visualizations and data plotting because of the graphs and plots that it produces. It also provides an object-oriented API, which can be used to embed those plots into applications.

You can design charts, graphs, pie charts, scatter plots, histograms, error charts, etc., with just a few lines of code, and people who have used MATLAB or other graph plotting programs before will find it easier to use.

Matplotlib is one of those plotting libraries that are really useful in data science projects — it provides an object-oriented API for embedding plots into applications. However, users need to write more code than usual while using this library to generate advanced visualizations.

Features of Matplotip

  • Supports dozens of backends and output types.
  • It has low memory consumption and better runtime behavior.
  • Can be embedded in various IDEs as well as Jupyter Lab, and Graphical User Interfaces (GUI).
  • Images and visualizations can be exported to multiple file formats.
  • Useful in creating advanced visualizations.
  • Provides a simple way to access large amounts of data.

Applications of Matplotlib

  • Visualize the distribution of data to gain instant insights
  • Make interactive figures that can zoom, pan, and update.
  • Create publication-quality plots.
  • Export to many file formats.
  • Outlier detection using a scatterplot etc.

4. Seaborn

Seaborn has a high-level interface and helps with Statistical data visualization using Matplotlib.

When using this library, you get to benefit from an extensive gallery of visualizations (including complex ones like joint plots and time series)

Seaborn is simply faster as a visualization tool – we can pass the entire data, and it does a lot of the work for you.

Features of Seaborn

  • Has a wide variation of plots such as relational plots, categorical plots, matrix plots, and distribution plots.
  • It has a lot of themes for styling visualizations.
  • Works well with other libraries like NumPy as well as Pandas data structures.
  • Fast at visualizing univariate and bivariate data.

Applications of Seaborn

  • Used in multiple IDEs
  • Provides an easy-to-use interface for creating informative statistical graphics.
  • Makes it able to efficiently communicate insights from complex data sets.

5. Scikit-Learn

Scikit-learn is indispensable for machine learning algorithms and tools, with almost all the machine learning algorithms you may need available.

Scikit-learn is designed to be interpolated into NumPy and SciPy.

It is used for data science projects based on Python.

Features of Scikit-Learn

  • Has almost all the popular supervised learning algorithms such as Decision Trees, Support Vector Machines, and Linear Regression.
  • Also has all popular unsupervised learning algorithms like clustering and PCA (Principal Component Analysis).
  • Feature extraction capabilities to extract features from data to define attributes in images and text data.
  • Open source.

Applications of Scikit-Learn

  • Image processing.
  • Model selection.
  • Spam detection.
  • Customer segmentation.
  • Grouping experimentation.
  • Developing neuroimages.

6. Statsmodels

Statsmodels is used for statistical modeling and hypothesis testing. It also provides classes and functions for the estimation of many statistical models, as well as for conducting statistical data exploration.

It has a function for statistical analysis to achieve high-performance outcomes while processing large statistical data sets.  (https://www.mygreatlearning.com/blog/open-source-python-libraries/)

Features of Statsmodels

  • Linear regression models
  • Generalized Estimating Equations (GEE) for longitudinal or one-way clustered data.
  • Time series analysis.
  • Statistical tests.

Applications of Statsmodels

  • Helps data scientists run models and get results quickly and easily.
  • Aids in projects that involve heavy statistics and machine learning in Python.
  • It has a syntax much closer to R, so, for those who are transitioning to Python, StatsModels is a good choice.

7. TensorFlow

TensorFlow is developed by Google Brain and is vital for deep learning and neural networks.

It is basically a framework for defining and running computations that involve tensors, which are multi-dimensional arrays with a uniform type (called a dtype). When compared to other libraries like Torch and Theano, Tensorflow offers higher computational graph visualizations that are native.

Features of TensorFlow

  • enable ease of use via pre-trained models and research with state-of-the-art models.
  • Seamless library management backed by Google
  • Helps you build your own models.
  • Can be deployed on the web, on mobile and edge, and on servers.
  • TensorFlow offers smooth performance, quick upgrades, and frequent new releases to provide you with the latest features.

Applications of TensorFlow

  • TensorFlow is used across various scientific fields.
  • Speech recognition.
  • Object identification.
  • Helps in working with artificial neural networks that need to handle multiple data sets.
  • Time-series analysis.

8. PyTorch

Introduced by Facebook in 2017, PyTorch is a Python library that gives the user a blend of 2 high-level features – tensor computations with GPU acceleration and the development of Deep Neural Networks on a tape-based auto diff system.

PyTorch is a framework that is essential for deep learning research and development. It’s also used for other tasks, for example, creating dynamic computational graphs and calculating gradients automatically. It eases these tasks for data scientists. PyTorch is based on Torch, which is an open-source deep-learning library implemented in C, with a wrapper in Lua.

PyTorch is one of the most commonly preferred deep learning research libraries that provides maximum flexibility and speed

Features of PyTorch

  • Supports features such as metrics, logging, multimodel serving, and the creation of RESTful endpoints.
  • Robust ecosystem.
  • Dynamic graph computation, which lets users change network behavior on the fly, rather than waiting for all the code to be executed.
  • Since PyTorch is based on Python, it can be used with popular libraries such as NumPy and SciPy.

Applications of PyTorch

  • Creating dynamic computational graphs and calculating gradients automatically.
  • Natural language processing
  • Reinforcement learning
  • Image classification.


The Natural Language Toolkit (NLTK) is a platform used for building Python programs that work with human language data and is important for natural language processing tasks.

It contains a set of tools that provide processing solutions for numerical and symbolic language processing. The toolkit also comes with a dynamic discussion forum that allows you to discuss and bring up any issues relating to NLTK.

Features of NLTK

  • Tokenization
  • Offers sentiment analysis through its built-in classifier, which enables determining the ratio of positive to negative engagements about a specific topic.
  • Access to corpora.
  • Library for chatbots.

Applications of NLTK

  • Used quite often in education and research.
  • Language detection
  • Named Entity Recognition
  • Tagging

10. XGBoost

XGBoost is a portable, flexible, and efficient library that is critical for gradient boosting and predictive modeling. It helps teams resolve many data science problems through the parallel tree boosting that it offers. Another advantage is that developers can run the same code on major distributed environments such as Hadoop, SGE, and MPI.

Boosting is a resilient and robust method that prevents and curbs over-fitting quite easily.

XGBoost performs very well on medium, and small, data with subgroups and structured datasets with few features. It is a great approach to go for because the large majority of real-world problems involve classification and regression, two tasks that XGBoost does excellently.

Features of XGBoost

  • XGBoost can detect and learn from non-linear data patterns.
  • Available for many programming languages like C++, JAVA, Python, and Julia.
  • Can run distributed thanks to distributed servers and clusters like Hadoop and Spark, so you can process enormous amounts of data.
  • Parallelization.
  • Includes different regularization penalties to avoid overfitting.

Applications of XGBoost

  • Recommendation systems,
  • Can be used to solve regression and classification problems
  • XGBoost can be applied to ranking problems.

Why use Python for Data Science

Python has been preferred by data scientists, analysts, and others due to its ease and versatility. Some more reasons why you should use Python for data science include

  • Ability to run code across different platforms (Windows, Mac OSX, Linux, etc.)
  • You can run your code on different machines without making any further changes.
  • Extensive libraries to make coding easier
  • Community support if you need help.


In conclusion, Python alone is not sufficient for a career in data science for sure. However, learning these Know Python Packages For a Successful Data Scientist can help you start your journey to becoming a great data scientist!

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