Python has been one of the most popular programming language used by data scientists and software developers to perform data science tasks. It's because Python is a very easy and user friendly language built with a lots of open source libraries. There are several data science Python libraries available as of now. While some of them are already popular, others are improving inch-by-inch to reach the acceptance levels of their peers. You might have heard about some of them, but don't know when to use them, what are its significant features and the advantages.

In this blog, I will briefly outline 10 most useful Python libraries for data scientists and engineers.

  1. Panda: Pandas stand for Python Data Analysis Library. It is a perfect tool for data wrangling or munging. It is designed for quick and easy data manipulation, reading, aggregation, and visualization. Pandas also allows converting data structures to DataFrame objects, handling missing data, and adding/deleting columns from DataFrame, imputing missing files, and plotting data with histogram or plot box.
  2. NumPy: NumPy (Numerical Python) is a perfect tool for scientific computing and performing basic and advanced array operations. The library offers many handy features performing operations on n-arrays and matrices in Python. It helps to process arrays that store values of the same data type and makes performing math operations on arrays (and their vectorization) easier. In fact, the vectorization of mathematical operations on the NumPy array type increases performance and accelerates the execution time.
  3. Scipy: The SciPy library is one of the core packages that make up the SciPy stack. Now, there is a difference between SciPy Stack and SciPy, the library. SciPy builds on the NumPy array object and is part of the stack which includes tools like Matplotlib, Pandas, and SymPy with additional tools. SciPy library contains modules for efficient mathematical routines as linear algebra, interpolation, optimization, integration, and statistics. The main functionality of the SciPy library is built upon NumPy and its arrays. SciPy makes significant use of NumPy.
  4. Matplotlib: This is a standard data science library that helps to generate data visualizations such as two-dimensional diagrams and graphs. 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. Histogram, bar plots, scatter plots, area plot to pie plot, Matplotlib can depict a wide range of  visualizations. Matplotlib also facilitates labels, grids, legends, and some more formatting entities with Matplotlib. Basically, everything that can be drawn!
  5. Seaborn: So when you read the official documentation on Seaborn, it is defined as the data visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. Putting it simply, seaborn is an extension of Matplotlib with advanced features. So, what is the difference between Matplotlib and Seaborn? Matplotlib is used for basic plotting; bars, pies, lines, scatter plots and stuff whereas, seaborn provides a variety of visualization patterns with less complex and fewer syntax.
  6. Scikit Learn:  Introduced to the world as a Google Summer of Code project, Scikit Learn is a robust machine learning library for Python. It features ML algorithms like SVMs, random forests, k-means clustering, spectral clustering, mean shift, cross-validation and more... Even NumPy, SciPy and related scientific operations are supported by Scikit Learn with Scikit Learn being a part of the SciPy Stack.
  7. Tensorflow:
    TensorFlow is a popular Python framework for machine learning and deep learning, which was developed at Google Brain. It's the best tool for tasks like object identification, speech recognition, and many others. It helps in working with artificial neural networks that need to handle multiple data sets. The library includes various layer-helpers (tflearn, tf-slim, skflow), which make it even more functional. TensorFlow is constantly expanded with its new releases – including fixes in potential security vulnerabilities or improvements in the integration of TensorFlow and GPU.
  8. Keras:
    Keras is a great library for building neural networks and modeling. It's very straightforward to use and provides developers with a good degree of extensibility. The library takes advantage of other packages, (Theano or TensorFlow) as its backends. Moreover, Microsoft integrated CNTK (Microsoft Cognitive Toolkit) to serve as another backend. It's a great pick if you want to experiment quickly using compact systems – the minimalist approach to design really pays off!
  9. PyTorch:  PyTorch is a framework that is perfect for data scientists who want to perform deep learning tasks easily. The tool allows performing tensor computations with GPU acceleration. It's also used for other tasks – for example, for creating dynamic computational graphs and calculating gradients automatically. PyTorch is based on Torch, which is an open-source deep learning library implemented in C, with a wrapper in Lua.

Conclusion

This list is by no means complete! The Python ecosystem offers many other tools that can be helpful for data science work. Data scientists and software engineers involved in data science projects that use Python will use many of these tools, as they are essential for building high-performing ML models in Python.

Do you know other useful Python libraries for data science and ML projects? Let us know what other tools you find essential to the Python data ecosystem!