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Top 10 Python Libraries for Data Science

by Edward Kenyon Posted on

For quite some time now, Python has fascinated data scientists. The more I interact with resources, literature, courses, trainings, and people in data science, the more in-depth knowledge of Python I acquire. That said, when I first started developing my Python skills, I had a whole list of libraries to learn about. And so, after a while…

Data Science professionals know exactly what Python libraries to use in data science, but when asked to name them or specify their function in an interview, we often get caught up or perhaps don’t remember more than 5 libraries (this happened to me: / )

Today I prepared a list of 10 Python libraries that help in Data Science, when to use them, what are their features and benefits.

In this article I’ve summarized the 10 most useful Python libraries for data scientists and engineers, based on my recent experience and research.

Pandas

Pandas is an open-source Python package that provides high-performance, easy-to-use data structures and analysis tools for labeled data in the Python programming language. Pandas stands for Python Data Analysis Library. Did anyone know about it?

When to use it? Pandas is the perfect tool for data processing. It is designed for fast and easy data processing, reading, aggregation, and visualization.

Pandas takes data in a CSV or TSV file or SQL database and creates a Python object with rows and columns called a data frame. A data frame is very similar to a table in statistical software, say Excel or SPSS.

What can you do with Pandas?

  1. index, manipulate, rename, sort, merge the data frame;
  2. update, add, remove columns from the data frame;
  3. Restore missing files, process missing data or NAN;
  4. Construct a histogram or rectangular chart.

This makes Pandas a fundamental library in learning Python for Data Science.

NumPy

NumPy is one of the most fundamental packages in Python, a versatile array processing package. It provides high-performance multidimensional array objects and tools for working with arrays. NumPy is an efficient container of universal multidimensional data.

The basic NumPy object is a homogeneous multidimensional array. It is a table of elements or numbers of the same data type, indexed by a set of natural numbers. In NumPy, the dimensions are called axes, and the number of axes is called a rank. The NumPy array class is called ndarray, aka array.

When to use it? NumPy is used to handle arrays that hold values of the same data type. NumPy facilitates mathematical operations on arrays and their vectorization. It greatly improves performance and therefore speeds up execution time.

What can I do with NumPy?

  1. basic array operations: add, multiply, slice, align, reshape, index arrays;
  2. Advanced array operations: stack arrays, partitioning, broadcast arrays;
  3. Working with DateTime or linear algebra;
  4. Basic slicing and advanced indexing in NumPy Python.

SciPy

The SciPy library is one of the key packages that make up the SciPy stack. Now there is a difference between the SciPy Stack and the SciPy library. SciPy is based on the NumPy array object and is part of a stack that includes tools like Matplotlib, Pandas and SymPy with additional tools.

The SciPy library contains modules for efficient mathematical procedures such as linear algebra, interpolation, optimization, integration and statistics. The main functionality of the SciPy library is built on NumPy and its arrays.

When to use? SciPy uses arrays as a basic data structure. It has various modules for common scientific programming tasks such as linear algebra, integration, matanalysis, ordinary differential equations, and signal processing.

What can you do with SciPy?

  1. Mathematical, scientific, engineering calculations;
  2. Numerical integration and optimization procedures;
  3. Finding minima and maxima of functions;
  4. Calculation of function integrals;
  5. Support for special functions;
  6. Working with genetic algorithms;
  7. Solving ordinary differential equations. 4.

Matplotlib

This is by far my favorite and most basic Python library. You can create stories with data visualized with Matplotlib. Another library from the SciPy stack, Matplotlib, builds 2D shapes.

When to use. Matplotlib is a Python library that provides an API for embedding graphs into applications. It is very similar to MATLAB, which is built into the Python programming language.

What can I do with Matplotlib?

Histograms, bar charts, point charts, pie charts – Matplotlib can display a wide range of visualizations. With a little effort, with Matplotlib, you can create any visualization you want:

  1. line diagrams;
  2. point diagrams;
  3. Diagrams with areas;
  4. Bar charts and histograms;
  5. Pie charts;
  6. Stem-leaf charts;
  7. Contour charts;
  8. Vector fields;
  9. Spectrograms.

Matplotlib also facilitates the use of labels, grids, legends and some other formatting objects. Basically, it’s about everything you can draw!

Seaborn

So, when you read the official documentation on Seaborn, it is defined as a data visualization library based on Matplotlib, providing a high-level interface for depicting interesting and informative statistical plots. Simply put, seaborn is an extension to Matplotlib with additional features.

So what is the difference between Matplotlib and seaborn? Matplotlib is used for basic construction of bar, pie, line, point charts, etc., while Seaborn provides many visualization templates with fewer syntax rules, and simpler ones at that.
What can I do with Seaborn?

  1. define relationships between multiple variables (correlation);
  2. Observe qualitative variables for aggregated statistics;
  3. Analyze univariate or bivariate distributions and compare between different subsets of data;
  4. Construct linear regression models for dependent variables;
  5. Provide multi-level abstractions, multi-story grids.

Seaborn is a great option for R visualization libraries such as corrplot and ggplot.

Scikit Learn

Scikit Learn, introduced to the world as a Google Summer of Code project, is a robust machine learning library for Python. It includes ML algorithms such as SVM, random forests, k-means clustering, spectral clustering, mean shift, cross-validation, and many others. Even NumPy, SciPy and related scientific operations are supported by Scikit Learn, with Scikit Learn being part of the SciPy Stack.

When to use. Scikit-learn provides a number of supervised and unsupervised learning algorithms through a consistent interface in Python. Scikit learn will be your guide for making supervised learning models, such as Naive Bayes, group unlabeled data, such as KMeans.

What can you do with Scikit Learn?

  1. Classification: spam detection, image recognition;
  2. Clustering: drug exposure, stock price;
  3. Regression: customer segmentation, clustering experiment results;
  4. Dimensionality reduction: visualization, increased efficiency;
  5. Model selection: improved accuracy due to parameter tuning;
  6. Pre-processing: preparation of input data in the form of text for processing with machine learning algorithms.

Scikit Learn focuses on data modeling; not data manipulation. For generalization and manipulation we have NumPy and Pandas.

TensorFlow

TensorFlow is an AI library that helps developers create large-scale neural networks with many layers using data flow graphs. TensorFlow also makes it easy to build deep learning models, promotes modern ML / AI technology, and enables easy deployment of ML-based applications.

One of the most developed websites among all the libraries is TensorFlow. Giants like Google, Coca-Cola, Airbnb, Twitter, Intel, DeepMind all use TensorFlow!

When to use? TensorFlow is quite effective when it comes to classifying, perceiving, understanding, detecting, predicting and creating data.

What can you do with TensorFlow?

  1. Voice/sound recognition – IoT, automotive, security, UX/UI, telecom;
  2. sentiment analysis – mostly for CRM or CX;
  3. Text Applications – Threat Detection, Google Translate, Gmail Smart Reply;
  4. Face Recognition – Facebook’s Deep Face, Photo tagging, Smart Unlock;
  5. Timeline – Amazon, Google and Netflix recommendations;
  6. Video detection – motion detection, real-time threat detection in games, security, airports.

Keras

Keras is TensorFlow’s high-level API for creating and training deep neural network code. It is an open-source neural network library in Python. With Keras, statistical modeling, working with images and text is much easier with simplified coding for deep learning.

What is the difference between Keras and TensorFlow?

Keras is a neural network library written in Python, while TensorFlow is an open source library for various machine learning tasks. TensorFlow provides both high-level and low-level APIs, while Keras provides only high-level APIs. Keras is designed for Python and makes it more user-friendly, modular, and componentizable than TensorFlow.

What can you do with Keras?

  1. define percentage accuracy;
  2. Function to calculate losses;
  3. Create custom function layers;
  4. Built-in data and image processing functions;
  5. Functions with repeating blocks of code: 20, 50, 100 layers deep.

Statsmodels

Statsmodels is a versatile Python package that provides simple calculations for descriptive statistics and estimation and statistical model building.

What can I do with Statsmodels?

  1. linear regression;
  2. Correlation;
  3. the method of least squares (OLS);
  4. survival analysis;
  5. Generalized linear models and Bayesian model;
  6. Single-factor and two-factor analysis, hypothesis testing (basically, what R can do!).

Plotly

Plotly is a typical graphics library for Python. Users can import, copy, paste, or transfer data to be analyzed and visualized. Plotly offers an isolated version of Python (where you can run Python limited in its capabilities). Now it remains to be seen what the limited version means, but I know for a fact that Plotly makes it easy!

When to use it? You can use Plotly if you want to create and display shapes, update shapes, hover over text for details. Plotly also has the additional feature of sending data to cloud servers. That’s interesting!

What can I do with Plotly?

The Plotly chart library has a wide range of charts that you can build:

  1. basic charts: line, pie, point, bubble, Gantt, Sunburst, tree, sankey, area charts;
  2. Statistical and Seaborn styles: errors, histograms, Facet and Trellis charts, tree charts, scree charts, trend lines;
  3. scientific maps: contour, ternary plot, logarithmic plot, vector fields, Carpet plot, radar chart, Wind Rose and Polar plot heat maps;
  4. Financial charts;
  5. Maps;
  6. Subplots;
  7. Transformations;
  8. Jupyter Widgets interaction.

Plotly is a typical graph library. Think about visualization and Plotly does it!