The Top 10 Python Libraries for Data Science

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Python has become the most widely used programming language today — especially in the world of data science because it is a high-performance language, easy to learn and debug, and has extensive library support. Each of these libraries has a particular focus. Some manage image and textual data, while others focus on data mining, neural networks, and data visualization. Python can be used for statistical analysis and building predictive models. When it comes to solving data science tasks and challenges, data enthusiasts, analysts, engineers, and scientists are leveraging the power of Python.

In this article, I will be talking about the top ten most useful Python libraries for data science and machine learning.

Some of the libraries have been installed if you are using the Anaconda distribution. All you have to do is import the library or install it with pip if it is not available on your machine.

1. Pandas: Pandas’ name is derived from “panel data,” an econometrics term for multidimensional structured data sets and “Python data analysis.” It is well-known that cleaning and transforming data is very important in data analysis and analytics. Pandas provides rich data structures and functions designed to make working with structured data fast, easy, and expressive. Pandas allows you to import data from different file formats such as CSV, JSON, SQL, and Microsoft Excel. It’s based on two main data structures: “Series” (one-dimensional) and “DataFrames” (two-dimensional). The dataframe is very similar to tables in statistical software such as Excel and SPSS. Pandas allows various data manipulation operations, such as handling and imputing missing data, indexing, adding and deleting columns from a dataframe, merging, reshaping, selecting, as well as data wrangling, munging features, and visualization.
2. NumPy: NumPy (Numerical Python), one of the most fundamental and general-purpose array processing packages in Python, is a tool for scientific computing and performing element-wise and advanced array operations. It is the foundation of many libraries, such as SciPy and scikit-learn for machine learning. NumPy dimensions are called axes, the numberof axesis called rank, and its array class is called ndarray. NumPy facilitates math operations on arrays and their vectorization. The vectorization of mathematical operations on the NumPy array increases performance and execution time. The basic array operations are addition, multiplication, reshaping, indexing, and slicing arrays. One of its primary purposes with regards to data analysis is that it is the primary container for data to be passed between algorithms.
3. SciPy: SciPy (Scientific Python) is another core library for high-level scientific and technical computing. It is built on NumPy and therefore extends its capabilities and provides many user-friendly and efficient routines for scientific and numerical calculations. SciPy contains modules that handle data integration, data optimization, data interpolation, data modification, linear algebra, probability theory, random number generation, integral calculus, Fourier transforms, and more.
4. Matplotlib: Matplotlib is the most widely used visualization library in Python. It is a low-level library for creating two-dimensional diagrams and graphs. Matplotlib accommodates legends, labels, and grids. Stories can be told with Matplotlib. Some of the plots that can be created with Matplotlib are pie charts, bar charts, scatter plots, histograms, line plots, and more. Many popular plotting libraries such as seaborn are designed to work with matplotlib.
5. Seaborn:Seaborn is a high-level data visualization library built on Matplotlib integrated with the NumPy and Pandas data structures. It serves as a useful tool for plotting appealing statistical graphics, heatmaps, and other kind of visualizations that summarize data. There is a rich gallery of visualizations, including some complex types like time series, joint plots, and violin diagrams. Seaborn’s data graphics can include bar charts and histograms, pie charts, scatter plots, etc. Seaborn can be used to determine relationships between variables (correlation), plot linear regression models for dependent variables, view and observe categorical variables, and more.
6. Scikit-Learn: Scikit-Learn is a free machine learning Python library used in data mining tasks and predictive modeling such as regression, classification, and clustering. It features supervised and unsupervised machine learning algorithms such as decision trees, SVMs, Naïve Bayes, random forests, cross-validation, k-means clustering, and more. Scikit-Learn is supported by NumPy, SciPy. Matplotlib, Pandas, and many more.
7. TensorFlow: TensorFlow is a popular framework developed by Google for machine learning and deep learning. It is used to define and run computations on tensors. TensorFlow efficiently does classification as well as data creation and predictions. It is the best tool for tasks such as natural language processing, object identification, speech recognition, motion detection, and more. It works with artificial neural networks that run large datasets and allows easy deployment of machine learning and deep learning applications. It provides multiple layers of abstraction so you can choose what you need for your model. With TensorFlow, you can easily build and train machine learning models such as Keras. It also allows you to deploy machine learning models anywhere either on the cloud, your browser, or your local machine. Large companies such as Google, Twitter, Coca-Cola, Airbnb, and Netflix use TensorFlow.
8. Keras: Keras is a free and open-source neural-network library in Python. It is a library for training and building neural networks and modeling. Unlike TensorFlow, which provides both high-level and low-level APIs, Keras provides only high-level APIs. It has optimizers, layers, activation functions, and so on. Keras supports tools that make it easier to work with different types of images and textual data in deep neural networks. It can be used alongside libraries such as TensorFlow, Theano, and more. You can do many tasks using Keras, such as writing functions with repeating code blocks that are multiple layers deep.
9. Statsmodels: Statsmodels is a Python library that can implement machine learning and provides computations for descriptive statistics and statistical data analysis, such as statistical models estimation, hypothesis testing, Bayesian model, linear regression, correlation, and more.
10. BeautifulSoup: BeautifulSoup is popular Python library for data scraping and web crawling on different websites using the website’s API. It pulls data out of HTML and XML files. Beautiful Soup supports the HTML parser included in Python’s standard library, but it also supports a number of third-party Python parsers.

Do you know other useful and helpful Python libraries used in data science and machine learning? Drop them in the comments section below and share useful information about them.

Thank you for reading.