Introduction 

Are you a Data Science professional using Python libraries for everyday analysis? If you are using Python libraries for Data Science, this blog will help you understand them. Python has more than 137,000 libraries that are assisting IT professionals in various ways. While businesses and organizations rely heavily on Data Science to find key market insights, people are also using Data Science in their everyday lives. Data Science aims for greater heights as its market is expected to reach USD 178 billion by 2025. This is the right time to get into the Data Science sector. 

Data science professionals can perform data analytics easily with the help of Python libraries. Freshers can also set themselves apart from other candidates if they know Python libraries for Data Science. Read on to know the top 20 Python libraries to use in 2021 for Data Science.

Table of Contents 

  1. Matplotlib
  2. Pandas 
  3. NumPy 
  4. Theano
  5. PyTorch 
  6. SciPy 
  7. Requests 
  8. SQLAlchemy 
  9. Scrapy 
  10. BeautifulSoup
  11. Keras 
  12. Scikit-Learn 
  13. TensorFlow 
  14. XGBoost
  15. Seaborn 
  16. Bokeh 
  17. Plotly
  18. PyBrain 
  19. NLTK
  20. Gensim 

About Python 

Python is an interactive programming language that supports various programming paradigms like functional, object-oriented, reflective, etc. It helps programmers and developers to write logical code for their projects. Python provides many libraries that are a chunk of code to be reused by developers/programmers. A Python library is a collection of core modules (chunks of code) that are ready-made and can be included in your projects. A package in Python is also a library that requires a package manager for installation. You can also define a Python library as a code module that can be used multiple times. Using Python for Data Science is easy as well as feasible. 

Often, people confuse the Python standard library with Data Science libraries. The Python standard library constitutes the semantics and syntax of Python and is embedded in the core Python. Data science libraries are pieces of code (modules) that can perform data science tasks. Let us now discuss the top 20 Python libraries for Data Science. 

1) Matplotlib

Numerical plotting is an essential step during data analysis and management. Matplotlib is a 2D numerical plotting library for Data Science. Matplotlib can also be used with Python command shells like IPython. Various types of plots can be created with the help of Matplotlib, like histograms, power spectra, scatterplots, error charts, etc. Matplotlib creates a MATLAB-like interface for generating plots, styling plot lines, manipulating axes properties, etc. You can also generate plots in hardcopy formats via Matplotlib for publication purposes.

2) Pandas 

Pandas is another Data Science library that is used to generate data structures. Pandas provide flexibility in creating data structures for Data Science as you can create multidimensional, tabular, heterogenous, etc. data structures. Data manipulation and time series can also be performed with the help of operations provided by Pandas. This BSD licensed library is built over NumPy libraries in Python and is open-source. The Pandas package also contains various methods for filtering the big data (large chunks of data).

3) NumPy

NumPy is another useful Python package for Data science that is registered under the BSD license. Data science requires working on complex mathematical operations, especially during scientific computation. You can perform various complex mathematical operations with the help of NumPy like linear algebra problems, Fourier transformation, and many others. Generic data that can work with any data type can also be treated using NumPy. Large chunks of data or datasets can be reshaped/arranged with the help of the array interface provided by NumPy.  

4) Theano 

Array operations are not difficult and can be easily performed without using an external library. But what if the arrays are multidimensional? This is where Theano comes in and helps in distributed and parallel computing. Theano is also used together with NumPy in the ‘numpy.ndarray’ function. Besides allowing data scientists to perform multidimensional array operations, it also helps in unit testing. Theano dynamically generates C code that can identify errors/flaws in the model under consideration.  

5) PyTorch 

PyTorch is one of the most-used Python libraries for Data Science and machine learning. Data scientists also use PyTorch APIs to study deep neural networks. Data scientists can design computational graphs dynamically via PyTorch. This includes various complex tasks like transitioning in graph mode and fast tensor computation. It also helps in testing and deployment as the resources can be easily scaled. PyTorch has gained many users over the years and is a prominent Python machine learning library.  

6)SciPy

Based on NumPy, SciPy is another Python library that is used to solve complex mathematical problems. SciPy is used for various mathematical problems relating to statistics, linear algebra, integration, optimizations, etc. Numerical computation is an important aspect of Data Science, and SciPy can guide data scientists in solving complex mathematical problems. 

7) Requests 

Requests is a Python library used for sending HTTP requests, forming data, adding headers, etc. The response data (HTTP response) is also collected/accessed with the help of Requests. An HTTP response is a packet of information provided to the client by the server in response to the client’s request. Earlier, the ‘urllib2’ module was used for HTTP processes, but it is outdated in the current scenario. The API of Requests is not broken as compared to that of ‘urllib2’.

8) SQLAlchemy

SQLAlchemy helps in accessing enterprise databases efficiently. It includes the prominent enterprise-level patterns for high-performance while accessing databases. The two major parts of SQLAlchemy are SQLAlchemy ORM and SQLAlchemy Core. SQLAlchemy core provides a layer of abstraction over Python database APIs and behaviors. It also provides users with SQL expressions and schema. SQLAlchemy ORM is a self-sustained object-relational mapper in itself. Developers can gain more control of their database via SQLAlchemy along with automating redundant tasks.

9) Scrapy 

If you are into data scraping where the data is extracted from the screen (display data), Scrapy is an essential Python library for you. Scrapy lets you enhance the screen-scraping process along with web crawling. Data scientists use Scrapy for data mining and also for automated testing. Scrapy is an open-source framework used by many IT professionals worldwide to extract data from websites. Scrapy is written in Python and is highly portable as it can run on Linux, Windows, BSD, and Mac. Many expert developers recommend Python for data analysis and scraping because of its high interactivity. 

10) BeautifulSoup

BeautifulSoup is a Python library used for data scraping and mining. It helps data scientists build a web crawler that crawls through webpages. Besides retrieving the data, you can also arrange it in the required format using BeautifulSoup. Recently, its latest version, BS4 (BeautifulSoup 4), was launched. The scrapped HTML data contains a lot of messy web data that is not understandable by the users. BS4 organizes the messy web data into XML structures that are easy to understand, and hence data analysis is performed. 

11) Keras 

Keras is an open-source Python library used widely by artificial intelligence, deep learning, and data science professionals. Neural networks are also used in Data Science for analyzing observational data (photos or audio). Developers can use Keras for modeling/building neural networks with a minimalistic design approach. The latest version of Keras, i.e., Keras 2.4, only supports the ‘TensorFlow’ backend. All the earlier supported backends have been removed in the latest version of Keras. 

12) Scikit-Learn 

Data scientists use Scikit-Learn for statistical modeling of data, including classification, reduction, clustering, and regression. It is built upon the Python libraries NumPy and Matplotlib. It is an industry-standard package used by data scientists for specific functionalities. Reducing the dimensionality of data is one of the most useful functionalities of Scikit-Learn as the resultant data will be less complex. Scikit-Learn prepared the data for easy summarization, feature selection, and visualization.

13) TensorFlow 

TensorFlow is one of the most commonly used Python libraries for data processing and modeling, along with being an important Python machine learning library. Since it is one of the most-used Python libraries, new updates are often added quickly compared to other libraries. Besides implementing machine learning and deep learning applications, TensorFlow also helps convert ideas into code and then faster modeling and publication. Developed at Google Brain, TensorFlow is highly preferred for processes like object identification and speech recognition. 

14) XGBoost 

While many Python libraries are capable of solving Data Science problems, the speed and accuracy of XGBoost can’t be ignored. A parallel tree booster is provided by XGBoost that is used to solve data science problems. The parallel tree boosting is also known as GBDT or GBM. Problems with scales beyond billions of examples can also be solved easily by XGBoost by using only a few resources. Data scientists also use XGBoost for optimizing the sparse data via sparse aware tree learning. 

15) Seaborn 

Seaborn is a Python library based on Matplotlib and is widely used for data visualization. Various statistical models can be developed by data scientists, like heatmaps, using Seaborn. The number of choices provided by Seaborn to visualize the data is remarkable as you get time-series visualization, joint plots, violin diagrams, and many more. Seaborn performs semantic mapping and aggregation (statistical) to create informative plots with rich insights. 

16) Bokeh 

Bokeh lets you create scalable visualizations for your data that make it easy to understand. While there are other libraries for creating visualizations, Bokeh is preferred only for creating visualizations inside browsers. Besides letting you create simple plots, Bokeh also lets developers create custom plots. For specialized use cases, JavaScript widgets are also added by many developers/data scientists. 

17) Plotly 

Plotly is another Python library widely used for data visualization. Plotly is open-source and helps in understanding data easily. Plotly offers various features for data visualization like crosstalk integration, linked views, animation, etc. The anomalies/outliers in our data set can be easily identified using Plotly, making the data well-appointed. The plots created by Plotly are highly customizable, along with being visually attractive. You can also add buttons to your plot, create a dropdown menu, and add sliders to the plot through Plotly. 

18) PyBrain 

PyBrain is a Python library widely used by fresher data scientists as it offers flexible modules and algorithms. The flexible Data Science models provided by PyBrain also help in advance research processes. The gallery of algorithms in PyBrain is huge and is related to neural networks, supervised and unsupervised learning, etc. PyBrain aims at providing easy-to-use modules for Data Science and Machine Learning beginners. Licensed under BSD, PyBrain is open-source and among the free-to-use Python libraries for Data Science. 

19) NLTK 

Natural Language Toolkit, abbreviated as NLTK, is an important Python library used by data scientists. Various chores related to natural language processing can be done via NLTK, like text tagging, tokenization, semantic reasoning, etc. Complex AI (Artificial Intelligence) tasks can also be accomplished with the help of NLTK. Initially, NLTK was developed to promote various teaching models in AI and machine learning like the linguistic model, cognitive theory, etc. It is now driving real-world developments of AI algorithms and learning models. 

20) Gensim 

Last, in our Python libraries list, Gensim is a useful Python data analytics library. Many times, data scientists have to perform in-memory database processing to reduce the load on database servers. Gensim is the perfect Python library for processing the data stored in an in-memory database. Its built-in algorithms like HDP (Hierarchical Dirichlet Processes), LSA (Latent Semantic Analysis), and word2vec are widely used for processing unstructured digital texts.

Conclusion 

Data analytics include various processes like data processing, classification, visualization, etc. There are numerous Python libraries for Data Science, and it depends on the user what type of project he/she is working on. For example, if your project requires complex Data Science problems to be solved quickly, opt for XGBoost. If you are working on creating better visualizations, opt for Seaborn or Plotly. 

Want to learn more? Check out our 11-month Postgraduate Certificate Diploma in Data Science course. Start working with robust and powerful Python libraries for Data Science today! 

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