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Top 21 Python Libraries a Data Scientist must know

Python is an abundant source of libraries. A Python library is a gathering of functions that assist one to perform many actions. It has myriad inbuilt libraries. Python contains ample libraries for data science.

This tutorial covers python libraries for data scientist.

Python Libraries for data scientists
Python categorizes these libraries according to their title role in data science.

Let’s see Python libraries for data scientist:

A. Data Cleaning and Data Manipulation

  • Pandas
  • NumPy
  • Spacy
  • SciPy

B. Data Gathering

  • Beautiful Soap
  • Scrapy
  • Selenium

C. Data Visualisation

  • Matplotlib
  • Seaborn
  • Bokeh
  • Plotly

D. Data Modelling

  • Scikit-Learn
  • PyTorch
  • TensorFlow
  • Theano

E. Image Processing

  • Scikit-Image
  • Pillow
  • OpenCV

F. Audio Processing

  • pyAudioAnalysis
  • Librosa
  • Madmom

1) Pandas

Pandas is one of the most popular data analysis and data manipulation libraries. It is an open-source library.

DataFrame is the chief data structure of the Pandas library. It stores and manages the data in the table. It can be done by manipulating rows and columns. It allows dataset joining, merging and reshaping.

Hence, when millions of petabytes of data are to be analyzed, Pandas is much helpful in this case. Using Pandas, Data can be easily and effectively analyzed.

2) NumPy

Numerical Python, in short, NumPy, is an open-source library. It is an incredible Python library for scientific calculations. It also allows for accomplishing matrix operations.

NumPy is used to perform operations on the array. As it works on an array, it permits us to reorganize a large set of data.

3) Spacy

Till now, Pandas and NumPy taught us to clean and manipulate data.

Spacy manipulates free data into structured data. It is used as an NLP (Natural Language Processing) library. Many human languages are also supported by this library.

4) SciPy

It is an open-source library which is based on the concept of NumPy which provides many effective numerical routines. It can perform integration and linear algebra and has high-level features for data manipulating and visualizing.

SciPy is a key library for data processing.

5) Beautiful Soap

It is one of the most popular libraries used for data scraping. Further, this data is given the required format.

With the support of Beautiful Soap, specific content from the webpage can be extracted. Using the same, HTML markup can be detached and the information can be protected.

6) Scrapy

Scrapy is an alternative library of Python used for large scale web scraping. It is an open-source Python library which is very dissolute and modest to operate. It is beneficial for mining the data from the website.

Scrapy is a collection of all the efficient tools required to abstract from websites, process them and structure them the way you want.

7) Selenium

Selenium is a library which automatically tests the web browser. It is also used for testing purposes in industries. It offers essential features to draw-out data and captures it in a future usable format.

Selenium is slower from other Python libraries.

8) Matplotlib

Matplotlib is one of the most famous 2D graphical Python libraries used for data visualization. Not only 2D graphs, but it can also be useful to generate 3D graphs. It is helpful to generate graphs, bar charts, histograms, scatterplots, etc.

9) Seaborn

Seaborn is based on Matplotlib. It enhances the visualizing features of Matplotlib. This popular Python library provides a gallery full of visualizations including time series, joint plots, etc.

Seaborn offers efficient tools for revealing the pattern of data in a more colorful manner.

10) Bokeh

Boke is a Python library used to provide interactive visualization. It is dependent on Matplotlib. It targets interactivity and offers interactive designs in a web browser.

11) Plotly

Plotly supports interactive web apps. It provides the advantage to create an upmarket graph in very fewer lines of code.

Plotly can fulfill any kind of visual requirement in a short period.

12) Scikit-Learn

Scikit-learn is used for modeling data. It is a savior for Machine Learning projects. It has numerous supervised and unsupervised ML algorithms. Its main target is on quality of code, performance and decent documentation.

13) PyTorch

PyTorch is an open-source library. It is a useful tool for deep learning programs that provide high speed. It fulfills many data-centric demands.

The cloud-based environment is provided by PyTorch which enables easy scaling of resources.

14) TensorFlow

TensorFlow is one of the most popular frameworks for Data Science, Deep Learning and Machine Learning. It is an open-source framework that enables you to build models, test them and train them accordingly. It is the best tool for voice recognition and object identification.

15) Theano

Theano is the Python library used to perform large multi-dimensional array operations. It allows performing array-based mathematical operations.

Theano has GPU based infrastructure. Hence, it can perform operations in a faster manner as compared to CPU.

16) Scikit-Image

Scikit-Image is a Python library which performs image processing. It is a combination of various functions that are helpful for multiple image processing.

Scikit-Learn is a tool that has ample functionality including Image segmentation, color modification, etc.

17) Pillow

Pillow is an advanced version of the Python Imaging Library. This library offers several image processing standards.

Pillow is helpful in image-enhancing like blurring, smoothing, etc. Using Pillow, you can add text to an image.

18) OpenCV

OpenCV resolves computer vision issues. It makes use of NumPy to convert OpenCV array to and from NumPy array. It performs many tasks including motion tracking, gesture recognition, etc.

19) pyAudioAnalysis

pyAudioAnalysis is the Python library used for audio processing. It performs various audio features like classification, extraction, segmentation, etc.

pyAudioAnalysis is also efficient in classifying unknown sounds and extracting audio. This special tool of Python also helps to detect audio chunks and remove unnecessary slots from heavy recordings.

20) Librosa

This Python library is rich in features that can analyze audio and music features. It can extract remarkable features of the audio segment such as beats, tempo, rhythm, etc.

Librosa can deliver building blocks that are useful parts to create a music retrieval system.

21) Madmom

Madmom is an audio processing library capable of performing Music Information Retrieval (MIR) tasks. It is proficient in performing music data analysis tasks.

Some libraries like NumPy, SciPy, etc. are pre-requisite for the execution of Madmom.

Conclusion

Python has ample libraries that fulfil the requirements of every field. It has various libraries that deal with a particular field. These python libraries for data scientist are extremely useful as it helps in decision making.

The bundle of libraries are capable enough to work on large sets of data.