Ayesha SaharTable of Contents
Introduction
As you guys know, Python is a popular language that is assisting developers in creating PC, games, mobile, and other types of applications. This is possible due to its libraries that are almost more than 137,000 in number. Crazy right? Moreover, in this world that revolves around data where buyers demand relevant information while buying, big companies or even small start-ups require "data scientists" to get valuable insights by processing massive data sets.
The results of such an analysis guide them in critical decision making, business operations, and various other tasks that require valuable information to be completed efficiently. Now you might be thinking, how do data scientists accomplish all this? The answer is simple, as mentioned above, they use various libraries to perform these tasks.
So, here is a list of the top 10 most useful Python libraries for data scientists;
1. Pandas
It was basically created as a community library project and released around 2008. It provides various high-performance, easy-to-use data structures and operations for manipulating data in the form of numerical tables and time series. Pandas also has multiple tools for reading and writing data between in-memory data structures and different file formats.
Features and Applications:
• Good syntax with various functionalities that gives you the freedom to even deal with some missing data.
• It helps you make your own function and then run it across a series of data.
• It has high-level abstraction.
• It contains high-level data structures & manipulation tools.
• It helps to perform data wrangling and data cleaning.
• It is used in a variety of academic areas, commercial areas, statistics, finance and even neuroscience.
• It has time-series-specific functionality like date range generation, date shifting, linear regression, and moving window.
Documentation:
https://pandas.pydata.org/docs/
2. NumPy
NumPy is actually the fundamental package for scientific numerical computation in Python as it contains a powerful N-dimensional array object. It is quite popular among developers and data scientists who are aware of the technologies which are dealing with data-oriented stuff. It is a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and also provides tools for working with them.
Features and Applications:
• It provides fast and precompiled functions for numerical calculations.
• It is used in data analysis.
• The Array-oriented computing increases its efficiency.
• It also forms the base of other libraries like SciPy and scikit-learn.
• It supports the concepts of Object-Oriented Programming (OOP).
• It performs compact computations with vectorization.
• It can create a powerful N-dimensional array.
Documentation:
3. TensorFlow
It was designed by Google to compute data low graphs with the empowered machine learning algorithms and to fulfill the high demand for the training neural networks work. It is an open-source library. Its performance is quite high and it has a flexible architecture. It also allows you to deploy Machine Learning models in places like the cloud, your browser, or even your own device. TensorFlow is available for Python, C APIs, C++, Java, JavaScript, Go, Swift, etc.
Features and Applications:
• It is optimized for speed and makes use of techniques such as XLA to perform quick linear algebra operations.
• It is easily trainable on both CPU and GPU.
• It can visualize each and every single part of the graph with ease.
• It can perform speech and image recognition.
• In neural machine learning, it reduces error by 50 to 60%.
• It can also perform video detection.
Documentation:
https://www.tensorflow.org/guide
4. Scikit-learn
Scikit-Learn is used for performing data analysis and mining-related tasks. It is also open-source like TensorFlow and licensed under the BSD. Anyone can access it. It is developed mainly over the Numpy, Scipy, and Matplotlib.
Features and Applications:
• It works well with complex data.
• It is quite useful for extracting features from images and text.
• It has a lot of algorithms for the purpose of implementing standard machine learning and even data mining tasks.
• It allows dimensionality reduction, model selection, and pre-processing.
• It can also perform clustering, classification, and regression.
Documentation:
https://scikit-learn.org/stable/
5. Sci-Py
SciPy (Scientific Python) is another free and open-source Python library for data science and is mainly used for high-level computations. It has around 19,000 comments on GitHub with about 600 contributors. It is widely used for scientific and technical computations because it extends NumPy while providing many user-friendly routines for scientific calculations.
Features and Applications:
• It is used for multi-dimensional image processing
• It has can solve Fourier transforms, and differential equations.
• Due to its optimized algorithms, it can do linear algebra computations quite efficiently.
Documentation:
6. Matplotlib
Matplotlib has various powerful but beautiful visualizations. It is a Python library used for plotting. It has around 26,000 comments on GitHub with about 700 contributors. It is extensively used for data visualization due to the graphs and plots that it creates. It also provides an object-oriented API. This API can be used to embed the created plots into applications.
Features and Applications:
• It offers many charts and customizations from histograms to even scatterplots.
• It is useful while performing data exploration for a machine learning project.
• You can use it regardless of which operating system you’re using or which output format you wish to use as it supports various backends and output types.
• It can perform correlation analysis of variables.
• It has low memory consumption and good runtime behavior.
Documentation:
https://matplotlib.org/stable/contents.html
7. Seaborn
It is a Python data visualization library. Seaborn is based on Matplotlib and is integrated with the NumPy and pandas data structures. It provides various dataset-oriented plotting functions that operate on data frames and arrays that have whole datasets within them. The Seaborn data graphics it can create include bar charts, pie charts, histograms, scatterplots, error charts, etc. It also has many tools for choosing color palettes that are used to reveal patterns in the data.
Features and Applications:
• It has a high-level interface.
• It can draw attractive yet informative statistical graphics.
• It performs the necessary statistical aggregation and mapping functions that allows users to create the plots that they want.
Documentation:
https://seaborn.pydata.org/tutorial.html
8. Keras
It is one of the most powerful Python libraries. It allows high-level neural network APIs for integration that execute over the top of TensorFlow, Theano, and CNTK. It was created mainly for reducing challenges faced in complex researches allowing users to compute faster. It is also open-source. It provides a user-friendly environment.
Features and Applications:
• It allows fast prototyping.
• It supports the recurrent and convolution networks individually.
• It is a high-level neural network library
• It is simple to use but is also powerful.
• By using Keras, users can simply add new modules as a class or even as a function.
Documentation:
9. Scrapy
It is one of the most popular and fast web crawling frameworks written in Python. It is also open-source. Scrapy is mainly used to extract the data from the web page with the help of selectors. These selectors are based on the XPath.
Features and Applications:
• It can perform large-scale web-scraping.
• After extracting and processing the data from websites, users can store it in their preferred format.
• It can also be used to gather data from APIs.
Documentation:
https://docs.scrapy.org/en/latest/
10. BeautifulSoup
BeautifulSoup is also a popular choice for data scientists. It is commonly known for web crawling and data scraping.
Features and Applications:
• It performs web scraping from HTML and XML documents.
• It can automatically detect encodings.
• Users can navigate a parsed document and extract the data from various web pages.
Documentation:
https://crummy.com/software/BeautifulSoup/bs4/doc/
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