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Bay Path MS Degree in Applied Data Science: Maximize value with data


PYTHON LIBRARIES DATA SCIENTISTS SHOULD KNOW IN 2022

Let’s have a look at the Python libraries that every data scientist should know
in 2022, to maintain and improve their coding journey.

By Nisha Arya, KDnuggets on April 11, 2022 in Python


--------------------------------------------------------------------------------


As more people enter the tech world trying to tackle Data Scientists, Data
Analysts, Machine Learning Engineer roles, and more; the programming language
Python becomes more popular. Due to its simplified syntax, the Python language
is known to be one of the most accessible programming languages available.

As Data Science becomes more popular, there are new libraries that are being
released to help solve the challenges faced in Data Science. It can be very
overwhelming to learn the ins and outs of libraries; however, there are some
that are vital to our learning.
Below are Python libraries that every Data Scientist should know in 2022, to
maintain and improve their coding journey. 

 


PANDAS

 




Source: Wikipedia

 



Pandas was created by Wes McKinney in 2008, as a Python library for data
manipulation and analysis. Wes McKinney built Pandas based on their need for a
powerful and flexible analysis tool. 



Pandas can deal with:

 * Handling missing data (represented as NaN)
 * Flexible reshaping and pivoting of datasets
 * Indexing, manipulation, renaming, merging, and joining of datasets
 * Time series-specific functionality
 * and much more

Core Task: Data Manipulation and Analysis

How to install Pandas: Pandas Installation 

pip install pandas


 

Get the Book: Python for Data Analysis by Wes McKinney

 


NUMPY

 



Source: Wikipedia

 



NumPy is another library used for Python, which is used for mathematical
functions. It is popular in processing multidimensional array objects, and
various derived objects (such as masked arrays and matrices) and is mostly used
in machine learning computations. The software includes linear algebra, Fourier
transform, and matrix calculation functions. 

NumPy can deal with:

 * Array operations such as add, multiply, cut, sort, index
 * Working with linear algebra
 * Basic slicing and advanced indexing in Numpy Python
 * Adding/Removing/Sorting Elements

Core Task: Processing arrays, using mathematical functions

How to install NumPy: NumPy Installation

pip install numpy


 


SCIPY

 



Source: SciPy
 



SciPy stands for Scientific Python. SciPy is a free and open-source Python
library, which is a collection of mathematical algorithms and functions built
mainly on the NumPy extension of Python. 

SciPy:

 * Can manipulate and visualize data
 * contains a variety of sub-packages that help to solve the most common
   challenges and problems related to scientific computation.
 * Can deal with linear algebra, integration, ordinary differential equations,
   calculus, and signal processing
 * Is easy to use and understand and has a fast computational power.
 * It can operate on an array of NumPy libraries.

Core Task: Solve scientific and mathematical problems

How to install SciPy: SciPy Installation

pip install scipy



conda install scipy


 

 


MATPLOTLIB

 



Source: GitHub
 



Matplotlib is a numerical extension of NumPy, which is a cross-platform, data
visualization and graphical plotting library for Python. It is used in
conjunction with NumPy to provide an effective environment that is an
open-source alternative for MatLab. 

Matplotlib can:

 * Create quality plots of data.
 * Create Line charts, Scatter charts, Bar charts and histograms, Pie charts,
   Stem plots, Spectrograms
 * Make interactive figures that can zoom in and out, pan, and update.
 * Customize the style and layout of the visualisation.
 * Export to different file formats

Core Task: Creating static, animated, and/or interactive visualizations in
Python
How to install Matplotlib: Matplotlib Installation

pip install matplotlib



conda install matplotlib


 

GitHub: Matplotlib
Tutorials: Matplotlib tutorials 

Books for further reading:

 * Mastering matplotlib by Duncan M. McGreggor
 * Interactive Applications Using Matplotlib by Benjamin Root
 * Matplotlib for Python Developers by Sandro Tosi

 


SEABORN

 



 



Seaborn is a library that has been built on top of matplotlib and is closely
integrated with pandas data structures. It provides a high-level interface for
drawing attractive and informative statistical graphs using its plotting
functions to help you further explore and understand your data. 

Seaborn can:

 * Create Scatter Plot. Histogram, Bar Plot, Box and Whiskers Plot, and more
 * show a linear relationship between two or three data points
 * comfortably handle Pandas’ data frames more than matplotlib
 * Perform semantic mapping and statistical aggregation to produce informative
   plots.

Core Task: Making statistical graphics in Python

How to install Seaborn: Seaborn Installation

pip install seaborn



conda install seaborn


 


SCIKIT-LEARN

 



Source: Wikipedia
 



Scikit-learn is a free software machine learning library, that contains
effective tools for machine learning and statistical modeling such as
classification, regression, clustering, and dimensionality reduction.

The main benefits of sci-kit learn are that it is open-source, easy to use,
properly documented, and versatile used.

Scikit-learn can be used in:

 * Supervised learning and Unsupervised learning
 * Clustering and Dimensionality Reduction
 * Ensemble methods
 * Cross-validation
 * Feature extraction and selection

Core Task: Machine learning and statistical modeling

How to install Sci-kit Learn: Sci-kit Learn Installation

pip install scikit-learn


 

Further reading:

 * Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by
   Aurelien Geron 

 


TENSORFLOW

 



Source: Wikipedia
 



TensorFlow was built by the Google Brain Team and is an open-source library for
deep learning applications. Tensorflow also makes it easy to build deep learning
models by helping developers create large-scale neural networks with many layers
using data flow graphs.

TensorFlow can/have been used on:

 * Voice and sound recognition
 * Sentiment analysis, classifying texts
 * Text applications such as Google Translate, Gmail, and more. 
 * Facial recognition such as Facebook Deep Face, Photo tagging, and more

Core Task: Develop and train models using Python

How to install TensorFlow: TensorFlow Installation

pip install tensorflow


 

Books for further reading:

 * Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by
   Aurelien Geron 
 * Learning TensorFlow: A Guide to Building Deep Learning Systems by Itay
   Lieder, Tom Hope, and Yehezkel S. Resheff
 * TensorFlow for Deep Learning: From Linear Regression to Reinforcement
   Learning by Bharath Ramsundar and Reza Bosagh

 
 
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is
particularly interested in providing Data Science career advice or tutorials and
theory-based knowledge around Data Science. She also wishes to explore the
different ways Artificial Intelligence is/can benefit the longevity of human
life. A keen learner, seeking to broaden her tech knowledge and writing skills,
whilst helping guide others.
 





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Published on April 11, 2022 by Nisha Arya

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