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 Data Science Libraries in Python

Python Data Science

Introduction

There is no doubt that the Python programming language has become the leading programming language in the field  of data science and machine learning , and it has become one of the languages ​​required as a condition in job opportunities ,and jobs related to data science and analysis.

The following figure represents the relationship between programming languages known and functions announced at the site  indeed ,so that these functions was related to  machine learning  and  Data Science , and the shape is clear to us that the Python language of top programming languages in these areas.

Python and other languages

The above gives a clear indication of the direction of skills and languages required in the functions  of data science and  machine learning , and in this article I will give you a quick and very brief about the most important overview  of Python libraries  in  science data  and  machine learning  , which should you have to you speak ,when your business functions such as data analyst Or a data scientist or in the field of machine education.

The first Python Data Science library Numpy

The Numpy library is considered one of the basic mathematical libraries in scientific computing in Python, as many scientists and researchers rely on it to perform large ,and complex calculations on their data and in their scientific tests.

The Numpy library has the following features:

• Strong support for N-Dimensional arrays
• Fast and highly efficient operations when dealing with arrays, and optimal use of resources when processing.
• Support linear algebra and Fourier transform operations in an easy and fast way.
• Many other libraries depend on it, such as Pandas, theanets, and others.

Some library learning resources:

1. https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.0_T9D90
2. http://cs231n.github.io/python-numpy-tutorial/
3. http://www.python-course.eu/numpy.php
4. https://www.datacamp.com/community/tutorials/python-numpy-tutorial#gs.Ereixfc
5. https://vimeo.com/77263537?utm_source=datafloq&utm_medium=ref&utm_campaign=datafloq

The second Python Data Science library Pandas

Pandas library provides  the  necessary capabilities to make data analysis and processing easy and fast, through what it provides of structures and software tools based mainly on  Numpy .

Features and characteristics of the Pandas library:

1. The library provides a so-called Data Frame, which makes it easy to import and manipulate data.
2. Pandas library provides the necessary capabilities to import data from files in various formats and transfer them to random memory.
3. The library facilitates operations (Data Preprocessing) such as data cleaning, processing null values ​​in it, and performing exploratory operations on data.
4. Pandas library offers the possibility of reshaping the data structure.
5. Makes it easy to merge data together or split it into multiple frames.

And many other features and advantages.

Some library learning resources:

1. http://pandas.pydata.org/pandas-docs/stable/10min.html
2. https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python#gs.zMakrWo
3. https://www.dataquest.io/blog/pandas-python-tutorial/

The third Python Data Science library, Scipy

This library comes at the top of the pyramid of scientific libraries in Python, and serves the aspect  of data analysis  and machine learning  strongly, and is not limited to that, as it provides huge capabilities in the field of signal processing, image processing, and complex mathematical operations.

The Scipy library is based   on a mixture of popular libraries such as  Numpy, Pandas, Matplotlib, Sympy , IPython ,   and others.

The library offers a wide range of algorithms and scientific packages ,that are particularly (but not limited to) mathematical operations, also statistical functions and machine learning.

The Scipy library features:

• It offers extensive functions in the field of probability ,and statistics via the stats module.
• Conducting spatial analysis through a set of specialized algorithms found in the spatial module.
• The library offers tools for signal processing.
• The library provides a set of functions for processing multidimensional matrices for image processing.
• Support for linear algebra ,and Fourier transform operations.

And many other features and advantages.

Some library learning resources:

1. https://www.datacamp.com/community/tutorials/python-scipy-tutorial#gs.hGF3Hnw
2. https://www.youtube.com/watch?v=oYTs9HwFGbY
3. https://docs.scipy.org/doc/scipy/reference/tutorial/?utm_source=datafloq&utm_medium=ref&utm_campaign=datafloq

Fourth Python Data Science library Matplotlib

This library one of the most Python libraries in the Visualization, and enables you to build your graphics graphs and illustrations are easy and flexible, also this library in several libraries and other tools ,including a library and linked Pandas  tool  Jupyter , to let you call the graphics commands directly on the frame data and interactive way.

The flexibility in the library enables you to arrange shapes and graphics in an easy and convenient way, and you can also export the output as files in multiple formats, including Pdf, Jpg, SVG, PNG, BMP, GIF and others.

Some library learning resources:

1. https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python#gs.HdUg0vY
2. https://www.labri.fr/perso/nrougier/teaching/matplotlib/
3. https://pythonprogramming.net/matplotlib-python-3-basics-tutorial/

Fifth Python Data Science Library Scikit-learn

This library is considered one of the most important  libraries for machine learning  in the Python language ,and is based on the previous library Scipy. Also this library provides users with a set of machine learning algorithms and makes it easy for them to run and implement these algorithms directly on the data.

The following link lists the algorithms for machine learning that are in the library  here

Some library learning resources:

1. https://www.youtube.com/watch?utm_source=datafloq&utm_medium=ref&utm_campaign=datafloq&v=4ONBVNm3isI
2. http://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/
3. http://www.dataschool.io/machine-learning-with-scikit-learn/
4. http://scikit-learn.org/stable/tutorial/index.html?utm_source=datafloq&utm_medium=ref&utm_campaign=datafloq

summary

The previous libraries are not the only ones in  data science  and  machine learning  in Python ,but they are distinguished and widely used among scientists and researchers in this field.

If you have the basics of the Python language, and you want to start specializing in the field of data science and analysis or machine learning ,so I advise you to master the use of these libraries for their power and wide use.

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