Python is a powerful science package fair, the choice of the Python Big Data couple is justified by its robust packages that meet the data science and analytical needs of programs.

Some of the top libraries that contribute to the popularity of Python include :

1. Tensorflow

Tensorflow is the best known library for high performance computing.

This library deals with calculations involving tensors and is used in various scientific fields.

Applications of tensorflow include :

* Image and Voice Recognition.
* Video detection.
* Text based applications.

This library is mainly characterised by :

* Parallel Computing to run complex programs.
* Error reduction with a rate of up to 60% for machine  learning problems.
* Updating and resolution of very frequent bugs.

2. Numpy

It is the fundamental module of numerical calculation in Python.

It enables the processing of high-performance multidimensional arrays of objects.

Numpy also manages the problem of slowness by providing functionalities and methods that work efficiently on these arrays.

The numpy module's applications are numerous, such as :

* Data analysis.
* Father module of some other libraries such as Scipy or matplotlib .
* Creates powerful N dimensional tables.
* Application with Matlab.

The strength of the numpy module is justified by :

* Fast precompiled functions for basic calculations.
* Supports the object-oriented approach.
* Table programming oriented for better results.

3. Scipy

Here we are at the Scipy library, it is more Data Science oriented.

It comes from the numpy module.

SciPy is a library widely used in Big Data for scientific and technical computing.

This library contains different modules for :

* Optimisation.
* Linear algebra.
* Interpolation.
* Image and signal processing.

Scipy is characterized by :

* Multidimensional image processing tools.
* Predefined functions to solve differential equation problems.
* Advanced features for data manipulation and visualisation.

4. Pandas

Pandas is an essential module in data processing.

It is one of the most popular libraries in Data Science.

Pandas provides a wide variety of data structures that are easy to manipulate.

Among the applications of this library are :

* ETL: the process of extracting, transforming and storing data.
* Data cleansing and visualisation.
* Widely used in studies of customer behaviour in marketing.

5. Matplotlib

Finally we present you Matplotlib, or the library of your tracings.

It allows you to draw 2D diagrams so that you can visualise the results.

These diagrams can be plots, bar charts, histograms, power spectra, diffusion plots or more.

This module has several applications including :

* The visualisation of the correlation between variables.
* Visualisation of the distribution of data .
* Visualisation of model confidence intervals up to the 95% level .

NB : A tip, try to understand what is in every library it is not enough to use only

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