10 Python Libraries For Data Scientists.

If you are a data scientist, these Python libraries are for you! Master them to perform well in your job, Explore now:

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Pandas

This library mainly provides data manipulation and analysis tools,which are used for analyzing data using its powerful data structures for manipulating numerical tables and time series.

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Numpy

The NumPy library is popular for array and matrix processing using a set of mathematical functions.  This library is mostly used in machine learning computations.

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Statsmodels

For rigorous Statsmodels is a fantastic library. This multipurpose library is a mix of multiple Python libraries, drawing on Matplotlib for its graphical functionalities.

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Seaborn

 Some of the associations that aren’t immediately visible can be represented in a visual context, which helps data scientists better comprehend the models.

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Requests

This is another different library module in Python used for sending HTTP requests and supports functionalities like adding headers, the formation of data and accessing. 

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Scipy

In Python, the scipy library is one of the open-source libraries mainly used in mathematical and scientific computations, technical and engineering computations.

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Sqllite 3

Python programming language provides a library for database operations. This library is mainly used for database operations using sql queries.

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Keras

Francois Chollet created it, and it was initially launched in 2015. Keras provides tools for constructing models, visualizing graphs, and analyzing datasets.

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TensorFlow

TensorFlow is an open-source library for deep learning applications built by the Google Brain Team. Initially conceived for numeric computations, flexible and wide range of tools.

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SciKit-Learn

DBSCAN, gradient boosting, support vector machines, and random forests are among the classification, regression, and clustering methods included in SciKit-Learn.

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