Python Libraries Every Data Scientist Must Know.
Tags: python, datascience, machinelearning, computerscience
Published: 2021-09-02 20:02:42

######Pandas.

Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,
built on top of the Python programming language.

Uses the following data structures;

* **DataFrame** is a 2-dimensional data structure that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns.

* **Series** represent one-dimensional data structures, similar to an array.

**Applications**
* General data wrangling and data cleaning
* ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format
* Used in academic and commercial areas, including statistics, finance and neuroscience. 
* Time-series-specific functionality, such as date range generation, linear regression and date shifting.

[Read more about pandas](https://pandas.pydata.org/docs/getting_started/intro_tutorials/index.html)


######Numpy.
Numpy stands for  **Numerical Python**.
It is a Python library that provides a multidimensional array object and an assortment of routines for fast operations on arrays, including mathematical, logical,  sorting, selecting,  discrete Fourier transforms, basic linear algebra and many others.

**Applications**
* Extensively used in data analysis 
* Creates powerful N-dimensional array
* Forms the base of other libraries, such as SciPy and scikit-learn
* Replacement of MATLAB when used with SciPy and matplotlib

[Read more about numpy](https://numpy.org/doc/stable/user/absolute_beginners.html)

######Scikit-learn.
It is the most useful  library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling.

**Applications**
* clustering
* classification
* regression
* model selection
* dimensionality reduction

[Read more about Scikit-learn ](https://scikit-learn.org/stable/getting_started.html#)

######Matplotlib
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

**Applications**
* Correlation analysis of variables
* Outlier detection using a scatter plot etc.
* Visualize the distribution of data to gain instant insights


######Seaborn
Seaborn is a Python data visualization library based on matplotlib. 
It provides a high-level interface for drawing attractive and informative statistical graphics.

Seaborn has important features that helps in;
* Built in themes for styling matplotlib graphics
* Visualizing univariate and bivariate data
* Fitting in and visualizing linear regression models
* Plotting statistical time series data

[Read more about Seaborn ](https://seaborn.pydata.org/tutorial.html)

######TensorFlow
TensorFlow is an end-to-end open source platform for machine learning consisting of comprehensive, flexible ecosystem of tools, libraries and community resources that lets  developers easily build and deploy ML powered applications.

**Applications**

* Speech and image recognition 
* Text-based applications 
* Time-series analysis
* Video detection

[Read More about TensorFlow ](https://www.tensorflow.org/tutorials)

######Keras
Similar to TensorFlow, Keras is a popular library that is used extensively for deep learning and neural network modules. 
Keras supports both the TensorFlow and Theano backends.

**Applications**
* For developing and evaluating deep learning models.

[Read more about Keras](https://keras.io/getting_started/intro_to_keras_for_engineers/)


######SciPy
SciPy in Python is an open-source library used for solving mathematical, scientific, engineering, and technical problems.

It allows users to manipulate the data and visualize the data using a wide range of high-level Python commands. 
SciPy is built on the Python NumPy extention.

**Applications**

* Solving differential equations and the Fourier transform
* Optimization algorithms
* Linear algebra

[Read more about SciPy](https://docs.scipy.org/doc/scipy/reference/tutorial/general.html)

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