Python Libraries Every Data Scientist Must Know.

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.

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

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

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

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

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.

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