Matplotlib-Seaborn-python

Matplotlib is the basic visualizing or plotting library of the python programming language. Matplotlib is a powerful tool for executing a variety of tasks. It is able to create different types of visualization reports like line plots, scatter plots, histograms, bar charts, pie charts, box plots, and many more different plots. This library also supports 3-dimensional plotting. Matplotlib was originally written by John D. Hunter, has an active development community, and is distributed under a BSD-style license. It was first released in 2003. The official website is www.matplotlib.org.

What is Matplotlib Inline?

%matplotlib inline is how we use inline in a jupyter document. With this backend, the output of plotting commands is displayed inline within frontends like the Jupyter notebook, directly below the code cell that produced it. The resulting plots will then also be stored in the notebook document. When using the 'inline' backend, your matplotlib graphs will be included in your notebook, next to the code.

ANALYSIS :

UNIVARIATE ANALYSIS BIVARIATE ANALYSIS MULTIVARIATE ANALYSIS

SEABORN LIBRARY

Seaborn is a powerful visualization tool developed by Michael Waskom. The name ‘Seaborn’ was given after the fictional character Samuel Norman Seaborn of the television serial drama The West Wing. Hence while performing visualization in Seaborn , its name is being contradicted to sns. It is built on top of Matplotlib. Visualization plays an important role when we try to explore and understand data, Seaborn is aimed to make it easier and the centre of the process.The Seaborn library is built on top of Matplotlib and offers many advanced data visualization capabilities. Though, the Seaborn library can be used to draw a variety of charts such as matrix plots, grid plots, regression plots etc. Why should choose Seaborn? · It is one of the powerful visualization tool providing a variety of visualization patterns. · It uses fewer syntax comparing to Matplotlib. Hence it is easy to use. · It works very well with Pandas Dataframe with more integrated functionality.

TYPES OF PLOTS :

NUMERICAL DATA PLOT :

--relplot()

--scatterplot()

--lineplot()

CATEGORICAL DATA PLOT :

--catplot()

--boxplot()

--stripplot()

--swarmplot()

--etc...

VISUALIZING DISTRIBUTION OF DATA :

--distplot()

--kdeplot()

--jointplot()

--rugplot()

LINEAR REGRESSION & RELATIONSHIP:

--regplot()

--lmplot()