Basic Statistics and Data Visualization

Doing exploratory, diagnostic and descriptive statistics is the first and very crucial part of any data analytics project.

Here are some more details on each of the steps involved in Exploratory Data Analysis ( EDA)

Let’s now look at examples on how to accomplish these tasks in Python.

You can find all the inbuilt datasets in the seaborn library using the below command-

seaborn.get_dataset_names()

The following datasets are available-

[‘anscombe’,

‘attention’,

‘brain_networks’,

‘car_crashes’,

‘diamonds’,

‘dots’,

‘exercise’,

‘flights’,

‘fmri’,

‘gammas’,

‘iris’,

‘mpg’,

‘planets’,

‘tips’,

‘titanic’]

EDA1EDA2EDA3EDA4EDA5EDA6EDA7EDA8EDA9EDA10

EDA11EDA12EDA13EDA14EDA15EDA16EDA17EDA18EDA19EDA20EDA21EDA22EDA23EDA24EDA25EDA26EDA27EDA28

Cheers!

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