(1) Import libraries

import pandas as pd
from pycaret.datasets import get_data

from autoviz.AutoViz_Class import AutoViz_Class

Imported AutoViz_Class version: 0.0.83. Call using:
    AV = AutoViz_Class()
    AV.AutoViz(filename, sep=',', depVar='', dfte=None, header=0, verbose=0,
                            lowess=False,chart_format='svg',max_rows_analyzed=150000,max_cols_analyzed=30)
Note: verbose=0 or 1 generates charts and displays them in your local Jupyter notebook.
      verbose=2 does not show plot but creates them and saves them in AutoViz_Plots directory in your local machine.

# Record version of key libraries
from importlib.metadata import version

print('autoviz==%s' % version('autoviz'))

autoviz==0.0.83

(2) Get data

# Select a pre-packaged data for testing
df = get_data('diabetes', verbose=True)

	Number of times pregnant	Plasma glucose concentration a 2 hours in an oral glucose tolerance test	Diastolic blood pressure (mm Hg)	Triceps skin fold thickness (mm)	2-Hour serum insulin (mu U/ml)	Body mass index (weight in kg/(height in m)^2)	Diabetes pedigree function	Age (years)	Class variable
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

(3) Perform EDA

# Generate report on data - including uni/paired variables distribution, correlation and missing values
# NOTE - set max_rows_analyzed, max_cols_analyzed for large data set
# NOTE - charts can be saved to a local directory by setting verbose=2

AV = AutoViz_Class()

report = AV.AutoViz(
    filename='', # must be set if passing dataframe directly
    dfte=df,
    depVar='Class variable',
    verbose=0,
    lowess=True # only set True for data with less than 100k rows
)

Shape of your Data Set loaded: (768, 9)
############## C L A S S I F Y I N G  V A R I A B L E S  ####################
Classifying variables in data set...
    Number of Numeric Columns =  2
    Number of Integer-Categorical Columns =  6
    Number of String-Categorical Columns =  0
    Number of Factor-Categorical Columns =  0
    Number of String-Boolean Columns =  0
    Number of Numeric-Boolean Columns =  0
    Number of Discrete String Columns =  0
    Number of NLP String Columns =  0
    Number of Date Time Columns =  0
    Number of ID Columns =  0
    Number of Columns to Delete =  0
    8 Predictors classified...
        This does not include the Target column(s)
        No variables removed since no ID or low-information variables found in data set

################ Binary_Classification VISUALIZATION Started #####################
Using Lowess Smoothing. This might take a few minutes for large data sets...

Using Lowess Smoothing. This might take a few minutes for large data sets...
Total Number of Scatter Plots = 3

Time to run AutoViz (in seconds) = 12.914

 ###################### VISUALIZATION Completed ########################