Autoviz & Autovizwidget

Autoviz is an open-source python library that mainly works on visualizing the relationship of the data, it can find the most impactful features and plot creative visualization in just one line of code.

Autoviz is incredibly fast and highly useful.

For using autoviz first we need to import the autoviz class and instantiate it.

#importing Autoviz class

from autoviz.AutoViz_Class import AutoViz_Class

#Instantiate the AutoViz class

AV = AutoViz_Class()        

After initiating the Autoviz class we just need to run a command which will create a visualization of the dataset.

AV.AutoViz("Advertising.csv", depVar='Sales')        

If we know the dependent variable in the dataset which is dependent on other variables, then we can pass it as an argument and visualize the data according to the Dependent Variable.

Understanding the Report

The above command will create a report which will contain the following attributes:

A.????Scatter plot of all continuous variables

B.?????Distplot , Boxplot , Probability Plot of all variables

C. Violin Plots of all continuous variables

D. Heatmap of continuous variables

Autovizwidget

1.??Convenience methods

Library inspect pandas DataFrame and select the X and Y axis automatically. Simply use the display_dataframe method:

from autovizwidget.widget.utils import display_dataframe

display_dataframe(df)        

2. Specifying an encoding

Alternatively, we can specify our own encoding by hand and then create a widget from it

Creating an Encoding

Encodings is how we indicate to the library which features it will use to create visualizations declaratively.

Below, we'll select column 'TV' as the X axis, column 'Sales' as the Y axis,

and we'll take the min of the 'Sales' column when plotting against the 'TV' column.

?
from autovizwidget.widget.encoding import Encoding
 
encoding = Encoding(chart_type=Encoding.chart_type_table, x='TV', y='Sales', y_aggregation=Encoding.y_agg_min)        

Visualizing

We can now create an AutoVizWidget with our Pandas Dataframe (df) and the encoding we created.

from autovizwidget.widget.autovizwidget import AutoVizWidget
AutoVizWidget(df, encoding)        

You can check my GitHub profile for code.