Automated Data Visualizations in Python

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Creating data visualizations in Python can be a challenge. For some it an be easy, but for most (and particularly new people to the language) they always have to search for the commands in the documentation or using some search engine. Over the past few years we have seem more and more libraries coming available to assist with many of the routine and tedious steps in most data science and machine learning projects. I’ve written previously about some data profiling libraries in Python. These are good up to a point, but additional work/code is needed to explore the data to suit your needs. One of these Python libraries, designed to make your initial work on a new data set easier is called AutoViz. It’s good to see there is continued development work on this library, which can be really help for creating initial sets of charts for all the variables in your data set, plus it has some additional features which help to make it very useful and cuts down on some of the additional code you might need to write.

The outputs from AutoViz are very extensive, and are just too long to show in this post. The images below will give you an indication of what if typically generated. I’d encourage you to install the library and run it on one of your data sets to see the full extent of what it can do. For this post, I’ll concentrate on some of the commands/parameters/settings to get the most out of AutoViz.

Firstly there is the install via pip command or install using Anaconda.

pip3 install autoviz

For comparison purposes I’m going to use the same data set as I’ve used in the data profiling post (see above). Here’s a quick snippet from that post to load the data and perform the data profiling (see post for output)

import pandas as pd
import pandas_profiling as pp

#load the data into a pandas dataframe
df = pd.read_csv("/Users/brendan.tierney/Downloads/Video_Games_Sales_as_at_22_Dec_2016.csv")

We can not skip to using AutoViz. It supports the loading and analzying of data sets direct from a file or from a pandas dataframe. In the following example I’m going to use the dataframe (df) created above.

from autoviz import AutoViz_Class

AV = AutoViz_Class()
df2 = AV.AutoViz(filename="", dfte=df)  #for a file, fill in the filename and remove dfte parameter

This will analyze the data and create lots and lots of charts for you. Some are should in the following image. One the helpful features is the ‘data cleaning improvements’ section where it has performed a data quality assessment and makes some suggestions to improve the data, maybe as part of the data preparation/cleaning step.

There is also an option for creating different types of out put with perhaps the Bokeh charts being particularly useful.

  • chart_format='bokeh': interactive Bokeh dashboards are plotted in Jupyter Notebooks.
  • chart_format='server', dashboards will pop up for each kind of chart on your web browser.
  • chart_format='html', interactive Bokeh charts will be silently saved as Dynamic HTML files under AutoViz_Plots directory
df2 = AV.AutoViz(filename="", dfte=df, chart_format='bokeh')

The next example allows for the report and charts to be focused on a particular dependent or target variable, particular in scenarios where classification will be used.

df2 = AV.AutoViz(filename="", dfte=df, depVar="Platform")

A little warning when using this library. It can take a little bit of time for it to run and create all the different charts. On the flip side, it save you from having to write lots and lots of code!

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