dataprep.eda: a newcomer in data analysis

Share this post

A new arrival in the world of profiling

In 2016 Pandas Profiling made its appearance, and with this library opened the world of data preparation in the Python universe (oh so essential phase of a Data science project). I had also devoted an article on the subject here.

2020 will undoubtedly be the year of “dataprep” and more particularly of dataprep.eda! indeed the dataprep project is broken down into several sub-projects (including that of eda: Exploratory Data Analysis, and dataprep.data_connector) … but others are in progress and given the name of the library we can easily s ‘imagine what they will cover.

In short, let’s see what this bookstore has in the belly …

Installation of dataprep.eda

Like any Python library, it all starts with installation and deployment. For this nothing is more efficient than using the pip command:

pip install dataprep

For more information on the library, go to you will also find (in English) user documents, etc.

If you want to use conda or even mamba type the following commands:

conda install -c conda-forge mamba
mamba install -c conda-forge -c sfu-db dataprep

The dataprep library is really very simple and has 4 main functions that we will see later:

Distribution analysis

As often, we will use the titanic data to look at the distribution of this data set in a single line:

import pandas as pd
from dataprep.eda import *
from pandas_profiling import ProfileReport
data = pd.read_csv("../datasources/titanic/train.csv")

A simple call to the plot () function on a Pandas daraframe is enough to display the following graphs:

Dans cet article on va passer au en revue le petit nouveau du profiling Python : datapre.eda

Each column presents a distribution graph (a bar of course). Clicking on the “Show Stats Info” header provides general information (Number of missing data, duplicates, etc.).

You don’t necessarily want to have all the columns? no problem, you just need to specify the desired column (s) to the plot () function as follows.

Single column details

plot(data, "Age")

In this detail visualization you will even have access to additional viz to help you in your analysis. A certain number of tabs (Stats, Histogram, etc.) allow you to visualize at a glance – almost – all the information of the desired column.

Confront 2 columns

It is also often convenient to look at two columns between them, once again the plot () function allows us to do this very easily. Below we will display in the same viz the age and price data of the ticket:

plot(data, "Age", "Fare")

Once again several tabs are proposed in order to present the data either for example under a point graph (scatter plot) or box and whisker plot (box plot) or under a hexagonal point graph (hexagonal binning plot ):

Missing Values ​​Analysis

An important aspect – especially for machine learning projects – is the analysis of missing data. dataprep.eda offers the plot_correlation () function


As before, several tabs are available to present different ways of viewing this information (heat map, bars, etc.). Note that if you have categorical data the graphs will be different… try it!

Correlation analysis

Another very interesting aspect is the correlation analysis which allows you to discover the degree of connection between the columns of the dataset. We saw this notion in a previous article so I won’t go over the different calculation methods .

With datapre.eda, we don’t ask ourselves any questions and we use the plot_correlation () function


Report creation with create_report ()

Report generation is started using the command



This bookstore, as I said in the introduction, is recent (2020) and already very promising. After a few tests we can already say that compared to Pandas Profiling we find these advantages:

  • Clearly a better design of APIs
  • According to some benchmarks – and especially via the promises of the designers – this library is up to 100 times faster! to check anyway.
  • Intelligent and adaptive visualization (admittedly much less static)
  • Allows the handling of much larger data (which was clearly a limitation of Pandas Profiling).
Share this post

Benoit Cayla

In more than 15 years, I have built-up a solid experience around various integration projects (data & applications). I have, indeed, worked in nine different companies and successively adopted the vision of the service provider, the customer and the software editor. This experience, which made me almost omniscient in my field naturally led me to be involved in large-scale projects around the digitalization of business processes, mainly in such sectors like insurance and finance. Really passionate about AI (Machine Learning, NLP and Deep Learning), I joined Blue Prism in 2019 as a pre-sales solution consultant, where I can combine my subject matter skills with automation to help my customers to automate complex business processes in a more efficient way. In parallel with my professional activity, I run a blog aimed at showing how to understand and analyze data as simply as possible: Learning, convincing by the arguments and passing on my knowledge could be my caracteristic triptych.

View all posts by Benoit Cayla →

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Fork me on GitHub