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KDnuggets Home » News » 2021 » Feb » Tutorials, Overviews » Powerful Exploratory Data Analysis in just two lines of code ( 21:n08 )

Silver BlogPowerful Exploratory Data Analysis in just two lines of code

EDA is a fundamental early process for any Data Science investigation. Typical approaches for visualization and exploration are powerful, but can be cumbersome for getting to the heart of your data. Now, you can get to know your data much faster with only a few lines of code... and it might even be fun!

By Francois Bertrand, coder & designer for data visualization and games.

Exploratory data analysis (EDA) is an essential early step in most data science projects, and it often consists of taking the same steps to characterize a dataset (e.g., find out data types, missing information, distribution of values, correlations, etc.). Given the repetitiveness and similarity of such tasks, there are a few libraries that automate and help kickstart the process.

One of the latest is a new open-source Python library called Sweetviz (GitHub), created by a few contributors and myself for just that purpose. It takes pandas dataframes and creates a self-contained HTML report.

It packs a powerful punch: in addition to creating insightful and beautiful visualizations with just two lines of code, it provides analysis that would take a lot more time to generate manually, including some that no other library provides so quickly, such as:

  • Comparison of 2 datasets(e.g., Train vs. Test)
  • Visualization of the target valueagainst all other variables (e.g., “What was the survival rate of male vs. female” etc.)

Here is a link to a report generated by Sweetviz for the well-known sample Titanic Survivor dataset. We will be analyzing this report in this article.


EDA made... fun?!


Being able to get so much information so quickly about the target value and compare different areas of the dataset almost instantly transforms this initial step from being out all about tedium to being quicker, interesting, and even to some degree… fun! (to this data geek, at least!) Of course, EDA is a much longer process, but at least that first step is a lot smoother. Let’s see how it works out with the well-known sample dataset.


Analyzing the Titanic dataset


For this article, we will be analyzing the sample Titanic Survivor dataset you can find here.

After installation of Sweetviz (using pip install sweetviz), simply load the pandas dataframes as you normally would, then call either analyze()compare() or compare_intra() depending on your need (more on that below). The full documentation can be found on GitHub. For now, let's start with the case at hand, loading it as so:

import sweetviz
import pandas as pd
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")


We now have 2 dataframes (train and test), and we would like to analyze the target value “Survived”. I want to point out in this case, we know the name of the target column in advance, but it is always optional to specify a target column. We can generate a report with this line of code:

my_report =[train, "Train"], [test, "Test"], "Survived")


Running this command will perform the analysis and create the report object. To get the output, simply use the show_html() command:

my_report.show_html("Report.html") # Not providing a filename will default to SWEETVIZ_REPORT.html


After generating the file, it will open it through your default browser and should look something like this:

There’s a lot to unpack, so let’s take it one step at a time!

Summary display

The summary shows us the characteristics of both dataframes side-by-side. We can immediately identify that the testing set is roughly half the size of the training set, but that it contains the same features. That legend at the bottom shows us that the training set does contain the “Survived” target variable but that the testing set does not.

Note that Sweetviz does a best guess at determining the data type of each column, between numerical, category/boolean and text. These can be overridden—more on that below.


Hovering your mouse over the “Associations” button in the summary will make the Associations graph appear on the right-hand side:

This graph is a composite of the visuals from Drazen Zaric: Better Heatmaps and Correlation Matrix Plots in Python and concepts from Shaked Zychlinski: The Search for Categorical Correlation.

Basically, in addition to showing the traditional numerical correlations, it unifies in a single graph both numerical correlation but also the uncertainty coefficient (for categorical-categorical) and correlation ratio (for categorical-numerical). Squares represent categorical-featured-related variables, and circles represent numerical-numerical correlations. Note that the trivial diagonal is left empty for clarity.

IMPORTANT: categorical-categorical associations (provided by the uncertainty coefficient) are ASYMMETRICAL, meaning that each row represents how much the row title (on the left) gives information on each column. For example, “Sex”, “Pclass” and “Fare” are the elements that give the most information on “Survived”. For the Titanic dataset, this information is rather symmetrical, but it is not always the case.

Finally, it is worth noting these correlation/association methods shouldn’t be taken as gospel as they make some assumptions on the underlying distribution of data and relationships. However, they can be a very useful starting point.

Target variable

When a target variable is specified, it will show up first, in a special black box.

IMPORTANT: only numerical and boolean features can be targets currently.

We can gather from this summary that “Survived” has no missing data in the training set (891, 100%), that there are 2 distinct possible values (accounting for less than 1% of all values), and from the graph, it can be estimated that roughly 60% did not survive.

Detail area (categorical/boolean)

When you move the mouse to hover over any of the variables, an area to the right will showcase the details. The content of the details depends on the type of variable being analyzed. In the case of a categorical (or boolean) variable, as is the case with the target, the analysis is as follows:

IMPORTANT: a “widescreen” monitor is required to see the full detail area for the moment.

Here, we can see the exact statistics for each class, where 62% did not survive, and 38% survived. You also get the detail of the associations for each of the other features.

Numerical data

Numerical data shows more information on its summary. Here, we can see that in this case, about 20% of data is missing (21% in the test data, which is very consistent).

Note that the target value (“Survived” in this case) is plotted as a line right over the distribution graph. This enables instant analysis of the target distribution with regard to other variables.

Interestingly, we can see from the graph on the right that the survival rate is pretty consistent across all ages, except for the youngest, which have a higher survival rate. It would look like “women and children first” was not just talk.

Detail area (numerical)

As with the categorical data type, the numerical data type shows some extra information in its detail area. Noteworthy here are the buttons on top of the graph. These buttons change how many “bins” are shown in the graph. You can select the following:

  • Auto
  • 5
  • 15
  • 30

Note: to get to these buttons, you need to “lock in place” the current feature by clicking on it. The feature then has a RED OUTLINE to show it is locked in place, and you can access the detail area.

For example, selecting “30” yields a much more granular graph:

Text data

For now, anything that the system does not consider numerical or categorical will be deemed as “text”. Text features currently only show count (percentage) as stats.


FeatureConfig: forcing data types, skipping columns


In many cases, there are “label” columns that you may not want to analyze (although target analysis can provide insights on the distribution of target values based on labeling). In other cases, you may want to force some values to be marked as categorical even though they are numerical in nature.

To do all this, simply create a FeatureConfig object and pass it in to the analyze/compare function. You can specify either a string or a list to kwargs skipforce_cat, and force_text:

feature_config = sweetviz.FeatureConfig(skip="PassengerId", force_cat=["Ticket"])
my_report =[train, "Train"], [test, "Test"], "Survived", feature_config)


Comparing sub-populations (e.g., Male vs Female)


Even if you are only looking at a single dataset, it can be very useful to study the characteristics of different subpopulations within that dataset. To do so, Sweetviz provides the compare_intra() function. To use it, you provide a boolean test that splits the population (here we try train["Sex"] == 'male', to get a sense of the different gender populations), and give a name to each subpopulation. For example:

my_report = sweetviz.compare_intra(train, train["Sex"] == 'male', ["Male", "Female"], 'Survived')
my_report.show_html() # Not providing a filename will default to SWEETVIZ_REPORT.html


yields the following analysis: (for this screenshot I used feature_config to skip showing the analysis of the “Sex” feature, as it is redundant)

Note that the target value (“Survived” in this case) is now plotted as separate lines, one for each compared dataset (e.g., male in blue, female in orange).


Putting it all together


EDA is a fluid, artistic process that must be uniquely adapted to each set of data and situation. However, a tool like Sweetviz can help kickstart the process and get rid of a lot of the initial minutiae of characterizing datasets to provide insights right off the bat. Let’s go through all the features for the Titanic dataset to see what that could look like.

Individual fields


  • The distribution of IDs and survivability is even and ordered as you would hope/expect, so no surprises here.
  • No missing data


  • About twice as many males as females, but…
  • Females were much more likely to survive than males
  • Looking at the correlations, Sex is correlated with Fare, which is and isn’t surprising…
  • Similar distribution between Train and Test
  • No missing data


  • 20% missing data, consistent missing data and distribution between Train and Test
  • Young-adult-centric population, but ages 0–70 well-represented
  • Surprisingly evenly distributed survivability, except for a spike at the youngest age
  • Using 30 bins in the histogram in the detail window, you can see that this survivability spike is really for the youngest (about <= 5 years old), as at about 10 years old, survivability is really low.
  • Age seems related to Siblings, Pclass, and Fare, and a bit more surprisingly to Embarked


  • No missing data, data seems pretty clean
  • All names are distinct, which is not surprising


  • Survivability closely follows class (first class most likely to survive, third class least likely)
  • Similar distribution between Train and Test
  • No missing data


  • There seems to be a survival spike at 1 and to some degree 2, but (looking at the detail pane not shown here) there is a sharp drop-off at 3 and greater. Large families couldn’t make it or perhaps were poorer?
  • Similar distribution between Train and Test
  • No missing data


  • Similar distribution between Train and Test
  • No missing data


  • ~80% distinct values, so about 1 in 5 shared tickets on average
  • The highest frequency ticket was 7, which is generally consistent with the maximum number of siblings (8)
  • No missing data, data seems pretty clean


  • As expected, and as with Pclass, the higher fares survived better (although sample size gets pretty thin at higher levels)
  • A Correlation Ratio of 0.26 for “Survived” is relatively high, so it would tend to support this theory
  • About 30% distinct values feels a bit high as you would expect fewer set prices but looks like there is a lot of granularity, so that’s ok
  • Only 1 missing record in the Test set, data pretty consistent between Train and Test


  • A lot of missing data (up to 78%), but consistent between Train and Test
  • Maximum frequency is 4, which would make sense to have 4 people maximum in a cabin


  • 3 distinct values (S, C, Q)
  • Only 2 missing rows in Train data. Data seems pretty consistent between Train and Test
  • Survivability somewhat higher at C; could this be a location with richer people?
  • Either way, “Embarked” shows an Uncertainty Coefficient of only 0.03 for “Survived”, so it may not be very significant


General analysis


  • Overall, most data is present and seems consistent and make sense; no major outliers or huge surprises

Test versus Training data

  • Test has about 50% fewer rows
  • Train and Test are very closely matched in the distribution of missing data
  • Train and Test data values are very consistent across the board

Association/correlation analysis

  • Sex, Fare, and Pclass give the most information on Survived
  • As expected, Fare and Pclass are highly correlated
  • Age seems to tell us a good amount regarding Pclass, siblings, and to some degree, Fare, which would be somewhat expected. It seems to tell us a lot about “Embarked” which is a bit more surprising.

Missing data

  • There is no significant missing data except for Age (~20%) and Cabin (~77%) (and an odd one here and there on other features)




All this information from just two lines of code! Sweetviz also now supports notebook integration (you can insert reports inline) and new features!

Using Sweetviz easily gives me a significant jump-start when I start looking at a new dataset. It’s worth pointing out I also find it useful later in the analysis process, for example, during feature-generation, to get a quick overview of how new features play out. I hope you will find it as a useful tool in your own data analysis.

Original. Reposted with permission.



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