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Visualize and compare datasets, target values and associations, with one line of code.
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In-depth EDA (target analysis, comparison, feature analysis, correlation) in two lines of code!
Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Output is a fully self-contained HTML application.
The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks.
Usage and parameters are described below, you can also find an article describing its features in depth and see examples in action HERE.
Sweetviz development is still ongoing! Please let me know if you run into any data, compatibility or install issues! Thank you for reporting any BUGS in the issue tracking system here, and I welcome your feedback and questions on usage/features in the brand-new GitHub "Discussions" tab right here!.
Example HTML report using the Titanic dataset
Example Notebook w/docs on Colab (Jupyter/other notebooks should also work)
Medium Article describing its features in depth
KD Nugget articles:
(see below for docs on these features)
Some people have experienced mixed results behavior upgrading through pip. To update to the latest from an existing install, it is recommended to pip uninstall sweetviz first, then simply install.
Sweetviz currently supports Python 3.6+ and Pandas 0.25.3+. Reports are output using the base "os" module, so custom environments such as Google Colab which require custom file operations are not yet supported, although I am looking into a solution.
The best way to install sweetviz (other than from source) is to use pip:
pip install sweetviz
In some rare cases, users have reported errors such as ModuleNotFoundError: No module named 'sweetviz' and AttributeError: module 'sweetviz' has no attribute 'analyze'.
In those cases, we suggest the following:
sweetviz.py, as that interferes with the library itself. Delete or rename that script (and any associated .pyc files), and try again.pip uninstall sweetviz, then reinstallingCreating a report is a quick 2-line process:
DataframeReport object using one of: analyze(), compare() or compare_intra()show_xxx() function to render the report. You can now use either html or notebook report options, as well as scaling: (more info on these options below)There are 3 main functions for creating reports:
To analyze a single dataframe, simply use the analyze(...) function, then the show_html(...) function:
import sweetviz as sv
my_report = sv.analyze(my_dataframe)
my_report.show_html() # Default arguments will generate to "SWEETVIZ_REPORT.html"
When run, this will output a 1080p widescreen html app in your default browser:
The analyze() function can take multiple other arguments:
analyze(source: Union[pd.DataFrame, Tuple[pd.DataFrame, str]],
target_feat: str = None,
feat_cfg: FeatureConfig = None,
pairwise_analysis: str = 'auto',
verbosity: str = 'default'):
my_df or [my_df, "Training"]skip, force_cat, force_num and force_text. The "force_" arguments override the built-in type detection. They can be constructed as follows:feature_config = sv.FeatureConfig(skip="PassengerId", force_text=["Age"])
full, progress_only (to only display the progress bar but not report generation messages) and off (fully quiet, except for errors or warnings). Default verbosity can also be set in the INI override, under the "General" heading (see "The Config file" section below for details).pairwise_analysis="on" (or ="off") since processing that many features would take a long time. This parameter also covers the generation of the association graphs (based on Drazen Zaric's concept):To compare two data sets, simply use the compare() function. Its parameters are the same as analyze(), except with an inserted second parameter to cover the comparison dataframe. It is recommended to use the [dataframe, "name"] format of parameters to better differentiate between the base and compared dataframes. (e.g. [my_df, "Train"] vs my_df)
my_report = sv.compare([my_dataframe, "Training Data"], [test_df, "Test Data"], "Survived", feature_config)
Another way to get great insights is to use the comparison functionality to split your dataset into 2 sub-populations.
Support for this is built in through the compare_intra() function. This function takes a boolean series as one of the arguments, as well as an explicit "name" tuple for naming the (true, false) resulting datasets. Note that internally, this creates 2 separate dataframes to represent each resulting group. As such, it is more of a shorthand function of doing such processing manually.
my_report = sv.compare_intra(my_dataframe, my_dataframe["Sex"] == "male", ["Male", "Female"], "Survived", feature_config)
Once you have created your report object (e.g. my_report in the examples above), simply pass it into one of the two `show' functions:
show_html( filepath='SWEETVIZ_REPORT.html',
open_browser=True,
layout='widescreen',
scale=None)
show_html(...) will create and save an HTML report at the given file path. There are options for:
'widescreen' or 'vertical'. The widescreen layout displays details on the right side of the screen, as the mouse goes over each feature. The new (as of 2.0) vertical layout is more compact horizontally and enables expanding each detail area upon clicking.scale = 0.8 or None) to scale the entire report. This is very useful to fit reports to any output.show_notebook( w=None,
h=None,
scale=None,
layout='widescreen',
filepath=None,
file_layout=None,
file_scale=None)
show_notebook(...) is new as of 2.0 and will embed an IFRAME element showing the report right inside a notebook (e.g. Jupyter, Google Colab, etc.).
Note that since notebooks are generally a more constrained visual environment, it is probably a good idea to use custom width/height/scale values (w, h, scale) and even set custom default values in an INI override (see below). The options are:
layout and/or scale for the report itself). Can be as a percentage string (w="100%") or number of pixels (w=900).h=700) or "Full" to stretch the window to be as tall as all the features (h="Full").show_html(), above.show_html(), above.layout for show_html(), above)Selected from shared topics, language and repository description—not editorial ratings.
MugundhanY /
A comprehensive data analysis comparing the city of Louisville between the years 2008 and 2016. Utilized Python for data exploration and visualization, employing both Sweetviz and Pandas Profiling for in-depth insights. The analysis covers various aspects, providing a holistic view of the city's dynamics and changes over the specified time frame.
27/100 health