Customize reports - YData Profiling (original) (raw)

Customizing the report

In some situations, a user might want to customize the appearance of the report to match personal preferences or a corporate brand.ydata-profiling offers two major customization dimensions: the styling of the HTML report and the styling of the visualizations and plots contained within.

Customizing the report's theme

Several aspects of the report can be customized. The table below shows the available settings:

Parameter Type Default Description
html.minify_html bool True If True, the output HTML is minified using the htmlmin package.
html.use_local_assets bool True If True, all assets (stylesheets, scripts, images) are stored locally. If False, a CDN is used for some stylesheets and scripts.
html.inline boolean True If True, all assets are contained in the report. If False, then a web export is created, where all assets are stored in the '[REPORT_NAME]_assets/' directory.
html.navbar_show boolean True Whether to include a navigation bar in the report
html.style.theme string None Select a bootswatch theme. Available options: flatly (dark) and united (orange)
html.style.logo string nan A base64 encoded logo, to display in the navigation bar.
html.style.primary_color string #337ab7 The primary color to use in the report.
html.style.full_width boolean False By default, the width of the report is fixed. If set to True, the full width of the screen is used.

See the available changing settingsto see how to change and apply these settings.

Customizing the visualizations

Plot rendering options

A way how to pass arguments to the underlying matplotlib visualization engine is to use the plot argument when computing the profile. It is possible to change the default format of images to png (default is SVG) using the key-pair image_format: "png" and also the resolution of the images using dpi: 800. An example would be:

Customize plots rendering
1 2 3 4 5 6 profile = ProfileReport( planets, title="YData Profiling Report", explorative=True, plot={"dpi": 200, "image_format": "png"}, )

Pie charts

Pie charts are used to plot the frequency of categories in categorical (or boolean) features. By default, a feature is considered as categorical if it does not have more than 10 distinct values. This threshold can be configured with the plot.pie.max_unique setting.

Control pie charts frequency
1 2 3 profile = ProfileReport(pd.DataFrame(["a", "b", "c"])) # Changing the *max_unique* threshold to 2 will make feature non-categorical profile.config.plot.pie.max_unique = 2

If the feature is not considered as categorical, the pie chart will not be displayed. All pie charts can therefore be removed by setting: plot.pie.max_unique = 0.

The pie chart's colors can be configured to any recognised matplotlib colour plot.pie.colors setting.

Control pie charts colors
1 2 profile = ProfileReport(pd.DataFrame([1, 2, 3])) profile.config.plot.pie.colors = ["gold", "b", "#FF796C"]

Colour palettes

The palettes used in visualizations like correlation matrices and missing values overview can also be customized via the plot argument. To customize the palette used by the correlation matrix, use the correlation key:

Changing visualizations color palettes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 from ydata_profiling import ProfileReport profile = ProfileReport( df, title="YData Profiling Report", explorative=True, plot={"correlation": {"cmap": "RdBu_r", "bad": "#000000"}}, ) Similarly, the palette for *Missing values* can be changed using ``missing`` argument: ``` python linenums="1" python from ydata_profiling import ProfileReport profile = ProfileReport( df, title="YData Profiling Report", explorative=True, plot={"missing": {"cmap": "RdBu_r"}}, )

ydata-profiling accepts all cmap values (colormaps) accepted by matplotlib. The list of available colour maps can be accessed here. Alternatively, it is possible to create custom palettes.