Available settings

A set of options is available in order to customize the behaviour of pandas-profiling and the appearance of the generated report. The depth of customization allows the creation of behaviours highly targeted at the specific dataset being analysed. The available settings are listed below. To learn how to change them, check Changing settings.

General settings

Global report settings:

Parameter	Type	Default	Description
title	string	Pandas Profiling Report	Title for the report, shown in the header and title bar.
pool_size	integer	0	Number of workers in thread pool. When set to zero, it is set to the number of CPUs available.
progress_bar	boolean	True	If True, pandas-profiling will display a progress bar.

Variable summary settings

Settings related with the information displayed for each variable.

Parameter	Type	Default	Description
sort	None, asc or desc	None	Sort the variables asc (ending), desc (ending) or None (leaves original sorting).
variables.descriptions	dict	{}	Ability to display a description alongside the descriptive statistics of each variable ({‘var_name’: ‘Description’}).
vars.num.quantiles	list[float]	[0.05,0.25,0.5,0.75,0.95]	The quantiles to calculate. Note that .25, .5 and .75 are required for the computation of other metrics (median and IQR).
vars.num.skewness_threshold	integer	20	Warn if the skewness is above this threshold.
vars.num.low_categorical_threshold	integer	5	If the number of distinct values is smaller than this number, then the series is considered to be categorical. Set to 0 to disable.
vars.num.chi_squared_threshold	float	0.999	Set to 0 to disable chi-squared calculation.
vars.cat.length	boolean	True	Check the string length and aggregate values (min, max, mean, media).
vars.cat.characters	boolean	False	Check the distribution of characters and their Unicode properties. Often informative, but may be computationally expensive.
vars.cat.words	boolean	False	Check the distribution of words. Often informative, but may be computationally expensive.
vars.cat.cardinality_threshold	integer	50	Warn if the number of distinct values is above this threshold.
vars.cat.imbalance_threshold	float	0.5	Warn if the imbalance score is above this threshold.
vars.cat.n_obs	integer	5	Display this number of observations.
vars.cat.chi_squared_threshold	float	0.999	Same as above, but for categorical variables.
vars.bool.n_obs	integer	3	Same as above, but for boolean variables.
vars.bool.imbalance_threshold	float	0.5	Warn if the imbalance score is above this threshold.

Configuration example

profile = df.profile_report(
    sort="ascending",
    vars={
        "num": {"low_categorical_threshold": 0},
        "cat": {
            "length": True,
            "characters": False,
            "words": False,
            "n_obs": 5,
        },
    },
)

profile.config.variables.descriptions = {
    "files": "Files in the filesystem",
    "datec": "Creation date",
    "datem": "Modification date",
}

profile.to_file("report.html")

Missing data overview plots

Settings related with the missing data section and the visualizations it can include.

Parameter	Type	Default	Description
missing_diagrams.bar	boolean	True	Display a bar chart with counts of missing values for each column.
missing_diagrams.matrix	boolean	True	Display a matrix of missing values. Similar to the bar chart, but might provide overview of the co-occurrence of missing values in rows.
missing_diagrams.heatmap	boolean	True	Display a heatmap of missing values, that measures nullity correlation (i.e. how strongly the presence or absence of one variable affects the presence of another).

Configuration example: disable heatmap for large datasets

profile = df.profile_report(
    missing_diagrams={
        "heatmap": False,
    }
)
profile.to_file("report.html")

Correlations

Settings regarding correlation metrics and thresholds.

Parameter	Type	Default	Description
correlations.pearson.calculate	boolean	True	Whether to calculate this coefficient
correlations.pearson.warn_high_correlations	boolean	True	Show warning for correlations higher than the threshold
correlations.pearson.threshold	float	0.9	Warning threshold
correlations.spearman.calculate	boolean	True	Whether to calculate this coefficient
correlations.spearman.warn_high_correlations	boolean	False	Show warning for correlations higher than the threshold
correlations.spearman.threshold	float	0.9	Warning threshold
correlations.kendall.calculate	boolean	True	Whether to calculate this coefficient
correlations.kendall.warn_high_correlations	boolean	False	Show warning for correlations higher than the threshold
correlations.kendall.threshold	float	0.9	Warning threshold
correlations.phi_k.calculate	boolean	True	Whether to calculate this coefficient
correlations.phi_k.warn_high_correlations	boolean	False	Show warning for correlations higher than the threshold
correlations.phi_k.threshold	float	0.9	Warning threshold
correlations.cramers.calculate	boolean	True	Whether to calculate this coefficient
correlations.cramers.warn_high_correlations	boolean	True	Show warning for correlations higher than the threshold
correlations.cramers.threshold	float	0.9	Warning threshold
correlations.auto.calculate	boolean	True	Whether to calculate this coefficient
correlations.auto.warn_high_correlations	boolean	True	Show warning for correlations higher than the threshold
correlations.auto.threshold	float	0.9	Warning threshold

For instance, to disable all correlation computations (may be relevant for large datasets):

profile = df.profile_report(
    title="Report without correlations",
    correlations={
        "auto": {"calculate": False},
        "pearson": {"calculate": False},
        "spearman": {"calculate": False},
        "kendall": {"calculate": False},
        "phi_k": {"calculate": False},
        "cramers": {"calculate": False},
    },
)

# or using a shorthand that is available for correlations
profile = df.profile_report(
    title="Report without correlations",
    correlations=None,
)

Interactions

Settings related with the interactions section.

Parameter	Type	Default	Description
interactions.continuous	boolean	True	Generate a 2D scatter plot (or hexagonal binned plot) for all continuous variable pairs.
interactions.targets	list	[]	When a list of variable names is given, only interactions between these and all other variables are computed.

Report’s appearance

Settings related with the appearance and style of the report.

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		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.