Advanced Usage¶

A set of options is available in order to adapt the report generated.

General 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.

The configuration can be changed in the following ways:

Configuration example¶

# Change the config when creating the report
profile = df.profile_report(title="Pandas Profiling Report", pool_size=1)

# Change the config after
profile.set_variable("html.minify_html", False)

profile.to_file("output.html")

Variable summary settings¶

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 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 zero to disable chi squared calculation.
`vars.cat.length`	boolean	True	Check the string length and aggregate values (min, max, mean, media).
`vars.cat.unicode`	boolean	False	Check the distribution of characters and their Unicode properties. 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.n_obs`	integer	5	Display this number of observations.
`vars.cat.chi_squared_threshold`	float	0.999	Same as above.
`vars.bool.n_obs`	integer	3	Same as above.

Configuration example¶

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

profile.set_variable('variables.descriptions',
    {
      'files': 'Files in the filesystem',
      'datec': 'Creation date',
      'datem': 'Modification date',
    }
)

profile.to_file("report.html")

Missing data overview plots¶

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).
`missing_diagrams.dendrogram`	boolean	True	Display a dendrogram. Provides insight in the co-occurrence of missing values (i.e. columns that are both filled or both none).

Configuration example: disable heatmap and dendrogram for large datasets¶

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

The missing data diagrams are generated by the missingno package.

Correlations¶

Parameter	Type	Default	Description
`correlations.pearson.calculate`	boolean	True	Whether to calculate this coefficient
`correlations.pearson.warn_high_correlations`	boolean	True	Warn 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	Warn 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	Warn 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	Warn 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	Warn for correlations higher than the threshold
`correlations.cramers.threshold`	float	0.9	Warning threshold

Disable all correlations:

profile = df.profile_report(
     title="Report without correlations",
     correlations={
         "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¶

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 given.

The HTML 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.

Using a custom configuration file¶

To set the configuration of pandas-profiling using a custom file, you can start one of the sample configuration files below. Then, change the configuration to your liking.

from pandas_profiling import ProfileReport

profile = ProfileReport(df, config_file="your_config.yml")
profile.to_file("report.html")

Sample configuration files¶

A great way to get an overview of the possible configuration is to look through sample configuration files. The repository contains the following files:

default configuration file (default),
minimal configuration file (minimal computation, optimized for performance)

Configuration shorthands¶

It’s possible to disable certain groups of features through configuration shorthands.

# Disable samples, correlations, missing diagrams and duplicates at once
r = ProfileReport(samples=None, correlations=None, missing_diagrams=None, duplicates=None, interactions=None)

# Or use the .set_variable method
r = ProfileReport()
r.set_variable("samples", None)
r.set_variable("duplicates", None)
r.set_variable("correlations", None)
r.set_variable("missing_diagrams", None)
r.set_variable("interactions", None)