pandas_profiling.profile_report.ProfileReport
- class pandas_profiling.profile_report.ProfileReport(df=None, minimal=False, explorative=False, sensitive=False, dark_mode=False, orange_mode=False, tsmode=False, sortby=None, sample=None, config_file=None, lazy=True, typeset=None, summarizer=None, config=None, **kwargs)[source]
Generate a profile report from a Dataset stored as a pandas DataFrame.
Used as is, it will output its content as an HTML report in a Jupyter notebook.
Generate a ProfileReport based on a pandas DataFrame
Config processing order (in case of duplicate entries, entries later in the order are retained): - config presets (e.g. config_file, minimal arguments) - config groups (e.g. explorative and sensitive arguments) - custom settings (e.g. config argument) - custom settings **kwargs (e.g. title)
- Parameters
df (
Optional
[DataFrame
]) – the pandas DataFrameminimal (
bool
) – minimal mode is a default configuration with minimal computationconfig_file (
Union
[Path
,str
,None
]) – a config file (.yml), mutually exclusive with minimallazy (
bool
) – compute when neededsample (
Optional
[dict
]) – optional dict(name=”Sample title”, caption=”Caption”, data=pd.DataFrame())typeset (
Optional
[VisionsTypeset
]) – optional user typeset to use for type inferencesummarizer (
Optional
[BaseSummarizer
]) – optional user summarizer to generate custom summary output**kwargs – other arguments, for valid arguments, check the default configuration file.
- __init__(df=None, minimal=False, explorative=False, sensitive=False, dark_mode=False, orange_mode=False, tsmode=False, sortby=None, sample=None, config_file=None, lazy=True, typeset=None, summarizer=None, config=None, **kwargs)[source]
Generate a ProfileReport based on a pandas DataFrame
Config processing order (in case of duplicate entries, entries later in the order are retained): - config presets (e.g. config_file, minimal arguments) - config groups (e.g. explorative and sensitive arguments) - custom settings (e.g. config argument) - custom settings **kwargs (e.g. title)
- Parameters
df (
Optional
[DataFrame
]) – the pandas DataFrameminimal (
bool
) – minimal mode is a default configuration with minimal computationconfig_file (
Union
[Path
,str
,None
]) – a config file (.yml), mutually exclusive with minimallazy (
bool
) – compute when neededsample (
Optional
[dict
]) – optional dict(name=”Sample title”, caption=”Caption”, data=pd.DataFrame())typeset (
Optional
[VisionsTypeset
]) – optional user typeset to use for type inferencesummarizer (
Optional
[BaseSummarizer
]) – optional user summarizer to generate custom summary output**kwargs – other arguments, for valid arguments, check the default configuration file.
Methods
__init__
([df, minimal, explorative, ...])Generate a ProfileReport based on a pandas DataFrame
compare
(other[, config])Compare this report with another ProfileReport Alias for:
` pandas_profiling.compare([report1, report2], config=config) `
See pandas_profiling.compare for details.dump
(output_file)Dump ProfileReport to file
dumps
()Serialize ProfileReport and return bytes for reproducing ProfileReport or Caching.
get_description
()Return the description (a raw statistical summary) of the dataset.
get_duplicates
()Get duplicate rows and counts based on the configuration
get_rejected_variables
()Get variables that are rejected for analysis (e.g.
get_sample
()Get head/tail samples based on the configuration
invalidate_cache
([subset])Invalidate report cache.
load
(load_file)Load ProfileReport from file
loads
(data)Deserialize the serialized report
to_expectation_suite
([suite_name, ...])All parameters default to True to make it easier to access the full functionality of Great Expectations out of the box. :type suite_name:
Optional
[str
] :param suite_name: The name of your expectation suite :type data_context:Optional
[Any
] :param data_context: A user-specified data context :type save_suite:bool
:param save_suite: Boolean to determine whether to save the suite to .json as part of the method :type run_validation:bool
:param run_validation: Boolean to determine whether to run validation as part of the method :type build_data_docs:bool
:param build_data_docs: Boolean to determine whether to build data docs, save the .html file, and open data docs in your browser :type handler:Optional
[Handler
] :param handler: The handler to use for building expectation.to_file
(output_file[, silent])Write the report to a file.
to_html
()Generate and return complete template as lengthy string
to_json
()Represent the ProfileReport as a JSON string
to_notebook_iframe
()Used to output the HTML representation to a Jupyter notebook.
to_widgets
()The ipython notebook widgets user interface.
Attributes
config
description_set
df
df_hash
html
json
report
summarizer
typeset
widgets