DataFrame methods

_obj = pandas_obj instance-attribute

__init__(pandas_obj)

assert_data(condition, subset=None, pass_message=' ✔️ Assertion passed ', fail_message=' ㄨ Assertion failed ', raise_exception=True, exception_to_raise=DataError, message_shows_condition=True, verbose=False)

Tests whether Dataframe meets condition. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
condition	Callable	Assertion criteria in the form of a lambda function, such as lambda df: df.shape[0]>10.	required
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. Applied after fn. Subsetting can also be done within the condition, such as lambda df: df['column_name'].sum()>10	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assertion passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assertion failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
message_shows_condition	bool	Whether the fail/pass message should also print the assertion criteria	True
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_datetime(subset=None, pass_message=' ✔️ Assert datetime passed ', fail_message=None, raise_exception=True, exception_to_raise=TypeError, verbose=False)

Tests whether Dataframe or subset of columns is datetime or timestamp. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert datetime passed '
fail_message	Union[str, None]	Message to display if the condition fails.	None
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	TypeError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_float(subset=None, pass_message=' ✔️ Assert float passed ', fail_message=None, raise_exception=True, exception_to_raise=TypeError, verbose=False)

Tests whether Dataframe or subset of columns is floats. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert float passed '
fail_message	Union[str, None]	Message to display if the condition fails.	None
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	TypeError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_greater_than(min, or_equal_to=True, subset=None, pass_message=' ✔️ Assert minimum passed ', fail_message=' ㄨ Assert minimum failed ', raise_exception=True, exception_to_raise=DataError, verbose=False)

Tests whether Dataframe or subset of columns is > or >= a value. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
min	Any	the minimum value to compare DataFrame to. Accepts any type that can be used in >, such as int, float, str, datetime	required
or_equal_to	bool	whether to test for >= min (True) or > min (False)	True
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert minimum passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assert minimum failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_int(subset=None, pass_message=' ✔️ Assert integeer passed ', fail_message=None, raise_exception=True, exception_to_raise=TypeError, verbose=False)

Tests whether Dataframe or subset of columns is integers. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert integeer passed '
fail_message	Union[str, None]	Message to display if the condition fails.	None
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	TypeError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_less_than(max, or_equal_to=True, subset=None, pass_message=' ✔️ Assert maximum passed ', fail_message=' ㄨ Assert maximum failed ', raise_exception=True, exception_to_raise=DataError, verbose=False)

Tests whether Dataframe or subset of columns is < or <= a value. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
max	Any	the max value to compare DataFrame to. Accepts any type that can be used in <, such as int, float, str, datetime	required
or_equal_to	bool	whether to test for <= min (True) or < max (False)	True
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert maximum passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assert maximum failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_negative(subset=None, assert_not_null=True, pass_message=' ✔️ Assert negative passed ', fail_message=' ㄨ Assert negative failed ', raise_exception=True, exception_to_raise=DataError, verbose=False)

Tests whether Dataframe or subset of columns has all negative values. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against.`	None
assert_not_null	bool	Whether to also enforce that data has no nulls.	True
pass_message	str	Message to display if the condition passes.	' ✔️ Assert negative passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assert negative failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_not_null(subset=None, pass_message=' ✔️ Assert no nulls passed ', fail_message=' ㄨ Assert no nulls failed ', raise_exception=True, exception_to_raise=DataError, verbose=False)

Tests whether Dataframe or subset of columns has no nulls. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert no nulls passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assert no nulls failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_null(subset=None, pass_message=' ✔️ Assert all nulls passed ', fail_message=' ㄨ Assert all nulls failed ', raise_exception=True, exception_to_raise=DataError, verbose=False)

Tests whether Dataframe or subset of columns has all nulls. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert all nulls passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assert all nulls failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_positive(subset=None, assert_not_null=True, pass_message=' ✔️ Assert positive passed ', fail_message=' ㄨ Assert positive failed ', raise_exception=True, exception_to_raise=DataError, verbose=False)

Tests whether Dataframe or subset of columns has all positive values. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
assert_not_null	bool	Whether to also enforce that data has no nulls.	True
pass_message	str	Message to display if the condition passes.	' ✔️ Assert positive passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assert positive failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_str(subset=None, pass_message=' ✔️ Assert string passed ', fail_message=None, raise_exception=True, exception_to_raise=TypeError, verbose=False)

Tests whether Dataframe or subset of columns is strings. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert string passed '
fail_message	Union[str, None]	Message to display if the condition fails.	None
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	TypeError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_timedelta(subset=None, pass_message=' ✔️ Assert timedelta passed ', fail_message=None, raise_exception=True, exception_to_raise=TypeError, verbose=False)

Tests whether Dataframe or subset of columns is of type timedelta. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert timedelta passed '
fail_message	Union[str, None]	Message to display if the condition fails.	None
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	TypeError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_type(dtype, subset=None, pass_message=' ✔️ Assert type passed ', fail_message=None, raise_exception=True, exception_to_raise=TypeError, verbose=False)

Tests whether Dataframe or subset of columns meets type assumption. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
dtype	Type[Any]	The required variable type	required
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert type passed '
fail_message	Union[str, None]	Message to display if the condition fails.	None
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	TypeError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

assert_unique(subset=None, pass_message=' ✔️ Assert unique passed ', fail_message=' ㄨ Assert unique failed ', raise_exception=True, exception_to_raise=DataError, verbose=False)

Tests whether Dataframe or subset of columns has no duplicate rows. Optionally raises an exception. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
subset	Union[str, List, None]	Optional, which column or columns to check the condition against. `	None
pass_message	str	Message to display if the condition passes.	' ✔️ Assert unique passed '
fail_message	str	Message to display if the condition fails.	' ㄨ Assert unique failed '
raise_exception	bool	Whether to raise an exception if the condition fails.	True
exception_to_raise	Type[BaseException]	The exception to raise if the condition fails and raise_exception is True.	DataError
verbose	bool	Whether to display the pass message if the condition passes.	False

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

columns(fn=lambda df: df, subset=None, check_name='🏛️ Columns')

Prints the column names of a DataFrame, without modifying the DataFrame itself.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before printing columns. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before printing their names. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check to preface the result with.	'🏛️ Columns'

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

describe(fn=lambda df: df, subset=None, check_name='📏 Distributions', **kwargs)

Displays descriptive statistics about a DataFrame without modifying the DataFrame itself.

See Pandas docs for describe() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas describe(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before running Pandas describe(). Applied after fn.	None
check_name	Union[str, None]	An optional name for the check to preface the result with.	'📏 Distributions'
**kwargs	Any	Optional, additional arguments that are accepted by Pandas describe() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

disable_checks(enable_asserts=True)

Turns off Pandas Checks globally, such as in production mode. Calls to .check functions will not be run. Does not modify the DataFrame itself.

Args enable_assert: Optionally, whether to also enable or disable assert statements

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

dtypes(fn=lambda df: df, subset=None, check_name='🗂️ Data types')

Displays the data types of a DataFrame's columns without modifying the DataFrame itself.

See Pandas docs for dtypes for additional usage information.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas dtypes. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before running Pandas .dtypes. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check to preface the result with.	'🗂️ Data types'

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

enable_checks(enable_asserts=True)

Globally enables Pandas Checks. Subequent calls to .check methods will be run. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
enable_asserts	bool	Optionally, whether to globally enable or disable calls to .check.assert_data().	True

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

function(fn=lambda df: df, subset=None, check_name=None)

Applies an arbitrary function on a DataFrame and shows the result, without modifying the DataFrame itself.

Example

.check.function(fn=lambda df: df.shape[0]>10, check_name='Has at least 10 rows?') which will result in 'True' or 'False'

Parameters:

Name	Type	Description	Default
fn	Callable	A lambda function to apply to the DataFrame. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before running Pandas describe(). Applied after fn.	None
check_name	Union[str, None]	An optional name for the check to preface the result with.	None

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

get_mode(check_name='🐼🩺 Pandas Checks mode')

Displays the current values of Pandas Checks global options enable_checks and enable_asserts. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
check_name	Union[str, None]	An optional name for the check. Will be used as a preface the printed result.	'🐼🩺 Pandas Checks mode'

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

head(n=5, fn=lambda df: df, subset=None, check_name=None)

Displays the first n rows of a DataFrame, without modifying the DataFrame itself.

See Pandas docs for head() for additional usage information.

Parameters:

Name	Type	Description	Default
n	int	The number of rows to display.	5
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas head(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before running Pandas head(). Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

hist(fn=lambda df: df, subset=[], check_name=None, **kwargs)

Displays a histogram for the DataFrame, without modifying the DataFrame itself.

See Pandas docs for hist() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas hist(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before running Pandas hist(). Applied after fn.	[]
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None
**kwargs	Any	Optional, additional arguments that are accepted by Pandas hist() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

Note

If more than one column is passed, displays a grid of histograms

Only renders in interactive mode (IPython/Jupyter), not in terminal

info(fn=lambda df: df, subset=None, check_name='ℹ️ Info', **kwargs)

Displays summary information about a DataFrame, without modifying the DataFrame itself.

See Pandas docs for info() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas info(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before running Pandas info(). Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	'ℹ️ Info'
**kwargs	Any	Optional, additional arguments that are accepted by Pandas info() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

memory_usage(fn=lambda df: df, subset=None, check_name='💾 Memory usage', **kwargs)

Displays the memory footprint of a DataFrame, without modifying the DataFrame itself.

See Pandas docs for memory_usage() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas memory_usage(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before running Pandas memory_usage(). Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	'💾 Memory usage'
**kwargs	Any	Optional, additional arguments that are accepted by Pandas info() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

Note

Include argument deep=True to get further memory usage of object dtypes in the DataFrame. See Pandas docs for memory_usage() for more info.

ncols(fn=lambda df: df, subset=None, check_name='🏛️ Columns')

Displays the number of columns in a DataFrame, without modifying the DataFrame itself.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before counting the number of columns. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before counting the number of columns. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	'🏛️ Columns'

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

ndups(fn=lambda df: df, subset=None, check_name=None, **kwargs)

Displays the number of duplicated rows in a DataFrame, without modifying the DataFrame itself.

See Pandas docs for duplicated() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before counting the number of duplicates. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before counting duplicate rows. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None
**kwargs	Any	Optional, additional arguments that are accepted by Pandas duplicated() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

nnulls(fn=lambda df: df, subset=None, by_column=True, check_name='👻 Rows with NaNs')

Displays the number of rows with null values in a DataFrame, without modifying the DataFrame itself.

See Pandas docs for isna() for additional usage information.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before counting the number of rows with a null. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string to select a subset of columns before counting nulls.	None
by_column	bool	If True, count null values with each column separately. If False, count rows with a null value in any column. Applied after fn.	True
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	'👻 Rows with NaNs'

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

nrows(fn=lambda df: df, subset=None, check_name='☰ Rows')

Displays the number of rows in a DataFrame, without modifying the DataFrame itself.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before counting the number of rows. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string name of one column to limit which columns are considered when counting rows. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	'☰ Rows'

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

nunique(column, fn=lambda df: df, check_name=None, **kwargs)

Displays the number of unique rows in a single column, without modifying the DataFrame itself.

See Pandas docs for nunique() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
column	str	The name of a column to count uniques in. Applied after fn.	required
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas nunique(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None
**kwargs	Any	Optional, additional arguments that are accepted by Pandas nunique() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

plot(fn=lambda df: df, subset=None, check_name='', **kwargs)

Displays a plot of the DataFrame, without modifying the DataFrame itself.

See Pandas docs for plot() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas plot(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string name of one column to limit which columns are plotted. Applied after fn.	None
check_name	Union[str, None]	An optional title for the plot.	''
**kwargs	Any	Optional, additional arguments that are accepted by Pandas plot() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

Note

Plots are only displayed when code is run in IPython/Jupyter, not in terminal.

If you pass a 'title' kwarg, it becomes the plot title, overriding check_name

print(object=None, fn=lambda df: df, subset=None, check_name=None, max_rows=10)

Displays text, another object, or (by default) the current DataFrame's head. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
object	Any	Object to print. Can be anything printable: str, int, list, another DataFrame, etc. If None, print the DataFrame's head (with max_rows rows).	None
fn	Callable	An optional lambda function to apply to the DataFrame before printing object. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string name of one column to limit which columns are printed. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None
max_rows	int	Maximum number of rows to print if object=None.	10

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

print_time_elapsed(start_time, lead_in='Time elapsed', units='auto')

Displays the time elapsed since start_time.

Parameters:

Name	Type	Description	Default
start_time	float	The index time when the stopwatch started, which comes from the Pandas Checks start_timer()	required
lead_in	Union[str, None]	Optional text to print before the elapsed time.	'Time elapsed'
units	str	The units in which to display the elapsed time. Allowed values: "auto", "milliseconds", "seconds", "minutes", "hours" or shorthands "ms", "s", "m", "h".	'auto'

Raises:

Type	Description
ValueError	If units is not one of allowed values.

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

reset_format()

Globally restores all Pandas Checks formatting options to their default "factory" settings. Does not modify the DataFrame itself.

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

set_format(**kwargs)

Configures selected formatting options for Pandas Checks. Does not modify the DataFrame itself.

Run pandas_checks.describe_options() to see a list of available options.

For example, .check.set_format(check_text_tag= "h1", use_emojis=False`) will globally change Pandas Checks to display text results as H1 headings and remove all emojis.

Parameters:

Name	Type	Description	Default
**kwargs	Any	Pairs of setting name and its new value.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

set_mode(enable_checks, enable_asserts)

Configures the operation mode for Pandas Checks globally. Does not modify the DataFrame itself.

Parameters:

Name	Type	Description	Default
enable_checks	bool	Whether to run any Pandas Checks methods globally. Does not affect .check.assert_data().	required
enable_asserts	bool	Whether to run calls to Pandas Checks .check.assert_data() statements globally.	required

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

shape(fn=lambda df: df, subset=None, check_name='📐 Shape')

Displays the Dataframe's dimensions, without modifying the DataFrame itself.

See Pandas docs for shape for additional usage information.

Parameters:

Name	Type	Description	Default
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas shape. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string name of one column to limit which columns are considered when printing the shape. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	'📐 Shape'

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

Note

See also .check.nrows() and .check.ncols()

tail(n=5, fn=lambda df: df, subset=None, check_name=None)

Displays the last n rows of the DataFrame, without modifying the DataFrame itself.

See Pandas docs for tail() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
n	int	Number of rows to show.	5
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas tail(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string name of one column to limit which columns are displayed. Applied after fn.	None
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

unique(column, fn=lambda df: df, check_name=None)

Displays the unique values in a column, without modifying the DataFrame itself.

See Pandas docs for unique() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
column	str	Column to check for unique values.	required
fn	Callable	An optional lambda function to apply to the DataFrame before calling Pandas unique(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

Note

fn is applied to the dataframe before selecting column. If you want to select the column before modifying it, set column=None and start fn with a column selection, i.e. fn=lambda df: df["my_column"].stuff()

value_counts(column, fn=lambda df: df, max_rows=10, check_name=None, **kwargs)

Displays the value counts for a column, without modifying the DataFrame itself.

See Pandas docs for value_counts() for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
column	str	Column to check for value counts.	required
max_rows	int	Maximum number of rows to show in the value counts.	10
fn	Callable	An optional lambda function to apply to the DataFrame before running Pandas value_counts(). Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
check_name	Union[str, None]	An optional name for the check, to be printed as preface to the result.	None
**kwargs	Any	Optional, additional arguments that are accepted by Pandas value_counts() method.	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

Note

fn is applied to the dataframe before selecting column. If you want to select the column before modifying it, set column=None and start fn with a column selection, i.e. fn=lambda df: df["my_column"].stuff()

write(path, format=None, fn=lambda df: df, subset=None, verbose=False, **kwargs)

Exports DataFrame to file, without modifying the DataFrame itself.

Format is inferred from path extension like .csv.

This functions uses the corresponding Pandas export function such as to_csv(). See Pandas docs for those functions for additional usage information, including more configuration options you can pass to this Pandas Checks method.

Parameters:

Name	Type	Description	Default
path	str	Path to write the file to.	required
format	Union[str, None]	Optional file format to force for the export. If None, format is inferred from the file's extension in path.	None
fn	Callable	An optional lambda function to apply to the DataFrame before exporting. Example: lambda df: df.shape[0]>10. Applied before subset.	lambda df: df
subset	Union[str, List, None]	An optional list of column names or a string name of one column to limit which columns are exported. Applied after fn.	None
verbose	bool	Whether to print a message when the file is written.	False
**kwargs	Any	Optional, additional keyword arguments to pass to the Pandas export function (.to_csv).	{}

Returns:

Type	Description
DataFrame	The original DataFrame, unchanged.

Note

Exporting to some formats such as Excel, Feather, and Parquet may require you to install additional packages.