report.dtable

report.dtable(
    df,
    vars,
    stats=None,
    bycol=None,
    byrow=None,
    type='gt',
    labels=None,
    stats_labels=None,
    digits=2,
    notes='',
    counts_row_below=False,
    hide_stats=False,
    **kwargs,
)

Generate descriptive statistics tables and create a booktab style table in the desired format (gt or tex).

Parameters

Name	Type	Description	Default
df	pd.DataFrame	DataFrame containing the table to be displayed.	required
vars	list	List of variables to be included in the table.	required
stats	list	List of statistics to be calculated. The default is None, that sets [‘count’,‘mean’, ‘std’]. All pandas aggregation functions are supported.	`None`
bycol	list	List of variables to be used to group the data by columns. The default is None.	`None`
byrow	str	Variable to be used to group the data by rows. The default is None.	`None`
type	str	Type of table to be created. The default is ‘gt’. Type can be ‘gt’ for great_tables, ‘tex’ for LaTeX or ‘df’ for dataframe.	`'gt'`
labels	dict	Dictionary containing the labels for the variables. The default is None.	`None`
stats_labels	dict	Dictionary containing the labels for the statistics. The default is None. The function uses a default labeling which will be replaced by the labels in the dictionary.	`None`
digits	int	Number of decimal places to round the statistics to. The default is 2.	`2`
notes	str	Table notes to be displayed at the bottom of the table.	`''`
counts_row_below	bool	Whether to display the number of observations at the bottom of the table. Will only be carried out when each var has the same number of obs and when byrow is None. The default is False	`False`
hide_stats	bool	Whether to hide the names of the statistics in the table header. When stats are hidden and the user provides no notes string the labels of the stats are listed in the table notes. The default is False.	`False`
kwargs	dict	Additional arguments to be passed to the make_table function.	`{}`

Returns

Name	Type	Description
	A table in the specified format.

Examples

For more examples, take a look at the regression tables and summary statistics vignette.

import pyfixest as pf

# load data
df = pf.get_data()
pf.dtable(df, vars = ["Y", "X1", "X2", "f1"])

	N	Mean	Std. Dev.
Y	999	-0.13	2.30
X1	999	1.04	0.81
X2	1000	-0.13	3.05
f1	999	14.21	8.70