summary_cat()

Returns a data table as a Pandas DataFrame that includes the counts and percentages of each category. If there are missing data present (numpy.nan), they will be excluded from the counts. However, if the missing data is coded as a string, it will be included as it’s own category.

Arguments

summary_cat(group1, ascending= False)

group1, can be a Pandas Series or DataFrame with multiple columns stated

ascending, determines the output ascending order or not. Default is
descending.

returns

Pandas DataFrame

Examples

import numpy, pandas, researchpy

numpy.random.seed(123)

df = pandas.DataFrame(numpy.random.randint(2, size= (101, 2)),
                  columns= ['disease', 'treatment'])

# Handles a single Pandas Series
researchpy.summary_cat(df['disease'])

	Variable	Outcome	Count	Percent
0	disease	0	53	52.48
1		1	48	47.52

# Can handle multiple Series, although the output is not pretty
researchpy.summary_cat(df[['disease', 'treatment']])

	Variable	Outcome	Count	Percent
0	disease	0	53	52.48
1		1	48	47.52
2	treatment	1	52	51.49
3		0	49	48.51

# If missing is a string, it will show up as it's own category
df['disease'][0] = ""

researchpy.summary_cat(df['disease'])

	Variable	Outcome	Count	Percent
0	disease	0	52	51.49
1		1	48	47.52
2			1	0.99

# However, is missing is a numpy.nan, it will be excluded from the counts
df['disease'][0] = numpy.nan

researchpy.summary_cat(df['disease'])

	Variable	Outcome	Count	Percent
0	disease	0	52	52.0
1		1	48	48.0

# Results can easily be exported using many methods including the default
# Pandas exporting methods
results = researchpy.summary_cat(df['disease'])

results.to_csv("summary_cats.csv", index= False)

# This is the default, showing for comparison of immediately below
researchpy.summary_cat(df['disease'], ascending= False)

	Variable	Outcome	Count	Percent
0	disease	0	52	52.0
1		1	48	48.0

researchpy.summary_cat(df['disease'], ascending= True)

	Variable	Outcome	Count	Percent
0	disease	1	48	48.0
1		0	52	52.0