bigframes.pandas.crosstab

bigframes.pandas.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, *, session: Session | None = None) → DataFrame

Compute a simple cross tabulation of two (or more) factors.

By default, computes a frequency table of the factors unless an array of values and an aggregation function are passed.

Examples:

>>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",
...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
>>> b = np.array(["one", "one", "one", "two", "one", "one",
...               "one", "two", "two", "two", "one"], dtype=object)
>>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",
...               "shiny", "dull", "shiny", "shiny", "shiny"],
...              dtype=object)
>>> bpd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
b    one        two
c   dull shiny dull shiny
a
bar    1     2    1     0
foo    2     2    1     2

[2 rows x 4 columns]

Parameters:

• index (array-like, Series, or list of arrays/Series) – Values to group by in the rows.

• columns (array-like, Series, or list of arrays/Series) – Values to group by in the columns.

• values (array-like, optional) – Array of values to aggregate according to the factors. Requires aggfunc be specified.

• rownames (sequence, default None) – If passed, must match number of row arrays passed.

• colnames (sequence, default None) – If passed, must match number of column arrays passed.

• aggfunc (function, optional) – If specified, requires values be specified as well.

Returns:

Cross tabulation of the data.

Return type:

DataFrame