hipdf.crosstab

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hipdf.crosstab#

21 min read time

Applies to Linux

hipdf.crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False, margins_name='All', dropna=None, normalize=False)#

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.

Parameters#

indexarray-like, Series, or list of arrays/Series

Values to group by in the rows.

columnsarray-like, Series, or list of arrays/Series

Values to group by in the columns.

valuesarray-like, optional

Array of values to aggregate according to the factors. Requires aggfunc be specified.

rownameslist of str, default None

If passed, must match number of row arrays passed.

colnameslist of str, default None

If passed, must match number of column arrays passed.

aggfuncfunction, optional

If specified, requires values be specified as well.

margins : Not supported margins_name : Not supported dropna : Not supported normalize : Not supported

Returns#

DataFrame

Cross tabulation of the data.

Examples#

>>> a = cudf.Series(["foo", "foo", "foo", "foo", "bar", "bar",
...               "bar", "bar", "foo", "foo", "foo"], dtype=object)
>>> b = cudf.Series(["one", "one", "one", "two", "one", "one",
...               "one", "two", "two", "two", "one"], dtype=object)
>>> c = cudf.Series(["dull", "dull", "shiny", "dull", "dull", "shiny",
...               "shiny", "dull", "shiny", "shiny", "shiny"],
...              dtype=object)
>>> cudf.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
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