def crosstab(index, columns, values=None, rownames=None, colnames=None,
aggfunc=None, margins=False, margins_name='All', dropna=True,
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
----------
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.
aggfunc : function, optional
If specified, requires `values` be specified as well
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
margins : boolean, default False
Add row/column margins (subtotals)
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.
.. versionadded:: 0.21.0
dropna : boolean, default True
Do not include columns whose entries are all NaN
normalize : boolean, {'all', 'index', 'columns'}, or {0,1}, default False
Normalize by dividing all values by the sum of values.
- If passed 'all' or `True`, will normalize over all values.
- If passed 'index' will normalize over each row.
- If passed 'columns' will normalize over each column.
- If margins is `True`, will also normalize margin values.
.. versionadded:: 0.18.1
Notes
-----
Any Series passed will have their name attributes used unless row or column
names for the cross-tabulation are specified.
Any input passed containing Categorical data will have **all** of its
categories included in the cross-tabulation, even if the actual data does
not contain any instances of a particular category.
In the event that there aren't overlapping indexes an empty DataFrame will
be returned.
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)
>>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
... # doctest: +NORMALIZE_WHITESPACE
b one two
c dull shiny dull shiny
a
bar 1 2 1 0
foo 2 2 1 2
>>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
>>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
>>> crosstab(foo, bar) # 'c' and 'f' are not represented in the data,
# and will not be shown in the output because
# dropna is True by default. Set 'dropna=False'
# to preserve categories with no data
... # doctest: +SKIP
col_0 d e
row_0
a 1 0
b 0 1
>>> crosstab(foo, bar, dropna=False) # 'c' and 'f' are not represented
# in the data, but they still will be counted
# and shown in the output
... # doctest: +SKIP
col_0 d e f
row_0
a 1 0 0
b 0 1 0
c 0 0 0
Returns
-------
crosstab : DataFrame
"""
index = com.maybe_make_list(index)
columns = com.maybe_make_list(columns)
rownames = _get_names(index, rownames, prefix='row')
colnames = _get_names(columns, colnames, prefix='col')
common_idx = _get_objs_combined_axis(index + columns, intersect=True,
sort=False)
data = {}
data.update(zip(rownames, index))
data.update(zip(colnames, columns))
if values is None and aggfunc is not None:
raise ValueError("aggfunc cannot be used without values.")
if values is not None and aggfunc is None:
raise ValueError("values cannot be used without an aggfunc.")
from pandas import DataFrame
df = DataFrame(data, index=common_idx)
if values is None:
df['__dummy__'] = 0
kwargs = {'aggfunc': len, 'fill_value': 0}
else:
df['__dummy__'] = values
kwargs = {'aggfunc': aggfunc}
table = df.pivot_table('__dummy__', index=rownames, columns=colnames,
margins=margins, margins_name=margins_name,
dropna=dropna, **kwargs)
# Post-process
if normalize is not False:
table = _normalize(table, normalize=normalize, margins=margins,
margins_name=margins_name)
return table
def crosstab(index,
columns,
values=None,
rownames=None,
colnames=None,
aggfunc=None,
margins=False,
margins_name='All',
dropna=True,
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
----------
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
margins : boolean, default False
Add row/column margins (subtotals)
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.
.. versionadded:: 0.21.0
dropna : boolean, default True
Do not include columns whose entries are all NaN
normalize : boolean, {'all', 'index', 'columns'}, or {0,1}, default False
Normalize by dividing all values by the sum of values.
- If passed 'all' or `True`, will normalize over all values.
- If passed 'index' will normalize over each row.
- If passed 'columns' will normalize over each column.
- If margins is `True`, will also normalize margin values.
.. versionadded:: 0.18.1
Returns
-------
crosstab : DataFrame
Notes
-----
Any Series passed will have their name attributes used unless row or column
names for the cross-tabulation are specified.
Any input passed containing Categorical data will have **all** of its
categories included in the cross-tabulation, even if the actual data does
not contain any instances of a particular category.
In the event that there aren't overlapping indexes an empty DataFrame will
be returned.
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)
>>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
... # doctest: +NORMALIZE_WHITESPACE
b one two
c dull shiny dull shiny
a
bar 1 2 1 0
foo 2 2 1 2
>>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
>>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
>>> crosstab(foo, bar) # 'c' and 'f' are not represented in the data,
# and will not be shown in the output because
# dropna is True by default. Set 'dropna=False'
# to preserve categories with no data
... # doctest: +SKIP
col_0 d e
row_0
a 1 0
b 0 1
>>> crosstab(foo, bar, dropna=False) # 'c' and 'f' are not represented
# in the data, but they still will be counted
# and shown in the output
... # doctest: +SKIP
col_0 d e f
row_0
a 1 0 0
b 0 1 0
c 0 0 0
"""
index = com.maybe_make_list(index)
columns = com.maybe_make_list(columns)
rownames = _get_names(index, rownames, prefix='row')
colnames = _get_names(columns, colnames, prefix='col')
common_idx = _get_objs_combined_axis(index + columns,
intersect=True,
sort=False)
data = {}
data.update(zip(rownames, index))
data.update(zip(colnames, columns))
if values is None and aggfunc is not None:
raise ValueError("aggfunc cannot be used without values.")
if values is not None and aggfunc is None:
raise ValueError("values cannot be used without an aggfunc.")
from pandas import DataFrame
df = DataFrame(data, index=common_idx)
if values is None:
df['__dummy__'] = 0
kwargs = {'aggfunc': len, 'fill_value': 0}
else:
df['__dummy__'] = values
kwargs = {'aggfunc': aggfunc}
table = df.pivot_table('__dummy__',
index=rownames,
columns=colnames,
margins=margins,
margins_name=margins_name,
dropna=dropna,
**kwargs)
# Post-process
if normalize is not False:
table = _normalize(table,
normalize=normalize,
margins=margins,
margins_name=margins_name)
return table
def crosstab(
index,
columns,
values=None,
rownames=None,
colnames=None,
aggfunc=None,
margins=False,
margins_name: str = "All",
dropna: bool = True,
normalize=False,
) -> "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.
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.
margins : bool, default False
Add row/column margins (subtotals).
margins_name : str, default 'All'
Name of the row/column that will contain the totals
when margins is True.
.. versionadded:: 0.21.0
dropna : bool, default True
Do not include columns whose entries are all NaN.
normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False
Normalize by dividing all values by the sum of values.
- If passed 'all' or `True`, will normalize over all values.
- If passed 'index' will normalize over each row.
- If passed 'columns' will normalize over each column.
- If margins is `True`, will also normalize margin values.
Returns
-------
DataFrame
Cross tabulation of the data.
See Also
--------
DataFrame.pivot : Reshape data based on column values.
pivot_table : Create a pivot table as a DataFrame.
Notes
-----
Any Series passed will have their name attributes used unless row or column
names for the cross-tabulation are specified.
Any input passed containing Categorical data will have **all** of its
categories included in the cross-tabulation, even if the actual data does
not contain any instances of a particular category.
In the event that there aren't overlapping indexes an empty DataFrame will
be returned.
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)
>>> pd.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
Here 'c' and 'f' are not represented in the data and will not be
shown in the output because dropna is True by default. Set
dropna=False to preserve categories with no data.
>>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
>>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
>>> pd.crosstab(foo, bar)
col_0 d e
row_0
a 1 0
b 0 1
>>> pd.crosstab(foo, bar, dropna=False)
col_0 d e f
row_0
a 1 0 0
b 0 1 0
c 0 0 0
"""
index = com.maybe_make_list(index)
columns = com.maybe_make_list(columns)
rownames = _get_names(index, rownames, prefix="row")
colnames = _get_names(columns, colnames, prefix="col")
common_idx = None
pass_objs = [
x for x in index + columns if isinstance(x, (ABCSeries, ABCDataFrame))
]
if pass_objs:
common_idx = get_objs_combined_axis(pass_objs,
intersect=True,
sort=False)
data: Dict = {}
data.update(zip(rownames, index))
data.update(zip(colnames, columns))
if values is None and aggfunc is not None:
raise ValueError("aggfunc cannot be used without values.")
if values is not None and aggfunc is None:
raise ValueError("values cannot be used without an aggfunc.")
from pandas import DataFrame
df = DataFrame(data, index=common_idx)
if values is None:
df["__dummy__"] = 0
kwargs = {"aggfunc": len, "fill_value": 0}
else:
df["__dummy__"] = values
kwargs = {"aggfunc": aggfunc}
table = df.pivot_table(
"__dummy__",
index=rownames,
columns=colnames,
margins=margins,
margins_name=margins_name,
dropna=dropna,
**kwargs,
)
# Post-process
if normalize is not False:
table = _normalize(table,
normalize=normalize,
margins=margins,
margins_name=margins_name)
return table
