def test_empty(self):
# product of empty factors
X = [[], [0, 1], []]
Y = [[], [], ['a', 'b', 'c']]
for x, y in zip(X, Y):
expected1 = np.array([], dtype=np.asarray(x).dtype)
expected2 = np.array([], dtype=np.asarray(y).dtype)
result1, result2 = cartesian_product([x, y])
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
# empty product (empty input):
result = cartesian_product([])
expected = []
tm.assert_equal(result, expected)
def test_empty(self):
# product of empty factors
X = [[], [0, 1], []]
Y = [[], [], ['a', 'b', 'c']]
for x, y in zip(X, Y):
expected1 = np.array([], dtype=np.asarray(x).dtype)
expected2 = np.array([], dtype=np.asarray(y).dtype)
result1, result2 = cartesian_product([x, y])
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
# empty product (empty input):
result = cartesian_product([])
expected = []
tm.assert_equal(result, expected)
def test_simple(self):
x, y = list('ABC'), [1, 22]
result1, result2 = cartesian_product([x, y])
expected1 = np.array(['A', 'A', 'B', 'B', 'C', 'C'])
expected2 = np.array([1, 22, 1, 22, 1, 22])
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
def test_datetimeindex(self):
# regression test for GitHub issue #6439
# make sure that the ordering on datetimeindex is consistent
x = date_range('2000-01-01', periods=2)
result = [Index(y).day for y in cartesian_product([x, x])]
expected = [np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])]
assert_equal(result, expected)
def test_simple(self):
x, y = list('ABC'), [1, 22]
result1, result2 = cartesian_product([x, y])
expected1 = np.array(['A', 'A', 'B', 'B', 'C', 'C'])
expected2 = np.array([1, 22, 1, 22, 1, 22])
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
def test_datetimeindex(self):
# regression test for GitHub issue #6439
# make sure that the ordering on datetimeindex is consistent
x = date_range('2000-01-01', periods=2)
result = [Index(y).day for y in cartesian_product([x, x])]
expected = [np.array([1, 1, 2, 2]), np.array([1, 2, 1, 2])]
assert_equal(result, expected)
def _apply_1d(self, func, axis):
axis_name = self._get_axis_name(axis)
ax = self._get_axis(axis)
ndim = self.ndim
values = self.values
# iter thru the axes
slice_axis = self._get_axis(axis)
slice_indexer = [0]*(ndim-1)
indexer = np.zeros(ndim, 'O')
indlist = list(range(ndim))
indlist.remove(axis)
indexer[axis] = slice(None, None)
indexer.put(indlist, slice_indexer)
planes = [ self._get_axis(axi) for axi in indlist ]
shape = np.array(self.shape).take(indlist)
# all the iteration points
points = cartesian_product(planes)
results = []
for i in range(np.prod(shape)):
# construct the object
pts = tuple([ p[i] for p in points ])
indexer.put(indlist, slice_indexer)
obj = Series(values[tuple(indexer)],index=slice_axis,name=pts)
result = func(obj)
results.append(result)
# increment the indexer
slice_indexer[-1] += 1
n = -1
while (slice_indexer[n] >= shape[n]) and (n > (1-ndim)):
slice_indexer[n-1] += 1
slice_indexer[n] = 0
n -= 1
# empty object
if not len(results):
return self._constructor(**self._construct_axes_dict())
# same ndim as current
if isinstance(results[0],Series):
arr = np.vstack([ r.values for r in results ])
arr = arr.T.reshape(tuple([len(slice_axis)] + list(shape)))
tranp = np.array([axis]+indlist).argsort()
arr = arr.transpose(tuple(list(tranp)))
return self._constructor(arr,**self._construct_axes_dict())
# ndim-1 shape
results = np.array(results).reshape(shape)
if results.ndim == 2 and axis_name != self._info_axis_name:
results = results.T
planes = planes[::-1]
return self._construct_return_type(results,planes)
def _apply_1d(self, func, axis):
axis_name = self._get_axis_name(axis)
ax = self._get_axis(axis)
ndim = self.ndim
values = self.values
# iter thru the axes
slice_axis = self._get_axis(axis)
slice_indexer = [0]*(ndim-1)
indexer = np.zeros(ndim, 'O')
indlist = list(range(ndim))
indlist.remove(axis)
indexer[axis] = slice(None, None)
indexer.put(indlist, slice_indexer)
planes = [ self._get_axis(axi) for axi in indlist ]
shape = np.array(self.shape).take(indlist)
# all the iteration points
points = cartesian_product(planes)
results = []
for i in range(np.prod(shape)):
# construct the object
pts = tuple([ p[i] for p in points ])
indexer.put(indlist, slice_indexer)
obj = Series(values[tuple(indexer)],index=slice_axis,name=pts)
result = func(obj)
results.append(result)
# increment the indexer
slice_indexer[-1] += 1
n = -1
while (slice_indexer[n] >= shape[n]) and (n > (1-ndim)):
slice_indexer[n-1] += 1
slice_indexer[n] = 0
n -= 1
# empty object
if not len(results):
return self._constructor(**self._construct_axes_dict())
# same ndim as current
if isinstance(results[0],Series):
arr = np.vstack([ r.values for r in results ])
arr = arr.T.reshape(tuple([len(slice_axis)] + list(shape)))
tranp = np.array([axis]+indlist).argsort()
arr = arr.transpose(tuple(list(tranp)))
return self._constructor(arr,**self._construct_axes_dict())
# ndim-1 shape
results = np.array(results).reshape(shape)
if results.ndim == 2 and axis_name != self._info_axis_name:
results = results.T
planes = planes[::-1]
return self._construct_return_type(results,planes)
def test_simple(self):
x, y = list('ABC'), [1, 22]
result = cartesian_product([x, y])
expected = [
np.array(['A', 'A', 'B', 'B', 'C', 'C']),
np.array([1, 22, 1, 22, 1, 22])
]
assert_equal(result, expected)
def test_datetimeindex(self):
# regression test for GitHub issue #6439
# make sure that the ordering on datetimeindex is consistent
x = date_range('2000-01-01', periods=2)
result1, result2 = [Index(y).day for y in cartesian_product([x, x])]
expected1 = np.array([1, 1, 2, 2], dtype=np.int32)
expected2 = np.array([1, 2, 1, 2], dtype=np.int32)
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
def test_datetimeindex(self):
# regression test for GitHub issue #6439
# make sure that the ordering on datetimeindex is consistent
x = date_range('2000-01-01', periods=2)
result1, result2 = [Index(y).day for y in cartesian_product([x, x])]
expected1 = np.array([1, 1, 2, 2], dtype=np.int32)
expected2 = np.array([1, 2, 1, 2], dtype=np.int32)
tm.assert_numpy_array_equal(result1, expected1)
tm.assert_numpy_array_equal(result2, expected2)
def setUp(self):
tm._skip_if_no_scipy()
import scipy.sparse
# SparseSeries inputs used in tests, the tests rely on the order
self.sparse_series = []
s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan])
s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0), (1, 2, 'a', 1),
(1, 1, 'b', 0), (1, 1, 'b', 1),
(2, 1, 'b', 0), (2, 1, 'b', 1)],
names=['A', 'B', 'C', 'D'])
self.sparse_series.append(s.to_sparse())
ss = self.sparse_series[0].copy()
ss.index.names = [3, 0, 1, 2]
self.sparse_series.append(ss)
ss = pd.Series([nan] * 12,
index=cartesian_product(
(range(3), range(4)))).to_sparse()
for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]):
ss[k] = v
self.sparse_series.append(ss)
# results used in tests
self.coo_matrices = []
self.coo_matrices.append(
scipy.sparse.coo_matrix(([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])),
shape=(3, 4)))
self.coo_matrices.append(
scipy.sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),
shape=(3, 4)))
self.coo_matrices.append(
scipy.sparse.coo_matrix(([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])),
shape=(3, 2)))
self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)],
[(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]]
self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]]
def setUp(self):
tm._skip_if_no_scipy()
import scipy.sparse
# SparseSeries inputs used in tests, the tests rely on the order
self.sparse_series = []
s = pd.Series([3.0, nan, 1.0, 2.0, nan, nan])
s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
(1, 2, 'a', 1),
(1, 1, 'b', 0),
(1, 1, 'b', 1),
(2, 1, 'b', 0),
(2, 1, 'b', 1)],
names=['A', 'B', 'C', 'D'])
self.sparse_series.append(s.to_sparse())
ss = self.sparse_series[0].copy()
ss.index.names = [3, 0, 1, 2]
self.sparse_series.append(ss)
ss = pd.Series([
nan
] * 12, index=cartesian_product((range(3), range(4)))).to_sparse()
for k, v in zip([(0, 0), (1, 2), (1, 3)], [3.0, 1.0, 2.0]):
ss[k] = v
self.sparse_series.append(ss)
# results used in tests
self.coo_matrices = []
self.coo_matrices.append(scipy.sparse.coo_matrix(
([3.0, 1.0, 2.0], ([0, 1, 1], [0, 2, 3])), shape=(3, 4)))
self.coo_matrices.append(scipy.sparse.coo_matrix(
([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(3, 4)))
self.coo_matrices.append(scipy.sparse.coo_matrix(
([3.0, 1.0, 2.0], ([0, 1, 1], [0, 0, 1])), shape=(3, 2)))
self.ils = [[(1, 2), (1, 1), (2, 1)], [(1, 1), (1, 2), (2, 1)],
[(1, 2, 'a'), (1, 1, 'b'), (2, 1, 'b')]]
self.jls = [[('a', 0), ('a', 1), ('b', 0), ('b', 1)], [0, 1]]
def pivot_table(data,
values=None,
index=None,
columns=None,
aggfunc='mean',
fill_value=None,
margins=False,
dropna=True):
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in the
pivot table will be stored in MultiIndex objects (hierarchical indexes) on
the index and columns of the result DataFrame
Parameters
----------
data : DataFrame
values : column to aggregate, optional
index : a column, Grouper, array which has the same length as data, or list of them.
Keys to group by on the pivot table index.
If an array is passed, it is being used as the same manner as column values.
columns : a column, Grouper, array which has the same length as data, or list of them.
Keys to group by on the pivot table column.
If an array is passed, it is being used as the same manner as column values.
aggfunc : function, default numpy.mean, or list of functions
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names (inferred
from the function objects themselves)
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
rows : kwarg only alias of index [deprecated]
cols : kwarg only alias of columns [deprecated]
Examples
--------
>>> df
A B C D
0 foo one small 1
1 foo one large 2
2 foo one large 2
3 foo two small 3
4 foo two small 3
5 bar one large 4
6 bar one small 5
7 bar two small 6
8 bar two large 7
>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
small large
foo one 1 4
two 6 NaN
bar one 5 4
two 6 7
Returns
-------
table : DataFrame
"""
index = _convert_by(index)
columns = _convert_by(columns)
if isinstance(aggfunc, list):
pieces = []
keys = []
for func in aggfunc:
table = pivot_table(data,
values=values,
index=index,
columns=columns,
fill_value=fill_value,
aggfunc=func,
margins=margins)
pieces.append(table)
keys.append(func.__name__)
return concat(pieces, keys=keys, axis=1)
keys = index + columns
values_passed = values is not None
if values_passed:
if isinstance(values, (list, tuple)):
values_multi = True
else:
values_multi = False
values = [values]
else:
values = list(data.columns.drop(keys))
if values_passed:
to_filter = []
for x in keys + values:
if isinstance(x, Grouper):
x = x.key
try:
if x in data:
to_filter.append(x)
except TypeError:
pass
if len(to_filter) < len(data.columns):
data = data[to_filter]
grouped = data.groupby(keys)
agged = grouped.agg(aggfunc)
table = agged
if table.index.nlevels > 1:
to_unstack = [
agged.index.names[i] or i for i in range(len(index), len(keys))
]
table = agged.unstack(to_unstack)
if not dropna:
try:
m = MultiIndex.from_arrays(cartesian_product(table.index.levels))
table = table.reindex_axis(m, axis=0)
except AttributeError:
pass # it's a single level
try:
m = MultiIndex.from_arrays(cartesian_product(table.columns.levels))
table = table.reindex_axis(m, axis=1)
except AttributeError:
pass # it's a single level or a series
if isinstance(table, DataFrame):
if isinstance(table.columns, MultiIndex):
table = table.sortlevel(axis=1)
else:
table = table.sort_index(axis=1)
if fill_value is not None:
table = table.fillna(value=fill_value, downcast='infer')
if margins:
table = _add_margins(table,
data,
values,
rows=index,
cols=columns,
aggfunc=aggfunc)
# discard the top level
if values_passed and not values_multi:
table = table[values[0]]
if len(index) == 0 and len(columns) > 0:
table = table.T
return table
def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
fill_value=None, margins=False, dropna=True, **kwarg):
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in the
pivot table will be stored in MultiIndex objects (hierarchical indexes) on
the index and columns of the result DataFrame
Parameters
----------
data : DataFrame
values : column to aggregate, optional
index : list of column names or arrays to group on
Keys to group on the x-axis of the pivot table
columns : list of column names or arrays to group on
Keys to group on the y-axis of the pivot table
aggfunc : function, default numpy.mean, or list of functions
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names (inferred
from the function objects themselves)
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
rows : kwarg only alias of index [deprecated]
cols : kwarg only alias of columns [deprecated]
Examples
--------
>>> df
A B C D
0 foo one small 1
1 foo one large 2
2 foo one large 2
3 foo two small 3
4 foo two small 3
5 bar one large 4
6 bar one small 5
7 bar two small 6
8 bar two large 7
>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
small large
foo one 1 4
two 6 NaN
bar one 5 4
two 6 7
Returns
-------
table : DataFrame
"""
# Parse old-style keyword arguments
rows = kwarg.pop('rows', None)
if rows is not None:
warnings.warn("rows is deprecated, use index", FutureWarning)
if index is None:
index = rows
else:
msg = "Can only specify either 'rows' or 'index'"
raise TypeError(msg)
cols = kwarg.pop('cols', None)
if cols is not None:
warnings.warn("cols is deprecated, use columns", FutureWarning)
if columns is None:
columns = cols
else:
msg = "Can only specify either 'cols' or 'columns'"
raise TypeError(msg)
if kwarg:
raise TypeError("Unexpected argument(s): %s" % kwarg.keys())
index = _convert_by(index)
columns = _convert_by(columns)
if isinstance(aggfunc, list):
pieces = []
keys = []
for func in aggfunc:
table = pivot_table(data, values=values, index=index, columns=columns,
fill_value=fill_value, aggfunc=func,
margins=margins)
pieces.append(table)
keys.append(func.__name__)
return concat(pieces, keys=keys, axis=1)
keys = index + columns
values_passed = values is not None
if values_passed:
if isinstance(values, (list, tuple)):
values_multi = True
else:
values_multi = False
values = [values]
else:
values = list(data.columns.drop(keys))
if values_passed:
to_filter = []
for x in keys + values:
try:
if x in data:
to_filter.append(x)
except TypeError:
pass
if len(to_filter) < len(data.columns):
data = data[to_filter]
grouped = data.groupby(keys)
agged = grouped.agg(aggfunc)
table = agged
if table.index.nlevels > 1:
to_unstack = [agged.index.names[i]
for i in range(len(index), len(keys))]
table = agged.unstack(to_unstack)
if not dropna:
try:
m = MultiIndex.from_arrays(cartesian_product(table.index.levels))
table = table.reindex_axis(m, axis=0)
except AttributeError:
pass # it's a single level
try:
m = MultiIndex.from_arrays(cartesian_product(table.columns.levels))
table = table.reindex_axis(m, axis=1)
except AttributeError:
pass # it's a single level or a series
if isinstance(table, DataFrame):
if isinstance(table.columns, MultiIndex):
table = table.sortlevel(axis=1)
else:
table = table.sort_index(axis=1)
if fill_value is not None:
table = table.fillna(value=fill_value, downcast='infer')
if margins:
table = _add_margins(table, data, values, rows=index,
cols=columns, aggfunc=aggfunc)
# discard the top level
if values_passed and not values_multi:
table = table[values[0]]
if len(index) == 0 and len(columns) > 0:
table = table.T
return table
def test_simple(self):
x, y = list('ABC'), [1, 22]
result = cartesian_product([x, y])
expected = [np.array(['A', 'A', 'B', 'B', 'C', 'C']),
np.array([ 1, 22, 1, 22, 1, 22])]
assert_equal(result, expected)
def pivot_table(data, values=None, index=None, columns=None, aggfunc='mean',
fill_value=None, margins=False, dropna=True,
margins_name='All'):
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in the
pivot table will be stored in MultiIndex objects (hierarchical indexes) on
the index and columns of the result DataFrame
Parameters
----------
data : DataFrame
values : column to aggregate, optional
index : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The list
can contain any of the other types (except list).
Keys to group by on the pivot table index. If an array is passed, it
is being used as the same manner as column values.
columns : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The list
can contain any of the other types (except list).
Keys to group by on the pivot table column. If an array is passed, it
is being used as the same manner as column values.
aggfunc : function or list of functions, default numpy.mean
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names (inferred
from the function objects themselves)
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.
Examples
--------
>>> df
A B C D
0 foo one small 1
1 foo one large 2
2 foo one large 2
3 foo two small 3
4 foo two small 3
5 bar one large 4
6 bar one small 5
7 bar two small 6
8 bar two large 7
>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
small large
foo one 1 4
two 6 NaN
bar one 5 4
two 6 7
Returns
-------
table : DataFrame
"""
index = _convert_by(index)
columns = _convert_by(columns)
if isinstance(aggfunc, list):
pieces = []
keys = []
for func in aggfunc:
table = pivot_table(data, values=values, index=index,
columns=columns,
fill_value=fill_value, aggfunc=func,
margins=margins)
pieces.append(table)
keys.append(func.__name__)
return concat(pieces, keys=keys, axis=1)
keys = index + columns
values_passed = values is not None
if values_passed:
if com.is_list_like(values):
values_multi = True
values = list(values)
else:
values_multi = False
values = [values]
else:
values = list(data.columns.drop(keys))
if values_passed:
to_filter = []
for x in keys + values:
if isinstance(x, Grouper):
x = x.key
try:
if x in data:
to_filter.append(x)
except TypeError:
pass
if len(to_filter) < len(data.columns):
data = data[to_filter]
grouped = data.groupby(keys)
agged = grouped.agg(aggfunc)
table = agged
if table.index.nlevels > 1:
to_unstack = [agged.index.names[i] or i
for i in range(len(index), len(keys))]
table = agged.unstack(to_unstack)
if not dropna:
try:
m = MultiIndex.from_arrays(cartesian_product(table.index.levels))
table = table.reindex_axis(m, axis=0)
except AttributeError:
pass # it's a single level
try:
m = MultiIndex.from_arrays(cartesian_product(table.columns.levels))
table = table.reindex_axis(m, axis=1)
except AttributeError:
pass # it's a single level or a series
if isinstance(table, DataFrame):
if isinstance(table.columns, MultiIndex):
table = table.sortlevel(axis=1)
else:
table = table.sort_index(axis=1)
if fill_value is not None:
table = table.fillna(value=fill_value, downcast='infer')
if margins:
table = _add_margins(table, data, values, rows=index,
cols=columns, aggfunc=aggfunc,
margins_name=margins_name)
# discard the top level
if values_passed and not values_multi:
table = table[values[0]]
if len(index) == 0 and len(columns) > 0:
table = table.T
return table
def pivot_table(data,
values=None,
index=None,
columns=None,
aggfunc='mean',
fill_value=None,
margins=False,
dropna=True,
margins_name='All'):
"""
Create a spreadsheet-style pivot table as a DataFrame. The levels in the
pivot table will be stored in MultiIndex objects (hierarchical indexes) on
the index and columns of the result DataFrame
Parameters
----------
data : DataFrame
values : column to aggregate, optional
index : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The list
can contain any of the other types (except list).
Keys to group by on the pivot table index. If an array is passed, it
is being used as the same manner as column values.
columns : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The list
can contain any of the other types (except list).
Keys to group by on the pivot table column. If an array is passed, it
is being used as the same manner as column values.
aggfunc : function or list of functions, default numpy.mean
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names (inferred
from the function objects themselves)
fill_value : scalar, default None
Value to replace missing values with
margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
dropna : boolean, default True
Do not include columns whose entries are all NaN
margins_name : string, default 'All'
Name of the row / column that will contain the totals
when margins is True.
Examples
--------
>>> df
A B C D
0 foo one small 1
1 foo one large 2
2 foo one large 2
3 foo two small 3
4 foo two small 3
5 bar one large 4
6 bar one small 5
7 bar two small 6
8 bar two large 7
>>> table = pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
small large
foo one 1 4
two 6 NaN
bar one 5 4
two 6 7
Returns
-------
table : DataFrame
See also
--------
DataFrame.pivot : pivot without aggregation that can handle
non-numeric data
"""
index = _convert_by(index)
columns = _convert_by(columns)
if isinstance(aggfunc, list):
pieces = []
keys = []
for func in aggfunc:
table = pivot_table(data,
values=values,
index=index,
columns=columns,
fill_value=fill_value,
aggfunc=func,
margins=margins,
margins_name=margins_name)
pieces.append(table)
keys.append(func.__name__)
return concat(pieces, keys=keys, axis=1)
keys = index + columns
values_passed = values is not None
if values_passed:
if is_list_like(values):
values_multi = True
values = list(values)
else:
values_multi = False
values = [values]
# GH14938 Make sure value labels are in data
for i in values:
if i not in data:
raise KeyError(i)
to_filter = []
for x in keys + values:
if isinstance(x, Grouper):
x = x.key
try:
if x in data:
to_filter.append(x)
except TypeError:
pass
if len(to_filter) < len(data.columns):
data = data[to_filter]
else:
values = data.columns
for key in keys:
try:
values = values.drop(key)
except (TypeError, ValueError):
pass
values = list(values)
grouped = data.groupby(keys)
agged = grouped.agg(aggfunc)
table = agged
if table.index.nlevels > 1:
to_unstack = [
agged.index.names[i] or i for i in range(len(index), len(keys))
]
table = agged.unstack(to_unstack)
if not dropna:
try:
m = MultiIndex.from_arrays(cartesian_product(table.index.levels),
names=table.index.names)
table = table.reindex_axis(m, axis=0)
except AttributeError:
pass # it's a single level
try:
m = MultiIndex.from_arrays(cartesian_product(table.columns.levels),
names=table.columns.names)
table = table.reindex_axis(m, axis=1)
except AttributeError:
pass # it's a single level or a series
if isinstance(table, DataFrame):
if isinstance(table.columns, MultiIndex):
table = table.sortlevel(axis=1)
else:
table = table.sort_index(axis=1)
if fill_value is not None:
table = table.fillna(value=fill_value, downcast='infer')
if margins:
if dropna:
data = data[data.notnull().all(axis=1)]
table = _add_margins(table,
data,
values,
rows=index,
cols=columns,
aggfunc=aggfunc,
margins_name=margins_name)
# discard the top level
if values_passed and not values_multi and not table.empty:
table = table[values[0]]
if len(index) == 0 and len(columns) > 0:
table = table.T
return table
def test_simple(self):
x, y = list("ABC"), [1, 22]
result = cartesian_product([x, y])
expected = [np.array(["A", "A", "B", "B", "C", "C"]), np.array([1, 22, 1, 22, 1, 22])]
assert_equal(result, expected)