# pylint: disable=E1103
from pandas.core.dtypes.common import is_list_like, is_scalar
from pandas.core.reshape.concat import concat
from pandas import Series, DataFrame, MultiIndex, Index
from pandas.core.groupby import Grouper
from pandas.core.reshape.util import cartesian_product
from pandas.compat import range, lrange, zip
from pandas import compat
import pandas.core.common as com
import numpy as np
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):
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 and \
(table.columns.nlevels > 1):
table = table[values[0]]
if len(index) == 0 and len(columns) > 0:
table = table.T
# GH 15193 Makse sure empty columns are removed if dropna=True
if isinstance(table, DataFrame) and dropna:
table = table.dropna(how='all', axis=1)
return table
DataFrame.pivot_table = pivot_table
def _add_margins(table, data, values, rows, cols, aggfunc,
margins_name='All'):
if not isinstance(margins_name, compat.string_types):
raise ValueError('margins_name argument must be a string')
exception_msg = 'Conflicting name "{0}" in margins'.format(margins_name)
for level in table.index.names:
if margins_name in table.index.get_level_values(level):
raise ValueError(exception_msg)
grand_margin = _compute_grand_margin(data, values, aggfunc, margins_name)
# could be passed a Series object with no 'columns'
if hasattr(table, 'columns'):
for level in table.columns.names[1:]:
if margins_name in table.columns.get_level_values(level):
raise ValueError(exception_msg)
if len(rows) > 1:
key = (margins_name,) + ('',) * (len(rows) - 1)
else:
key = margins_name
if not values and isinstance(table, Series):
# If there are no values and the table is a series, then there is only
# one column in the data. Compute grand margin and return it.
return table.append(Series({key: grand_margin[margins_name]}))
if values:
marginal_result_set = _generate_marginal_results(table, data, values,
rows, cols, aggfunc,
grand_margin,
margins_name)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
else:
marginal_result_set = _generate_marginal_results_without_values(
table, data, rows, cols, aggfunc, margins_name)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
row_margin = row_margin.reindex(result.columns)
# populate grand margin
for k in margin_keys:
if isinstance(k, compat.string_types):
row_margin[k] = grand_margin[k]
else:
row_margin[k] = grand_margin[k[0]]
margin_dummy = DataFrame(row_margin, columns=[key]).T
row_names = result.index.names
try:
result = result.append(margin_dummy)
except TypeError:
# we cannot reshape, so coerce the axis
result.index = result.index._to_safe_for_reshape()
result = result.append(margin_dummy)
result.index.names = row_names
return result
def _compute_grand_margin(data, values, aggfunc,
margins_name='All'):
if values:
grand_margin = {}
for k, v in data[values].iteritems():
try:
if isinstance(aggfunc, compat.string_types):
grand_margin[k] = getattr(v, aggfunc)()
elif isinstance(aggfunc, dict):
if isinstance(aggfunc[k], compat.string_types):
grand_margin[k] = getattr(v, aggfunc[k])()
else:
grand_margin[k] = aggfunc[k](v)
else:
grand_margin[k] = aggfunc(v)
except TypeError:
pass
return grand_margin
else:
return {margins_name: aggfunc(data.index)}
def _generate_marginal_results(table, data, values, rows, cols, aggfunc,
grand_margin,
margins_name='All'):
if len(cols) > 0:
# need to "interleave" the margins
table_pieces = []
margin_keys = []
def _all_key(key):
return (key, margins_name) + ('',) * (len(cols) - 1)
if len(rows) > 0:
margin = data[rows + values].groupby(rows).agg(aggfunc)
cat_axis = 1
for key, piece in table.groupby(level=0, axis=cat_axis):
all_key = _all_key(key)
# we are going to mutate this, so need to copy!
piece = piece.copy()
try:
piece[all_key] = margin[key]
except TypeError:
# we cannot reshape, so coerce the axis
piece.set_axis(cat_axis, piece._get_axis(
cat_axis)._to_safe_for_reshape())
piece[all_key] = margin[key]
table_pieces.append(piece)
margin_keys.append(all_key)
else:
margin = grand_margin
cat_axis = 0
for key, piece in table.groupby(level=0, axis=cat_axis):
all_key = _all_key(key)
table_pieces.append(piece)
table_pieces.append(Series(margin[key], index=[all_key]))
margin_keys.append(all_key)
result = concat(table_pieces, axis=cat_axis)
if len(rows) == 0:
return result
else:
result = table
margin_keys = table.columns
if len(cols) > 0:
row_margin = data[cols + values].groupby(cols).agg(aggfunc)
row_margin = row_margin.stack()
# slight hack
new_order = [len(cols)] + lrange(len(cols))
row_margin.index = row_margin.index.reorder_levels(new_order)
else:
row_margin = Series(np.nan, index=result.columns)
return result, margin_keys, row_margin
def _generate_marginal_results_without_values(
table, data, rows, cols, aggfunc,
margins_name='All'):
if len(cols) > 0:
# need to "interleave" the margins
margin_keys = []
def _all_key():
if len(cols) == 1:
return margins_name
return (margins_name, ) + ('', ) * (len(cols) - 1)
if len(rows) > 0:
margin = data[rows].groupby(rows).apply(aggfunc)
all_key = _all_key()
table[all_key] = margin
result = table
margin_keys.append(all_key)
else:
margin = data.groupby(level=0, axis=0).apply(aggfunc)
all_key = _all_key()
table[all_key] = margin
result = table
margin_keys.append(all_key)
return result
else:
result = table
margin_keys = table.columns
if len(cols):
row_margin = data[cols].groupby(cols).apply(aggfunc)
else:
row_margin = Series(np.nan, index=result.columns)
return result, margin_keys, row_margin
def _convert_by(by):
if by is None:
by = []
elif (is_scalar(by) or
isinstance(by, (np.ndarray, Index, Series, Grouper)) or
hasattr(by, '__call__')):
by = [by]
else:
by = list(by)
return by
def crosstab(index, columns, values=None, rownames=None, colnames=None,
aggfunc=None, margins=False, 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)
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
array([foo, foo, foo, foo, bar, bar,
bar, bar, foo, foo, foo], dtype=object)
>>> b
array([one, one, one, two, one, one,
one, two, two, two, one], dtype=object)
>>> c
array([dull, dull, shiny, dull, dull, shiny,
shiny, dull, shiny, shiny, shiny], dtype=object)
>>> 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
>>> 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,
# but they still will be counted in the output
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')
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.")
if values is None:
df = DataFrame(data)
df['__dummy__'] = 0
table = df.pivot_table('__dummy__', index=rownames, columns=colnames,
aggfunc=len, margins=margins, dropna=dropna)
table = table.fillna(0).astype(np.int64)
else:
data['__dummy__'] = values
df = DataFrame(data)
table = df.pivot_table('__dummy__', index=rownames, columns=colnames,
aggfunc=aggfunc, margins=margins, dropna=dropna)
# Post-process
if normalize is not False:
table = _normalize(table, normalize=normalize, margins=margins)
return table
def _normalize(table, normalize, margins):
if not isinstance(normalize, bool) and not isinstance(normalize,
compat.string_types):
axis_subs = {0: 'index', 1: 'columns'}
try:
normalize = axis_subs[normalize]
except KeyError:
raise ValueError("Not a valid normalize argument")
if margins is False:
# Actual Normalizations
normalizers = {
'all': lambda x: x / x.sum(axis=1).sum(axis=0),
'columns': lambda x: x / x.sum(),
'index': lambda x: x.div(x.sum(axis=1), axis=0)
}
normalizers[True] = normalizers['all']
try:
f = normalizers[normalize]
except KeyError:
raise ValueError("Not a valid normalize argument")
table = f(table)
table = table.fillna(0)
elif margins is True:
column_margin = table.loc[:, 'All'].drop('All')
index_margin = table.loc['All', :].drop('All')
table = table.drop('All', axis=1).drop('All')
# to keep index and columns names
table_index_names = table.index.names
table_columns_names = table.columns.names
# Normalize core
table = _normalize(table, normalize=normalize, margins=False)
# Fix Margins
if normalize == 'columns':
column_margin = column_margin / column_margin.sum()
table = concat([table, column_margin], axis=1)
table = table.fillna(0)
elif normalize == 'index':
index_margin = index_margin / index_margin.sum()
table = table.append(index_margin)
table = table.fillna(0)
elif normalize == "all" or normalize is True:
column_margin = column_margin / column_margin.sum()
index_margin = index_margin / index_margin.sum()
index_margin.loc['All'] = 1
table = concat([table, column_margin], axis=1)
table = table.append(index_margin)
table = table.fillna(0)
else:
raise ValueError("Not a valid normalize argument")
table.index.names = table_index_names
table.columns.names = table_columns_names
else:
raise ValueError("Not a valid margins argument")
return table
def _get_names(arrs, names, prefix='row'):
if names is None:
names = []
for i, arr in enumerate(arrs):
if isinstance(arr, Series) and arr.name is not None:
names.append(arr.name)
else:
names.append('%s_%d' % (prefix, i))
else:
if len(names) != len(arrs):
raise AssertionError('arrays and names must have the same length')
if not isinstance(names, list):
names = list(names)
return names