/usr/lib/python3/dist-packages/pandas/tools/pivot.py is in python3-pandas 0.13.1-2ubuntu2.
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from pandas import Series, DataFrame
from pandas.core.index import MultiIndex
from pandas.tools.merge import concat
from pandas.tools.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, rows=None, cols=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
rows : list of column names or arrays to group on
Keys to group on the x-axis of the pivot table
cols : 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
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', rows=['A', 'B'],
... cols=['C'], aggfunc=np.sum)
>>> table
small large
foo one 1 4
two 6 NaN
bar one 5 4
two 6 7
Returns
-------
table : DataFrame
"""
rows = _convert_by(rows)
cols = _convert_by(cols)
if isinstance(aggfunc, list):
pieces = []
keys = []
for func in aggfunc:
table = pivot_table(data, values=values, rows=rows, cols=cols,
fill_value=fill_value, aggfunc=func,
margins=margins)
pieces.append(table)
keys.append(func.__name__)
return concat(pieces, keys=keys, axis=1)
keys = rows + cols
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(rows), 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=rows,
cols=cols, aggfunc=aggfunc)
# discard the top level
if values_passed and not values_multi:
table = table[values[0]]
if len(rows) == 0 and len(cols) > 0:
table = table.T
return table
DataFrame.pivot_table = pivot_table
def _add_margins(table, data, values, rows, cols, aggfunc):
grand_margin = _compute_grand_margin(data, values, aggfunc)
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.
row_key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All'
return table.append(Series({row_key: grand_margin['All']}))
if values:
marginal_result_set = _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin)
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)
if not isinstance(marginal_result_set, tuple):
return marginal_result_set
result, margin_keys, row_margin = marginal_result_set
key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All'
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
result = result.append(margin_dummy)
result.index.names = row_names
return result
def _compute_grand_margin(data, values, aggfunc):
if values:
grand_margin = {}
for k, v in data[values].iteritems():
try:
if isinstance(aggfunc, compat.string_types):
grand_margin[k] = getattr(v, aggfunc)()
else:
grand_margin[k] = aggfunc(v)
except TypeError:
pass
return grand_margin
else:
return {'All': aggfunc(data.index)}
def _generate_marginal_results(table, data, values, rows, cols, aggfunc, grand_margin):
if len(cols) > 0:
# need to "interleave" the margins
table_pieces = []
margin_keys = []
def _all_key(key):
return (key, 'All') + ('',) * (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)
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):
if len(cols) > 0:
# need to "interleave" the margins
margin_keys = []
def _all_key():
if len(cols) == 1:
return 'All'
return ('All', ) + ('', ) * (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 (np.isscalar(by) or isinstance(by, (np.ndarray, Series))
or hasattr(by, '__call__')):
by = [by]
else:
by = list(by)
return by
def crosstab(rows, cols, values=None, rownames=None, colnames=None,
aggfunc=None, margins=False, dropna=True):
"""
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
----------
rows : array-like, Series, or list of arrays/Series
Values to group by in the rows
cols : 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
aggfunc : function, optional
If no values array is passed, computes a frequency table
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
Notes
-----
Any Series passed will have their name attributes used unless row or column
names for the cross-tabulation are specified
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
Returns
-------
crosstab : DataFrame
"""
rows = com._maybe_make_list(rows)
cols = com._maybe_make_list(cols)
rownames = _get_names(rows, rownames, prefix='row')
colnames = _get_names(cols, colnames, prefix='col')
data = {}
data.update(zip(rownames, rows))
data.update(zip(colnames, cols))
if values is None:
df = DataFrame(data)
df['__dummy__'] = 0
table = df.pivot_table('__dummy__', rows=rownames, cols=colnames,
aggfunc=len, margins=margins, dropna=dropna)
return table.fillna(0).astype(np.int64)
else:
data['__dummy__'] = values
df = DataFrame(data)
table = df.pivot_table('__dummy__', rows=rownames, cols=colnames,
aggfunc=aggfunc, margins=margins, dropna=dropna)
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
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