diff --git a/doc/source/release.rst b/doc/source/release.rst index 90f7585ba7ab9..4a4040d638141 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -102,6 +102,7 @@ pandas 0.13 set _ref_locs (:issue:`4403`) - Fixed an issue where hist subplots were being overwritten when they were called using the top level matplotlib API (:issue:`4408`) + - Fixed (:issue:`3334`). Margins did not compute if values is the index. pandas 0.12 =========== diff --git a/doc/source/v0.13.0.txt b/doc/source/v0.13.0.txt index 0a62322fa2996..9623339f42b07 100644 --- a/doc/source/v0.13.0.txt +++ b/doc/source/v0.13.0.txt @@ -11,35 +11,12 @@ API changes - ``read_excel`` now supports an integer in its ``sheetname`` argument giving the index of the sheet to read in (:issue:`4301`). - - Text parser now treats anything that reads like inf ("inf", "Inf", "-Inf", - "iNf", etc.) as infinity. (:issue:`4220`, :issue:`4219`), affecting - ``read_table``, ``read_csv``, etc. - - ``pandas`` now is Python 2/3 compatible without the need for 2to3 thanks to - @jtratner. As a result, pandas now uses iterators more extensively. This - also led to the introduction of substantive parts of the Benjamin - Peterson's ``six`` library into compat. (:issue:`4384`, :issue:`4375`, - :issue:`4372`) - - ``pandas.util.compat`` and ``pandas.util.py3compat`` have been merged into - ``pandas.compat``. ``pandas.compat`` now includes many functions allowing - 2/3 compatibility. It contains both list and iterator versions of range, - filter, map and zip, plus other necessary elements for Python 3 - compatibility. ``lmap``, ``lzip``, ``lrange`` and ``lfilter`` all produce - lists instead of iterators, for compatibility with ``numpy``, subscripting - and ``pandas`` constructors.(:issue:`4384`, :issue:`4375`, :issue:`4372`) - - deprecated ``iterkv``, which will be removed in a future release (was just - an alias of iteritems used to get around ``2to3``'s changes). - (:issue:`4384`, :issue:`4375`, :issue:`4372`) - - ``Series.get`` with negative indexers now returns the same as ``[]`` (:issue:`4390`) Enhancements ~~~~~~~~~~~~ - ``read_html`` now raises a ``URLError`` instead of catching and raising a ``ValueError`` (:issue:`4303`, :issue:`4305`) - - Added a test for ``read_clipboard()`` and ``to_clipboard()`` (:issue:`4282`) - - Clipboard functionality now works with PySide (:issue:`4282`) - - Added a more informative error message when plot arguments contain - overlapping color and style arguments (:issue:`4402`) Bug Fixes ~~~~~~~~~ @@ -52,22 +29,9 @@ Bug Fixes - Fixed bug in ``PeriodIndex.map`` where using ``str`` would return the str representation of the index (:issue:`4136`) + + - Fixed (:issue:`3334`). Margins did not compute if values is the index. - - Fixed test failure ``test_time_series_plot_color_with_empty_kwargs`` when - using custom matplotlib default colors (:issue:`4345`) - - - Fix running of stata IO tests. Now uses temporary files to write - (:issue:`4353`) - - - Fixed an issue where ``DataFrame.sum`` was slower than ``DataFrame.mean`` - for integer valued frames (:issue:`4365`) - - - ``read_html`` tests now work with Python 2.6 (:issue:`4351`) - - - Fixed bug where ``network`` testing was throwing ``NameError`` because a - local variable was undefined (:issue:`4381`) - - - Suppressed DeprecationWarning associated with internal calls issued by repr() (:issue:`4391`) See the :ref:`full release notes ` or issue tracker diff --git a/pandas/tools/pivot.py b/pandas/tools/pivot.py index effcc3ff7695f..8171b4e019954 100644 --- a/pandas/tools/pivot.py +++ b/pandas/tools/pivot.py @@ -2,11 +2,8 @@ from pandas import Series, DataFrame from pandas.core.index import MultiIndex -from pandas.core.reshape import _unstack_multiple 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 @@ -149,17 +146,64 @@ def pivot_table(data, values=None, rows=None, cols=None, aggfunc='mean', DataFrame.pivot_table = pivot_table -def _add_margins(table, data, values, rows=None, cols=None, aggfunc=np.mean): - grand_margin = {} - for k, v in compat.iteritems(data[values]): - try: - if isinstance(aggfunc, compat.string_types): - grand_margin[k] = getattr(v, aggfunc)() - else: - grand_margin[k] = aggfunc(v) - except TypeError: - pass +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, basestring): + 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, basestring): + 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 = [] @@ -198,28 +242,48 @@ def _all_key(key): row_margin = row_margin.stack() # slight hack - new_order = [len(cols)] + lrange(len(cols)) + new_order = [len(cols)] + range(len(cols)) row_margin.index = row_margin.index.reorder_levels(new_order) else: row_margin = Series(np.nan, index=result.columns) - key = ('All',) + ('',) * (len(rows) - 1) if len(rows) > 1 else 'All' + return result, margin_keys, row_margin - row_margin = row_margin.reindex(result.columns) - # populate grand margin - for k in margin_keys: - if len(cols) > 0: - row_margin[k] = grand_margin[k[0]] - else: - row_margin[k] = grand_margin[k] - margin_dummy = DataFrame(row_margin, columns=[key]).T +def _generate_marginal_results_without_values(table, data, rows, cols, aggfunc): + if len(cols) > 0: + # need to "interleave" the margins + margin_keys = [] - row_names = result.index.names - result = result.append(margin_dummy) - result.index.names = row_names + def _all_key(): + if len(cols) == 1: + return 'All' + return ('All', ) + ('', ) * (len(cols) - 1) - return result + 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): diff --git a/pandas/tools/tests/test_pivot.py b/pandas/tools/tests/test_pivot.py index 57e7d2f7f6ae9..88fc3008b1e00 100644 --- a/pandas/tools/tests/test_pivot.py +++ b/pandas/tools/tests/test_pivot.py @@ -1,14 +1,11 @@ -import datetime import unittest import numpy as np from numpy.testing import assert_equal -import pandas from pandas import DataFrame, Series, Index, MultiIndex from pandas.tools.merge import concat from pandas.tools.pivot import pivot_table, crosstab -from pandas.compat import range, u, product import pandas.util.testing as tm @@ -75,18 +72,9 @@ def test_pivot_table_dropna(self): pv_col = df.pivot_table('quantity', 'month', ['customer', 'product'], dropna=False) pv_ind = df.pivot_table('quantity', ['customer', 'product'], 'month', dropna=False) - m = MultiIndex.from_tuples([(u('A'), u('a')), - (u('A'), u('b')), - (u('A'), u('c')), - (u('A'), u('d')), - (u('B'), u('a')), - (u('B'), u('b')), - (u('B'), u('c')), - (u('B'), u('d')), - (u('C'), u('a')), - (u('C'), u('b')), - (u('C'), u('c')), - (u('C'), u('d'))]) + m = MultiIndex.from_tuples([(u'A', u'a'), (u'A', u'b'), (u'A', u'c'), (u'A', u'd'), + (u'B', u'a'), (u'B', u'b'), (u'B', u'c'), (u'B', u'd'), + (u'C', u'a'), (u'C', u'b'), (u'C', u'c'), (u'C', u'd')]) assert_equal(pv_col.columns.values, m.values) assert_equal(pv_ind.index.values, m.values) @@ -211,17 +199,20 @@ def _check_output(res, col, rows=['A', 'B'], cols=['C']): # no rows rtable = self.data.pivot_table(cols=['AA', 'BB'], margins=True, aggfunc=np.mean) - tm.assert_isinstance(rtable, Series) + self.assert_(isinstance(rtable, Series)) for item in ['DD', 'EE', 'FF']: gmarg = table[item]['All', ''] self.assertEqual(gmarg, self.data[item].mean()) def test_pivot_integer_columns(self): # caused by upstream bug in unstack + from pandas.util.compat import product + import datetime + import pandas d = datetime.date.min data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'], - [d + datetime.timedelta(i) for i in range(20)], [1.0])) + [d + datetime.timedelta(i) for i in xrange(20)], [1.0])) df = pandas.DataFrame(data) table = df.pivot_table(values=4, rows=[0, 1, 3], cols=[2]) @@ -245,6 +236,9 @@ def test_pivot_no_level_overlap(self): tm.assert_frame_equal(table, expected) def test_pivot_columns_lexsorted(self): + import datetime + import numpy as np + import pandas n = 10000 @@ -296,6 +290,28 @@ def test_pivot_complex_aggfunc(self): tm.assert_frame_equal(result, expected) + def test_margins_no_values_no_cols(self): + # Regression test on pivot table: no values or cols passed. + result = self.data[['A', 'B']].pivot_table(rows=['A', 'B'], aggfunc=len, margins=True) + result_list = result.tolist() + self.assertEqual(sum(result_list[:-1]), result_list[-1]) + + def test_margins_no_values_two_rows(self): + # Regression test on pivot table: no values passed but rows are a multi-index + result = self.data[['A', 'B', 'C']].pivot_table(rows=['A', 'B'], cols='C', aggfunc=len, margins=True) + self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0]) + + def test_margins_no_values_one_row_one_col(self): + # Regression test on pivot table: no values passed but row and col defined + result = self.data[['A', 'B']].pivot_table(rows='A', cols='B', aggfunc=len, margins=True) + self.assertEqual(result.All.tolist(), [4.0, 7.0, 11.0]) + + def test_margins_no_values_two_row_two_cols(self): + # Regression test on pivot table: no values passed but rows and cols are multi-indexed + self.data['D'] = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k'] + result = self.data[['A', 'B', 'C', 'D']].pivot_table(rows=['A', 'B'], cols=['C', 'D'], aggfunc=len, margins=True) + self.assertEqual(result.All.tolist(), [3.0, 1.0, 4.0, 3.0, 11.0]) + class TestCrosstab(unittest.TestCase):