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Merge branch 'master' into multi-index-join
2 parents a6c9733 + 699a48b commit f668710

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‎pandas/core/generic.py

+293-20
Original file line numberDiff line numberDiff line change
@@ -7660,7 +7660,6 @@ def truncate(self, before=None, after=None, axis=None, copy=True):
76607660
5 e j
76617661
76627662
For Series, only rows can be truncated.
7663-
76647663
>>> df['A'].truncate(before=2, after=4)
76657664
2 b
76667665
3 c
@@ -8487,19 +8486,21 @@ def compound(self, axis=None, skipna=None, level=None):
84878486
cls.compound = compound
84888487

84898488
cls.cummin = _make_cum_function(
8490-
cls, 'cummin', name, name2, axis_descr, "cumulative minimum",
8489+
cls, 'cummin', name, name2, axis_descr, "minimum",
84918490
lambda y, axis: np.minimum.accumulate(y, axis), "min",
8492-
np.inf, np.nan)
8491+
np.inf, np.nan, _cummin_examples)
84938492
cls.cumsum = _make_cum_function(
8494-
cls, 'cumsum', name, name2, axis_descr, "cumulative sum",
8495-
lambda y, axis: y.cumsum(axis), "sum", 0., np.nan)
8493+
cls, 'cumsum', name, name2, axis_descr, "sum",
8494+
lambda y, axis: y.cumsum(axis), "sum", 0.,
8495+
np.nan, _cumsum_examples)
84968496
cls.cumprod = _make_cum_function(
8497-
cls, 'cumprod', name, name2, axis_descr, "cumulative product",
8498-
lambda y, axis: y.cumprod(axis), "prod", 1., np.nan)
8497+
cls, 'cumprod', name, name2, axis_descr, "product",
8498+
lambda y, axis: y.cumprod(axis), "prod", 1.,
8499+
np.nan, _cumprod_examples)
84998500
cls.cummax = _make_cum_function(
8500-
cls, 'cummax', name, name2, axis_descr, "cumulative max",
8501+
cls, 'cummax', name, name2, axis_descr, "maximum",
85018502
lambda y, axis: np.maximum.accumulate(y, axis), "max",
8502-
-np.inf, np.nan)
8503+
-np.inf, np.nan, _cummax_examples)
85038504

85048505
cls.sum = _make_min_count_stat_function(
85058506
cls, 'sum', name, name2, axis_descr,
@@ -8702,8 +8703,8 @@ def _doc_parms(cls):
87028703
Include only boolean columns. If None, will attempt to use everything,
87038704
then use only boolean data. Not implemented for Series.
87048705
**kwargs : any, default None
8705-
Additional keywords have no affect but might be accepted for
8706-
compatibility with numpy.
8706+
Additional keywords have no effect but might be accepted for
8707+
compatibility with NumPy.
87078708
87088709
Returns
87098710
-------
@@ -8761,24 +8762,296 @@ def _doc_parms(cls):
87618762
"""
87628763

87638764
_cnum_doc = """
8765+
Return cumulative %(desc)s over a DataFrame or Series axis.
8766+
8767+
Returns a DataFrame or Series of the same size containing the cumulative
8768+
%(desc)s.
87648769
87658770
Parameters
87668771
----------
8767-
axis : %(axis_descr)s
8772+
axis : {0 or 'index', 1 or 'columns'}, default 0
8773+
The index or the name of the axis. 0 is equivalent to None or 'index'.
87688774
skipna : boolean, default True
87698775
Exclude NA/null values. If an entire row/column is NA, the result
8770-
will be NA
8776+
will be NA.
8777+
*args, **kwargs :
8778+
Additional keywords have no effect but might be accepted for
8779+
compatibility with NumPy.
87718780
87728781
Returns
87738782
-------
8774-
%(outname)s : %(name1)s\n
8775-
8776-
8783+
%(outname)s : %(name1)s or %(name2)s\n
8784+
%(examples)s
87778785
See also
87788786
--------
87798787
pandas.core.window.Expanding.%(accum_func_name)s : Similar functionality
87808788
but ignores ``NaN`` values.
8789+
%(name2)s.%(accum_func_name)s : Return the %(desc)s over
8790+
%(name2)s axis.
8791+
%(name2)s.cummax : Return cumulative maximum over %(name2)s axis.
8792+
%(name2)s.cummin : Return cumulative minimum over %(name2)s axis.
8793+
%(name2)s.cumsum : Return cumulative sum over %(name2)s axis.
8794+
%(name2)s.cumprod : Return cumulative product over %(name2)s axis.
8795+
"""
8796+
8797+
_cummin_examples = """\
8798+
Examples
8799+
--------
8800+
**Series**
87818801
8802+
>>> s = pd.Series([2, np.nan, 5, -1, 0])
8803+
>>> s
8804+
0 2.0
8805+
1 NaN
8806+
2 5.0
8807+
3 -1.0
8808+
4 0.0
8809+
dtype: float64
8810+
8811+
By default, NA values are ignored.
8812+
8813+
>>> s.cummin()
8814+
0 2.0
8815+
1 NaN
8816+
2 2.0
8817+
3 -1.0
8818+
4 -1.0
8819+
dtype: float64
8820+
8821+
To include NA values in the operation, use ``skipna=False``
8822+
8823+
>>> s.cummin(skipna=False)
8824+
0 2.0
8825+
1 NaN
8826+
2 NaN
8827+
3 NaN
8828+
4 NaN
8829+
dtype: float64
8830+
8831+
**DataFrame**
8832+
8833+
>>> df = pd.DataFrame([[2.0, 1.0],
8834+
... [3.0, np.nan],
8835+
... [1.0, 0.0]],
8836+
... columns=list('AB'))
8837+
>>> df
8838+
A B
8839+
0 2.0 1.0
8840+
1 3.0 NaN
8841+
2 1.0 0.0
8842+
8843+
By default, iterates over rows and finds the minimum
8844+
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
8845+
8846+
>>> df.cummin()
8847+
A B
8848+
0 2.0 1.0
8849+
1 2.0 NaN
8850+
2 1.0 0.0
8851+
8852+
To iterate over columns and find the minimum in each row,
8853+
use ``axis=1``
8854+
8855+
>>> df.cummin(axis=1)
8856+
A B
8857+
0 2.0 1.0
8858+
1 3.0 NaN
8859+
2 1.0 0.0
8860+
"""
8861+
8862+
_cumsum_examples = """\
8863+
Examples
8864+
--------
8865+
**Series**
8866+
8867+
>>> s = pd.Series([2, np.nan, 5, -1, 0])
8868+
>>> s
8869+
0 2.0
8870+
1 NaN
8871+
2 5.0
8872+
3 -1.0
8873+
4 0.0
8874+
dtype: float64
8875+
8876+
By default, NA values are ignored.
8877+
8878+
>>> s.cumsum()
8879+
0 2.0
8880+
1 NaN
8881+
2 7.0
8882+
3 6.0
8883+
4 6.0
8884+
dtype: float64
8885+
8886+
To include NA values in the operation, use ``skipna=False``
8887+
8888+
>>> s.cumsum(skipna=False)
8889+
0 2.0
8890+
1 NaN
8891+
2 NaN
8892+
3 NaN
8893+
4 NaN
8894+
dtype: float64
8895+
8896+
**DataFrame**
8897+
8898+
>>> df = pd.DataFrame([[2.0, 1.0],
8899+
... [3.0, np.nan],
8900+
... [1.0, 0.0]],
8901+
... columns=list('AB'))
8902+
>>> df
8903+
A B
8904+
0 2.0 1.0
8905+
1 3.0 NaN
8906+
2 1.0 0.0
8907+
8908+
By default, iterates over rows and finds the sum
8909+
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
8910+
8911+
>>> df.cumsum()
8912+
A B
8913+
0 2.0 1.0
8914+
1 5.0 NaN
8915+
2 6.0 1.0
8916+
8917+
To iterate over columns and find the sum in each row,
8918+
use ``axis=1``
8919+
8920+
>>> df.cumsum(axis=1)
8921+
A B
8922+
0 2.0 3.0
8923+
1 3.0 NaN
8924+
2 1.0 1.0
8925+
"""
8926+
8927+
_cumprod_examples = """\
8928+
Examples
8929+
--------
8930+
**Series**
8931+
8932+
>>> s = pd.Series([2, np.nan, 5, -1, 0])
8933+
>>> s
8934+
0 2.0
8935+
1 NaN
8936+
2 5.0
8937+
3 -1.0
8938+
4 0.0
8939+
dtype: float64
8940+
8941+
By default, NA values are ignored.
8942+
8943+
>>> s.cumprod()
8944+
0 2.0
8945+
1 NaN
8946+
2 10.0
8947+
3 -10.0
8948+
4 -0.0
8949+
dtype: float64
8950+
8951+
To include NA values in the operation, use ``skipna=False``
8952+
8953+
>>> s.cumprod(skipna=False)
8954+
0 2.0
8955+
1 NaN
8956+
2 NaN
8957+
3 NaN
8958+
4 NaN
8959+
dtype: float64
8960+
8961+
**DataFrame**
8962+
8963+
>>> df = pd.DataFrame([[2.0, 1.0],
8964+
... [3.0, np.nan],
8965+
... [1.0, 0.0]],
8966+
... columns=list('AB'))
8967+
>>> df
8968+
A B
8969+
0 2.0 1.0
8970+
1 3.0 NaN
8971+
2 1.0 0.0
8972+
8973+
By default, iterates over rows and finds the product
8974+
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
8975+
8976+
>>> df.cumprod()
8977+
A B
8978+
0 2.0 1.0
8979+
1 6.0 NaN
8980+
2 6.0 0.0
8981+
8982+
To iterate over columns and find the product in each row,
8983+
use ``axis=1``
8984+
8985+
>>> df.cumprod(axis=1)
8986+
A B
8987+
0 2.0 2.0
8988+
1 3.0 NaN
8989+
2 1.0 0.0
8990+
"""
8991+
8992+
_cummax_examples = """\
8993+
Examples
8994+
--------
8995+
**Series**
8996+
8997+
>>> s = pd.Series([2, np.nan, 5, -1, 0])
8998+
>>> s
8999+
0 2.0
9000+
1 NaN
9001+
2 5.0
9002+
3 -1.0
9003+
4 0.0
9004+
dtype: float64
9005+
9006+
By default, NA values are ignored.
9007+
9008+
>>> s.cummax()
9009+
0 2.0
9010+
1 NaN
9011+
2 5.0
9012+
3 5.0
9013+
4 5.0
9014+
dtype: float64
9015+
9016+
To include NA values in the operation, use ``skipna=False``
9017+
9018+
>>> s.cummax(skipna=False)
9019+
0 2.0
9020+
1 NaN
9021+
2 NaN
9022+
3 NaN
9023+
4 NaN
9024+
dtype: float64
9025+
9026+
**DataFrame**
9027+
9028+
>>> df = pd.DataFrame([[2.0, 1.0],
9029+
... [3.0, np.nan],
9030+
... [1.0, 0.0]],
9031+
... columns=list('AB'))
9032+
>>> df
9033+
A B
9034+
0 2.0 1.0
9035+
1 3.0 NaN
9036+
2 1.0 0.0
9037+
9038+
By default, iterates over rows and finds the maximum
9039+
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
9040+
9041+
>>> df.cummax()
9042+
A B
9043+
0 2.0 1.0
9044+
1 3.0 NaN
9045+
2 3.0 1.0
9046+
9047+
To iterate over columns and find the maximum in each row,
9048+
use ``axis=1``
9049+
9050+
>>> df.cummax(axis=1)
9051+
A B
9052+
0 2.0 2.0
9053+
1 3.0 NaN
9054+
2 1.0 1.0
87829055
"""
87839056

87849057
_any_see_also = """\
@@ -8975,11 +9248,11 @@ def stat_func(self, axis=None, skipna=None, level=None, ddof=1,
89759248

89769249

89779250
def _make_cum_function(cls, name, name1, name2, axis_descr, desc,
8978-
accum_func, accum_func_name, mask_a, mask_b):
9251+
accum_func, accum_func_name, mask_a, mask_b, examples):
89799252
@Substitution(outname=name, desc=desc, name1=name1, name2=name2,
8980-
axis_descr=axis_descr, accum_func_name=accum_func_name)
8981-
@Appender("Return {0} over requested axis.".format(desc) +
8982-
_cnum_doc)
9253+
axis_descr=axis_descr, accum_func_name=accum_func_name,
9254+
examples=examples)
9255+
@Appender(_cnum_doc)
89839256
def cum_func(self, axis=None, skipna=True, *args, **kwargs):
89849257
skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
89859258
if axis is None:

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