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DOC: frame.py doctest fixing (pandas-dev#25097)
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@stijnvanhoey @Pingviinituutti
stijnvanhoey authored and Pingviinituutti committed Feb 28, 2019
1 parent 6cb8e1e commit e53b7d6
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2 changes: 1 addition & 1 deletion ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -206,7 +206,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then

MSG='Doctests frame.py' ; echo $MSG
pytest -q --doctest-modules pandas/core/frame.py \
-k"-axes -combine -itertuples -join -pivot_table -query -reindex -reindex_axis -round"
-k" -itertuples -join -reindex -reindex_axis -round"
RET=$(($RET + $?)) ; echo $MSG "DONE"

MSG='Doctests series.py' ; echo $MSG
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163 changes: 102 additions & 61 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -483,7 +483,7 @@ def axes(self):
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.axes
[RangeIndex(start=0, stop=2, step=1), Index(['coll', 'col2'],
[RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
dtype='object')]
"""
return [self.index, self.columns]
Expand Down Expand Up @@ -3016,28 +3016,30 @@ def query(self, expr, inplace=False, **kwargs):
Parameters
----------
expr : string
expr : str
The query string to evaluate. You can refer to variables
in the environment by prefixing them with an '@' character like
``@a + b``.
inplace : bool
Whether the query should modify the data in place or return
a modified copy
.. versionadded:: 0.18.0
kwargs : dict
a modified copy.
**kwargs
See the documentation for :func:`pandas.eval` for complete details
on the keyword arguments accepted by :meth:`DataFrame.query`.
.. versionadded:: 0.18.0
Returns
-------
q : DataFrame
DataFrame
DataFrame resulting from the provided query expression.
See Also
--------
pandas.eval
DataFrame.eval
eval : Evaluate a string describing operations on
DataFrame columns.
DataFrame.eval : Evaluate a string describing operations on
DataFrame columns.
Notes
-----
Expand Down Expand Up @@ -3076,9 +3078,23 @@ def query(self, expr, inplace=False, **kwargs):
Examples
--------
>>> df = pd.DataFrame(np.random.randn(10, 2), columns=list('ab'))
>>> df.query('a > b')
>>> df[df.a > df.b] # same result as the previous expression
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.query('A > B')
A B
4 5 2
The previous expression is equivalent to
>>> df[df.A > df.B]
A B
4 5 2
"""
inplace = validate_bool_kwarg(inplace, 'inplace')
if not isinstance(expr, compat.string_types):
Expand Down Expand Up @@ -5142,8 +5158,7 @@ def _combine_const(self, other, func):

def combine(self, other, func, fill_value=None, overwrite=True):
"""
Perform column-wise combine with another DataFrame based on a
passed function.
Perform column-wise combine with another DataFrame.
Combines a DataFrame with `other` DataFrame using `func`
to element-wise combine columns. The row and column indexes of the
Expand All @@ -5159,13 +5174,14 @@ def combine(self, other, func, fill_value=None, overwrite=True):
fill_value : scalar value, default None
The value to fill NaNs with prior to passing any column to the
merge func.
overwrite : boolean, default True
overwrite : bool, default True
If True, columns in `self` that do not exist in `other` will be
overwritten with NaNs.
Returns
-------
result : DataFrame
DataFrame
Combination of the provided DataFrames.
See Also
--------
Expand Down Expand Up @@ -5209,15 +5225,15 @@ def combine(self, other, func, fill_value=None, overwrite=True):
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 NaN
A B
0 0 -5.0
1 0 3.0
Example that demonstrates the use of `overwrite` and behavior when
the axis differ between the dataframes.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1],}, index=[1, 2])
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
A B C
0 NaN NaN NaN
Expand All @@ -5232,7 +5248,7 @@ def combine(self, other, func, fill_value=None, overwrite=True):
Demonstrating the preference of the passed in dataframe.
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1],}, index=[1, 2])
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
A B C
0 0.0 NaN NaN
Expand Down Expand Up @@ -5716,19 +5732,19 @@ def pivot(self, index=None, columns=None, values=None):
This first example aggregates values by taking the sum.
>>> table = pivot_table(df, values='D', index=['A', 'B'],
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
C large small
A B
bar one 4 5
two 7 6
foo one 4 1
two NaN 6
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
We can also fill missing values using the `fill_value` parameter.
>>> table = pivot_table(df, values='D', index=['A', 'B'],
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C large small
Expand All @@ -5740,12 +5756,11 @@ def pivot(self, index=None, columns=None, values=None):
The next example aggregates by taking the mean across multiple columns.
>>> table = pivot_table(df, values=['D', 'E'], index=['A', 'C'],
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': np.mean})
>>> table
D E
mean mean
D E
A C
bar large 5.500000 7.500000
small 5.500000 8.500000
Expand All @@ -5755,17 +5770,17 @@ def pivot(self, index=None, columns=None, values=None):
We can also calculate multiple types of aggregations for any given
value column.
>>> table = pivot_table(df, values=['D', 'E'], index=['A', 'C'],
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': [min, max, np.mean]})
>>> table
D E
mean max mean min
D E
mean max mean min
A C
bar large 5.500000 9 7.500000 6
small 5.500000 9 8.500000 8
foo large 2.000000 5 4.500000 4
small 2.333333 6 4.333333 2
bar large 5.500000 9.0 7.500000 6.0
small 5.500000 9.0 8.500000 8.0
foo large 2.000000 5.0 4.500000 4.0
small 2.333333 6.0 4.333333 2.0
"""

@Substitution('')
Expand Down Expand Up @@ -6903,41 +6918,67 @@ def round(self, decimals=0, *args, **kwargs):
columns not included in `decimals` will be left as is. Elements
of `decimals` which are not columns of the input will be
ignored.
*args
Additional keywords have no effect but might be accepted for
compatibility with numpy.
**kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
DataFrame :
A DataFrame with the affected columns rounded to the specified
number of decimal places.
See Also
--------
numpy.around
Series.round
numpy.around : Round a numpy array to the given number of decimals.
Series.round : Round a Series to the given number of decimals.
Examples
--------
>>> df = pd.DataFrame(np.random.random([3, 3]),
... columns=['A', 'B', 'C'], index=['first', 'second', 'third'])
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
... columns=['dogs', 'cats'])
>>> df
A B C
first 0.028208 0.992815 0.173891
second 0.038683 0.645646 0.577595
third 0.877076 0.149370 0.491027
>>> df.round(2)
A B C
first 0.03 0.99 0.17
second 0.04 0.65 0.58
third 0.88 0.15 0.49
>>> df.round({'A': 1, 'C': 2})
A B C
first 0.0 0.992815 0.17
second 0.0 0.645646 0.58
third 0.9 0.149370 0.49
>>> decimals = pd.Series([1, 0, 2], index=['A', 'B', 'C'])
dogs cats
0 0.21 0.32
1 0.01 0.67
2 0.66 0.03
3 0.21 0.18
By providing an integer each column is rounded to the same number
of decimal places
>>> df.round(1)
dogs cats
0 0.2 0.3
1 0.0 0.7
2 0.7 0.0
3 0.2 0.2
With a dict, the number of places for specific columns can be
specfified with the column names as key and the number of decimal
places as value
>>> df.round({'dogs': 1, 'cats': 0})
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
Using a Series, the number of places for specific columns can be
specfified with the column names as index and the number of
decimal places as value
>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
A B C
first 0.0 1 0.17
second 0.0 1 0.58
third 0.9 0 0.49
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
"""
from pandas.core.reshape.concat import concat

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23 changes: 15 additions & 8 deletions pandas/core/window.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2117,24 +2117,25 @@ def _constructor(self):

class EWM(_Rolling):
r"""
Provides exponential weighted functions.
Provide exponential weighted functions.
.. versionadded:: 0.18.0
Parameters
----------
com : float, optional
Specify decay in terms of center of mass,
:math:`\alpha = 1 / (1 + com),\text{ for } com \geq 0`
:math:`\alpha = 1 / (1 + com),\text{ for } com \geq 0`.
span : float, optional
Specify decay in terms of span,
:math:`\alpha = 2 / (span + 1),\text{ for } span \geq 1`
:math:`\alpha = 2 / (span + 1),\text{ for } span \geq 1`.
halflife : float, optional
Specify decay in terms of half-life,
:math:`\alpha = 1 - exp(log(0.5) / halflife),\text{ for } halflife > 0`
:math:`\alpha = 1 - exp(log(0.5) / halflife),\text{ for }
halflife > 0`.
alpha : float, optional
Specify smoothing factor :math:`\alpha` directly,
:math:`0 < \alpha \leq 1`
:math:`0 < \alpha \leq 1`.
.. versionadded:: 0.18.0
Expand All @@ -2143,14 +2144,19 @@ class EWM(_Rolling):
(otherwise result is NA).
adjust : bool, default True
Divide by decaying adjustment factor in beginning periods to account
for imbalance in relative weightings (viewing EWMA as a moving average)
for imbalance in relative weightings
(viewing EWMA as a moving average).
ignore_na : bool, default False
Ignore missing values when calculating weights;
specify True to reproduce pre-0.15.0 behavior
specify True to reproduce pre-0.15.0 behavior.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. The value 0 identifies the rows, and 1
identifies the columns.
Returns
-------
a Window sub-classed for the particular operation
DataFrame
A Window sub-classed for the particular operation.
See Also
--------
Expand Down Expand Up @@ -2188,6 +2194,7 @@ class EWM(_Rolling):
--------
>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
B
0 0.0
1 1.0
Expand Down

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