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CLN: consolidate Series.quantile and DataFrame.quantile #12469

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107 changes: 10 additions & 97 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,8 @@
import pandas.algos as _algos

from pandas.core.config import get_option
from pandas import _np_version_under1p9

from textwrap import dedent

# ---------------------------------------------------------------------
# Docstring templates
Expand Down Expand Up @@ -4919,108 +4920,20 @@ def f(s):

return data.apply(f, axis=axis)

def quantile(self, q=0.5, axis=0, numeric_only=True,
interpolation='linear'):
"""
Return values at the given quantile over requested axis, a la
numpy.percentile.

Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
0 <= q <= 1, the quantile(s) to compute
axis : {0, 1, 'index', 'columns'} (default 0)
0 or 'index' for row-wise, 1 or 'columns' for column-wise
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
.. versionadded:: 0.18.0
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:

* linear: `i + (j - i) * fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
* lower: `i`.
* higher: `j`.
* nearest: `i` or `j` whichever is nearest.
* midpoint: (`i` + `j`) / 2.

Returns
-------
@Substitution(dedent("""
quantiles : Series or DataFrame
If ``q`` is an array, a DataFrame will be returned where the
index is ``q``, the columns are the columns of self, and the
values are the quantiles.
If ``q`` is a float, a Series will be returned where the
index is the columns of self and the values are the quantiles.

Examples
--------

>>> df = DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
columns=['a', 'b'])
>>> df.quantile(.1)
a 1.3
b 3.7
dtype: float64
>>> df.quantile([.1, .5])
a b
0.1 1.3 3.7
0.5 2.5 55.0
"""
self._check_percentile(q)
per = np.asarray(q) * 100

if not com.is_list_like(per):
per = [per]
q = [q]
squeeze = True
else:
squeeze = False

if _np_version_under1p9:
if interpolation != 'linear':
raise ValueError("Interpolation methods other than linear "
"are not supported in numpy < 1.9")

def f(arr, per, interpolation):
if arr._is_datelike_mixed_type:
values = _values_from_object(arr).view('i8')
else:
values = arr.astype(float)
values = values[notnull(values)]
if len(values) == 0:
return NA
else:
if _np_version_under1p9:
return _quantile(values, per)
else:
return _quantile(values, per, interpolation=interpolation)

data = self._get_numeric_data() if numeric_only else self

axis = self._get_axis_number(axis)

if axis == 1:
data = data.T

# need to know which cols are timestamp going in so that we can
# map timestamp over them after getting the quantile.
is_dt_col = data.dtypes.map(com.is_datetime64_dtype)
is_dt_col = is_dt_col[is_dt_col].index

quantiles = [[f(vals, x, interpolation) for x in per]
for (_, vals) in data.iteritems()]

result = self._constructor(quantiles, index=data._info_axis,
columns=q).T
if len(is_dt_col) > 0:
result[is_dt_col] = result[is_dt_col].applymap(lib.Timestamp)
if squeeze:
if result.shape == (1, 1):
result = result.T.iloc[:, 0] # don't want scalar
else:
result = result.T.squeeze()
result.name = None # For groupby, so it can set an index name
return result
"""))
@Appender(_shared_docs['quantile'])
def quantile(self, q=0.5, axis=0, numeric_only=True,
interpolation='linear'):
return super(DataFrame,
self).quantile(q=q, axis=axis, numeric_only=numeric_only,
interpolation=interpolation)

def to_timestamp(self, freq=None, how='start', axis=0, copy=True):
"""
Expand Down
159 changes: 122 additions & 37 deletions pandas/core/generic.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,10 @@
SettingWithCopyError, SettingWithCopyWarning,
AbstractMethodError)
import pandas.core.nanops as nanops
from numpy import percentile as _quantile
from pandas.util.decorators import Appender, Substitution, deprecate_kwarg
from pandas.core import config
from pandas import _np_version_under1p9

# goal is to be able to define the docs close to function, while still being
# able to share
Expand Down Expand Up @@ -842,43 +844,7 @@ def __contains__(self, key):

@property
def empty(self):
"""True if NDFrame is entirely empty [no items], meaning any of the
axes are of length 0.

Notes
-----
If NDFrame contains only NaNs, it is still not considered empty. See
the example below.

Examples
--------
An example of an actual empty DataFrame. Notice the index is empty:

>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True

If we only have NaNs in our DataFrame, it is not considered empty! We
will need to drop the NaNs to make the DataFrame empty:

>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True

See also
--------
pandas.Series.dropna
pandas.DataFrame.dropna
"""
"""True if NDFrame is entirely empty [no items]"""
return not all(len(self._get_axis(a)) > 0 for a in self._AXIS_ORDERS)

def __nonzero__(self):
Expand Down Expand Up @@ -4110,6 +4076,125 @@ def ranker(data):

return ranker(data)

_shared_docs['quantile'] = ("""
Return values at the given quantile over requested axis, a la
numpy.percentile.

Parameters
----------
q : float or array-like, default 0.5 (50 percentile)
0 <= q <= 1, the quantile(s) to compute
axis : {0, 1, 'index', 'columns'} (default 0)
0 or 'index' for row-wise, 1 or 'columns' for column-wise
numeric_only : boolean, default None
Include only float, int, boolean data. If None, will attempt to use
everything, then use only numeric data
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
.. versionadded:: 0.18.0
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:

* linear: `i + (j - i) * fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
* lower: `i`.
* higher: `j`.
* nearest: `i` or `j` whichever is nearest.
* midpoint: (`i` + `j`) / 2.

Returns
-------
%s

Examples
--------

>>> s = Series([1, 2, 3, 4])
>>> s.quantile(.5)
2.5
>>> s.quantile([.25, .5, .75])
0.25 1.75
0.50 2.50
0.75 3.25
dtype: float64
>>> df = DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
columns=['a', 'b'])
>>> df.quantile(.1)
a 1.3
b 3.7
dtype: float64
>>> df.quantile([.1, .5])
a b
0.1 1.3 3.7
0.5 2.5 55.0
""")

@Appender(_shared_docs['quantile'] % '')
def quantile(self, q=0.5, axis=0, numeric_only=None,
interpolation='linear'):
if self.ndim >= 3:
msg = "quantile is not implemented on on Panel or PanelND objects."
raise NotImplementedError(msg)
elif self.ndim == 1:
result = self.to_frame().quantile(q=q, axis=axis,
numeric_only=numeric_only,
interpolation=interpolation)
if not com.is_list_like(q):
return result.iloc[0]
else:
return result[result.columns[0]]

self._check_percentile(q)
per = np.asarray(q) * 100

if not com.is_list_like(per):
per = [per]
q = [q]
squeeze = True
else:
squeeze = False

if _np_version_under1p9:
if interpolation != 'linear':
raise ValueError("Interpolation methods other than linear "
"are not supported in numpy < 1.9")

def f(arr, per, interpolation):
boxer = com.i8_boxer(arr) \
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you can combine these in 1
its either a needs_i8_conversion (where you need a boxer), or its not (and we upcast to float)

if com.needs_i8_conversion(arr) else lambda x: x
if arr._is_datelike_mixed_type:
values = _values_from_object(arr).view('i8')
else:
values = arr.astype(float)
values = values[notnull(values)]
if len(values) == 0:
return boxer(np.nan)
else:
if _np_version_under1p9:
return boxer(_quantile(values, per))
else:
return boxer(_quantile(values, per,
interpolation=interpolation))

data = self._get_numeric_data() if numeric_only else self

axis = self._get_axis_number(axis)

if axis == 1:
data = data.T

quantiles = [[f(vals, x, interpolation) for x in per]
for (_, vals) in data.iteritems()]

result = self._constructor(quantiles, index=data._info_axis,
columns=q).T
if squeeze:
if result.shape == (1, 1):
result = result.T.iloc[:, 0] # don't want scalar
else:
result = result.T.squeeze()
result.name = None # For groupby, so it can set an index name
return result

_shared_docs['align'] = ("""
Align two object on their axes with the
specified join method for each axis Index
Expand Down
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