From 1c351f77d3464088a6a1a59999feefd93d020d57 Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Mon, 24 Sep 2018 22:57:45 +0100
Subject: [PATCH 01/10] Add Mask kwarg

---
 pandas/core/nanops.py | 60 +++++++++++++++++++++++--------------------
 1 file changed, 32 insertions(+), 28 deletions(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 7619d47cbc8f9..6f42f04909ff9 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -1,12 +1,16 @@
-import itertools
 import functools
+import itertools
 import operator
 import warnings
 from distutils.version import LooseVersion
 
 import numpy as np
+
+import pandas.core.common as com
 from pandas import compat
 from pandas._libs import tslibs, lib
+from pandas.core.config import get_option
+from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
 from pandas.core.dtypes.common import (
     _get_dtype,
     is_float, is_scalar,
@@ -17,10 +21,7 @@
     is_datetime64_dtype, is_timedelta64_dtype,
     is_datetime_or_timedelta_dtype,
     is_int_or_datetime_dtype, is_any_int_dtype)
-from pandas.core.dtypes.cast import _int64_max, maybe_upcast_putmask
 from pandas.core.dtypes.missing import isna, notna, na_value_for_dtype
-from pandas.core.config import get_option
-import pandas.core.common as com
 
 _BOTTLENECK_INSTALLED = False
 _MIN_BOTTLENECK_VERSION = '1.0.0'
@@ -200,16 +201,18 @@ def _get_fill_value(dtype, fill_value=None, fill_value_typ=None):
 
 
 def _get_values(values, skipna, fill_value=None, fill_value_typ=None,
-                isfinite=False, copy=True):
+                isfinite=False, copy=True, mask=None):
     """ utility to get the values view, mask, dtype
     if necessary copy and mask using the specified fill_value
     copy = True will force the copy
     """
     values = com.values_from_object(values)
-    if isfinite:
-        mask = _isfinite(values)
-    else:
-        mask = isna(values)
+
+    if mask is None:
+        if isfinite:
+            mask = _isfinite(values)
+        else:
+            mask = isna(values)
 
     dtype = values.dtype
     dtype_ok = _na_ok_dtype(dtype)
@@ -315,19 +318,21 @@ def _na_for_min_count(values, axis):
         return result
 
 
-def nanany(values, axis=None, skipna=True):
-    values, mask, dtype, _ = _get_values(values, skipna, False, copy=skipna)
+def nanany(values, axis=None, skipna=True, mask=None):
+    values, mask, dtype, _ = _get_values(values, skipna, False, copy=skipna,
+                                         mask=mask)
     return values.any(axis)
 
 
-def nanall(values, axis=None, skipna=True):
-    values, mask, dtype, _ = _get_values(values, skipna, True, copy=skipna)
+def nanall(values, axis=None, skipna=True, mask=None):
+    values, mask, dtype, _ = _get_values(values, skipna, True, copy=skipna,
+                                         mask=mask)
     return values.all(axis)
 
 
 @disallow('M8')
-def nansum(values, axis=None, skipna=True, min_count=0):
-    values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
+def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
+    values, mask, dtype, dtype_max = _get_values(values, skipna, 0, mask=mask)
     dtype_sum = dtype_max
     if is_float_dtype(dtype):
         dtype_sum = dtype
@@ -341,9 +346,8 @@ def nansum(values, axis=None, skipna=True, min_count=0):
 
 @disallow('M8')
 @bottleneck_switch()
-def nanmean(values, axis=None, skipna=True):
-    values, mask, dtype, dtype_max = _get_values(values, skipna, 0)
-
+def nanmean(values, axis=None, skipna=True, mask=None):
+    values, mask, dtype, dtype_max = _get_values(values, skipna, 0, mask=mask)
     dtype_sum = dtype_max
     dtype_count = np.float64
     if is_integer_dtype(dtype) or is_timedelta64_dtype(dtype):
@@ -367,15 +371,14 @@ def nanmean(values, axis=None, skipna=True):
 
 @disallow('M8')
 @bottleneck_switch()
-def nanmedian(values, axis=None, skipna=True):
-
+def nanmedian(values, axis=None, skipna=True, mask=None):
     def get_median(x):
         mask = notna(x)
         if not skipna and not mask.all():
             return np.nan
         return np.nanmedian(x[mask])
 
-    values, mask, dtype, dtype_max = _get_values(values, skipna)
+    values, mask, dtype, dtype_max = _get_values(values, skipna, mask=mask)
     if not is_float_dtype(values):
         values = values.astype('f8')
         values[mask] = np.nan
@@ -439,7 +442,6 @@ def nanstd(values, axis=None, skipna=True, ddof=1):
 @disallow('M8')
 @bottleneck_switch(ddof=1)
 def nanvar(values, axis=None, skipna=True, ddof=1):
-
     values = com.values_from_object(values)
     dtype = values.dtype
     mask = isna(values)
@@ -465,7 +467,7 @@ def nanvar(values, axis=None, skipna=True, ddof=1):
     avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
     if axis is not None:
         avg = np.expand_dims(avg, axis)
-    sqr = _ensure_numeric((avg - values)**2)
+    sqr = _ensure_numeric((avg - values) ** 2)
     np.putmask(sqr, mask, 0)
     result = sqr.sum(axis=axis, dtype=np.float64) / d
 
@@ -520,22 +522,24 @@ def reduction(values, axis=None, skipna=True):
 
 
 @disallow('O')
-def nanargmax(values, axis=None, skipna=True):
+def nanargmax(values, axis=None, skipna=True, mask=None):
     """
     Returns -1 in the NA case
     """
-    values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf')
+    values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',
+                                         mask=mask)
     result = values.argmax(axis)
     result = _maybe_arg_null_out(result, axis, mask, skipna)
     return result
 
 
 @disallow('O')
-def nanargmin(values, axis=None, skipna=True):
+def nanargmin(values, axis=None, skipna=True, mask=None):
     """
     Returns -1 in the NA case
     """
-    values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf')
+    values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf',
+                                         mask=mask)
     result = values.argmin(axis)
     result = _maybe_arg_null_out(result, axis, mask, skipna)
     return result
@@ -636,7 +640,7 @@ def nankurt(values, axis=None, skipna=True):
     with np.errstate(invalid='ignore', divide='ignore'):
         adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
         numer = count * (count + 1) * (count - 1) * m4
-        denom = (count - 2) * (count - 3) * m2**2
+        denom = (count - 2) * (count - 3) * m2 ** 2
 
     # floating point error
     #

From 9b28848d577475d127b57ae14a061e56057b2902 Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Thu, 27 Sep 2018 21:58:57 +0100
Subject: [PATCH 02/10] Add doc strings and further mask kwargs

---
 pandas/core/nanops.py | 272 +++++++++++++++++++++++++++++++++++++++---
 1 file changed, 255 insertions(+), 17 deletions(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 6f42f04909ff9..7b9e222571540 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -319,12 +319,64 @@ def _na_for_min_count(values, axis):
 
 
 def nanany(values, axis=None, skipna=True, mask=None):
+    """
+    Check if any elements along an axis evaluate to True.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Returns
+    -------
+    result : bool
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 2])
+    >>> nanops.nanany(s)
+    True
+
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([np.nan])
+    >>> nanops.nanany(s)
+    False
+    """
     values, mask, dtype, _ = _get_values(values, skipna, False, copy=skipna,
                                          mask=mask)
     return values.any(axis)
 
 
 def nanall(values, axis=None, skipna=True, mask=None):
+    """
+    Check if all elements along an axis evaluate to True.
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Returns
+    -------
+    result : bool
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nanall(s)
+    True
+
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 0])
+    >>> nanops.nanall(s)
+    False
+    """
     values, mask, dtype, _ = _get_values(values, skipna, True, copy=skipna,
                                          mask=mask)
     return values.all(axis)
@@ -332,6 +384,24 @@ def nanall(values, axis=None, skipna=True, mask=None):
 
 @disallow('M8')
 def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
+    """
+    Sum the elements along an axis ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    min_count: int
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nansum(s)
+    3.0
+    """
     values, mask, dtype, dtype_max = _get_values(values, skipna, 0, mask=mask)
     dtype_sum = dtype_max
     if is_float_dtype(dtype):
@@ -347,6 +417,23 @@ def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
 @disallow('M8')
 @bottleneck_switch()
 def nanmean(values, axis=None, skipna=True, mask=None):
+    """
+    Compute the mean of the element along an axis ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 2, np.nan])
+    >>> nanops.nanmean(s)
+    1.5
+    """
     values, mask, dtype, dtype_max = _get_values(values, skipna, 0, mask=mask)
     dtype_sum = dtype_max
     dtype_count = np.float64
@@ -372,6 +459,21 @@ def nanmean(values, axis=None, skipna=True, mask=None):
 @disallow('M8')
 @bottleneck_switch()
 def nanmedian(values, axis=None, skipna=True, mask=None):
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, np.nan, 2, 2])
+    >>> nanops.nanmedian(s)
+    2.0
+    """
     def get_median(x):
         mask = notna(x)
         if not skipna and not mask.all():
@@ -434,17 +536,53 @@ def _get_counts_nanvar(mask, axis, ddof, dtype=float):
 
 @disallow('M8')
 @bottleneck_switch(ddof=1)
-def nanstd(values, axis=None, skipna=True, ddof=1):
-    result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof))
+def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
+    """
+    Compute the standard deviation along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nanstd(s)
+    1.0
+    """
+    result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof,
+                            mask=mask))
     return _wrap_results(result, values.dtype)
 
 
 @disallow('M8')
 @bottleneck_switch(ddof=1)
-def nanvar(values, axis=None, skipna=True, ddof=1):
+def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
+    """
+    Compute the variance along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nanvar(s)
+    1.0
+    """
     values = com.values_from_object(values)
     dtype = values.dtype
-    mask = isna(values)
+    if mask is None:
+        mask = isna(values)
     if is_any_int_dtype(values):
         values = values.astype('f8')
         values[mask] = np.nan
@@ -480,12 +618,31 @@ def nanvar(values, axis=None, skipna=True, ddof=1):
 
 
 @disallow('M8', 'm8')
-def nansem(values, axis=None, skipna=True, ddof=1):
+def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
+    """
+    Compute the standard error in the mean along given axis while ignoring NaNs
+
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, np.nan, 2, 3])
+    >>> nanops.nansem(s)
+     0.5773502691896258
+    """
+
     # This checks if non-numeric-like data is passed with numeric_only=False
     # and raises a TypeError otherwise
-    nanvar(values, axis, skipna, ddof=ddof)
+    nanvar(values, axis, skipna, ddof=ddof, mask=mask)
 
-    mask = isna(values)
+    if mask is None:
+        mask = isna(values)
     if not is_float_dtype(values.dtype):
         values = values.astype('f8')
     count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)
@@ -524,7 +681,23 @@ def reduction(values, axis=None, skipna=True):
 @disallow('O')
 def nanargmax(values, axis=None, skipna=True, mask=None):
     """
-    Returns -1 in the NA case
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 2, 3, np.nan, 4])
+    >>> nanops.nanargmax(s)
+    4
+
+    Returns
+    --------
+    The index of max value in specified axis or -1 in the NA case
     """
     values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',
                                          mask=mask)
@@ -536,7 +709,23 @@ def nanargmax(values, axis=None, skipna=True, mask=None):
 @disallow('O')
 def nanargmin(values, axis=None, skipna=True, mask=None):
     """
-    Returns -1 in the NA case
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 2, 3, np.nan, 4])
+    >>> nanops.nanargmin(s)
+    0
+
+    Returns
+    --------
+    The index of min value in specified axis or -1 in the NA case
     """
     values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf',
                                          mask=mask)
@@ -546,17 +735,30 @@ def nanargmin(values, axis=None, skipna=True, mask=None):
 
 
 @disallow('M8', 'm8')
-def nanskew(values, axis=None, skipna=True):
+def nanskew(values, axis=None, skipna=True, mask=None):
     """ Compute the sample skewness.
 
     The statistic computed here is the adjusted Fisher-Pearson standardized
     moment coefficient G1. The algorithm computes this coefficient directly
     from the second and third central moment.
 
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1,np.nan, 1, 2])
+    >>> nanops.nanskew(s)
+    1.7320508075688787
     """
-
     values = com.values_from_object(values)
-    mask = isna(values)
+    if mask is None:
+        mask = isna(values)
     if not is_float_dtype(values.dtype):
         values = values.astype('f8')
         count = _get_counts(mask, axis)
@@ -605,16 +807,31 @@ def nanskew(values, axis=None, skipna=True):
 
 
 @disallow('M8', 'm8')
-def nankurt(values, axis=None, skipna=True):
-    """ Compute the sample excess kurtosis.
+def nankurt(values, axis=None, skipna=True, mask=None):
+    """
+    Compute the sample excess kurtosis
 
     The statistic computed here is the adjusted Fisher-Pearson standardized
     moment coefficient G2, computed directly from the second and fourth
     central moment.
 
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1,np.nan, 1, 3, 2])
+    >>> nanops.nankurt(s)
+    -1.2892561983471076
     """
     values = com.values_from_object(values)
-    mask = isna(values)
+    if mask is None:
+        mask = isna(values)
     if not is_float_dtype(values.dtype):
         values = values.astype('f8')
         count = _get_counts(mask, axis)
@@ -672,8 +889,29 @@ def nankurt(values, axis=None, skipna=True):
 
 
 @disallow('M8', 'm8')
-def nanprod(values, axis=None, skipna=True, min_count=0):
-    mask = isna(values)
+def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
+    """
+    Parameters
+    ----------
+    values : ndarray
+    axis : int
+    skipna : bool
+    min_count: int
+    mask : ndarray nan-mask if known
+
+    Examples
+    --------
+    >>> import pandas.core.nanops as nanops
+    >>> s = pd.Series([1, 2, 3, np.nan])
+    >>> nanops.nanprod(s)
+    6.0
+
+    Returns
+    --------
+    The product of all elements on a given axis. ( NaNs are treated as 1)
+    """
+    if mask is None:
+        mask = isna(values)
     if skipna and not is_any_int_dtype(values):
         values = values.copy()
         values[mask] = 1

From 2b4e23e84e1bfe31bdcd578ba1c2fb9a7c488d9c Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Thu, 27 Sep 2018 22:15:11 +0100
Subject: [PATCH 03/10] Add nanmin and nanmax

---
 pandas/core/nanops.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 7b9e222571540..b73f5835d55d9 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -653,9 +653,9 @@ def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
 
 def _nanminmax(meth, fill_value_typ):
     @bottleneck_switch()
-    def reduction(values, axis=None, skipna=True):
+    def reduction(values, axis=None, skipna=True, mask=None):
         values, mask, dtype, dtype_max = _get_values(
-            values, skipna, fill_value_typ=fill_value_typ, )
+            values, skipna, fill_value_typ=fill_value_typ, mask=mask)
 
         if ((axis is not None and values.shape[axis] == 0) or
                 values.size == 0):

From 424e252fdb7f8f93b841bc244b180f07b3b2f12c Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Thu, 27 Sep 2018 22:15:25 +0100
Subject: [PATCH 04/10] Add unit test

---
 pandas/tests/test_nanops.py | 36 +++++++++++++++++++++++++++++++-----
 1 file changed, 31 insertions(+), 5 deletions(-)

diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index b6c2c65fb6dce..7104187226bfe 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -1,19 +1,19 @@
 # -*- coding: utf-8 -*-
 from __future__ import division, print_function
 
+import warnings
 from functools import partial
 
-import pytest
-import warnings
 import numpy as np
+import pytest
 
 import pandas as pd
-from pandas import Series, isna
-from pandas.core.dtypes.common import is_integer_dtype
 import pandas.core.nanops as nanops
-import pandas.util.testing as tm
 import pandas.util._test_decorators as td
+import pandas.util.testing as tm
+from pandas import Series, isna
 from pandas.compat.numpy import _np_version_under1p13
+from pandas.core.dtypes.common import is_integer_dtype
 
 use_bn = nanops._USE_BOTTLENECK
 
@@ -1041,3 +1041,29 @@ def test_numpy_ops_np_version_under1p13(numpy_op, expected):
             assert result == expected
     else:
         assert result == expected
+
+
+@pytest.mark.parametrize("operation",[
+    nanops.nanany,
+    nanops.nanall,
+    nanops.nansum,
+    nanops.nanmean,
+    nanops.nanmedian,
+    nanops.nanstd,
+    nanops.nanvar,
+    nanops.nansem,
+    nanops.nanargmax,
+    nanops.nanargmin,
+    nanops.nanmax,
+    nanops.nanmin,
+    nanops.nanskew,
+    nanops.nankurt,
+    nanops.nanprod,
+])
+def test_nanops_independent_of_mask_param(operation):
+    # GH22764
+    s = pd.Series([1, 2, np.nan, 3, np.nan, 4])
+    mask = s.isna()
+    median_expected = operation(s)
+    median_result = operation(s, mask=mask)
+    assert median_expected == median_result

From 0e42c571e5f16b139cb6718e5aa726daf09662c7 Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Sat, 29 Sep 2018 12:23:52 +0100
Subject: [PATCH 05/10] Pep8

---
 pandas/tests/test_nanops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas/tests/test_nanops.py b/pandas/tests/test_nanops.py
index 7104187226bfe..b06463d3c07aa 100644
--- a/pandas/tests/test_nanops.py
+++ b/pandas/tests/test_nanops.py
@@ -1043,7 +1043,7 @@ def test_numpy_ops_np_version_under1p13(numpy_op, expected):
         assert result == expected
 
 
-@pytest.mark.parametrize("operation",[
+@pytest.mark.parametrize("operation", [
     nanops.nanany,
     nanops.nanall,
     nanops.nansum,

From 060c7652dbcd2161b7453966aaaf017c437a3f57 Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Sat, 29 Sep 2018 13:01:12 +0100
Subject: [PATCH 06/10] Improve my docstrings

---
 pandas/core/nanops.py | 104 +++++++++++++++++++++++++-----------------
 1 file changed, 63 insertions(+), 41 deletions(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index b73f5835d55d9..0af7a73e10e48 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -325,9 +325,10 @@ def nanany(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis : int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Returns
     -------
@@ -357,9 +358,10 @@ def nanall(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Returns
     -------
@@ -390,10 +392,11 @@ def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    min_count: int
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    min_count: int, default 0
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -423,9 +426,10 @@ def nanmean(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -463,9 +467,10 @@ def nanmedian(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -543,9 +548,13 @@ def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -568,9 +577,13 @@ def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -625,9 +638,13 @@ def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    ddof : int, default 1
+        Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
+        where N represents the number of elements.
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -684,9 +701,10 @@ def nanargmax(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -712,9 +730,10 @@ def nanargmin(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -745,9 +764,10 @@ def nanskew(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -818,9 +838,10 @@ def nankurt(values, axis=None, skipna=True, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------
@@ -894,10 +915,11 @@ def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
     Parameters
     ----------
     values : ndarray
-    axis : int
-    skipna : bool
-    min_count: int
-    mask : ndarray nan-mask if known
+    axis: int, optional
+    skipna : bool, default True
+    min_count: int, default 0
+    mask : ndarray[bool], optional
+        nan-mask if known
 
     Examples
     --------

From 67f2020f4fbb5ab6b65c0bfcc407f7b9e3a914f0 Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Sun, 30 Sep 2018 18:51:26 +0100
Subject: [PATCH 07/10] Add whatsnew

---
 doc/source/whatsnew/v0.24.0.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 851c1a3fbd6e9..a52e554394176 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -834,4 +834,5 @@ Other
 - :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
 - :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
 - Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
+- ``nanops`` methods now take an optional `mask` parameter to avoid recalculating mask if known. (:issue:`22764`)
 -

From 021cc6b8db91e708aee08dd49d64a3dc2a4288a3 Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Sun, 30 Sep 2018 18:58:05 +0100
Subject: [PATCH 08/10] Fix up docstrings

---
 pandas/core/nanops.py | 72 +++++++++++++++++++++++++++++++++++++------
 1 file changed, 62 insertions(+), 10 deletions(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 0af7a73e10e48..7433d50ff2548 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -391,13 +391,17 @@ def nansum(values, axis=None, skipna=True, min_count=0, mask=None):
 
     Parameters
     ----------
-    values : ndarray
+    values : ndarray[dtype]
     axis: int, optional
     skipna : bool, default True
     min_count: int, default 0
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : dtype
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -431,6 +435,12 @@ def nanmean(values, axis=None, skipna=True, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -472,6 +482,12 @@ def nanmedian(values, axis=None, skipna=True, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -556,6 +572,12 @@ def nanstd(values, axis=None, skipna=True, ddof=1, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -585,6 +607,12 @@ def nanvar(values, axis=None, skipna=True, ddof=1, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : float
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -646,6 +674,12 @@ def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -706,16 +740,17 @@ def nanargmax(values, axis=None, skipna=True, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    --------
+    result : int
+        The index of max value in specified axis or -1 in the NA case
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
     >>> s = pd.Series([1, 2, 3, np.nan, 4])
     >>> nanops.nanargmax(s)
     4
-
-    Returns
-    --------
-    The index of max value in specified axis or -1 in the NA case
     """
     values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='-inf',
                                          mask=mask)
@@ -735,16 +770,17 @@ def nanargmin(values, axis=None, skipna=True, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    --------
+    result : int
+        The index of min value in specified axis or -1 in the NA case
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
     >>> s = pd.Series([1, 2, 3, np.nan, 4])
     >>> nanops.nanargmin(s)
     0
-
-    Returns
-    --------
-    The index of min value in specified axis or -1 in the NA case
     """
     values, mask, dtype, _ = _get_values(values, skipna, fill_value_typ='+inf',
                                          mask=mask)
@@ -769,6 +805,12 @@ def nanskew(values, axis=None, skipna=True, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -843,6 +885,12 @@ def nankurt(values, axis=None, skipna=True, mask=None):
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : float64
+        Unless input is a float array, in which case use the same
+        precision as the input array.
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops
@@ -914,13 +962,17 @@ def nanprod(values, axis=None, skipna=True, min_count=0, mask=None):
     """
     Parameters
     ----------
-    values : ndarray
+    values : ndarray[dtype]
     axis: int, optional
     skipna : bool, default True
     min_count: int, default 0
     mask : ndarray[bool], optional
         nan-mask if known
 
+    Returns
+    -------
+    result : dtype
+
     Examples
     --------
     >>> import pandas.core.nanops as nanops

From 043ba31dea2ecfd4a28b564d53036c79786d1b20 Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Tue, 2 Oct 2018 22:32:54 +0100
Subject: [PATCH 09/10] Remove whatsnew

---
 doc/source/whatsnew/v0.24.0.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 704895eae8aee..a240f1fd85dd0 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -834,5 +834,4 @@ Other
 - :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
 - :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
 - Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
-- ``nanops`` methods now take an optional `mask` parameter to avoid recalculating mask if known. (:issue:`22764`)
 -

From dacdbec03c57d531d11bd1524610e313ab49b2fd Mon Sep 17 00:00:00 2001
From: alimcmaster1 <alimcmaster1@gmail.com>
Date: Tue, 2 Oct 2018 22:33:22 +0100
Subject: [PATCH 10/10] Update v0.24.0.txt

---
 doc/source/whatsnew/v0.24.0.txt | 1 -
 1 file changed, 1 deletion(-)

diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index a240f1fd85dd0..9b71ab656920d 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -834,4 +834,3 @@ Other
 - :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
 - :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
 - Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
--