From 0f6592fafadaf60f06c3c7509a6bc02050d5006a Mon Sep 17 00:00:00 2001
From: Thierry Moisan <thierry.moisan@gmail.com>
Date: Mon, 26 Nov 2018 19:49:17 -0500
Subject: [PATCH] DOC: fix pandas.DataFrame.quantile docstring and doctests

---
 ci/code_checks.sh    |  2 +-
 pandas/core/frame.py | 35 +++++++++++++++++------------------
 2 files changed, 18 insertions(+), 19 deletions(-)

diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 45cb1708258d7..129742d998f2c 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -147,7 +147,7 @@ if [[ -z "$CHECK" || "$CHECK" == "doctests" ]]; then
 
     MSG='Doctests frame.py' ; echo $MSG
     pytest -q --doctest-modules pandas/core/frame.py \
-        -k"-axes -combine -itertuples -join -pivot_table -quantile -query -reindex -reindex_axis -replace -round -set_index -stack"
+        -k"-axes -combine -itertuples -join -pivot_table -query -reindex -reindex_axis -replace -round -set_index -stack"
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
     MSG='Doctests series.py' ; echo $MSG
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 592825a7ea63b..958947ef6bfe0 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -7516,15 +7516,13 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
         Parameters
         ----------
         q : float or array-like, default 0.5 (50% quantile)
-            0 <= q <= 1, the quantile(s) to compute
+            Value between 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 True
+            Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
+        numeric_only : bool, default True
             If False, the quantile of datetime and timedelta data will be
-            computed as well
+            computed as well.
         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`:
 
@@ -7535,6 +7533,8 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
             * nearest: `i` or `j` whichever is nearest.
             * midpoint: (`i` + `j`) / 2.
 
+            .. versionadded:: 0.18.0
+
         Returns
         -------
         quantiles : Series or DataFrame
@@ -7545,15 +7545,19 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
             - 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
+        See Also
         --------
+        core.window.Rolling.quantile: Rolling quantile.
+        numpy.percentile: Numpy function to compute the percentile.
 
+        Examples
+        --------
         >>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
-                              columns=['a', 'b'])
+        ...                   columns=['a', 'b'])
         >>> df.quantile(.1)
         a    1.3
         b    3.7
-        dtype: float64
+        Name: 0.1, dtype: float64
         >>> df.quantile([.1, .5])
                a     b
         0.1  1.3   3.7
@@ -7563,20 +7567,15 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
         datetime and timedelta data.
 
         >>> df = pd.DataFrame({'A': [1, 2],
-                               'B': [pd.Timestamp('2010'),
-                                     pd.Timestamp('2011')],
-                               'C': [pd.Timedelta('1 days'),
-                                     pd.Timedelta('2 days')]})
+        ...                    'B': [pd.Timestamp('2010'),
+        ...                          pd.Timestamp('2011')],
+        ...                    'C': [pd.Timedelta('1 days'),
+        ...                          pd.Timedelta('2 days')]})
         >>> df.quantile(0.5, numeric_only=False)
         A                    1.5
         B    2010-07-02 12:00:00
         C        1 days 12:00:00
         Name: 0.5, dtype: object
-
-        See Also
-        --------
-        pandas.core.window.Rolling.quantile
-        numpy.percentile
         """
         self._check_percentile(q)