From 83dd67c24d6bb8c181a07aa4428c318ef23265f1 Mon Sep 17 00:00:00 2001
From: Sebastian Correa <43179146+sebastian-correa@users.noreply.github.com>
Date: Mon, 1 Apr 2024 14:14:03 -0300
Subject: [PATCH 1/2] Filter Mean of empty slice warning in nanmedian

When the array is full of NAs, it ends up in get_median which issues
that warning.
---
 pandas/core/nanops.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index a124e8679ae8e..d34781ee7f4c5 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -745,6 +745,10 @@ def nanmedian(values, *, axis: AxisInt | None = None, skipna: bool = True, mask=
     >>> s = pd.Series([1, np.nan, 2, 2])
     >>> nanops.nanmedian(s.values)
     2.0
+
+    >>> s = pd.Series([np.nan, np.nan, np.nan])
+    >>> nanops.nanmedian(s.values)
+    nan
     """
     # for floats without mask, the data already uses NaN as missing value
     # indicator, and `mask` will be calculated from that below -> in those
@@ -763,6 +767,9 @@ def get_median(x, _mask=None):
             warnings.filterwarnings(
                 "ignore", "All-NaN slice encountered", RuntimeWarning
             )
+            warnings.filterwarnings(
+                "ignore", "Mean of empty slice", RuntimeWarning
+            )
             res = np.nanmedian(x[_mask])
         return res
 

From 7eb1c9c62fd2ba1868effe1059151497fa43fa8d Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 1 Apr 2024 20:50:58 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 pandas/core/nanops.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index d34781ee7f4c5..2bc5f031ce69d 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -767,9 +767,7 @@ def get_median(x, _mask=None):
             warnings.filterwarnings(
                 "ignore", "All-NaN slice encountered", RuntimeWarning
             )
-            warnings.filterwarnings(
-                "ignore", "Mean of empty slice", RuntimeWarning
-            )
+            warnings.filterwarnings("ignore", "Mean of empty slice", RuntimeWarning)
             res = np.nanmedian(x[_mask])
         return res