From d7ce57e7b6280a8b2e285aaa1b1c5128b9b35877 Mon Sep 17 00:00:00 2001
From: Simon Hawkins <simonjayhawkins@gmail.com>
Date: Thu, 7 Jan 2021 18:47:08 +0000
Subject: [PATCH] Backport PR #39019: DOC: np.bool -> np.bool_

---
 .pre-commit-config.yaml               | 5 +++++
 asv_bench/benchmarks/groupby.py       | 2 +-
 doc/source/user_guide/basics.rst      | 2 +-
 doc/source/user_guide/cookbook.rst    | 2 +-
 pandas/tests/io/excel/test_writers.py | 2 +-
 5 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 717334bfe1299..90d65327ea980 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -144,6 +144,11 @@ repos:
             \#\ type:\s?ignore(?!\[)
         language: pygrep
         types: [python]
+    -   id: np-bool
+        name: Check for use of np.bool instead of np.bool_
+        entry: np\.bool[^_8]
+        language: pygrep
+        types_or: [python, cython, rst]
     -   id: no-os-remove
         name: Check code for instances of os.remove
         entry: os\.remove
diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py
index 6ce63ff8badca..6cc8e15786795 100644
--- a/asv_bench/benchmarks/groupby.py
+++ b/asv_bench/benchmarks/groupby.py
@@ -625,7 +625,7 @@ class TransformBools:
     def setup(self):
         N = 120000
         transition_points = np.sort(np.random.choice(np.arange(N), 1400))
-        transitions = np.zeros(N, dtype=np.bool)
+        transitions = np.zeros(N, dtype=np.bool_)
         transitions[transition_points] = True
         self.g = transitions.cumsum()
         self.df = DataFrame({"signal": np.random.rand(N)})
diff --git a/doc/source/user_guide/basics.rst b/doc/source/user_guide/basics.rst
index ffecaa222e1f9..8d38c12252df4 100644
--- a/doc/source/user_guide/basics.rst
+++ b/doc/source/user_guide/basics.rst
@@ -2229,7 +2229,7 @@ Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFra
 .. ipython:: python
 
    dft1 = pd.DataFrame({"a": [1, 0, 1], "b": [4, 5, 6], "c": [7, 8, 9]})
-   dft1 = dft1.astype({"a": np.bool, "c": np.float64})
+   dft1 = dft1.astype({"a": np.bool_, "c": np.float64})
    dft1
    dft1.dtypes
 
diff --git a/doc/source/user_guide/cookbook.rst b/doc/source/user_guide/cookbook.rst
index 5a6f56388dee5..77791b4b7e491 100644
--- a/doc/source/user_guide/cookbook.rst
+++ b/doc/source/user_guide/cookbook.rst
@@ -1406,7 +1406,7 @@ Often it's useful to obtain the lower (or upper) triangular form of a correlatio
     df = pd.DataFrame(np.random.random(size=(100, 5)))
 
     corr_mat = df.corr()
-    mask = np.tril(np.ones_like(corr_mat, dtype=np.bool), k=-1)
+    mask = np.tril(np.ones_like(corr_mat, dtype=np.bool_), k=-1)
 
     corr_mat.where(mask)
 
diff --git a/pandas/tests/io/excel/test_writers.py b/pandas/tests/io/excel/test_writers.py
index 197738330efe1..e67769bc774b0 100644
--- a/pandas/tests/io/excel/test_writers.py
+++ b/pandas/tests/io/excel/test_writers.py
@@ -492,7 +492,7 @@ def test_float_types(self, np_type, path):
 
     @pytest.mark.parametrize("np_type", [np.bool8, np.bool_])
     def test_bool_types(self, np_type, path):
-        # Test np.bool values read come back as float.
+        # Test np.bool8 and np.bool_ values read come back as float.
         df = DataFrame([1, 0, True, False], dtype=np_type)
         df.to_excel(path, "test1")