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new 1cd1841c06 GH-49150: [Doc][CI][Python] Doctests failing on rst files
due to pandas 3+ (#49088)
1cd1841c06 is described below
commit 1cd1841c06c2c5c849340549ba57fc015d50005a
Author: Rok Mihevc <[email protected]>
AuthorDate: Thu Feb 5 01:06:04 2026 +0100
GH-49150: [Doc][CI][Python] Doctests failing on rst files due to pandas 3+
(#49088)
Fixes: #49150
See https://github.com/apache/arrow/pull/48619#issuecomment-3823269381
### Rationale for this change
Fix CI failures
### What changes are included in this PR?
Tests are made more general to allow for Pandas 2 and Pandas 3 style string
types
### Are these changes tested?
By CI
### Are there any user-facing changes?
No
* GitHub Issue: #49150
Authored-by: Rok Mihevc <[email protected]>
Signed-off-by: Rok Mihevc <[email protected]>
---
.github/workflows/python.yml | 6 +++---
docs/source/python/data.rst | 2 +-
docs/source/python/ipc.rst | 12 ++++++------
docs/source/python/pandas.rst | 12 ++++++------
docs/source/python/parquet.rst | 6 +++---
5 files changed, 19 insertions(+), 19 deletions(-)
diff --git a/.github/workflows/python.yml b/.github/workflows/python.yml
index e5d367958d..bc7fe3cd68 100644
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -69,10 +69,10 @@ jobs:
- conda-python-3.12-no-numpy
include:
- name: conda-python-docs
- cache: conda-python-3.10
+ cache: conda-python-3.11
image: conda-python-docs
- title: AMD64 Conda Python 3.10 Sphinx & Numpydoc
- python: "3.10"
+ title: AMD64 Conda Python 3.11 Sphinx & Numpydoc
+ python: "3.11"
- name: conda-python-3.11-nopandas
cache: conda-python-3.11
image: conda-python
diff --git a/docs/source/python/data.rst b/docs/source/python/data.rst
index 279ec5dc61..22a3114fdd 100644
--- a/docs/source/python/data.rst
+++ b/docs/source/python/data.rst
@@ -684,7 +684,7 @@ When using :class:`~.DictionaryArray` with pandas, the
analogue is
6 NaN
7 baz
dtype: category
- Categories (3, object): ['foo', 'bar', 'baz']
+ Categories (3, str): ['foo', 'bar', 'baz']
.. _data.record_batch:
diff --git a/docs/source/python/ipc.rst b/docs/source/python/ipc.rst
index 9b4458c748..8f96363968 100644
--- a/docs/source/python/ipc.rst
+++ b/docs/source/python/ipc.rst
@@ -160,12 +160,12 @@ DataFrame output:
>>> with pa.ipc.open_file(buf) as reader:
... df = reader.read_pandas()
>>> df[:5]
- f0 f1 f2
- 0 1 foo True
- 1 2 bar None
- 2 3 baz False
- 3 4 None True
- 4 1 foo True
+ f0 f1 f2
+ 0 1 foo True
+ 1 2 bar None
+ 2 3 baz False
+ 3 4 NaN True
+ 4 1 foo True
Efficiently Writing and Reading Arrow Data
------------------------------------------
diff --git a/docs/source/python/pandas.rst b/docs/source/python/pandas.rst
index 9999a5b779..7aacaaff60 100644
--- a/docs/source/python/pandas.rst
+++ b/docs/source/python/pandas.rst
@@ -170,7 +170,7 @@ number of possible values.
>>> df = pd.DataFrame({"cat": pd.Categorical(["a", "b", "c", "a", "b",
"c"])})
>>> df.cat.dtype.categories
- Index(['a', 'b', 'c'], dtype='object')
+ Index(['a', 'b', 'c'], dtype='str')
>>> df
cat
0 a
@@ -182,7 +182,7 @@ number of possible values.
>>> table = pa.Table.from_pandas(df)
>>> table
pyarrow.Table
- cat: dictionary<values=string, indices=int8, ordered=0>
+ cat: dictionary<values=large_string, indices=int8, ordered=0>
----
cat: [ -- dictionary:
["a","b","c"] -- indices:
@@ -196,7 +196,7 @@ same categories of the Pandas DataFrame.
>>> column = table[0]
>>> chunk = column.chunk(0)
>>> chunk.dictionary
- <pyarrow.lib.StringArray object at ...>
+ <pyarrow.lib.LargeStringArray object at ...>
[
"a",
"b",
@@ -224,7 +224,7 @@ use the ``datetime64[ns]`` type in Pandas and are converted
to an Arrow
>>> df = pd.DataFrame({"datetime": pd.date_range("2020-01-01T00:00:00Z",
freq="h", periods=3)})
>>> df.dtypes
- datetime datetime64[ns, UTC]
+ datetime datetime64[us, UTC]
dtype: object
>>> df
datetime
@@ -234,9 +234,9 @@ use the ``datetime64[ns]`` type in Pandas and are converted
to an Arrow
>>> table = pa.Table.from_pandas(df)
>>> table
pyarrow.Table
- datetime: timestamp[ns, tz=UTC]
+ datetime: timestamp[us, tz=UTC]
----
- datetime: [[2020-01-01 00:00:00.000000000Z,...,2020-01-01
02:00:00.000000000Z]]
+ datetime: [[2020-01-01 00:00:00.000000Z,2020-01-01
01:00:00.000000Z,2020-01-01 02:00:00.000000Z]]
In this example the Pandas Timestamp is time zone aware
(``UTC`` on this case), and this information is used to create the Arrow
diff --git a/docs/source/python/parquet.rst b/docs/source/python/parquet.rst
index 638df963cd..30a84b3dc6 100644
--- a/docs/source/python/parquet.rst
+++ b/docs/source/python/parquet.rst
@@ -238,9 +238,9 @@ concatenate them into a single table. You can read
individual row groups with
>>> parquet_file.read_row_group(0)
pyarrow.Table
one: double
- two: string
+ two: large_string
three: bool
- __index_level_0__: string
+ __index_level_0__: large_string
----
one: [[-1,null,2.5]]
two: [["foo","bar","baz"]]
@@ -352,7 +352,7 @@ and improved performance for columns with many repeated
string values.
one: double
two: dictionary<values=string, indices=int32, ordered=0>
three: bool
- __index_level_0__: string
+ __index_level_0__: large_string
----
one: [[-1,null,2.5]]
two: [ -- dictionary:
