Description
Pandas version checks
-
I have checked that this issue has not already been reported.
-
I have confirmed this bug exists on the latest version of pandas.
-
I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
Here are my reproduction steps that does not work with PyArrow type:
df = pd.DataFrame({"a": pd.date_range("2018-01-01 00:00:00", "2018-01-07 00:00:00")}).astype({"a": "timestamp[ns][pyarrow]"})
date2pos = {date: i for i, date in enumerate(df['a'])}
df["a"].map(date2pos)
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
Name: a, dtype: float64Issue Description
For some reason pd.DataFrame.map() function does not work with PyArrow timestamp[ns][pyarrow] type and does not map values.
Expected Behavior
Here is an expected behavior that works with the default pandas type datetime64[ns]:
df = pd.DataFrame({"a": pd.date_range("2018-01-01 00:00:00", "2018-01-07 00:00:00")})
date2pos = {date: i for i, date in enumerate(df['a'])}
df["a"].map(date2pos)
0 0
1 1
2 2
3 3
4 4
5 5
6 6
Name: a, dtype: int64
Installed Versions
pandas : 2.2.3
numpy : 1.26.3
pytz : 2025.1
dateutil : 2.8.2
pip : 23.2.1
Cython : None
sphinx : None
IPython : 8.20.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.12.2
html5lib : None
hypothesis : None
gcsfs : 2023.12.2post1
jinja2 : 3.1.3
lxml.etree : None
matplotlib : 3.8.2
numba : 0.60.0
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 14.0.2
pyreadstat : None
pytest : 7.4.4
python-calamine : None
pyxlsb : None
s3fs : 2023.12.2
scipy : 1.12.0
sqlalchemy : 2.0.29
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2023.4
qtpy : None
pyqt5 : None