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
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snitish commentedon Apr 5, 2025
Thanks for reporting. Confirmed on main. Investigations and PRs to fix are welcome.
arthurlw commentedon Apr 5, 2025
take
KevsterAmp commentedon Jun 7, 2025
take
map()#61644