BUG: dtype keyword in Series constructor ignored for Series input in certain cases

[jonmooser]

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

import pandas as pd

try:
    s = pd.Series([1, 2, 'x', 4, 5], dtype=int)
    print("1. No exception")
except Exception as ex:
    print("1. Exception thrown", ex)

s = pd.Series([1, 2, 'x', 4, 5])
try:
    s2 = pd.Series(s, dtype=int)
    print("2. No exception")
except Exception as ex:
    print("2. Exception thrown", ex)

print("s2 dtype", s2.dtype)

Issue Description

The behavior of the above code doesn't seem right.
If I try to create a Series with dtype=int using a list that contains a string, it throws a ValueError, as expected.

But if the input to the Series constructor is another Series that contains a string, there is no exception and it quietly returns a new Series with dtype=object.

Is this expected? If so it's very counterintuitive. Perhaps the recommended way is
s.astype(int)
but it seems like this should produce the same behavior

Expected Behavior

In both of the cases above, it should throw a ValueError. Instead, only the first case does:

1. Exception thrown invalid literal for int() with base 10: 'x'
1. No exception
s2 dtype object

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.11.6.final.0
python-bits : 64
OS : Darwin
OS-release : 23.1.0
Version : Darwin Kernel Version 23.1.0: Mon Oct 9 21:27:24 PDT 2023; root:xnu-10002.41.9~6/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.2
numpy : 1.23.4
pytz : 2022.5
dateutil : 2.8.2
setuptools : 65.5.0
pip : 24.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.12.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.2
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.2
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.13.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[chaoyihu]

@jonmooser Thanks for raising this issue! If I understand it correctly, you are trying to change the dtype of a Series that is already initialized.

I agree it is counter-intuitive that s2 = pd.Series(s, dtype=int) ignores the customized dtype silently without raising.

However, I don't think the constructor should be used for casting. As you have mentioned, a better way of doing this might be Series.astype, in which case a ValueError is raised as expected:

>>> s = pd.Series([1, 2, 'x', 4, 5])  # dtype of s: object
>>> s3 = s.astype('int64')
ValueError: invalid literal for int() with base 10: 'x'

[jonmooser]

Thanks for looking into this. Glad there is a work around but it would be great if future versions are more consistent and intuitive.

[taranarmo]

The constructor doesn't use dtype keyword if Series passed in, source code link

        elif isinstance(data, Series):
            if index is None:
                index = data.index
                data = data._mgr.copy(deep=False)
            else:
                data = data.reindex(index)
                copy = False
                data = data._mgr

I guess warning might be raised if data is Series and dtype is not None. If so I can make a PR.
Personally I'd add cast data to the referenced dtype to make the code fail explicitly but I assume that's breaking change and could cause problems to (some) users.

[jorisvandenbossche]

The constructor doesn't use dtype keyword if Series passed in, source code link

The dtype is still used later on in that case:

pandas/pandas/core/series.py

Line 502 in 39bd3d3

data = sanitize_array(data, index, dtype, copy)

---

So while the dtype argument indeed seems to be ignored in certain cases as the example in the top post show, it is not ignored always. Quick example:

>>> ser_float = pd.Series([1.0], dtype=float)
>>> ser_float
0    1.0
dtype: float64
>>> pd.Series(ser_float, dtype="int64")
0    1
dtype: int64

I agree it is counter-intuitive that s2 = pd.Series(s, dtype=int) ignores the customized dtype silently without raising.

I fully agree this is counter-intuitive, and as shown above also inconsistent. So I think we should rather try to fix this.
When passing a dtype argument, some level of casting being performed in the constructor is unavoidable I think (although I agree that for the explicit action of casting a Series to a different dtype, using ser.astype(..) is the better and clearer option).