BUG: Concatenating dataframe and series with ignore_index = True drops the series name

[uzzell]

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

df = pd.DataFrame({'a': [0, 1], 'b': [2, 3]})
s = pd.Series([4, 5], name='c')
pd.concat([df, s])   # columns are ['a', 'b', 'c']
pd.concat([df, s], ignore_index=True)  # columns are ['a', 'b', 0]
pd.concat([df, s.to_frame()])  # columns are ['a', 'b', 'c']
pd.concat([df, s.to_frame()], ignore_index=True)  # columns are ['a', 'b', 'c']

Issue Description

When I concatenate a dataframe with a series and pass ignore_index=True, the series' name does not show up in the resulting dataframe. This is surprising, because the documentation for ignore_index says, "Note the index values on the other axes are still respected in the join."

This seems similar to #56257. That concerned the case in which the name of the series is the same as the name of one of the columns of the dataframe but did not involve ignore_index. That issue has a stale PR (#56362) that looks like it might fix this one, too.

Expected Behavior

Doing pd.concat([df, s], ignore_index=True) should preserve the name of the series.

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.12.2
python-bits : 64
OS : Windows
OS-release : 11
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 3, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252

pandas : 2.2.3
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
pip : 24.3.1
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None
None

[speco29]

It's true that you can not have the series index without converting into a Dataframe first. To merge a Series with a Dataframe without changing the Series into a Dataframe, while using ignore_index=True, you can convert the Series to a DataFrame temporarily within the pd.concat function. Here's how you can do it:

`import pandas as pd

df = pd.DataFrame({'a': [0, 1], 'b': [2, 3]})
s = pd.Series([4, 5], name='c')

Concatenating DataFrame and Series with ignore_index=True

result = pd.concat([df, s.to_frame().T], ignore_index=True)

print(result)
`

[rhshadrach]

Thanks for the report, agreed with the expectation to maintain the Series name here. Further investigations and PRs to fix are welcome!

[Anurag-Varma]

take

[Anurag-Varma]

Hi @rhshadrach , i am trying this bug and there is a wrong test case which has the below code and outputs.

Test case:
pandas/tests/reshape/concat/test_concat.py::TestConcatenate::test_concat_mixed_objs_columns

Python code for the Test case:

import numpy as np
import pandas as pd

# Create date index
index = pd.date_range("01-Jan-2013", periods=10, freq="h")

# Create array
arr = np.arange(10, dtype="int64")

# Create Series
s1 = pd.Series(arr, index=index)
s2 = pd.Series(arr, index=index)

# Create DataFrame from array
df = pd.DataFrame(arr.reshape(-1, 1), index=index)

# Concatenate along columns
result = pd.concat([s1, df, s2, s2, s1], axis=1)

# Expected DataFrame with correct column naming
expected = pd.DataFrame(
    np.repeat(arr, 5).reshape(-1, 5), index=index, columns=[0, 0, 1, 2, 3]
)

# Print results
print("Result with to_frame:")
print(pd.concat([s1.to_frame(), df, s2.to_frame(), s2.to_frame(), s1.to_frame()], axis=1))
print("Result after my code changes to the bug:")
print(result)
print("Expected from test case:")
print(expected)

Outputs:

# Result with to_frame:
                     0  0  0  0  0
2013-01-01 00:00:00  0  0  0  0  0
2013-01-01 01:00:00  1  1  1  1  1
2013-01-01 02:00:00  2  2  2  2  2
2013-01-01 03:00:00  3  3  3  3  3
2013-01-01 04:00:00  4  4  4  4  4
2013-01-01 05:00:00  5  5  5  5  5
2013-01-01 06:00:00  6  6  6  6  6
2013-01-01 07:00:00  7  7  7  7  7
2013-01-01 08:00:00  8  8  8  8  8
2013-01-01 09:00:00  9  9  9  9  9

# Result after my code changes to the bug:
                     0  0  0  0  0
2013-01-01 00:00:00  0  0  0  0  0
2013-01-01 01:00:00  1  1  1  1  1
2013-01-01 02:00:00  2  2  2  2  2
2013-01-01 03:00:00  3  3  3  3  3
2013-01-01 04:00:00  4  4  4  4  4
2013-01-01 05:00:00  5  5  5  5  5
2013-01-01 06:00:00  6  6  6  6  6
2013-01-01 07:00:00  7  7  7  7  7
2013-01-01 08:00:00  8  8  8  8  8
2013-01-01 09:00:00  9  9  9  9  9

# Expected from test case
                     0  0  1  2  3
2013-01-01 00:00:00  0  0  0  0  0
2013-01-01 01:00:00  1  1  1  1  1
2013-01-01 02:00:00  2  2  2  2  2
2013-01-01 03:00:00  3  3  3  3  3
2013-01-01 04:00:00  4  4  4  4  4
2013-01-01 05:00:00  5  5  5  5  5
2013-01-01 06:00:00  6  6  6  6  6
2013-01-01 07:00:00  7  7  7  7  7
2013-01-01 08:00:00  8  8  8  8  8
2013-01-01 09:00:00  9  9  9  9  9

Can you say if result with to_frame() is correct in the naming of the columns ?

I am thinking that the test case expected output is wrong here ! If that's the case, then i have to change the old test case from 10 years back and change the expected output