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Keywords {🔍}

object, bug, columns, pandas, issue, int, dtype, sign, code, foo, member, phofl, indexing, related, column, projects, frameisetitem, incorrectly, casts, haakonvt, version, index, futurewarning, fulldfiloc, nullable, newvals, jbrockmendel, isetitem, navigation, pull, requests, actions, security, closed, description, confirmed, exists, pddataframefoo, intcols, fulldf, works, intcolsastypeint, printfulldfdtypes, attempt, nonunique, behavior, expect, added, triage, reviewed,

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object issue description nullable extension arrays cast int64 columns personal information bug comment metadata assignees dtype=object import pandas type projects latest version future version triage issue projects milestone bug exists pandas issue main branch started giving future-proof values inplace df[df series/frames valid input special case avoid coercion milestone relationships current code int64 random futurewarning futurewarning referenced foo expected behavior convert column github object df2 = pd index initially indexing bug [/-] [+] bug columns columns[ full_df2 = full_df1 int_cols] = full_df1 isetitem df pd na code [df1 df1

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DiscussionForumPosting:
      context:https://schema.org
      headline:BUG: `frame.isetitem` incorrectly casts `Int64` columns to dtype `object`
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python import pandas as pd df1 = pd.DataFrame({"foo": [0, 0, 0]}, index=[1, 2, 3]) df2 = pd.DataFrame({"foo": [1, 1, 1]}, index=[2, 3, 4]) int_cols = [0, 1] full_df1 = pd.concat([df1, df2], axis="columns") full_df2 = full_df1.copy() # to showcase different behaviour # My current code: Works, but started giving me random FutureWarning as of v1.5 # (apparently happens when df1 and df2 have the same index initially, not relevant to this bug) full_df1.iloc[:, int_cols] = full_df1.iloc[:, int_cols].astype("Int64") print(full_df1.dtypes) # foo Int64 # foo Int64 # dtype: object # My attempt to future-proof my code (as I quite often have columns that are non-unique): full_df2.isetitem(int_cols, full_df2.iloc[:, int_cols].astype("Int64")) print(full_df2.dtypes) # foo object # foo object # dtype: object ``` ### Issue Description After trying to follow the instructions in the `FutureWarning` referenced in the code above, I got into trouble with nullable integer columns ending up as `dtype=object`. The warning in question: > FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)` ### Expected Behavior I would expect that `frame.isetitem(cols, ...)` would not cast `Int64` columns to `object`, but rather work exactly similar to how `frame.iloc[:, cols] = ...` works. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7 python : 3.8.13.final.0 python-bits : 64 OS : Darwin OS-release : 22.1.0 Version : Darwin Kernel Version 22.1.0: Sun Oct 9 20:15:09 PDT 2022; root:xnu-8792.41.9~2/RELEASE_ARM64_T6000 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.5.2 numpy : 1.23.2 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.4.1 pip : 22.3.1 Cython : 0.29.32 pytest : 6.2.5 hypothesis : None sphinx : 5.1.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.1.2 IPython : 8.4.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.5.3 numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : None </details>
      author:
         url:https://github.com/haakonvt
         type:Person
         name:haakonvt
      datePublished:2022-11-27T00:35:43.000Z
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      url:https://github.com/49922/pandas/issues/49922
      context:https://schema.org
      headline:BUG: `frame.isetitem` incorrectly casts `Int64` columns to dtype `object`
      articleBody:### Pandas version checks - [X] I have checked that this issue has not already been reported. - [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas. - [ ] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python import pandas as pd df1 = pd.DataFrame({"foo": [0, 0, 0]}, index=[1, 2, 3]) df2 = pd.DataFrame({"foo": [1, 1, 1]}, index=[2, 3, 4]) int_cols = [0, 1] full_df1 = pd.concat([df1, df2], axis="columns") full_df2 = full_df1.copy() # to showcase different behaviour # My current code: Works, but started giving me random FutureWarning as of v1.5 # (apparently happens when df1 and df2 have the same index initially, not relevant to this bug) full_df1.iloc[:, int_cols] = full_df1.iloc[:, int_cols].astype("Int64") print(full_df1.dtypes) # foo Int64 # foo Int64 # dtype: object # My attempt to future-proof my code (as I quite often have columns that are non-unique): full_df2.isetitem(int_cols, full_df2.iloc[:, int_cols].astype("Int64")) print(full_df2.dtypes) # foo object # foo object # dtype: object ``` ### Issue Description After trying to follow the instructions in the `FutureWarning` referenced in the code above, I got into trouble with nullable integer columns ending up as `dtype=object`. The warning in question: > FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)` ### Expected Behavior I would expect that `frame.isetitem(cols, ...)` would not cast `Int64` columns to `object`, but rather work exactly similar to how `frame.iloc[:, cols] = ...` works. ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7 python : 3.8.13.final.0 python-bits : 64 OS : Darwin OS-release : 22.1.0 Version : Darwin Kernel Version 22.1.0: Sun Oct 9 20:15:09 PDT 2022; root:xnu-8792.41.9~2/RELEASE_ARM64_T6000 machine : arm64 processor : arm byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8 pandas : 1.5.2 numpy : 1.23.2 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.4.1 pip : 22.3.1 Cython : 0.29.32 pytest : 6.2.5 hypothesis : None sphinx : 5.1.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.1.2 IPython : 8.4.0 pandas_datareader: None bs4 : 4.11.1 bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.5.3 numba : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None zstandard : None tzdata : None </details>
      author:
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