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npnan, dannyi, issue, pandas, import, bug, defaultdict, smapd, pdseries, missing, dtype, expected, nan, ids, sign, case, lines, result, commented, contributor, projects, differences, seriesmap, dtypes, description, numpy, pdseriesa, defaultdictlambda, object, edited, edits, idnpnan, pandaspandaslibslibpyx, val, floats, navigation, code, pull, requests, actions, security, closed, asishm, version, checks, confirmed, exists, collections, replaced, mapped,

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pandas/pandas/tests/apply/test_series_apply /pandas-dev/pandas/issues/17648 pandas/pandas/_libs/lib dannyi96 edits contributor personal information bug issue description works arrays import integerarray preallocated memory addresses int64 expected behavior manually setting arr[ pd import numpy issue description collections import defaultdict latest version type projects bug exists import pandas projects milestone triage issue code snippet id issue result = integerarray pandas main branch explicitly tested regular dicts def test_map_dict_na_key //github defaultdicts / objects observation - noticing layer - floats copied/set success cases github milestone relationships expected result correctly replaced appropriately mapped unexpected output nan_ = true s2 = pd nan key nan behaviour dtype='object' issue object id expected = series elif val d2 = defaultdict addresses

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DiscussionForumPosting:
      context:https://schema.org
      headline:BUG: differences in Series.map with defaultdict with different dtypes
      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. - [X] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np from collections import defaultdict s = pd.Series([1,2, np.nan]) s2 = pd.Series(['a', 'b', np.nan]) d = defaultdict(lambda: "missing", {1:'a', 2:'b', np.nan: 'c'}) d2 = defaultdict(lambda: -1, {'a': 1, 'b': 2, np.nan: 3}) s.map(d) s2.map(d2) ``` ### Issue Description In the first case the `np.nan` is not replaced with `'c'` but is instead mapped to the default value. ```python >>> s.map(d) 0 a 1 b 2 missing dtype: object ``` in the second case, `np.nan` is correctly replaced with `3` and not `-1` ```python >>> s2.map(d2) 0 1 1 2 2 3 dtype: int64 ``` ### Expected Behavior the first case to output ```python >>> s.map(d) 0 a 1 b 2 c dtype: object ``` I know I can use `.fillna` as a workaround - but this seems to be explicitly tested in the case of regular dicts here - https://github.com/pandas-dev/pandas/blob/49d4c075e015b5cec0c334554d9d980ae6bf0224/pandas/tests/apply/test_series_apply.py#L592-L598 but seems to be untested for defaultdicts / objects that implement `__missing__` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : b3552c54be2d49d52e6c0ae90db23ccb13bbfe0d python : 3.8.13.final.0 python-bits : 64 OS : Linux OS-release : 4.19.128-microsoft-standard Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.5.0.dev0+1579.gb3552c54be numpy : 1.23.3 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.4.0 pip : 22.2.2 Cython : 0.29.32 pytest : 7.1.3 hypothesis : 6.54.6 sphinx : 4.5.0 blosc : None feather : None xlsxwriter : 3.0.3 lxml.etree : 4.9.1 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : 2.9.3 jinja2 : 3.0.3 IPython : 8.5.0 pandas_datareader: 0.10.0 bs4 : 4.11.1 bottleneck : 1.3.5 brotli : fastparquet : 0.8.3 fsspec : 2021.11.0 gcsfs : 2021.11.0 matplotlib : 3.3.2 numba : 0.53.1 numexpr : 2.8.0 odfpy : None openpyxl : 3.0.10 pandas_gbq : 0.15.0 pyarrow : 2.0.0 pyreadstat : 1.1.9 pyxlsb : 1.0.9 s3fs : 2021.11.0 scipy : 1.9.1 snappy : sqlalchemy : 1.4.41 tables : 3.7.0 tabulate : 0.8.10 xarray : 2022.6.0 xlrd : 2.0.1 xlwt : 1.3.0 zstandard : 0.18.0 tzdata : None </details>
      author:
         url:https://github.com/asishm
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      datePublished:2022-09-27T15:15:25.000Z
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      url:https://github.com/48813/pandas/issues/48813
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      headline:BUG: differences in Series.map with defaultdict with different dtypes
      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. - [X] I have confirmed this bug exists on the main branch of pandas. ### Reproducible Example ```python import pandas as pd import numpy as np from collections import defaultdict s = pd.Series([1,2, np.nan]) s2 = pd.Series(['a', 'b', np.nan]) d = defaultdict(lambda: "missing", {1:'a', 2:'b', np.nan: 'c'}) d2 = defaultdict(lambda: -1, {'a': 1, 'b': 2, np.nan: 3}) s.map(d) s2.map(d2) ``` ### Issue Description In the first case the `np.nan` is not replaced with `'c'` but is instead mapped to the default value. ```python >>> s.map(d) 0 a 1 b 2 missing dtype: object ``` in the second case, `np.nan` is correctly replaced with `3` and not `-1` ```python >>> s2.map(d2) 0 1 1 2 2 3 dtype: int64 ``` ### Expected Behavior the first case to output ```python >>> s.map(d) 0 a 1 b 2 c dtype: object ``` I know I can use `.fillna` as a workaround - but this seems to be explicitly tested in the case of regular dicts here - https://github.com/pandas-dev/pandas/blob/49d4c075e015b5cec0c334554d9d980ae6bf0224/pandas/tests/apply/test_series_apply.py#L592-L598 but seems to be untested for defaultdicts / objects that implement `__missing__` ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : b3552c54be2d49d52e6c0ae90db23ccb13bbfe0d python : 3.8.13.final.0 python-bits : 64 OS : Linux OS-release : 4.19.128-microsoft-standard Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.5.0.dev0+1579.gb3552c54be numpy : 1.23.3 pytz : 2022.2.1 dateutil : 2.8.2 setuptools : 65.4.0 pip : 22.2.2 Cython : 0.29.32 pytest : 7.1.3 hypothesis : 6.54.6 sphinx : 4.5.0 blosc : None feather : None xlsxwriter : 3.0.3 lxml.etree : 4.9.1 html5lib : 1.1 pymysql : 1.0.2 psycopg2 : 2.9.3 jinja2 : 3.0.3 IPython : 8.5.0 pandas_datareader: 0.10.0 bs4 : 4.11.1 bottleneck : 1.3.5 brotli : fastparquet : 0.8.3 fsspec : 2021.11.0 gcsfs : 2021.11.0 matplotlib : 3.3.2 numba : 0.53.1 numexpr : 2.8.0 odfpy : None openpyxl : 3.0.10 pandas_gbq : 0.15.0 pyarrow : 2.0.0 pyreadstat : 1.1.9 pyxlsb : 1.0.9 s3fs : 2021.11.0 scipy : 1.9.1 snappy : sqlalchemy : 1.4.41 tables : 3.7.0 tabulate : 0.8.10 xarray : 2022.6.0 xlrd : 2.0.1 xlwt : 1.3.0 zstandard : 0.18.0 tzdata : None </details>
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