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int, pandas, employed, company, organization, bug, issue, typeerror, united, simonjayhawkins, sign, mattharrison, version, added, member, mehgarg, code, projects, pdpivottable, values, astypeage, works, expected, result, mentioned, commented, navigation, pandasdev, pull, requests, actions, security, closed, description, confirmed, exists, import, statesfully, student, person, earning, income, directly, business, trade, profession, pivottableindexcountrylive, columnsemploymentstatus, valuesage, aggfuncmean,

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nullable extension arrays personal information bug issue description import pandas latest version working student china pivot_table raising typeerror bug exists type projects projects milestone triage issue pandas working student margins=true explode type values='age' github main branch united states company / organization united kingdom aggregation raises code works expected behavior 0 united kingdom running jb2 enforce annotation bug ea dtype milestone relationships fully employed issue na merge/join expected result columns='employment_status' stack/unstack float64 dtype bad commit index='country_live' change age io data pivot_table pivot_table` china int64 'int64'} employed result

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DiscussionForumPosting:
      context:https://schema.org
      headline:BUG: TypeError: Int64 when using `pd.pivot_table` with `Int64` values
      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 import io data = ''',country_live,employment_status,age 26983,United States,Fully employed by a company / organization,21 51776,Turkey,Working student,18 53092,India,Fully employed by a company / organization,21 44612,France,Fully employed by a company / organization,21 7043,United States,"Self-employed (a person earning income directly from one's own business, trade, or profession)",60 50421,Other country,Fully employed by a company / organization,21 2552,United Kingdom,Fully employed by a company / organization,30 21166,China,Fully employed by a company / organization,21 29144,Italy,Fully employed by a company / organization,40 36828,United States,Fully employed by a company / organization,40''' df = pd.read_csv(io.StringIO(data)) (df .astype({'age': 'Int64'}) .pivot_table(index='country_live', columns='employment_status', values='age', aggfunc='mean') ) ``` ### Issue Description This aggregation raises a `TypeError: Int64` However, it you change *age* to `int64` the code works. Also note that it works if you perform the same operation via `.groupby`/`.unstack`: ie: ``` (df #.astype({'age': 'Int64'}) .pivot_table(index='country_live', columns='employment_status', values='age', aggfunc='mean') ) ``` This also works: ``` (df .astype({'age': 'Int64'}) .groupby(['country_live', 'employment_status']) .age .mean() .unstack() ) ``` ### Expected Behavior I expect to see the following result: | country_live | Fully employed by a company / organization | Self-employed (a person earning income directly from one's own business, trade, or profession) | Working student | |:---------------|:---------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------| | China | 21.0 | <NA> | <NA> | | France | 21.0 | <NA> | <NA> | | India | 21.0 | <NA> | <NA> | | Italy | 40.0 | <NA> | <NA> | | Other country | 21.0 | <NA> | <NA> | | Turkey | <NA> | <NA> | 18.0 | | United Kingdom | 30.0 | <NA> | <NA> | | United States | 30.5 | 60.0 | <NA> | ​ ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 4bfe3d07b4858144c219b9346329027024102ab6 python : 3.8.10.final.0 python-bits : 64 OS : Linux OS-release : 5.10.16.3-microsoft-standard-WSL2 Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.4.2 numpy : 1.19.4 pytz : 2022.1 dateutil : 2.8.1 pip : 20.0.2 setuptools : 44.0.0 Cython : 0.29.24 pytest : 7.1.1 hypothesis : 6.8.1 sphinx : 4.3.2 blosc : None feather : None xlsxwriter : 1.3.7 lxml.etree : 4.6.2 html5lib : 1.1 pymysql : None psycopg2 : 2.9.3 jinja2 : 3.0.3 IPython : 7.19.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : None brotli : None fastparquet : None fsspec : 2021.07.0 gcsfs : None markupsafe : 2.1.1 matplotlib : 3.3.3 numba : 0.52.0 numexpr : 2.8.1 odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : 8.0.0 pyreadstat : 1.1.4 pyxlsb : None s3fs : None scipy : 1.5.4 snappy : None sqlalchemy : 1.4.27 tables : None tabulate : 0.8.9 xarray : 0.18.2 xlrd : 2.0.1 xlwt : None zstandard : None </details>
      author:
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      url:https://github.com/47477/pandas/issues/47477
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      headline:BUG: TypeError: Int64 when using `pd.pivot_table` with `Int64` values
      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 import io data = ''',country_live,employment_status,age 26983,United States,Fully employed by a company / organization,21 51776,Turkey,Working student,18 53092,India,Fully employed by a company / organization,21 44612,France,Fully employed by a company / organization,21 7043,United States,"Self-employed (a person earning income directly from one's own business, trade, or profession)",60 50421,Other country,Fully employed by a company / organization,21 2552,United Kingdom,Fully employed by a company / organization,30 21166,China,Fully employed by a company / organization,21 29144,Italy,Fully employed by a company / organization,40 36828,United States,Fully employed by a company / organization,40''' df = pd.read_csv(io.StringIO(data)) (df .astype({'age': 'Int64'}) .pivot_table(index='country_live', columns='employment_status', values='age', aggfunc='mean') ) ``` ### Issue Description This aggregation raises a `TypeError: Int64` However, it you change *age* to `int64` the code works. Also note that it works if you perform the same operation via `.groupby`/`.unstack`: ie: ``` (df #.astype({'age': 'Int64'}) .pivot_table(index='country_live', columns='employment_status', values='age', aggfunc='mean') ) ``` This also works: ``` (df .astype({'age': 'Int64'}) .groupby(['country_live', 'employment_status']) .age .mean() .unstack() ) ``` ### Expected Behavior I expect to see the following result: | country_live | Fully employed by a company / organization | Self-employed (a person earning income directly from one's own business, trade, or profession) | Working student | |:---------------|:---------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------| | China | 21.0 | <NA> | <NA> | | France | 21.0 | <NA> | <NA> | | India | 21.0 | <NA> | <NA> | | Italy | 40.0 | <NA> | <NA> | | Other country | 21.0 | <NA> | <NA> | | Turkey | <NA> | <NA> | 18.0 | | United Kingdom | 30.0 | <NA> | <NA> | | United States | 30.5 | 60.0 | <NA> | ​ ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : 4bfe3d07b4858144c219b9346329027024102ab6 python : 3.8.10.final.0 python-bits : 64 OS : Linux OS-release : 5.10.16.3-microsoft-standard-WSL2 Version : #1 SMP Fri Apr 2 22:23:49 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : C.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.4.2 numpy : 1.19.4 pytz : 2022.1 dateutil : 2.8.1 pip : 20.0.2 setuptools : 44.0.0 Cython : 0.29.24 pytest : 7.1.1 hypothesis : 6.8.1 sphinx : 4.3.2 blosc : None feather : None xlsxwriter : 1.3.7 lxml.etree : 4.6.2 html5lib : 1.1 pymysql : None psycopg2 : 2.9.3 jinja2 : 3.0.3 IPython : 7.19.0 pandas_datareader: None bs4 : 4.10.0 bottleneck : None brotli : None fastparquet : None fsspec : 2021.07.0 gcsfs : None markupsafe : 2.1.1 matplotlib : 3.3.3 numba : 0.52.0 numexpr : 2.8.1 odfpy : None openpyxl : 3.0.5 pandas_gbq : None pyarrow : 8.0.0 pyreadstat : 1.1.4 pyxlsb : None s3fs : None scipy : 1.5.4 snappy : None sqlalchemy : 1.4.27 tables : None tabulate : 0.8.9 xarray : 0.18.2 xlrd : 2.0.1 xlwt : None zstandard : None </details>
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