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DiscussionForumPosting:
      context:https://schema.org
      headline:BUG: np.mean(pd.Series) != np.mean(pd.Series.values)
      articleBody:- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd import numpy as np a = pd.Series(np.random.normal(scale=0.1, size=(1_000_000,)).astype(np.float32)).pow(2) assert isinstance(np.mean(a), float) assert isinstance(np.mean(a.values), np.float32) assert abs(1 - np.mean(a)/np.mean(a.values)) > 4e-4 ``` #### Problem description 1. `pd.DataFrame.mean`/`pd.Series.mean`/`np.mean(pd.Series)` outputs a Python float instead of a numpy float. Since `np.mean(pd.Series.values)` does return an np float, I'm assuming for now that this should be fixed in pandas 2. if `dtype==np.float32`, then calling `mean` on a pandas object gives a significantly different result vs calling `mean` on the underlying numpy ndarray. #### Expected Output The output of `np.mean(a)` should be the same as `np.mean(a.values)`. additional tests ```python # both b and c ~1e-2 b = a.mean() # the pandas impl of mean assert isinstance(b, float) # PYTHON float, not numpy float? Ergo implicit f64 h = np.mean(a) assert isinstance(h, float) assert h == b c = a.values.mean() # the numpy impl of mean assert isinstance(c, np.float32) # as exprected print('\nerrors between pandas mean and numpy mean') print(f'relative error: {abs(1-b/c):.3e}') # ~ 5e-4 print(f'absolute error: {abs(b -c):.3e}') # ~ 5e-6 print(f'relative error after casting: {abs(1-np.float32(b)/c):.3e}') # ~ 5e-4 print(f'absolute error after casting: {abs(np.float32(b) -c):.3e}') # ~ 5e-6 d = a.sum() / len(a) assert isinstance(d, np.float64) # expected, because division. Note `sum` returns an np.float32 e = a.values.sum() / len(a) assert isinstance(e, np.float64) # expected, because division # these methods are equivalent assert d==e # and up to f32 precision equal to the numpy impl assert d.astype(np.float32) == c # the cherry on the cake f = a.astype(np.float64).mean() assert isinstance(f, float) # still not ideal, should be np.float64 g = a.astype(np.float64).values.mean() print('\nrelative error between pandas f64 mean and numpy f64 mean') print(f'relative error numpy f64/pandas f64: {abs(1-g/f):.3e}') # ~ 1e-14 -- 1e-16, not bad but I would have expected equality print('\nerrors between pandas f64 mean and numpy/pandas f32 mean') print(f'relative error pandas f32/pandas f64: {abs(1-b/f):.3e}') # ~ 5e-4 print(f'absolute error numpy f32/pandas f64: {abs(1-c/f):.3e}') # ~ 1e-7 -- 1e-9 # finally... h = np.mean(a) assert isinstance(h, float) assert h == b ``` output ```python errors between pandas mean and numpy mean relative error: 5.210e-04 absolute error: 5.204e-06 relative error after casting: 5.210e-04 absolute error after casting: 5.204e-06 relative error between pandas f64 mean and numpy f64 mean relative error numpy f64/pandas f64: 1.066e-14 errors between pandas f64 mean and numpy/pandas f32 mean relative error pandas f32/pandas f64: 5.214e-04 absolute error numpy f32/pandas f64: 2.399e-07 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.8.3.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-80-generic Version : #90-Ubuntu SMP Fri Jul 9 22:49:44 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.1 numpy : 1.21.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.1 setuptools : 52.0.0.post20210125 Cython : 0.29.23 pytest : 6.2.3 hypothesis : None sphinx : 4.0.1 blosc : None feather : None xlsxwriter : 1.3.8 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 3.0.0 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.9.0 fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.15 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.51.2 </details>
      author:
         url:https://github.com/sebasv
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      datePublished:2021-08-04T09:37:14.000Z
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      url:https://github.com/42878/pandas/issues/42878
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      headline:BUG: np.mean(pd.Series) != np.mean(pd.Series.values)
      articleBody:- [x] I have checked that this issue has not already been reported. - [x] I have confirmed this bug exists on the latest version of pandas. - [x] (optional) I have confirmed this bug exists on the master branch of pandas. --- #### Code Sample, a copy-pastable example ```python import pandas as pd import numpy as np a = pd.Series(np.random.normal(scale=0.1, size=(1_000_000,)).astype(np.float32)).pow(2) assert isinstance(np.mean(a), float) assert isinstance(np.mean(a.values), np.float32) assert abs(1 - np.mean(a)/np.mean(a.values)) > 4e-4 ``` #### Problem description 1. `pd.DataFrame.mean`/`pd.Series.mean`/`np.mean(pd.Series)` outputs a Python float instead of a numpy float. Since `np.mean(pd.Series.values)` does return an np float, I'm assuming for now that this should be fixed in pandas 2. if `dtype==np.float32`, then calling `mean` on a pandas object gives a significantly different result vs calling `mean` on the underlying numpy ndarray. #### Expected Output The output of `np.mean(a)` should be the same as `np.mean(a.values)`. additional tests ```python # both b and c ~1e-2 b = a.mean() # the pandas impl of mean assert isinstance(b, float) # PYTHON float, not numpy float? Ergo implicit f64 h = np.mean(a) assert isinstance(h, float) assert h == b c = a.values.mean() # the numpy impl of mean assert isinstance(c, np.float32) # as exprected print('\nerrors between pandas mean and numpy mean') print(f'relative error: {abs(1-b/c):.3e}') # ~ 5e-4 print(f'absolute error: {abs(b -c):.3e}') # ~ 5e-6 print(f'relative error after casting: {abs(1-np.float32(b)/c):.3e}') # ~ 5e-4 print(f'absolute error after casting: {abs(np.float32(b) -c):.3e}') # ~ 5e-6 d = a.sum() / len(a) assert isinstance(d, np.float64) # expected, because division. Note `sum` returns an np.float32 e = a.values.sum() / len(a) assert isinstance(e, np.float64) # expected, because division # these methods are equivalent assert d==e # and up to f32 precision equal to the numpy impl assert d.astype(np.float32) == c # the cherry on the cake f = a.astype(np.float64).mean() assert isinstance(f, float) # still not ideal, should be np.float64 g = a.astype(np.float64).values.mean() print('\nrelative error between pandas f64 mean and numpy f64 mean') print(f'relative error numpy f64/pandas f64: {abs(1-g/f):.3e}') # ~ 1e-14 -- 1e-16, not bad but I would have expected equality print('\nerrors between pandas f64 mean and numpy/pandas f32 mean') print(f'relative error pandas f32/pandas f64: {abs(1-b/f):.3e}') # ~ 5e-4 print(f'absolute error numpy f32/pandas f64: {abs(1-c/f):.3e}') # ~ 1e-7 -- 1e-9 # finally... h = np.mean(a) assert isinstance(h, float) assert h == b ``` output ```python errors between pandas mean and numpy mean relative error: 5.210e-04 absolute error: 5.204e-06 relative error after casting: 5.210e-04 absolute error after casting: 5.204e-06 relative error between pandas f64 mean and numpy f64 mean relative error numpy f64/pandas f64: 1.066e-14 errors between pandas f64 mean and numpy/pandas f32 mean relative error pandas f32/pandas f64: 5.214e-04 absolute error numpy f32/pandas f64: 2.399e-07 ``` #### Output of ``pd.show_versions()`` <details> INSTALLED VERSIONS ------------------ commit : c7f7443c1bad8262358114d5e88cd9c8a308e8aa python : 3.8.3.final.0 python-bits : 64 OS : Linux OS-release : 5.4.0-80-generic Version : #90-Ubuntu SMP Fri Jul 9 22:49:44 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.3.1 numpy : 1.21.1 pytz : 2021.1 dateutil : 2.8.1 pip : 21.1.1 setuptools : 52.0.0.post20210125 Cython : 0.29.23 pytest : 6.2.3 hypothesis : None sphinx : 4.0.1 blosc : None feather : None xlsxwriter : 1.3.8 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : 2.8.6 (dt dec pq3 ext lo64) jinja2 : 3.0.0 IPython : 7.22.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 0.9.0 fastparquet : None gcsfs : None matplotlib : 3.3.4 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : None pyxlsb : None s3fs : None scipy : 1.6.2 sqlalchemy : 1.4.15 tables : 3.6.1 tabulate : None xarray : None xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.51.2 </details>
      author:
         url:https://github.com/sebasv
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      url:https://github.com/42878/pandas/issues/42878
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