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BUG: DataFrame.describe TypeError for Int64 column instead of int64 #49019

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Closed
2 of 3 tasks
robert-schmidtke opened this issue Oct 10, 2022 · 2 comments
Closed
2 of 3 tasks
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Bug Needs Triage Issue that has not been reviewed by a pandas team member

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@robert-schmidtke
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robert-schmidtke commented Oct 10, 2022

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

# pandas Int64 column causes TypeError on describe
df = pd.DataFrame({"col": [1]}, dtype="Int64")
df.describe()


```python-traceback
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/rschmidtke/miniconda3/envs/pandas150/lib/python3.9/site-packages/pandas/core/generic.py", line 10947, in describe
    return describe_ndframe(
  File "/home/rschmidtke/miniconda3/envs/pandas150/lib/python3.9/site-packages/pandas/core/describe.py", line 99, in describe_ndframe
    result = describer.describe(percentiles=percentiles)
  File "/home/rschmidtke/miniconda3/envs/pandas150/lib/python3.9/site-packages/pandas/core/describe.py", line 179, in describe
    ldesc.append(describe_func(series, percentiles))
  File "/home/rschmidtke/miniconda3/envs/pandas150/lib/python3.9/site-packages/pandas/core/describe.py", line 246, in describe_numeric_1d
    return Series(d, index=stat_index, name=series.name, dtype=dtype)
  File "/home/rschmidtke/miniconda3/envs/pandas150/lib/python3.9/site-packages/pandas/core/series.py", line 471, in __init__
    data = sanitize_array(data, index, dtype, copy)
  File "/home/rschmidtke/miniconda3/envs/pandas150/lib/python3.9/site-packages/pandas/core/construction.py", line 623, in sanitize_array
    subarr = _try_cast(data, dtype, copy, raise_cast_failure)
  File "/home/rschmidtke/miniconda3/envs/pandas150/lib/python3.9/site-packages/pandas/core/construction.py", line 841, in _try_cast
    subarr = np.array(arr, dtype=dtype, copy=copy)
TypeError: float() argument must be a string or a number, not 'NAType'

Issue Description

When describing a DataFrame with an Int64 (or pd.Int64Dtype() for that matter) column instead of an int64 column, a TypeError is raised. There are several issues around TypeErrors for NA-like values, but I believe the above is different, as both the dtype as well as the data are well defined.

Expected Behavior

import pandas as pd

# pandas Int64 should behave like numpy int64
df = pd.DataFrame({"col": [1]}, dtype="int64")
df.describe()
#        col
# count  1.0
# mean   1.0
# std    NaN
# min    1.0
# 25%    1.0
# 50%    1.0
# 75%    1.0
# max    1.0

Installed Versions

INSTALLED VERSIONS

commit : 87cfe4e
python : 3.9.13.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0
numpy : 1.23.3
pytz : 2022.4
dateutil : 2.8.2
setuptools : 65.4.1
pip : 22.2.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
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

@robert-schmidtke robert-schmidtke added Bug Needs Triage Issue that has not been reviewed by a pandas team member labels Oct 10, 2022
@phofl
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phofl commented Oct 10, 2022

Hi, thanks for your report. This is a duplicate of #48778

@phofl phofl closed this as completed Oct 10, 2022
@robert-schmidtke
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Apologies for missing this and thanks for the quick reply!

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