Description
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
pd.set_option('compute.use_numba', True)
df = pd.DataFrame([[1, 2], [3, 4]])
df.info()
"""
...
ValueError: Unknown format code 'd' for object of type 'float'
"""Issue Description
I'm sorry to inform you that my current pandas environment can only be updated to version 1.5.2, and this bug exists in this version. If the bug has been fixed in version 1.5.3, I will wait for the maintainers to update it.
When compute.use_numba is disabled,
pd.set_option('compute.use_numba', False)
info = pd.core.frame.DataFrameInfo(df)
print(info.dtype_counts)
"""
int64 2
dtype: int64
"""
When compute.use_numba is enabled,
pd.set_option('compute.use_numba', True)
info = pd.core.frame.DataFrameInfo(df)
print(info.dtype_counts)
"""
int64 2.0
dtype: float64
"""
The count of data types in the dtype Series has changed from int to float. This causes an error in the code called by the DataFrame.info() function.
779 def add_dtypes_line(self) -> None:
780 """Add summary line with dtypes present in dataframe."""
781 collected_dtypes = [
--> 782 f"{key}({val:d})" for key, val in sorted(self.dtype_counts.items())
783 ]
784 self._lines.append(f"dtypes: {', '.join(collected_dtypes)}")
ValueError: Unknown format code 'd' for object of type 'float'
Expected Behavior
import pandas as pd
pd.set_option('compute.use_numba', True)
df = pd.DataFrame([[1, 2], [3, 4]])
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2 entries, 0 to 1
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 0 2 non-null int64
1 1 2 non-null int64
dtypes: int64(2)
memory usage: 160.0 bytes
Installed Versions
INSTALLED VERSIONS
commit : 8dab54d
python : 3.9.0.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-348.7.1.el8_5.x86_64
Version : #1 SMP Wed Dec 22 13:25:12 UTC 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None
pandas : 1.5.2
numpy : 1.23.5
pytz : 2022.7
dateutil : 2.8.2
setuptools : 65.6.3
pip : 23.0
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.8.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.5
brotli :
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.6.2
numba : 0.56.4
numexpr : 2.8.4
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 8.0.0
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.0
snappy : None
sqlalchemy : 2.0.3
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : 2022.7