Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
data = [0, 10.25, 11.11, 5.555, 50.55, 70.75, 98.99, 99.98, 99.99, 100.00]
df = pd.DataFrame(data)
f16_df = df.astype("float16").round(2)
f32_df = df.astype("float32").round(2)
f64_df = df.astype("float64").round(2)
f_df = df.astype(float).round(2)
f16t32_df = f16_df.astype("float32").round(2)
print(f"df:\n{df}\n")
print(f"f16:\n{f16_df}\n")
print(f"f32:\n{f32_df}\n")
print(f"f64:\n{f64_df}\n")
print(f"f:\n{f_df}\n")
print(f"f16t32:\n{f16t32_df}\n")df:
0
0 0.000
1 10.250
2 11.110
3 5.555
4 50.550
5 70.750
6 98.990
7 99.980
8 99.990
9 100.000
f16:
0
0 0.000000
1 10.250000
2 11.109375
3 5.558594
4 50.562500
5 70.750000
6 99.062500
7 100.000000
8 100.000000
9 100.000000
f32:
0
0 0.000000
1 10.250000
2 11.110000
3 5.560000
4 50.549999
5 70.750000
6 98.989998
7 99.980003
8 99.989998
9 100.000000
f64:
0
0 0.00
1 10.25
2 11.11
3 5.56
4 50.55
5 70.75
6 98.99
7 99.98
8 99.99
9 100.00
f:
0
0 0.00
1 10.25
2 11.11
3 5.56
4 50.55
5 70.75
6 98.99
7 99.98
8 99.99
9 100.00
f16t32:
0
0 0.000000
1 10.250000
2 11.110000
3 5.560000
4 50.560001
5 70.750000
6 99.059998
7 100.000000
8 100.000000
9 100.000000Issue Description
pd.DataFrame.round() does not seem to take effect when using NumPy floating point datatypes.
Notice that it only works for float64, which I assume is because that's the default, making it akin to Python's default float type (please correct my understanding if it is wrong).
What's worse is when this happens, boolean indexing does not behave as
intended. This is how I came across this issue:
f16_range_df = f16_df[f16_df <= 99.99]
print(f"f16:\n{f16_df}\n")
print(f"f16_range_df:\n{f16_range_df}\n")f16:
0
0 0.000000
1 10.250000
2 11.109375
3 5.558594
4 50.562500
5 70.750000
6 99.062500
7 100.000000
8 100.000000
9 100.000000
f16_range_df:
0
0 0.000000
1 10.250000
2 11.109375
3 5.558594
4 50.562500
5 70.750000
6 99.062500
7 100.000000
8 100.000000
9 100.000000Oddly, this behavior is observed when checking the inequality against 99.99, 99.98, 99.97, but starts behaving correctly at 99.96.
Expected Behavior
I would expect everything to be rounded to 2 decimal places, the way the float64 (f64), and float (f) datatypes are in the example above.
I would also expect that boolean indexing works within the range specified, according to what that dataframe shows as its values when printed.
Installed Versions
INSTALLED VERSIONS
commit : 8dab54d
python : 3.10.8.final.0
python-bits : 64
OS : Linux
OS-release : 6.0.12-arch1-1
Version : #1 SMP PREEMPT_DYNAMIC Thu, 08 Dec 2022 11:03:38 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.5.2
numpy : 1.23.5
pytz : 2021.3
dateutil : 2.8.2
setuptools : 58.1.0
pip : 22.3.1
Cython : 0.29.32
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.2
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None
None