Comparing floats in a pandas column
Due to imprecise float comparison you can or
your comparison with np.isclose
, isclose
takes a relative and absolute tolerance param so the following should work:
df['result'] = df['actual_credit'].ge(df['min_required_credit']) | np.isclose(df['actual_credit'], df['min_required_credit'])
@EdChum's answer works great, but using the pandas.DataFrame.round function is another clean option that works well without the use of numpy
.
df = pd.DataFrame( # adding a small difference at the thousandths place to reproduce the issue
data=[[0.3, 0.4], [0.5, 0.2], [0.400, 0.401], [0.2, 0.3]],
columns=['actual_credit', 'min_required_credit'])
df['result'] = df['actual_credit'].round(1) >= df['min_required_credit'].round(1)
print(df)
actual_credit min_required_credit result
0 0.3 0.400 False
1 0.5 0.200 True
2 0.4 0.401 True
3 0.2 0.300 False
You might consider using round()
to more permanently edit your dataframe, depending if you desire that precision or not. In this example, it seems like the OP suggests this is probably just noise and is just causing confusion.
df = pd.DataFrame( # adding a small difference at the thousandths place to reproduce the issue
data=[[0.3, 0.4], [0.5, 0.2], [0.400, 0.401], [0.2, 0.3]],
columns=['actual_credit', 'min_required_credit'])
df = df.round(1)
df['result'] = df['actual_credit'] >= df['min_required_credit']
print(df)
actual_credit min_required_credit result
0 0.3 0.4 False
1 0.5 0.2 True
2 0.4 0.4 True
3 0.2 0.3 False