I've got a pandas DataFrame with a float (on decimal) index which I use to look up values (similar to a dictionary). As floats are not exactly the value they are supposed to be multiplied everything by 10 and converted it to integers .astype(int) before setting it as index. However this seems to do a floor instead of rounding. Thus 1.999999999999999992 is converted to 1 instead of 2. Rounding with the pandas.DataFrame.round() method before does not avoid this problem as the values are still stored as floats.
The original idea (which obviously rises a key error) was this:
idx = np.arange(1,3,0.001)
s = pd.Series(range(2000))
s.index=idx
print(s[2.022])
trying with converting to integers:
idx_int = idx*1000
idx_int = idx_int.astype(int)
s.index = idx_int
for i in range(1000,3000):
print(s[i])
the output is always a bit random as the 'real' value of an integer can be slightly above or below the wanted value. In this case the index contains two times the value 1000 and does not contain the value 2999.
You are right, astype(int) does a conversion toward zero:
‘integer’ or ‘signed’: smallest signed int dtype
from pandas.to_numeric documentation (which is linked from astype() for numeric conversions).
If you want to round, you need to do a float round, and then convert to int:
df.round(0).astype(int)
Use other rounding functions, according your needs.
the output is always a bit random as the 'real' value of an integer can be slightly above or below the wanted value
Floats are able to represent whole numbers, making a conversion after round(0) lossless and non-risky, check here for details.