I am given a pandas multiindex DataFrame indexed with floats. Consider the following example:
arrays = [[0.21,0.21,0.21,0.22,0.22,0.22,0.23,0.23,0.23],
[0.81,0.8200000000000001,0.83,0.81,0.8200000000000001,0.83,0.81,0.8200000000000001,0.83]]
df = pd.DataFrame(np.random.randn(9, 2), index=arrays)
df
# 0 1
# 0.21 0.81 -2.234036 -0.145643
# 0.82 0.367248 -1.471617
# 0.83 -0.764520 0.686241
# 0.22 0.81 1.380429 1.546513
# 0.82 1.230707 1.826980
# 0.83 -1.198403 0.377323
# 0.23 0.81 -0.418367 -0.125763
# 0.82 0.682860 -0.119080
# 0.83 -1.802418 0.357573
I am given this DataFrame in this form. Now, if I want to retrieve the entry df.loc[(0.21, 0.82)] I get an error because the index doesn't really carry 0.82 but 0.8200000000000001. I don't know in advance where these problems occur in the index. How can I address this problem? My idea is to round both levels of the multiindex to the significant number of decimals, which is 2 in this case. But how can that be done? Is there a better solution?
Consider using integer numbers instead: multiply your floating-point numbers by 100 (or 1000) and convert to ints:
df.index = pd.MultiIndex.from_product([
(df.index.levels[0] * 100).astype(int),
(df.index.levels[1] * 100).astype(int)])
Integer numbers are precise, unlike floating-point numbers. Now, you can use df.loc[(21, 82)] to access your data.