how to calculate if statement for relative value rebalancing / Error: “The truth value of a Series is ambiguous”

Question

Below you find the code I wrote to calculate a relative change in value of df.a and df.b while df is a dataframe. What has to be calculated is basically df[\"c\"] = df

Answer 1

This is not a 100% solution, but should at least get you down a better path and fix a primary problem. The core problem I'm seeing here from a syntax side is that you are trying to mix vectorized and non-vectorized code. You could instead do something more like this:

>>> df['d1'] = df.a/df.a.iloc[df.d].values > df.b/df.b.iloc[df.d].values * (1+tolerance)

>>> df['d2'] = df.a/df.a.iloc[df.d].values * (1+tolerance) < df.b/df.b.iloc[df.d].values

>>> df['d'] = df['d1'] | df['d2']

>>> df

              a    b     c      d  t     d1     d2
2011-01-01  1.1  1.1  None  False  0  False  False
2011-01-02  1.2  1.5  None  False  1  False  False
2011-01-03  2.3  1.3  None   True  2   True  False
2011-01-04  1.4  1.6  None  False  3  False  False
2011-01-05  1.5  1.5  None  False  4  False  False
2011-01-06  1.8  1.1  None   True  5   True  False
2011-01-07  0.7  1.5  None   True  6  False   True
2011-01-08  1.8  1.7  None  False  7  False  False
2011-01-09  1.9  2.1  None  False  8  False  False
2011-01-10  2.0  2.1  None  False  9  False  False

That's not quite the answer you want, but hopefully shows you what is going on with the code and how you can fix it to get what you want (i.e. you don't need or want to be using a function and applying it here, just use standard pandas vectorized code).

If you can get that to work, the cleaner way to do that would be with np.where (either two of them sequentially, or nested).