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

Ok, I get the result you want, but this is still way too complicated and unefficient. I would be interested to see a superior solution:

import pandas as pd
import numpy as np
import datetime

randn = np.random.randn


rng = pd.date_range('1/1/2011', periods=10, freq='D')

df = pd.DataFrame({'a': [1.1, 1.2, 2.3, 1.4, 1.5, 1.8, 0.7, 1.8, 1.9, 2.0], 'b': [1.1, 1.5, 1.3, 1.6, 1.5, 1.1, 1.5, 1.7, 2.1, 2.1],'c':[None] * 10},index=rng)



df["d"]= [0,0,0,0,0,0,0,0,0,0]



df["t"]= np.arange(len(df))
tolerance = 0.3

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['e'] = df.d1*df.t
df['f'] = df.d2*df.t
df['g'] = df.e +df.f
df.ix[df.g > df.g.shift(1),"h"] = df.g * 1; df
df.h = df.h + 1
df.h = df.h.shift(1)
df['h'][0] = 0

df.h.fillna(method='ffill',inplace=True)
df["d"] = df.h
df["c"] = df.a/df.a.iloc[df.d].values

and that's the result:

              a    b         c  d  t     d1     d2  e  f  g  h
2011-01-01  1.1  1.1  1.000000  0  0  False  False  0  0  0  0
2011-01-02  1.2  1.5  1.090909  0  1  False  False  0  0  0  0
2011-01-03  2.3  1.3  2.090909  0  2   True  False  2  0  2  0
2011-01-04  1.4  1.6  1.000000  3  3  False  False  0  0  0  3
2011-01-05  1.5  1.5  1.071429  3  4  False  False  0  0  0  3
2011-01-06  1.8  1.1  1.285714  3  5   True  False  5  0  5  3
2011-01-07  0.7  1.5  1.000000  6  6  False   True  0  6  6  6
2011-01-08  1.8  1.7  1.000000  7  7  False  False  0  0  0  7
2011-01-09  1.9  2.1  1.055556  7  8  False  False  0  0  0  7
2011-01-10  2.0  2.1  1.111111  7  9  False  False  0  0  0  7

from here you can easily delete rows with e.g. del df['g']