I have the following dataframe:
data = [['BALANCED', 'Corp EUR'], ['YIELD', 'Corp USD'], ['GROWTH', 'HG CHF']]
df = pd.DataFrame(data, columns=['STRATEGY', 'ASSET_CLASS']
df
STRATEGY ASSET_CLASS
0 BALANCED Corp EUR
1 YIELD Corp USD
2 GROWTH HG CHF
My objective is to assign a specific value from a dictionary with multiple values depending on the value in column "STRATEGY". The dictionary containing the different asset classes looks as follows:
my_dict = {'HG CHF': [5, 2, 3, 4],
'Corp EUR': [4, 6, 8, 7],
'Corp USD': [9, 7.5, 5.3, 6],
}
my_dict
{'HG CHF': [5, 2, 3, 4],
'Corp EUR': [4, 6, 8, 7],
'Corp USD': [9, 7.5, 5.3, 6]}
In the dictionary, each value depicts a specific strategy. In this case, YIELD include all second values, BALANCED all third values and GROWTH all fourth values, respectively. Essentially, what I'd like to obtain is the following output:
STRATEGY ASSET_CLASS TARGET
0 BALANCED Corp EUR 8.0
1 YIELD Corp USD 7.5
2 GROWTH HG CHF 4.0
How can I correctly tell Python which value in the dictionary to access? Below command maps all values:
df['TARGET'] = df['ASSET_CLASS'].map(my_dict)
STRATEGY ASSET_CLASS TARGET
0 BALANCED Corp EUR [4, 6, 8, 7]
1 YIELD Corp USD [9, 7.5, 5.3, 6]
2 GROWTH HG CHF [5, 2, 3, 4]
Any suggestion is much appreciated!! Thanks a lot in advance!!
Use a second dictionary and a list comprehension with zip:
# which item should be picked?
nth = {'YIELD': 1, 'BALANCED': 2, 'GROWTH': 3}
df['TARGET'] = [
my_dict[a][nth[s]] for s, a in zip(df['STRATEGY'], df['ASSET_CLASS'])
]
Output:
STRATEGY ASSET_CLASS TARGET
0 BALANCED Corp EUR 8.0
1 YIELD Corp USD 7.5
2 GROWTH HG CHF 4.0
If there is a chance that the lists don't have enough items, you can add a safety check:
# let's pick the 5th item for "GROWTH"
nth = {'YIELD': 1, 'BALANCED': 2, 'GROWTH': 5}
df['TARGET'] = [
d[n] if (n := nth[s]) < len(d := my_dict[a]) else None
for s, a in zip(df['STRATEGY'], df['ASSET_CLASS'])
]
Example:
STRATEGY ASSET_CLASS TARGET
0 BALANCED Corp EUR 8.0
1 YIELD Corp USD 7.5
2 GROWTH HG CHF NaN