[Python] How do assign iterables to columns in pandas dataframes?

I have a dataframe containing rows which describe financial stocks. The following is a simplified version:

df = pd.DataFrame({'stockprice': [100, 103,240], 'Characteristic1': [1,3,3],'Characteristic2':[5,7,1],'Characteristic3':[1,4,6]},index=["Company A", "Company B","Company C"])

stockprice Characteristic1 Characteristic2 Characteristic3
Company A 100 1 5 1
Company B 103 3 7 4
Company C 240 3 1 6
>>>


I would like to add a column which should contain for each cell a long dictionary which will be generated based on some of these characteristics - a series of cashflows. Later I will want to do some calculation on this generated dictionary.

Here is a sample function which generates the dictionary, and then the assign function to put it into my dataframe:

def cashflow_series(ch1=1,ch2=2):
return {0:ch1,0.5:ch2,1:7,2:8,3:9}

df.assign(cashflows=lambda x:cashflow_series(ch1=x["Characteristic1"],ch2=x["Characteristic3"]))


This returns

stockprice Characteristic1 Characteristic2 Characteristic3 cashflows
Company A 100 1 5 1 NaN
Company B 103 3 7 4 NaN
Company C 240 3 1 6 NaN


How do I fix this?

I am wanting the new column 'cashflows' to contain a dictionary for each row, not a NaN.

I want something like this:

stockprice Characteristic1 Characteristic2 Characteristic3 cashflows
Company A 100 1 5 1 {0:1,..3:9}
Company B 103 3 7 4 {0:3,..3:9}
Company C 240 3 1 6 {0:3,..3:9}

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