multiply a single column shared by multiple pandas DataFrames by a number

user_19260183 0
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Lets say we have the following:

Jan_22 ={'A':221, 'B':119, 'C':455,'E':677}
Feb_22 ={'A':342, 'B':1223,'C':133,'D':3662,'G':321}
Mar_22 ={'A':252, 'C':53}
list = [Jan_22,Feb_22,Mar_22]
df = pd.DataFrame(list)
df

    A   B   C   D   E   G
0   221 119.0   455 NaN 677.0   NaN
1   342 1223.0  133 3662.0  NaN 321.0
2   252 NaN 53  NaN NaN NaN

This dataframe is made from a list of three dictionaries, each named after a month (Jan_22, etc.). The columns are not named by the month (just 0 is Jan,1 is Feb ,2 is Mar). What I want to do is divide the numbers in each column of the dataframe by its respective days in the month. How do I do this?

pandas date python
2 Answers

You can simply use the divide function of your pandas dataframe:

number_of_days = [31, 28.25, 31]
df = df.divide(number_of_days, axis=0)
user_9158690

Caveat! You are declaring a variable with the reserved word "list" so this may cause an error.

On the other hand, a possible solution to your problem would be:

Jan_22 ={'A':221, 'B':119, 'C':455,'E':677}
Feb_22 ={'A':342, 'B':1223,'C':133,'D':3662,'G':321}
Mar_22 ={'A':252, 'C':53}
lista = [Jan_22,Feb_22,Mar_22]

df = pd.DataFrame(lista)
for i in df.columns.values:
    df[i]= df[i].apply(lambda x: x*2) #You can change the two to the value you want
print(df)
user_180512170
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