I search the forum and see that it's possible to assign a list to a df cell, but can you assign one df to a df cell (i.e. nest dataframe to dataframe?)
A B C
0 Rep1 df_sales1(dataframe) df_bonus1(dataframe)
1 Rep2 df_sales2(dataframe) df_bonus2(dataframe)
Based on @ Mohamed Thasin ah below, I tried the following;
df1 = pd.DataFrame(np.random.randint(1,10, size = (3,3)), columns = ['A','B','C'])
df2 = pd.DataFrame(np.random.randint(1,10, size = (3,3)), columns = ['A','B','C'])
df3 = pd.DataFrame(np.random.randint(1,10, size = (3,3)), columns = ['A','B','C'])
dfs = pd.DataFrame({'idx': [0,1,2], 'dfs':[df1, df2, df3]})
dfs
idx dfs
0 0 A B C 0 3 4 6 1 3 5 5 2 6 2 5
1 1 A B C 0 2 9 9 1 6 5 5 2 9 1 1
2 2 A B C 0 3 7 2 1 8 5 1 2 9 4 8
Now I create another df_extra,
df_extra = pd.DataFrame(np.random.randint(10,100, size= (2,2)), columns = ['extra1', 'extra2'])
df_extra
extra1 extra2
0 60 86
1 97 75
Now I tried to add another column in the original df, and add the df_extra to that column, say in the second row. This is where I run into problems. I tried different method to add this df_extra as list as follows;
dfs.loc[1,'extra'] = [df_extra]
this creates error:
......
ValueError: setting an array element with a sequence.
How should I do that to get the following:
idx dfs extra
0 0 A B C 0 3 4 6 1 3 5 5 2 6 2 5 NaN
1 1 A B C 0 2 9 9 1 6 5 5 2 9 1 1 **[df_extra]**
2 2 A B C 0 3 7 2 1 8 5 1 2 9 4 8 NaN
