how to add a new column with different length into a existing dataframe

I am trying to go over a loop to add columns into a empty dataframe. each column might have a different length. Look like the final number of rows are defined by lenght of first column added. The columns with a Longer length will be cut values.

How to always keep all the values of each column when column length are different? Thanks

Here is case 1 with first column has lower length, then 2nd column’s value will be cut

import pandas as pd

df_profile=pd.DataFrame()
df_profile['A']=pd.Series([1,2,3,4])

df_profile['B']=pd.Series([10,20,30,40,50,60])

print(df_profile)

   A   B
0  1  10
1  2  20
2  3  30
3  4  40

Here are case 2 with first column has highest length, then it is find for other columns

import pandas as pd

df_profile=pd.DataFrame()
df_profile['A']=pd.Series([1,2,3,4,5,6,7,8])

df_profile['B']=pd.Series([10,20,30,40,50,60])

df_profile['C']=pd.Series([100,200,300,400,500,600])

df_profile['D']=pd.Series([100,200])

print(df_profile)

   A     B      C      D
0  1  10.0  100.0  100.0
1  2  20.0  200.0  200.0
2  3  30.0  300.0    NaN
3  4  40.0  400.0    NaN
4  5  50.0  500.0    NaN
5  6  60.0  600.0    NaN
6  7   NaN    NaN    NaN
7  8   NaN    NaN    NaN

>Solution :

You can use pd.concat to add another Series, e.g.:

# you have already this dataframe:
df_profile = pd.DataFrame()
df_profile["A"] = pd.Series([1, 2, 3, 4])

# you can use pd.concat to add another Series:
out = pd.concat([df_profile, pd.Series([10, 20, 30, 40, 50, 60], name="B")], axis=1)
print(out)

Prints:

     A   B
0  1.0  10
1  2.0  20
2  3.0  30
3  4.0  40
4  NaN  50
5  NaN  60