Question: I have a dataset with ["Uni", 'Region', "Profession", "Level_Edu", 'Financial_Base', 'Learning_Time', 'GENDER'] columns. All values in ["Uni", 'Region', "Profession"] are filled while ["Level_Edu", 'Financial_Base', 'Learning_Time', 'GENDER'] always contain NAs. For each column with NAs there are several possible values Level_Edu = ['undergrad', 'grad', 'PhD'] Financial_Base = ['personal', 'grant'] Learning_Time = ["morning", "day", "evening"] GENDER = ['Male', 'Female']  I want to generate all possible combinations of ["Level_Edu", 'Financial_Base', 'Learning_Time', 'GENDER'] for each observation in the initial data. So that each initial observation would be represented by 36 new observations (obtained by the formula of combinatorics: N1 * N2 * N3 * N4, where Ni is the length of the i-th vector of possible values for a column) Here is a Python code for recreating two initial observations and approximation of the result I desire to get (showing 3 combinations out of 36 for each initial observation I want). import pandas as pd import numpy as np sample_data_as_is = pd.DataFrame([["X1", "Y1", "Z1", np.nan, np.nan, np.nan, np.nan], ["X2", "Y2", "Z2", np.nan, np.nan, np.nan, np.nan]], columns=["Uni", 'Region', "Profession", "Level_Edu", 'Financial_Base', 'Learning_Time', 'GENDER']) sample_data_to_be = pd.DataFrame([["X1", "Y1", "Z1", "undergrad", "personal", "morning", 'Male'], ["X2", "Y2", "Z2", "undergrad", "personal", "morning", 'Male'], ["X1", "Y1", "Z1", "grad", "personal", "morning", 'Male'], ["X2", "Y2", "Z2", "grad", "personal", "morning", 'Male'], ["X1", "Y1", "Z1", "undergrad", "grant", "morning", 'Male'], ["X2", "Y2", "Z2", "undergrad", "grant", "morning", 'Male']], columns=["Uni", 'Region', "Profession", "Level_Edu", 'Financial_Base', 'Learning_Time', 'GENDER']) 

Answer: You can combine itertools.product and a cross-merge: from itertools import product data = {'Level_Edu': ['undergrad', 'grad', 'PhD'], 'Financial_Base': ['personal', 'grant'], 'Learning_Time': ['morning', 'day', 'evening'], 'GENDER': ['Male', 'Female']} out = (sample_data_as_is[['Uni', 'Region', 'Profession']] .merge(pd.DataFrame(product(*data.values()), columns=data.keys()), how='cross') )  Output:  Uni Region Profession Level_Edu Financial_Base Learning_Time GENDER 0 X1 Y1 Z1 undergrad personal morning Male 1 X1 Y1 Z1 undergrad personal morning Female 2 X1 Y1 Z1 undergrad personal day Male 3 X1 Y1 Z1 undergrad personal day Female 4 X1 Y1 Z1 undergrad personal evening Male .. .. ... ... ... ... ... ... 67 X2 Y2 Z2 PhD grant morning Female 68 X2 Y2 Z2 PhD grant day Male 69 X2 Y2 Z2 PhD grant day Female 70 X2 Y2 Z2 PhD grant evening Male 71 X2 Y2 Z2 PhD grant evening Female [72 rows x 7 columns]  If you want the specific order of rows/columns from your expected output: cols = ['Uni', 'Region', 'Profession'] out = (pd.DataFrame(product(*data.values()), columns=data.keys()) .merge(sample_data_as_is[cols], how='cross') [cols+list(data)] )  Output:  Uni Region Profession Level_Edu Financial_Base Learning_Time GENDER 0 X1 Y1 Z1 undergrad personal morning Male 1 X2 Y2 Z2 undergrad personal morning Male 2 X1 Y1 Z1 undergrad personal morning Female 3 X2 Y2 Z2 undergrad personal morning Female 4 X1 Y1 Z1 undergrad personal day Male .. .. ... ... ... ... ... ... 67 X2 Y2 Z2 PhD grant day Female 68 X1 Y1 Z1 PhD grant evening Male 69 X2 Y2 Z2 PhD grant evening Male 70 X1 Y1 Z1 PhD grant evening Female 71 X2 Y2 Z2 PhD grant evening Female [72 rows x 7 columns]