Python Forum

I have a large dataframe loaded from a CSV file.
I want to stack the columns related to each product into rows.

This is a sample of the dataframe, real dataset i have many coumns for more product quantities & Product Vales

import pandas as pd
import numpy as np

csvData = pd.DataFrame(data = {'Date':['01-09-17','01-09-17','02-09-17'],
                             'CustomerCode':['C100','C200','C100'],
                             'CustomerName':['CustomerA','CustomerB','CustomerA'],
                             'ProductA_Qty':[50,70,60],
                             'ProductA_Value':[1000,1400,1200],
                             'ProductB_Qty':[40,np.nan,20],
                             'ProductB_Value':[800,np.nan,400]
                             },columns=['Date', 'CustomerCode', 'CustomerName','ProductA_Qty','ProductA_Value','ProductB_Qty','ProductB_Value'])

I'm expecting an output similar to below table

Output:
Date                CustomerCode      CustomerName      Product          Qty     Value
01-09-2017     C100                     Customer A           Product A       50       1000
01-09-2017     C100                     Customer A           Product B       40        800
01-09-2017     C200                     Customer B           Product A       50       1000
01-09-2017     C200                     Customer B           Product B      null       null
02-09-2017     C100                     Customer A           Product A       60       1200
02-09-2017     C100                     Customer A           Product B       20        400

Code i tried is as follows, However the result is not as i expected;

new = pd.melt(csvData,
                   id_vars=['Date','CustomerCode','CustomerName'],
                   var_name='Product',
                   value_name='Values')

Result i'm getting from above code is;

Output:
Out[855]:
        Date CustomerCode CustomerName         Product  Values
0   01-09-17         C100    CustomerA    ProductA_Qty    50.0
1   01-09-17         C200    CustomerB    ProductA_Qty    70.0
2   02-09-17         C100    CustomerA    ProductA_Qty    60.0
3   01-09-17         C100    CustomerA  ProductA_Value  1000.0
4   01-09-17         C200    CustomerB  ProductA_Value  1400.0
5   02-09-17         C100    CustomerA  ProductA_Value  1200.0
6   01-09-17         C100    CustomerA    ProductB_Qty    40.0
7   01-09-17         C200    CustomerB    ProductB_Qty     NaN
8   02-09-17         C100    CustomerA    ProductB_Qty    20.0
9   01-09-17         C100    CustomerA  ProductB_Value   800.0
10  01-09-17         C200    CustomerB  ProductB_Value     NaN
11  02-09-17         C100    CustomerA  ProductB_Value   400.0

Highly appreciate if someone can help me to achive the expected result.

Thanks for your time & efforts.