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I have a DF like this:

ID Name Mail Phone1 Phone2 Mail1 Mail2 contact phone contact mail
70 DASS ARGENTINA SRL [email protected] 2664941642 2664941644 [email protected] [email protected] 115456789 [email protected]
71 PEPSI [email protected] 0456535365 7766554399 [email protected] [email protected] 8864545332 [email protected]
ID Name LOCATOR
70 DASS ARGENTINA SRL [email protected]
70 DASS ARGENTINA SRL 2664941642
70 DASS ARGENTINA SRL 2664941644
70 DASS ARGENTINA SRL [email protected]
70 DASS ARGENTINA SRL [email protected]
70 DASS ARGENTINA SRL 115456789
70 DASS ARGENTINA SRL [email protected]
71 PEPSI [email protected]
71 PEPSI 0456535365
71 PEPSI 7766554399
71 PEPSI [email protected]
71 PEPSI [email protected]
71 PEPSI 8864545332
71 PEPSI [email protected]

Is it possible to do anythong like this? I´ve tried with transpose function but I´m not getting the output like the example above

Maximiliano Vazquez
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1 Answers1

1

use pd.melt to flatten the dataframe and then sort and remove the unwanted columns

df.drop(columns='ID').melt(id_vars='Name', value_name='Locator').sort_values('Name').drop(columns='variable')

                  Name  Locator
0   DASS ARGENTINA SRL  [email protected]
2   DASS ARGENTINA SRL  2664941642
4   DASS ARGENTINA SRL  2664941644
6   DASS ARGENTINA SRL  [email protected]
8   DASS ARGENTINA SRL  [email protected]
10  DASS ARGENTINA SRL  115456789
12  DASS ARGENTINA SRL  [email protected]
1   PEPSI               [email protected]
3   PEPSI               456535365
5   PEPSI               7766554399
7   PEPSI               [email protected]
9   PEPSI               [email protected]
11  PEPSI               8864545332
13  PEPSI               [email protected]
Naveed
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