Check if a value in a df is in a row after

February 7, 2023

First of all, my English is not my native language, sorry for my mistakes.

I am looking to automate some tasks done via Excel with Python.
I have a dataframe ordered by date/time, and I want to check if a customer has contacted me again after already having a response.

So I have a dataframe like this:


|     Date   |     Tel      |
| ---------- | ------------ |
| 01-01-2023 | +33000000001 |
| 01-01-2023 | +33000000002 |
| 01-01-2023 | +33000000003 |
| 02-01-2023 | +33000000002 |
| 02-01-2023 | +33000000004 |

I’d like to add a column TRUE/FALSE if my client has contacted me later :

|     Date     |     Tel      |  Re-contact  | 
| ------------ | ------------ | ------------ |
| 01-01-2023   | +33000000001 |     FALSE    |
| 01-01-2023   | +33000000002 |     TRUE     |
| 01-01-2023   | +33000000003 |     FALSE    |
| 02-01-2023   | +33000000002 |     FALSE    |
| 02-01-2023   | +33000000004 |     FALSE    |

In Excel, I do this action as follows:

COUNTIFS(A2:A$5;A1)>0

And I would get my TRUE/FALSE if the phone number exists further in my list.

I looked at the documentation to see if a value existed in a list, but I couldn’t find a way to see if it existed further down. Also, I’m looking for a quick way to calculate it, as I have 100,000 rows in my dataframe.

# I've tried this so far: 

length = len(df.index) - 1
i = 1

for i in range(i, length):
    print(i)
    for x in df['number']:
    if x in df['number'][[i+1, length]]:
        df['Re-contact'] = 'TRUE'
    else:
        df['Re-contact'] = 'FALSE'
    i += 1

It feels very wrong to me, and my code takes too much time. I’m looking for a more efficient way to perform what I’m trying to do.

>Solution :

Use pandas.DataFrame.duplicated over Tel column to find repeated calls:

df['Re-contact'] = df.Tel.duplicated(keep='last')

         Date          Tel  Re-contact
0  01-01-2023  33000000001       False
1  01-01-2023  33000000002        True
2  01-01-2023  33000000003       False
3  02-01-2023  33000000002       False
4  02-01-2023  33000000004       False