Get first and last value for a sequence of pairs between two columns of a pandas dataframe

Question

I have a dataframe with 3 columns Replaced_ID, New_ID and Installation Date of New_ID.

Each New_ID replaces the Replaced_ID.

Replaced_ID      New_ID             Installation Date (of New_ID)
     3             5                    16/02/2018
     5             7                    17/05/2019
     7             9                    21/06/2019
     9             11                   23/08/2020
    25             39                   16/02/2017
    39             41                   16/08/2018

My goal is to get a dataframe which includes the first and last record of the sequence. I care only for the first Replaced_ID value and the last New_ID value.

i.e from above dataframe I want this

    Replaced_ID      New_ID             Installation Date (of New_ID)
        3              11                    23/08/2020
        25             41                    16/08/2018

Sorting by date and perform shift is not the solution here as far as I can imagine.

Also, I tried to join the columns New_ID with Replaced_ID but this is not the case because it returns only the previous sequence.

I need to find a way to get the sequence [3,5,7,9,11] & [25,41] combining the Replaced_ID & New_ID columns for all rows.

I care mostly about getting the first Replaced_ID value and the last New_ID value and not the Installation Date because I can perform join in the end.

Any ideas here? Thanks.

Answer 1

First, let's create the DataFrame:

import pandas as pd
import numpy as np
from io import StringIO

data = """Replaced_ID,New_ID,Installation Date (of New_ID)
3,5,16/02/2018
5,7,17/05/2019
7,9,21/06/2019
9,11,23/08/2020
25,39,16/02/2017
39,41,16/08/2018
11,14,23/09/2020
41,42,23/10/2020
"""
### note that I've added two rows to check whether it works with non-consecutive rows

### defining some short hands
r = "Replaced_ID"
n = "New_ID"
i = "Installation Date (of New_ID)"

df = pd.read_csv(StringIO(data),header=0,parse_dates=True,sep=",")
df[i] =  pd.to_datetime(df[i], )

And now for my actual solution:

a = df[[r,n]].values.flatten()
### returns a flat list of r and n values which clearly show duplicate entries, i.e.:
#  [ 3  5  5  7  7  9  9 11 25 39 39 41 11 14 41 42]

### now only get values that occur once, 
#   and reshape them nicely, such that the first column gives the lowest (replaced) id,
#   and the second column gives the highest (new) id, i.e.:
#    [[ 3 14]
#     [25 42]]
u, c = np.unique( a, return_counts=True)
res = u[c == 1].reshape(2,-1)

### now filter the dataframe where "New_ID" is equal to the second column of res, i.e. [14,42]:
#   and replace the entries in "r" with the "lowest possible values" of r
dfn = df[  df[n].isin(res[:,1].tolist()) ]
# print(dfn)
dfn.loc[:][r] = res[:,0]
print(dfn)

Which yields:

   Replaced_ID  New_ID Installation Date (of New_ID)
6            3      14                    2020-09-23
7           25      42                    2020-10-23