Recursive value in Dataframe

Question

I have something that has been bugging me for a while.

I have a Dataframe, looking like this:

           Amount  Balance       Date                           Text
90    -442.378339     5000 2018-02-12     PS Bar               04066
502   -183.229472     5000 2018-08-14               VDK EUR    39,95
957    -35.117568     5000 2019-01-28  Fakta Strandlodsve )))) 59260
1334   -15.234783     5000 2019-06-28                          Rente
1867  2934.846978     5000 2019-12-09           Netbank overfï¿½rsel
2304   -30.299886     5000 2019-05-21            MobilePay: Cathrine
2341    21.209920     5000 2019-06-03            MOBILEPAY: KRISTIAN
2357   -90.505760     5000 2019-06-04     DK-NOTA68957 NETTO GODTHÅB
2447  -182.799214     5000 2019-07-03               Forretning: REAL
2955  -121.199545     5000 2019-12-30     DK-NOTA17767 WANNASPORT.DK

I want to update the Balance column based on the Amount column. The balance should start at 5000, but for the second row, the balance should be 5000 - 442,378339 = 4557,62, and for the third row, it should be 4557,62 - 183,229472 = 4374,39.

I have tried df['Balance'] = df['Balance'] + df['Amount'].shift(1)

The second row is correct, but after that it just keeps taking the 5000, and not the previous computed value of Balance.

df['Balance'] - df['Amount'].cumsum() or df['Balacnce'] + df['Amount'].cumsum() — Scott Boston
– Scott Boston, Commented Feb 17, 2020 at 20:30
I knew it was something simple - thank you so much Scott! If you post an answer, I will approve it :) — Mathias Lund
– Mathias Lund, Commented Feb 17, 2020 at 20:32

Scott Boston · Accepted Answer · 2020-02-17 20:34:12Z

1

Let's use cumsum on 'Amount' column:

Try:

df['Balance'] + df['Amount'].cumsum()

Output:

90      4557.621661
502     4374.392189
957     4339.274621
1334    4324.039838
1867    7258.886816
2304    7228.586930
2341    7249.796850
2357    7159.291090
2447    6976.491876
2955    6855.292331
dtype: float64

answered Feb 17, 2020 at 20:34

Scott Boston

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