how to update rows based on previous row of dataframe python

Question

I have a time series data given below:

date    product price   amount
11/01/2019  A   10  20
11/02/2019  A   10  20
11/03/2019  A   25  15
11/04/2019  C   40  50
11/05/2019  C   50  60

I have a high dimensional data, and I have just added the simplified version with two columns {price, amount}. I am trying to transform it relatively based on time index illustrated below:

date    product price   amount
    11/01/2019  A   NaN NaN
    11/02/2019  A   0   0
    11/03/2019  A   15  -5
    11/04/2019  C   NaN NaN
    11/05/2019  C   10  10

I am trying to get relative changes of each product based on time indexes. If previous date does not exist for a specified product, I am adding "NaN".

Can you please tell me is there any function to do this?

Nathan Furnal · Accepted Answer · 2020-12-23 22:44:29Z

1

Group by product and use .diff()

df[["price", "amount"]] = df.groupby("product")[["price", "amount"]].diff()

output :

        date product  price  amount
0 2019-11-01       A    NaN     NaN
1 2019-11-02       A    0.0     0.0
2 2019-11-03       A   15.0    -5.0
3 2019-11-04       C    NaN     NaN
4 2019-11-05       C   10.0    10.0

answered Dec 23, 2020 at 22:44

Nathan Furnal

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3 Comments

user3104352 Over a year ago

Is there any way to insert columns witthout naming like [["price", "amount"]]? I have a high dimensional data and I am trying to inject the columns except {"date", "product"}.

Nathan Furnal Over a year ago

Sure, use something like cols = ["price", "amount"] and if you want the other cols then others = [c for c in df.columns if c not in ("price", "amount")]

Nathan Furnal Over a year ago

then you can select all the others with df[others].

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