Pandas：根据不同组中另一列的值过滤行（合计两列）

Question

I have a dataset like below in pandas dataframe:

Name    Shift   Data Type
Peter   0       12    A   
Peter   0       13    A
Peter   0       14    B
Sam     1       12    A
Sam     1       15    A
Sam     1       16    B
Sam     1       17    B
Mary    2       20    A
Mary    2       21    A
Mary    2       12    A

May anyone suggest how to show end result like the below? (logic is: if shift is 0, pick the 1st item under groupby "Name" and "type" columns; if shift is 1, pick the 2nd value under the groupby "Name" and "type" columns, etc... I have thought of nth(x) but I don't know how to put a variable on x in this case. Other workaround is fine that can generated the same result. Thank you.

Name    Shift   Data   Type
Peter   0       12     A
Peter   0       14     B
Sam     1       15     A
Sam     1       17     B
Mary    2       12     A

Answer 1

You can use groupby.cumcount()

Assuming your data is in a DataFrame called df , I think this should work for you:

df = df[df.groupby(['Name','Type']).cumcount()==df['Shift']]

It compares the cumulative count of rows with the same Name and Type to the values in the Shift column to determine which rows should be kept