Comparing previous row values in Pandas DataFrame in different column

Question

my input:

first=pd.Series([0,1680,5000,14999,17000])
last =pd.Series([4999,7501,10000,16777,21387])
dd=pd.concat([first, last], axis=1)

I trying find&compare second value in first column (e.g. 1680) and "range" previous row between first value in first column to first value in second column(e.g. from 0 to 4999). So in my condition value 1680 fall in range previous row between 0 to 4999, also 3td value in first column 5000 fall in range previous row between 1680 to 7501, but other values (e.g. 14999, 17000) not in range of previous rows.
My expect output something like this:
[1680], [5000] so show only values that fall in my condition
I trying with diff(): dd[0].diff().gt(dd[1]) or reshape/shift but not really success

I am not really necessary output as df, only value that fall in my condition, but if solution as df it will be great too — TeoK
– TeoK, Commented Aug 29, 2021 at 11:52

Corralien · Accepted Answer · 2021-08-29 11:53:58Z

1

Use shift and between to compare a row with the previous one:

>>> df[0].loc[df[0].between(df[0].shift(), df[1].shift())]
1    1680
2    5000
Name: 0, dtype: int64

Details of shift:

>>> pd.concat([df[0], df.shift()], axis=1)
       0        0        1
0      0      NaN      NaN
1   1680      0.0   4999.0
2   5000   1680.0   7501.0
3  14999   5000.0  10000.0
4  17000  14999.0  16777.0

answered Aug 29, 2021 at 11:53

Corralien

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