calculating a range based on the fields of a pandas dataframe

1
votes

I have a pandas dataframe

import pandas as pd
import numpy as np 

d = pd.DataFrame({
       'col': ['A', 'B', 'C', 'D'],
       'start': [1, 4, 6, 8], 
       'end': [4, 9, 10, 12]
    })

I'm trying to calculate a range field based on start and end fields such that the values for it are

[1, 2, 3, 4]
[4, 5, 6, 7, 8, 9]
[6, 7, 8, 9, 10]
[8, 9, 10, 11, 12]

I have tried the following options

d['range_'] = np.arange( d.start, d.end, 1)


d['range_'] = range(d['start'], d['end'])

but get the following errors

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

TypeError: 'Series' object cannot be interpreted as an integer <- second attempt

Any help would be appreciated

Thanks

2
pythonpandasnumpy

2 Answers

2
votes

Try this:

d.apply(lambda x: np.arange(x['start'], x['end']+1), axis=1)

Output:

0          [1, 2, 3, 4]
1    [4, 5, 6, 7, 8, 9]
2      [6, 7, 8, 9, 10]
3    [8, 9, 10, 11, 12]
dtype: object

Note: np.arange and range are not designed to accept pd.Series, therefore you can use apply rowwise to create ranges.

1
votes

IIUC

l = [list(range(x,y+1)) for x , y in zip(d.start,d.end)]
[[1, 2, 3, 4], [4, 5, 6, 7, 8, 9], [6, 7, 8, 9, 10], [8, 9, 10, 11, 12]]
d['range_']=l