Python: logical comparing with columns in panda's dataframe

Question

I have a dataframe where I want to determine when the ser_no and CTRY_NM are the same and differ. However, I want to be mindful of the ser_no changes and not make a false and false return true or a false/true return false.

Consider the following dataframe:

import pandas as pd
df = pd.DataFrame({'ser_no': [1, 1, 1, 2, 2, 2, 2, 3, 3, 3],
                'CTRY_NM': ['a', 'a', 'b', 'e', 'e', 'a', 'b', 'b', 'b', 'd']})
def check(key):
    return df[key] == df[key].shift(1)

match = check('ser_no') == check('CTRY_NM')

This returns:

However, at indices, 4 and 8 we have serial number changes. Since each serial number is a different machine, it doesn't make sense to have a logical comparison at these locations. When ser_no changes, how can I insert NaN instead of do a logical comparison?

You probably want to use groupby() first.

Corley Brigman
– Corley Brigman

2016-03-29 13:28:15 +00:00
Commented Mar 29, 2016 at 13:28 — Corley Brigman
– Corley Brigman, Commented Mar 29, 2016 at 13:28
@CorleyBrigman can you elaborate on how groupby will help?

dustin
– dustin

2016-03-29 13:32:17 +00:00
Commented Mar 29, 2016 at 13:32 — dustin
– dustin, Commented Mar 29, 2016 at 13:32

cncggvg · Accepted Answer · 2016-03-29 13:49:14Z

2

is this what you want?

def check(data, key):
    mask = data[key].shift(1) == data[key]
    mask.iloc[0] = np.nan
    return mask

df.groupby(by=['ser_no']).apply(lambda x: check(x, 'CTRY_NM'))

result

ser_no   
1       0   NaN
        1     1
        2     0
2       3   NaN
        4     1
        5     0
        6     0
3       7   NaN
        8     1
        9     0
Name: CTRY_NM, dtype: float64

answered Mar 29, 2016 at 13:49

cncggvg

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1 Comment

dustin Over a year ago

Yes that is what I was trying to achieve. Can you add some text with what is occurring so I have a better understanding?

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