Trouble with a python loop

Question

I'm having issues with a loop that I want to: a. see if a value in a DF row is greater than a value from a list b. if it is, concatenate the variable name and the value from the list as a string c. if it's not, pass until the loop conditions are met.

This is what I've tried.

import pandas as pd
import numpy as np

df = {'level': ['21', '22', '23', '24', '25', '26', '27', '28', '29', '30']
     , 'variable':'age'}

df = pd.DataFrame.from_dict(df)

knots = [0, 25]
df.assign(key = np.nan)
for knot in knots:
    if df['key'].items == np.nan:
        if df['level'].astype('int') > knot:
            df['key'] = df['variable']+"_"+knot.astype('str')
    else:
         pass
else:
     pass

However, this only yields the key column to have NaN values. I'm not sure why it's not placing the concatenation.

NaN/np.nan is special; it's not equal to anything, not even itself. You need to use np.isnan to test for it. I don't have enough experience to say how this works with pandas dataframes, so this isn't an answer, but the important part here is that df['key'].items == np.nan is guaranteed to be False. — ShadowRanger
– ShadowRanger, Commented Sep 13, 2019 at 13:10

abhilb · Accepted Answer · 2019-09-13 13:03:34Z

1

You can do something like this inside the for loop. No need of any if conditions:

df.loc[df['level'].astype('int') > 25, 'key'] = df.loc[df['level'].astype('int') > 25, 'variable'] + '_' + df.loc[df['level'].astype('int') > 25, 'level']

answered Sep 13, 2019 at 13:03

abhilb

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3 Comments

Jordan Over a year ago

Thanks @abhilb. However, the last know overwrites the previous knot. Somehow, I have to look to see if a value is None or nan, before putting in the above.

Jordan Over a year ago

check that last comment. this works. I just have to rearrange the knots values in ascending order. Thanks.

abhilb Over a year ago

happy to help :)

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