'How do I create a new dataframe column based on conditions of another dataframe?

I want to create a new dataframe column surv with lts and non_lts labels, where for all values of the clin["OS_MONTHS"] column in the clin dataframe, label the value as lts if value is <= 2*12, else label it as non_lts.

The criteria is:

import pandas as pd

non_lts = clin[(clin["OS_MONTHS"].astype(float)<= 2*12)]
lts = clin[~clin.isin(non_lts)].dropna()

but I want it as a dataframe surv.

My attempt:

# Survival info
def survival(clin):
    if clin["OS_MONTHS"].astype(float)<= 2*12:
        val = "lts"
    else:
        val = "non-lts"
    return val

clin['SURV'] = clin.apply(survival, axis=1)

Desired output surv

	surv
0	lts
1	non_lts

python pandas

Solution 1:^[1]

You may try this one using loc :

clin.loc[clin['OS_MONTHS'] <= 2*12, 'surv'] = 'lts' 
clin.loc[clin['OS_MONTHS'] > 2*12, 'surv'] = 'non_lts'

Sources

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Solution	Source
Solution 1	tlentali

'How do I create a new dataframe column based on conditions of another dataframe?

Solution 1:[1]

Sources

Related Questions

Solution 1:^[1]