pandas: how to assign a single column conditionally on multiple other columns? - pandas

Question

I'm confused about conditional assignment in Pandas.
I have this dataframe:
df = pd.DataFrame([
{ 'stripe_subscription_id': 1, 'status': 'past_due' },
{ 'stripe_subscription_id': 2, 'status': 'active' },
{ 'stripe_subscription_id': None, 'status': 'active' },
{ 'stripe_subscription_id': None, 'status': 'active' },
])
I'm trying to add a new column, conditionally based on the others:
def get_cancellation_type(row):
if row.stripe_subscription_id:
if row.status == 'past_due':
return 'failed_to_pay'
elif row.status == 'active':
return 'cancelled_by_us'
else:
return 'cancelled_by_user'
df['cancellation_type'] = df.apply(get_cancellation_type, axis=1)
This is fairly readable, but is it the standard way to do things?
I've been looking at pd.assign, and am not sure if I should be using that instead.

Answer 1

This should work, you can change or add the conditions however you want.
df.loc[(df['stripe_subscription_id'] != np.nan) & (df['status'] == 'past_due'), 'cancellation_type'] = 'failed_to_pay'
df.loc[(df['stripe_subscription_id'] != np.nan) & (df['status'] == 'active'), 'cancellation_type'] = 'cancelled_by_us'
df.loc[(df['stripe_subscription_id'] == np.nan), 'cancellation_type'] = 'cancelled_by_user'

Answer 2

You migth consider to use np.select
import pandas as pd
import numpy as np
condList = [df["status"]=="past_due",
df["status"]=="active",
~df["status"].isin(["past_due",
"active"])]
choiceList = ["failed_to_pay", "cancelled_by_us", "cancelled_by_user"]
df['cancellation_type'] = np.select(condList, choiceList)