How to iterate a vectorized if/else statement over additional columns?


import pandas as pd, numpy as np ltlist = [1, 2] org = {'ID': [1, 3, 4, 5, 6, 7], 'ID2': [3, 4, 5, 6, 7, 2]} ltlist_set = set(ltlist) org['LT'] = np.where(org['ID'].isin(ltlist_set), org['ID'], 0)

I'll need to check the ID2 column and write the ID in, unless it already has an ID.


ID ID2 LT 1 3 1 3 4 0 4 5 0 5 6 0 6 7 0 7 2 2



<strong>Option 1</strong>

You can nest numpy.where statements:

org['LT'] = np.where(org['ID'].isin(ltlist_set), 1, np.where(org['ID2'].isin(ltlist_set), 2, 0))

<strong>Option 2</strong>

Alternatively, you can use pd.DataFrame.loc sequentially:

org['LT'] = 0 # default value org.loc[org['ID2'].isin(ltlist_set), 'LT'] = 2 org.loc[org['ID'].isin(ltlist_set), 'LT'] = 1

<strong>Option 3</strong>

A third option is to use numpy.select:

conditions = [org['ID'].isin(ltlist_set), org['ID2'].isin(ltlist_set)] values = [1, 2] org['LT'] = np.select(conditions, values, 0) # 0 is default value


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