I have a large dataframe with customer data. I then have two lists with unique customer IDs and unique backup values. Both lists are the same length. I want a quicker solution to override a column in the dataset if the unique ID is in the list.
Here is what I am currently doing:
for unique_id, overrides_id in zip([list of unique_ids], [list of override ids]):
df.loc[df['Unique_ID_Column'] == unique_id, 'unique_id_backup'] = override_id
I assume there is a typo in the question and the assignment should be
df.loc[df['Unique_ID_Column'] == unique_id, 'unique_id_backup'] = overrides_id
I believe you could use the Series.map method. First you would set up a mapper dictionary {keys:values} where keys are elements from the unique_id list and values are overrides_id. Then you can use that mapper to override the values accordingly. You did not mention what to do with rows where the customer ID is not present in unique_id list.
df['unique_id_backup'] = df['Unique_ID_Column'].map(mapper)
This should do what you're after, replacing missing IDs with NaNs in the 'unique_id_backup' column. If you wanted to keep the original ID in case it's missing, you can do:
df['unique_id_backup'] = df['Unique_ID_Column'].map(lambda x:mapper.get(x,x))
Worked example:
import pandas as pd
df = pd.DataFrame(
{'Unique_ID_Column': [1, 2, 3, 4, 5],
'Some customer data': ['A', 'B', 'C', 'D', 'E']}
)
unique_id = [1, 3, 5]
override_id = [10, 30, 50]
mapper = dict(zip(unique_id, override_id))
df['unique_id_backup'] = df['Unique_ID_Column'].map(lambda x: mapper.get(x, x))
>>> Unique_ID_Column Some customer data unique_id_backup
0 1 A 10
1 2 B 2
2 3 C 30
3 4 D 4
4 5 E 50
overrides_idunused?groupbyinstead. There are plenty of examples, especially here on stack-overflow. Note you can build an index from the grouped dataframes. Here is an example.