Replacing values in a column based on condition of another dataframe

Asked Nov 28 '18 at 16:14

Active Nov 28 '18 at 16:20

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I have an airline dataset and in one month the airport codes are coded differently. I found a source to correct it, but can't seem to get it to work.

airline_df = pd.DataFrame({'airport':['ORD', 'LAS', 'BOS', 10721, 12889]})

airport_code = pd.DataFrame({'airport':['BOS', 'LAS', 'ORD'], 'airport_code':[10721, 12889, 13930]}

I also created a dictionary of the code:airport using

code_dict = airport_codes.set_index('airport_code').to_dict()['airport']

I am trying to replace all the airport values that are numbers with the airport name.

asked Nov 28 '18 at 16:14

Zakary Krumlinde

1

did you try replace? https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.replace.html – MisterMonk Nov 28 '18 at 16:17
or `map` + `fillna`: `airline_df.airport.map(code_dict).fillna(airline_df.airport)` – ALollz Nov 28 '18 at 16:18

Replacing values in a column based on condition of another dataframe

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