Updating a value in a pandas dataframe in an iterrows loop

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The rows you get back from iterrows are copies that are no longer connected to the original data frame, so edits don’t change your dataframe. Thankfully, because each item you get back from iterrows contains the current index, you can use that to access and edit the relevant row of the dataframe:

for index, row in rche_df.iterrows():
    if isinstance(row.wgs1984_latitude, float):
        row = row.copy()
        target = row.address_chi        
        dict_temp = geocoding(target)
        rche_df.loc[index, 'wgs1984_latitude'] = dict_temp['lat']
        rche_df.loc[index, 'wgs1984_longitude'] = dict_temp['long']

In my experience, this approach seems slower than using an approach like apply or map, but as always, it’s up to you to decide how to make the performance/ease of coding tradeoff.

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