从列表创建字典项目列表

Question

I am working on a project that involves going through two columns of latitude and longitude values. If the lat/long in one pair of columns are blank, then I need to figure out which pair of lat/long values in another two columns are (geographically) closest to those in the destination. The dataframe looks like this:

 origin_lat  |   origin_lon  |  destination_lat  |  destination_lon
----------------------------------------------------------------
 20.291326      -155.838488       25.145242          -98.491404
 25.611236      -80.551706        25.646763          -81.466360
 26.897654      -75.867564          nan                 nan

I am trying to build two dictionaries, one with the origin lat and long, and the other with the destination lat and long, in this format:

tmplist = [{'origin_lat': 39.7612992, 'origin_lon': -86.1519681}, 
           {'origin_lat': 39.762241,  'origin_lon': -86.158436 }, 
           {'origin_lat': 39.7622292, 'origin_lon': -86.1578917}]

What I want to do is for every row where the destination lat/lon are blank, compare the origin lat/lon in the same row to a dictionary of all the non-nan destination lat/lon values, then print the geographically closest lat/lon from the dictionary of destination lat/lon to the row in place of the nan values. I've been playing around with creating lists of dictionary objects but can't seem to build a dictionary in the correct format. Any help would be appreciated!

Answer 1

If df is your pandas.DataFrame , you can generate the requested dictionaries by iterating through the rows of df :

origin_dicts = [{'origin_lat': row['origin_lat'], 'origin_long': row['origin_lon']} for _, row in df.iterrows()]

and analogously for destination_dicts .

Remark: if the only reason for creating the dictionaries is the calculation of values replacing the nan -entries, it might be easier to do this directly on the data frame, eg

df['destination_lon'] = df.apply(find_closest_lon, axis=1)
df['destination_lat'] = df.apply(find_closest_lat, axis=1)

where find_closest_lon , find_closes_lat are functions receiving a data frame row as an argument and having access to the values of the origin-columns of the data frame.

Answer 2

The format that you want is the built-in 'records' format:

df[['origin_lat','origin_lon']].to_dict(orient = 'records')

produces

[{'origin_lat': 20.291326, 'origin_lon': -155.83848799999998},
 {'origin_lat': 25.611235999999998, 'origin_lon': -80.55170600000001},
 {'origin_lat': 26.897654, 'origin_lon': -75.867564}]

and of course you can equally have

df[['destination_lat','destination_lon']].to_dict(orient = 'records')

But I agree with @ctenar that you do not need to generate dictionaries for your ultimate task, Pandas provide enough functionality for that