Merge two dataframes of different lengths with matching ID and fill NaN values of main dataframe on-CodePudding

I have two dataframes, the main dataframe has two columns for Lat and Long some of which have values and some of which are NaN. I have another dataframe that is a subset of this main dataframe with Lat and Long filled in with values. I'd like to fill in the main DataFrame with these values based on matching ID.

Main DataFrame:

        ID      Lat         Long
0   9547507704  33.853682   -80.369867
1   9777677704  32.942332   -80.066165
2   5791407702  47.636067   -122.302559
3   6223567700  34.224719   -117.372550
4   9662437702  42.521828   -82.913680
... ... ... ...
968552  4395967002  NaN NaN
968553  6985647108  NaN NaN
968554  7996438405  NaN NaN
968555  9054647103  NaN NaN
968556  9184687004  NaN NaN

DataFrame to fill:

    ID          Lat         Long
0   2392497107  36.824257   -76.272486
1   2649457102  37.633918   -77.507746
2   2952437110  37.511077   -77.528711
3   3379937304  39.119430   -77.569008
4   3773127208  36.909731   -76.070420
... ... ... ...
23263   9512327001  37.371059   -79.194838
23264   9677417002  38.406665   -78.913133
23265   9715167306  38.761194   -77.454184
23266   9767568404  37.022287   -76.319882
23267   9872047407  38.823017   -77.057818

The two dataframes are of different lengths.

EDIT for clarification: I need to replace the NaN in the Lat & Long columns of the main DataFrame with the Lat & Long from the subset if ID matches in both DataFrames. My DataFrames are both >60 columns, I am only trying to replace the NaN for those two columns.

Edit:

I went with this mapping solution although it isn't exactly what I'm looking for, I know there is a much more simple solution.

#mapping coordinates to NaN values in main 
m = dict(zip(fill_df.ID,fill_df.Lat))
main_df.Lat = main_df.Lat.fillna(main_df.ID.map(m))
n = dict(zip(fill_df.ID,fill_df.Long))
main_df.Long = main_df.Long.fillna(main_df.ID.map(n))

CodePudding user response：

new_df = pd.merge(main_df, sub_df, how='left', on='ID')

I guess the left join will do the job.

CodePudding user response：

One approach is to use DataFrame.combine_first. This method aligns DataFrames on index and columns, so you need to set ID as the index of each DataFrame, call df_main.combine_first(df_filler), then reset ID back into a column. (Seems awkward; there's probably a more elegant approach.)

Assuming your main DataFrame is named df_main and your DataFrame to fill is named df_filler:

df_main.set_index('ID').combine_first(df_filler.set_index('ID')).reset_index()

CodePudding user response：

This should do the trick:

import math
A = pd.DataFrame({'ID' : [1, 2, 3], 'Lat':[4, 5, 6], 'Long': [7, 8, float('nan')]})
B = pd.DataFrame({'ID' : [2, 3], 'Lat':[5, 6], 'Long': [8, 9]})
print('Old table:')
print(A)
print('Fix table:')
print(B)
for i in A.index.to_list():
    for j in B.index.to_list():
        if not A['ID'][i] == B['ID'][j]:
            continue
        if math.isnan(A['Lat'][i]):
            A.at[i, 'Lat'] = B['Lat'][j] 
        if math.isnan(A['Long'][i]):
            A.at[i, 'Long'] = B['Long'][j]
print('New table:')
print(A)

Returns:

   ID  Lat  Long
0   1    4   7.0
1   2    5   8.0
2   3    6   NaN
Fix table:
   ID  Lat  Long
0   2    5     8
1   3    6     9
New table:
   ID  Lat  Long
0   1    4   7.0
1   2    5   8.0
2   3    6   9.0

Not very elegant but gets the job done :)

CodePudding user response：

A.set_index('ID').fillna(B[['ID', 'Lat', 'Long']].set_index('ID')).reset_index()

Example:

>>> A
   ID  Lat  Long  Dont Update
0   1    4   7.0          7.0
1   2    5   8.0          8.0
2   3    6   NaN          NaN
>>> B
   ID  Lat  Long  Dont Update
0   2    5     8           10
1   3    6     9           10
>>> A.set_index('ID').fillna(B[['ID', 'Lat', 'Long']].set_index('ID')).reset_index()
   ID  Lat  Long  Dont Update
0   1    4   7.0          7.0
1   2    5   8.0          8.0
2   3    6   9.0          NaN