CODEWITHSUNDEEP
pandasmultiple-columnsapply
Alex P
Alex P

Reputation: 137

Python and Pandas: apply per multiple columns

I am new to python and I was successful in using apply in a dataframe to create a new column inside a dataframe.

X['Geohash']=X[['Lat','Long']].apply (lambda column: geohash.encode(column[0],column[1],precision=8), axis=1)

this is calling the geohash function with the latitudes and longitudes per row and per column.

Now I have two new data frames one for Latitude and one for Longitude. Each dataframe has twenty columns and I want that the

.apply (lambda column: geohash.encode(column[0],column[1],precision=8), axis=1)

is called twenty times.

-First time the first dataframe-Latitude column with the first dataframe-Longitude column then

-the second time, the second dataframe-Latitude column with the second dataframe-Longitude column.

How I can do this iteration per column and at each iteration call the 

.apply (lambda column: geohash.encode(column[0],column[1],precision=8), axis=1)

What I want to get is a new dataframe with twenty columns with each column being the result of the geohash function.

Ideas will be appreciated.

Upvotes: 0

Views: 99

Answers (1)

Jim Eisenberg
Jim Eisenberg

Reputation: 1500

You can do this by creating an "empty" dataframe with 20 columns, and then using df.columns[i] to loop through your other dataframes - something like this:

output = pd.DataFrame({i:[] for i in range(20)})

This creates an empty dataframe with all the columns you wanted (numbered).

Now, let's say longitude and latitude dataframes are called 'lon' and 'lat'. We need to join them into one dataframe Then:

lonlat = lat.join(lon)
for i in range(len(output.columns)):
    output[output.columns[i]] = lonlat.apply(lambda column: geohash.encode(column[lat.columns[i]], 
                                                            column[lon.columns[i]],
                                                            precision=8), axis=1)

Upvotes: 1

Related Questions