Xjyuk

I have 9888562 records in dataframe and I would like to convert my lat, long to UTM x,y. according to my code, I have used pyproj package but because my data are too much it takes a long time and finally, it doesn't work.
I wonder whether you know another way or package that I can use for my data?

def rule(row):
 p = Proj(proj='utm',zone=10,ellps='WGS84', preserve_units=False)
 x,y = p(row["LON"], row["LAT"])
 return pd.Series("X": x , "Y": y)
My_data = My_data.merge(My_data.apply(rule, axis=1), left_index= True, right_index= True)

You can directly use shapely or GeoPandas but with 9888562 records It will take a long time to do (if you want a Progress bar during the pandas operations, you can use tqdm)

1) With your solution and the first 4 points

import pandas as pd
df = pd.DataFrame('LAT':[47.9767,47.9803,47.9801,47.9798], 'LON':[-122.2450,-122.2458,-122.2472,-122.2465])
My_data = df.merge(df.apply(rule, axis=1), left_index= True, right_index= True)
print(My_data)
 LAT LON X Y
0 47.9767 -122.2450 556344.793287 5.313986e+06
1 47.9803 -122.2458 556281.177583 5.314386e+06
2 47.9801 -122.2472 556176.922041 5.314363e+06
3 47.9798 -122.2465 556229.484294 5.314330e+06

2) With shapely

from shapely.geometry import Point
df['geometry'] = df.apply(lambda row: Point(row.LON, row.LAT), axis=1)
print(df)
 LAT LON geometry
0 47.9767 -122.2450 POINT (-122.245 47.9767)
1 47.9803 -122.2458 POINT (-122.2458 47.9803)
2 47.9801 -122.2472 POINT (-122.2472 47.9801)
3 47.9798 -122.2465 POINT (-122.2465 47.9798)
# change the projection of points
from functools import partial
from shapely.ops import transform
import pyproj 
project = partial(
 pyproj.transform,
 pyproj.Proj(init='epsg:4326'), # source coordinate system
 pyproj.Proj('+proj=utm +zone=10 +ellps=WGS84 +datum=WGS84 +units=m +no_defs')) # destination coordinate system
sdf = df.copy()
sdf['project'] = sdf.geometry.apply(lambda row: transform(project,row))
print(sdf)
 LAT LON geometry project
0 47.9767 -122.2450 POINT (-122.245 47.9767) POINT (556344.7932874847 5313986.318933257)
1 47.9803 -122.2458 POINT (-122.2458 47.9803) POINT (556281.1775833125 5314385.854768319)
2 47.9801 -122.2472 POINT (-122.2472 47.9801) POINT (556176.9220411006 5314362.605105238)
3 47.9798 -122.2465 POINT (-122.2465 47.9798) POINT (556229.4842943319 5314329.77191091)

3) Directly with GeoPandas

import geopandas as gpd
# change to GeoDataFrame
geodf = gpd.GeoDataFrame(df)
geodf.crs= 'init': 'epsg:4326'
# change the projection of geodf
geodf = geodf.to_crs("+proj=utm +zone=10 +ellps=WGS84 +datum=WGS84 +units=m +no_def")
print(geodf)
 LAT LON geometry
0 47.9767 -122.2450 POINT (556344.7932874847 5313986.318933257)
1 47.9803 -122.2458 POINT (556281.1775833125 5314385.854768319)
2 47.9801 -122.2472 POINT (556176.9220411006 5314362.605105238)
3 47.9798 -122.2465 POINT (556229.4842943319 5314329.77191091)

4) If you want the X and Y values, it is easy

geodf["x"] = geodf.geometry.apply(lambda row:row.x)
geodf["y"] = geodf.geometry.apply(lambda row:row.y)
print(geodf)
 LAT LON geometry x y
0 47.9767 -122.2450 POINT (556344.7932874847 5313986.318933257) 556344.793287 5.313986e+06
1 47.9803 -122.2458 POINT (556281.1775833125 5314385.854768319) 556281.177583 5.314386e+06
2 47.9801 -122.2472 POINT (556176.9220411006 5314362.605105238) 556176.922041 5.314363e+06
3 47.9798 -122.2465 POINT (556229.4842943319 5314329.77191091) 556229.484294 5.314330e+06