# Converting xy point to lat/long

I have data with columns x,y, address. And no any information about the projection of x,y points.

How can I convert these points to correct lat/long pairs?

``````1091969,199515,27 HEDGEWAY CT
1092046,199590,4 SARATOGA CIR
1091986,199663,6 SARATOGA CIR
1091928,199696,8 SARATOGA CIR
1091883,199758,2 LEXINGTON CIR
1091948,199825,4 LEXINGTON CIR
1091917,199913,6 LEXINGTON CIR
1091839,199946,8 LEXINGTON CIR
1092026,198875,20 WARNER AVE
1092013,198934,28 WARNER AVE
1092000,198992,32 WARNER AVE
1091988,199050,WARNER AVE
1091974,199111,48 WARNER AVE
1091953,199210,56 WARNER AVE
1091940,199269,62 WARNER AVE
1087464,233134,7 EDGEWOOD LN
``````

I've tried to use GeoPandas.

1. Create dataframe as epsg: 2236 crs, because its Florida , Nassau county.
2. Then convert to epsg:4326 (lat/long CRS)
3. Then I've checked a few points in Google Maps and tried to adopt just add difference in latitude and longitude as Point(p.x + 6.07406, p.y + 15.83704)

But it seems such a linear approach does not work.

``````    gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.coordinate_x, df.coordinate_y))
gdf.crs = {'init': 'epsg:2236'}
gdf['xy_geometry'] = gdf['geometry']

# convert to latitude/longitude coordinate reference system
gdf.to_crs({'init': 'epsg:4326'}, inplace=True)
gdf.rename(columns={'geometry': 'lat_long_geometry'}, inplace=True)

# correct geometry points with latitude & longitude offsets
gdf.lat_long_geometry = gdf.lat_long_geometry.apply(lambda p: Point(p.x + 6.07406, p.y + 15.83704))
``````