Using geopy, the latitudes and longitudes for some addresses in my dataset, showed different countries instead of the US.
And also, there is no limit for Geopy API so we will Geopy instead of OpenCage Geocoder.Output : Southeast Health Medical Center, Alma Street, Dothan, AL 36302, United States of AmericaOutput: ‘Southeast Health Campus, 1108, Ross Clark Circle, Morris Heights, Dothan, Houston County, Alabama, 36301, United States of AmericaFor reverse geocoding, as above first, we will convert latitude and longitude to list and zip them together.Since, we already created list, just like above we will create a loop to find address for each location coordinate and append them together.Finally, we have the address list of all hospitals in the US.For interested readers, I put the code in my GitHub Repo Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday.
Specifically, I am loading pygeocoder for its geo-functionality, pandas for its dataframe structures, and numpy for its missing value (np.nan) functionality.Geo-data comes in a wide variety of forms, in this case we have a Python dictionary of five latitude and longitude strings, with each coordinate in a coordinate pair separated by a comma.While technically unnecessary, because I originally come from R, I am a big fan of dataframes, so let us turn the dictionary of simulated data into a dataframe.You can see now that we have a a dataframe with five rows, with each now containing a string of latitude and longitude.
Once the library is installed, you will need an OpenCage geocoder account to generate an API key. The dataset can be downloaded from We have a data frame that contains the list of Facility Name of all Hospitals in the US and their addresses, so we just need to find location coordinates.First, we should convert the Address column to the list. Our software allows you to calculate exact distances to help give you the reliable data you need. This permits the identification of nearby street addresses, places, and/or areal subdivisions such as neighbourhoods, county, state, or country. To better understand this location coordinates let’s plot all this location coordinates as points in map using folium maps.Now, we can see the location points of all the hospitals in the USA. I used CircleMarker cluster to better help understand the regions with most number of hospitals.For reverse geocoding, I found the output format of Geopy API more detailed when compared to OpenCage Geocoder. Make learning your daily ritual.folium_map= folium.Map(location=[33.798259,-84.327062],zoom_start=4.4,tiles=’CartoDB dark_matter’)FastMarkerCluster(data[[‘latitudes’, ‘longitudes’]].values.tolist()).add_to(folium_map)folium.LayerControl().add_to(folium_map) for row in final.iterrows():result = geocoder.reverse_geocode(31.2158271,-85.3634326)
The amount of detail in a reverse geocoded location description may vary, for example, one might contain the full street … Before we can work with the data, we’ll need to 1) separate the strings into latitude and longitude and 2) convert them into floats. Good luck!Everything on this site is available on GitHub. Working with OpenCage geocoder and pandas. In the tutorial below, I use pygeocoder, a wrapper for Google’s geo-API, to both geocode and reverse geocode.First we want to load the packages we will want to use in the script. This is exactly what we want to see, one column of floats for latitude and one column of floats for longitude.To reverse geocode, we feed a specific latitude and longitude pair, in this case the first row (indexed as ‘0’) into pygeocoder’s reverse_geocoder function.Now we can take can start pulling out the data that we want.For geocoding, we need to submit a string containing an address or location (such as a city) into the geocode function.
in Google's system) Geocoding is the process of transforming a street address or other description of a location into a (latitude, longitude) coordinate. Specifically, I am loading pygeocoder for its geo-functionality, pandas for its dataframe structures, and numpy for its missing value (np.nan) functionality. However, not all strings are formatted in a way that Google’s geo-API can make sense of them.