I’ve exported my location data from Google but one thing they have in the resulting json is that both latitudes and longitudes are stored as integers. For example:

```
-37785037 # latitude
144939405 # longitude
```

Since I know where I roughly was at that time it’s clear that the actual coords are -37.785.. and 144.939…, i.e. somewhere in northern suburbs of Melbourne, AU.

Also, by examining the data I established that in all cases the values have a 7-digit precision. So reading this json I’m converting coordinates to integers using this very ugly construct: first I convert int to string, then take integer part, add a dot, add floating part and then convert all to float:

```
loc = {}
for i in data['locations']:
try:
t = dt.datetime.strptime(i['timestamp'], "%Y-%m-%dT%H:%M:%S.%fZ")
except ValueError:
t = dt.datetime.strptime(i['timestamp'], "%Y-%m-%dT%H:%M:%SZ")
lat = float(f"{str(i['latitudeE7'])[:-7]}.{str(i['latitudeE7'])[-7:]}")
lon = float(f"{str(i['longitudeE7'])[:-7]}.{str(i['longitudeE7'])[-7:]}")
loc[t] = {
'latitude': lat,
'longitude': lon
}
```

This works, but looks hella ugly.

Is there a cleaner way of converting an int to float when you know the precision of the resulting float?

### >Solution :

If precision is fixed, simply dividing your value by 1e6 will give you the correct answer:

`-37785037/1e6=-37.785037`