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How to determine deviation value in latitude and longitude values?

I have a script that takes the latitude and longitude values and stores them in the database(So caching). If there is the same location, I want to pull it from the database without having to calculate it again. However, I want to keep this location (latitude-longitude) information between a certain deviation value, since there will be very small differences depending on various environments during the acquisition of location information. For example:
First Location Value
latitude: 51.21318
longitude : 16.82032
Another Location Value
latitude: 51.21319
longitude : 16.82033
In the example above, if the control is provided with one-to-one matching, it will be perceived as two different location information and the database will be added. But I want to keep it here by specifying a certain deviation. I plan to set max and min values and provide control between these values. For example:
deviation = 0.005
max_lot = latitude + (latitude * deviation)
min_lot = latitude - (latitude * deviation)
I plan to provide a check for those within a certain range between these values and the database. Is there any formula or accepted value in the literature to determine the deviation value I want to ask here? What do you think is the best way?

Most effective pathway to compare geographic locations? solutions pls

Say I have a location A, with IP address, and a geolocator API with latitude and longitude. Now I want to find all instances that are within a 25-mile radius of location A. How can I compute this with the least amount of steps?
solution A: I can compute all distances between location A and all instances in the database, and display instances within 25 radius. (way too slow especially if I want a dynamic location, with a large database of locations)
solution B: I can group all instances in terms of zip code in addition to IP, and (lat, long). so that fewer distances between location A and instances needed to be computed. (better, but what if the IP address is at the border of another zip code, this will add to the amount of needed computation)
solution C: I can use trigonometry. using the latitude and longitude of location A. i can find each instance with in the 25 mile radius.
Can someone please describe a better way of comparing distances? ideas and suggestions are much appreciated (if further explanation is needed, pls ask) Thanks.

I'd use a combination of your proposed solutions a and c. You can query your database directly using a filter that only selects the locations within a 25 miles radius (or any other radius). Calculating the longitudinal difference in miles is a little tricky because the mileage of one degree in longitude differs with latitude. Kudos go out to this explanation: https://gis.stackexchange.com/questions/142326/calculating-longitude-length-in-miles#142327
Assuming you have the following DB schema with existing locations (only latitude and longitude as columns):
CREATE TABLE location (
lat REAL,
lon REAL
);
You're able to filter only the locations within a 25 mile radius using this query:
query = """
SELECT (lat - ?) AS difflat,
(lon - ?) AS difflon
FROM location
WHERE POWER(POWER(difflat * 69.172, 2) + POWER(difflon * COS(lat*3.14/180.) * 69.172, 2), 0.5) < ?;
"""
Using the query then like this:
radius = 25 #miles
cursor.execute(query, (querylocation['lat'], querylocation['lat'], radius))
SQLite3 unfortunately doesn't support basic mathematical functions like COS and POWER but they can easily be created:
import math
con = sqlite3.connect(db_path)
con.create_function('POWER', 2, math.pow)
con.create_function('COS', 1, math.cos)

How do i get a list of all the latitudes and longitudes from a database within a specific area?

I have a table in a Postgres DB containing places and their corresponding latitude and longitude values.
Places (id, name, lat, lng) # primary-key(id)
I will get an input of a pair of latitudes and longitudes forming a rectangle.
{
"long_ne": 12.34,
"lat_ne": 34.45,
"long_sw": 15.34,
"lat_sw": 35.56
}
I want to get all the rows that fall inside the rectangle.
The rows can't be sorted based on their lat-lng values as that will cause trouble while inserting new values.
What would be the best way to go about solving this to optimize queries to get the result?
I can obviously do it using the WHERE clause, but would it be the ost optimized solution? There would be a massive number of rows in the table and is there a way this query can be optimized to speed up the result?

It this what you want?
select *
from places
where
lat between :lat_no and :lat_sw
and lng between :long_no and :long_sw
Where :lat_no, :lat_ws :long_no and :long_sw are the input parameters to the query.
This gives you all rows whose latitude and longitude fall between the square boundaries. Note that between considers the inteval inclusive of their bounds on both ends. You can change this as needed with inequality conditions, for example, this makes the match inclusive on the lower bound and exclusive on the upper bound:
where
lat >= :lat_no and lat < :lat_sw
and lng >= :long_no and lng < :long_sw

Won't a simple SQL query suffice here?
SELECT *
FROM Places
WHERE (lat > lat_sw)
AND (long > long_sw)
AND (lat < lat_ne)
AND (long < long_ne)

How to delete CSV table values outside of a longitude/latitude radius?

I have a csv file with a table that has the columns Longitude, Latitude, and Wind Speed. I have a code that takes a csv file and deletes values outside of a specified bound. I would like to retain values whose longitude/latitude is within a 0.5 lon/lat radius of a point located at -71.5 longitude and 40.5 latitude.
My example code below deletes any values whose longitude and latitude isn't between -71 to -72 and 40 to 41 respectively. Of course, this retains values within a square bound ±0.5 lon/lat around my point of interest. But I am interested in finding values within a circular bound with radius 0.5 lon/lat of my point of interest. How should I modify my code?
import pandas as pd
import numpy
df = pd.read_csv(r"C:\\Users\\xil15102\\Documents\\results\\EasternLongIsland50.csv") #file path
indexNames=df[(df['Longitude'] <= -72)|(df['Longitude']>=-71)|(df['Latitude']<=40)|(df['Latitude']>=41)].index
df.drop(indexNames,inplace=True)
df.to_csv(r"C:\\Users\\xil15102\\Documents\\results\\EasternLongIsland50.csv")

Basically you need to check if a value is a certain distance from a central point (-71.5 and 40.5); to do this use the pythagorean theorem/distance formula:
d = sqrt(dx^2+dy^2).
So programmatically, I would do this like:
from math import sqrt
drop_indices = []
for row in range(len(df)):
if (sqrt(abs(-71.5 - df[row]['Longitude'])*abs(-71.5 - df[row]['Longitude']) + abs(40.5-df[row]['Latitude'])*abs(40.5-df[row]['Latitude']))) > 0.5:
drop_indices.append(row)
df.drop(drop_indices)
Sorry that is a sort for disgusting way to get rid of the rows and your way looks much better, but the code should work.

You should write a function to calculate the distance from your point of interest and drop those. Some help here. Pretty sure the example below should work if you implement is_not_in_area as a function to calculate the distance and check if dist < 0.5.
df = df.drop(df[is_not_in_area(df.lat, df.lon)].index)
(This code lifted from here)
Edit: drop the ones that aren't in area, not the ones that are haha.

Read value over time at certain coordinate from netCDF with python

I have a netCDF file with a grid (each step 0.25°).
What I want is the value of the variable, lets say tempMax, at a certain gridpoint, over the last 50 years.
I am aware that you read the data into python like this
lon = numpy.array(file.variables['longitude'][:])
lat = numpy.array(file.variables['latitude'][:])
temp = numpy.array(file.variables['tempMax'][:])
time = numpy.array(file.variables['time'][:])
That leaves me with an array and I do not know how to "untangle" it.
How to get the value at a certain coordinate (stored in temp) over the whole time (stored in time)?
S display is the value over the time at the certain coordinate.
Any ideas how I could achieve that?
Thanks!

I'm guessing that tempMax is 3D (time x lat x lon) and should then be read in as
temp = ncfile.variables['tempMAx'][:,:,:]
(Note two things: (1) if you're using Python v2, it's best to avoid the word file and instead use something like ncfile as shown above, (2) temp will be automatically stored as a numpy.ndarray simply with the call above, you don't need to use the numpy.array() command during the read in of variables.)
Now you can extract temperatures for all times at a certain location with
temp_crd = temp[:,lat_idx,lon_idx]
where lat_idx and lon_idx are integers corresponding to the index of the latitude and longitude coordinates. If you know these indices beforehand, great, just plug them in, e.g. temp_crd = temp[:,25,30]. (You can use the tool ncdump to view the contents of a netCDF file, https://www.unidata.ucar.edu/software/netcdf/docs/netcdf/ncdump.html)
The more likely case is that you know the coordinates, but not their indices beforehand. Let's say you want temperatures at 50N and 270E. You can use the numpy.where function to extract the indices of the coordinates given the lat and lon arrays that you've already read in.
lat_idx = numpy.where(lat==50)[0][0]
lon_idx = numpy.where(lon==270)[0][0]
tmp_crd = temp[:,lat_idx,lon_idx]