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Combine latitude and longitude into same column while adding an 'x or y' column in Python

I have a database that requires input data to be in an atypical format. Normally latitude and longitude would be in separate columns. In this case I need to bring them into the same column and then add an additional column to differentiate coordinate type. I am a python novice so I am in a bit of a bind as to where to start.
I need to get from this:
Location
Latitude
Longitude
Place1
32.123
120.123
Place2
31.321
121.321
To this:
Location
Lat/Long
Coords
Place1
Latitude
32.123
Place1
Longitude
120.123
Place2
Latitude
31.321
Place2
Longitude
121.321
Edit - This is a simplified example. The data I'm working with has a dozen other columns all of which I would like to preserve, only lengthening with the lat/long columns.
My initial thought was to create two exports of dataframe, one where I take the latitude field and one where I take the longitude field, and then recombine them (duplicating the records), but then calculating the values for the 'Lat/Long' and 'Coords' fields respectively. I do not know a) whether or not this is the right approach or; b) a clean path to get there. Any thoughts would be appreciated.

How do I reshape longitude data in an xarray of nc files

The current format of the longitude data is in the form (0,360) as shown in the picture. I want to get it to be (-180,180) where (-180,0) is equal to (180,360).

Assuming you have a column of longitude data (it's hard to tell from your sample), something like this should work:
pick = pd['longitude']>180
pd.loc[pick,'longitude'] = pd.loc[pick,'longitude'] - 360
The first line chooses the rows where longitude needs adjusting. The second line does the math for only those rows.

Why i am gething different h3 index with same latitude and longitude

I am working with a SQlite hexagon index database and other information, the Hexagons index is the primary key. This database is generated by code written in python, and other codes written in C use the hexagonal index to access the information stored in the database.
for res_hex in [12,11,10,9,8]:
index_hex = h3.geo_to_h3(sonde[1], sonde[0], res_hex)
sonde[1] is latitude, sonde[0] is longitude res_hex is the resolution.
In fact, I have a list of objects represented by their latitude and longitude in a text file, I calculate the indexes around them with different resolutions (8 to 12). that I enter the database.
But my problem is that when I calculate the hexagon in code c with lat, lon and the resolution, I do not find it in the base. This even if the calculation is based on the same file.
GeoCoord geo = {latitude, longitude};
H3Index currentIndex = geoToH3(&geo, resolution);
Thanks for your help

I have find a solution, in C Lat/Lon must be in radians but it is not the case in python

Pandas - How do I look for a set of values in a column and if it is present return a value in another column

I am new to pandas. I have a csv file which has a latitude and longitude columns and also a tile ID column, the file has around 1 million rows. I have a list of around a hundred tile ID's and want to get the latitude and longitude coordinates for these tile ID's. Currently I have:
good_tiles_str = [str(q) for q in good_tiles]#setting list elements to string data type
file['tile'] = file.tile.astype(str)#setting title column to string data type
for i in range (len(good_tiles_str)):
x = good_tiles_str[i]
lat = file.loc[file['tile'].str.contains(x), 'BL_Latitude'] #finding lat coordinates
long = file.loc[file['tile'].str.contains(x), 'BL_Longitude'] #finding long coordinates
print(lat)
print(long)
This method is very slow and I know it is not the correct way as I heard you should not use for loops like this whilst using pandas. Also, it does not work as it doesn't find all the latitude and longitude points for the tile ID's
Any help would be very gladly appreciated

There is no need to iterate rows explicitly , I think as far as I understood your question.
If you wish a particular assignment given a condition, you can do so explicitly. Here's one way using numpy.where; we use ~ to indicate "negative".
rule1= file['tile'].str.contains(x)
rule2= file['tile'].str.contains(x)
file['flag'] = np.where(rule1 , 'BL_Latitude', " " )
file['flag'] = np.where(rule2 & ~rule1, 'BL_Longitude', file['flag'])

Try this:
search_for = '|'.join(good_tiles_str)
good = file[file.tile.str.contains(search_for)]
good = good[['BL_Latitude', 'BL_Longitude']].drop_duplicates()

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]