Python - Converting a dataframe with columns x, y and a variable "A" into a netCDF file

My (simplified) data structure is as follows:

> x = [1,1,2,2,3,3,4,4,...n,n]

> y = [1,2,1,2,1,2,1,2,...1,2]

> A = [7,5,6,5,4,6,2,5,...4,3]

"A" is a variable which is linked to coordinates x and y. Dataframe consists of three columns. The variables are being read originally top down. Starting with x = 1 and y = 1, going down to y = max and after that x = 2, y from 1 to y_max -> next x = 3 and so on. So, this is 2 dimensional data, each value of "variable A" has a coordinate value of x and y in the same row in my dataframe.

However when I convert this directly to netCDF with

> Data.to_netcdf("filename.nc")

I get massive amount of x and y variables (dimension ends up being an index from 1 to n). For example if my x coordinate goes from 1 to 5 like 1,1,1,2,2,2,3,3,3,4,4,4,5,5,5 the netCDF will have 15 x -coordinates while I would like it to only have 5 of them. And same happens with the y -coordinates. I have tried many other approaches but I do not end up with anything useful.

I would like to have a netCDF with "A" as a variable and x and y as dimensions without them being repeated multiple times. My real dataset has more than a hundred x values and nearly a hundred y values. So every x value is repeated y times and vice versa.

Edit:

Here was the original code as requested by the answer giver @mozway

import pandas as pd

S_2017 = pd.read_csv("S_2017.csv")

EachValue = []
for i in range(124):
    Lon_min = 19.3 + i*0.1
    Lon_max = Lon_min + 0.1
    for j in range(45):
        S_2017_Analyze = S_2017
        Lat_max = 64.2 - j*0.1
        Lat_min = Lat_max - 0.1
        S_2017_Analyze = S_2017_Analyze[S_2017_Analyze.iloc[:,1] >= Lon_min]
        S_2017_Analyze = S_2017_Analyze[S_2017_Analyze.iloc[:,1] <= Lon_max]
        S_2017_Analyze = S_2017_Analyze[S_2017_Analyze.iloc[:,2] >= Lat_min]
        S_2017_Analyze = S_2017_Analyze[S_2017_Analyze.iloc[:,2] <= Lat_max]
        S_Sum_2017 = S_2017_Analyze.iloc[:,3].sum()
        Pixel_S_2017 = [round(Lat_min,2),round(Lon_min,2),S_Sum_2017]
        EachValue.append(Pixel_S_2017)
DataFrame = pd.DataFrame(EachValue,columns=["Latitude","Longitude","S_Sum_2017"])

And here is the solution by @mozway which I applied

import xarray as xr 

S_2017 = pd.DataFrame({'Lat':S_2017.iloc[:,0]
                       'Lon':S_2017.iloc[:,1]
                       'Variable':S_2017.iloc[:,2]
                       })
xr.Dataset.from_dataframe(S_2017.set_index(["Latitude","Longitude"])).to_netcdf("S_2017.nc")

IIUC, you could set the x/y as index, convert to xarray and then to netCDF:

import pandas as pd
import xarray as xr

df = pd.DataFrame({'x': [1,1,2,2,3,3,4,4],
                   'y': [1,2,1,2,1,2,1,2],
                   'A': [7,5,6,5,4,6,2,5],
                   })

xr.Dataset.from_dataframe(df.set_index(['x', 'y'])).to_netcdf('filename.nc')

Dataset:

<xarray.Dataset>
Dimensions:  (x: 4, y: 2)
Coordinates:
  * x        (x) int32 1 2 3 4
  * y        (y) int32 1 2
Data variables:
    A        (x, y) int32 ...

Underlying A:

array([[7, 5],
       [6, 5],
       [4, 6],
       [2, 5]])