如何比较同一csv文件中的多个值？

Question

我正在实现以下两遍算法，如下所述。

    Two-pass algorithm

    To compute the connected components of a raster, suppose we have binary
    raster B. In order to identify connected regions, the algorithm makes two
    passes. On the first pass the operator scans the raster pixel by pixel until it
    comes to a point p where B(i, j) = 1. It then examines the four neighbors
    that are already passed in the scan, i.e. the west, north-west, north and northeast pixel (considering 8-connectivity) and labels the pixel p as follows:
    1. If all four neighbors are 0, assign a new label to p, else
    2. If only one neighboring pixel has value 1, assign pixel p with the same
    label, else
    3. If more than one neighboring pixel has value 1, assign the lowest label
    to p and add a note to the list of equivalences.
    After completing the first pass, a second scan is made through the raster,
    during which each label is replaced by the label assigned to it in the equivalence list.

有一个包含经度，纬度，温度和标签字段的数据集，如下图所示，在该数据集中我必须实现上述两次通过算法。

pass1的实现如下

    for i in np.arange(41.70, 42.10, 0.05):
        #print(round(i,2), end=', ')
        for j in np.arange(12.30, 12.80, 0.05):
        #   print(round(j,2), end=', ')
            for k in np.arange(0,25,5):
                #print("\n")
                #print(round(i,2),round(j,2),k, end=', ')
                xmax = round(i+0.05,2)
                ymax = round(j+ 0.05,2)
                zmax = round(k+5,2)
                #print("Voxel",xmax,ymax,zmax)
                v = []
                #x1 = []
                #y1 = []
                #z1 = []
                count = 0
                label = 0
                with open('a.csv') as csvfile:
                    plots = csv.reader(csvfile,delimiter=',')
                    for rows in plots:
                        if(float(rows[0]) >= i and float(rows[0])<= xmax and float(rows[1]) >=j and float(rows[1])<=ymax and float(rows[2])>=k and float(rows[2])<=zmax):
                            #print("points", float(rows[0]),float(rows[1]),float(rows[2]))

                            #x1.append(rows[0])
                            #y1.append(rows[1])
                            #z1.append(rows[2])
                            count= count+1
                            label = label + 1

                            break


                    #f = open("demofile2.txt", "a")
                    #f.write(str(i)+","+str(j)+","+str(k)+","+str(count)+"\n")
                    #f.write(text)
                    #f.close()
                    #print(count)
                if(count > 0):
                    print(xmax,ymax,zmax,count)
                        #label = label + 1
                    f = open("demofile3.txt", "a")
                    f.write(str(xmax)+","+str(ymax)+","+str(zmax)+","+str(label)+"\n")
                    f.close()

Pass2的实施

    for i in np.arange(41.85, 42.10, 0.05):
           for j in np.arange(12.45, 12.70, 0.05):
               for k in np.arange(0,26,5):
                print("\n")
                df = pd.read_csv('demofile3.txt')
                x = round(i,2)
                y= round(j,2)
                z =k

                #neighbour1 (x-1,y,z)
                x = float(round(i,2)-0.5)
                y= round(j,2)
                z =k
                df1=df[(df['longitude'] == str(x)) & (df['latitude'] == str(y)) & (df['temperature'] == str(z))]
                print(df1)

现在我被困在pass2的实现中，其中print（df1）给出了这样的结果

    Empty DataFrame
    Columns: [longitude, latitude, temperature, label]
    Index: []  

如何在python中实现此逻辑？如何在同一个csv中比较多个值？

Answer 1

第二次通行证的实施，直至获得邻居的标签

for i in np.arange(41.85, 42.10, 0.05):
    #print(round(i,2), end=', ')
    for j in np.arange(12.45, 12.70, 0.05):
    #   print(round(j,2), end=', ')
        for k in np.arange(0,26,5):
            print("\n")
            df = pd.read_csv('demofile3.txt')
            x = round(i,2)
            y= round(j,2)
            z =k

            #neighbour1 (x-1,y,z) west
            x = float(round(i,2)-0.05)
            y= round(j,2)
            z =k
            #df1 = df['longitude']
            #df2 = df['latitude']
            #df3 = df['temperature']
            #print(x,y,z)
            df1=df[(df['longitude'] == x) & (df['latitude'] == y) &(df['temperature'] == z)]
            isempty = df1.empty
            if(isempty):
                print('No neighbour 1')
            else:
                print("neighbour1",df1)
                print(df1['label'])
            #print(df1)

            #neighbour2 (x1,y-1,z) north 
            x = round(i,2)
            y= float(round(j,2)-0.05)
            z =k
            #df1 = df['longitude']
            #df2 = df['latitude']
            #df3 = df['temperature']
            #print(x,y,z)
            df2=df[(df['longitude'] == x) & (df['latitude'] == y) &(df['temperature'] == z)]
            isempty = df2.empty
            if(isempty):
                print('No neighbour 2')
            else:
                print("neighbour2",df2)
                print(df2['label'])




            #neighbour3 (x1-1,y-1,z) north - west
            x = float(round(i,2)-0.05)
            y= float(round(j,2)-0.05)
            z =k
            #df1 = df['longitude']
            #df2 = df['latitude']
            #df3 = df['temperature']
            #print(x,y,z)
            df3=df[(df['longitude'] == x) & (df['latitude'] == y) &(df['temperature'] == z)]
            isempty = df3.empty
            if(isempty):
                print('No neighbour 3')
            else:
                print("neighbour3",df3)
                print(df3['label'])

            #neighbour4 (x1,y-1,z+1) north - east
            x = round(i,2)
            y= float(round(j,2)-0.05)
            z =k+5
            #df1 = df['longitude']
            #df2 = df['latitude']
            #df3 = df['temperature']
            #print(x,y,z)
            df4=df[(df['longitude'] == x) & (df['latitude'] == y) &(df['temperature'] == z)]
            isempty = df1.empty
            if(isempty):
                print('No neighbour 4')
            else:
                print("neighbour4",df4)
                print(df4['label'])