How to plot in 3D with a double entry table - Matplotlib

maybe contour is enough

b = np.array([0,5,15,25,40,60,80,100,120,140,160,180,200])
a = np.array([0,23,45,90,120,180])
x, y = np.meshgrid(a, b)
z = np.random.randint(-50,-40, (x.shape))

scm = plt.contourf(x, y, z, cmap='inferno')
plt.colorbar(scm)
plt.xticks(a)
plt.yticks(b)
plt.xlabel('Distance')
plt.ylabel('Angle')
plt.show()

displays

You can get a contour plot with something like this (but for the data shown it is not very interesting since all the values are constant at -45):

df = pd.read_csv(sep=';')
df = df.set_index('Distance')
x = df.index
y = df.columns.astype(int)
z = df.values
X,Y = np.meshgrid(x,y)
Z = z.T
plt.contourf(X,Y,Z,cmap='jet')
plt.colorbar()
plt.show()

Welcome to stackoverflow, your question can be split into several steps:

Step 1 - read the data

I have stored your data in a file called data.txt.

I don't know Pandas very well but this can also be handled with the nice simple function of Numpy called loadtxt. Your data is a bit problematic because of the text 'Distance' value in the first column and first row. But don't panic we load the file as a matrix of strings:

raw_data = np.loadtxt('data.txt', delimiter=';', dtype=np.string_)

Step 2 - transform the raw data

To extract the wanted data from the raw data we can do the following:

angle    = raw_data[0 , 1:].astype(float)
distance = raw_data[1:, 0 ].astype(float)
data     = raw_data[1:, 1:].astype(float)

With indexing the raw data we select the data that we want and with astype we change the string values to numbers.

Intermediate step - making the data a bit fancier

Your data was a bit boring, only the value -45, i took the liberty to make it a bit fancier:

data = (50 + angle[np.newaxis,:]) / (10 + np.sqrt(distance[:,np.newaxis])) 

Step 4 - make a wireframe plot

The example at matplotlib.org looks easy enough:

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X, Y, Z)
plt.show()

But the trick is to get the X, Y, Z parameters right...

Step 3 - make the X and Y data

The Z data is simply our data values:

Z = data

The X and Y should also be 2D array's such that plot_wireframe can find the x and y for each value of Z in the 2D arrays X an Y at the same array locations. There is a Numpy function to create these 2D array's:

X, Y = np.meshgrid(angle, distance)

Step 5 - fancing it up a bit

ax.set_xticks(angle)
ax.set_yticks(distance[::2])
ax.set_xlabel('angle')
ax.set_ylabel('distance')

Putting it together

All steps together in the right order:

# necessary includes...
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np

raw_data = np.loadtxt('data.txt', delimiter=';', dtype=np.string_)

angle    = raw_data[0 , 1:].astype(float)
distance = raw_data[1:, 0 ].astype(float)
data     = raw_data[1:, 1:].astype(float)


# make the example data a bit more interesting...
data = (50 + angle[np.newaxis,:]) / (10 + np.sqrt(distance[:,np.newaxis])) 

# setting up the plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# the trickey part creating the data that plot_wireframe wants
Z = data
X, Y = np.meshgrid(angle, distance)

ax.plot_wireframe(X, Y, Z)

# fancing it up a bit
ax.set_xticks(angle)
ax.set_yticks(distance[::2])
ax.set_xlabel('angle')
ax.set_ylabel('distance')

# and showing the plot ...
plt.show()