I have a question I have a dataset which contain 1200060 rows and 3 column. column contain points and I have to draw a 3D plot for it. I’m using the code below but I don’t know where is the error.
fig = plt.figure() ax = plt.axes(projection="3d") count = len(data1.index) z_line = np.linspace(0, count, 1000) x_line = np.cos(z_line) y_line = np.sin(z_line) ax.plot3D(x_line, y_line, z_line, 'gray') z_points = count * data1[['z']] x_points = np.cos(z_points) + 0.1 * data1[['x']] y_points = np.sin(z_points) + 0.1 * data1[['y']] ax.scatter3D(x_points, y_points, z_points, c=z_points, cmap='hsv') plt.show()
error is:
ValueError: shape mismatch: objects cannot be broadcast to a single shape
I tried this one too but unsuccessful
fig = plt.figure()
ax = plt.axes(projection="3d")
x = np.linspace(len(data1['z'].index), len(data1['y'].index), len(data1['x'].index))
y = np.linspace(len(data1['z'].index), len(data1['y'].index), len(data1['x'].index))
X = data1[['x']]
Y = data1[['y']]
Z = data1[['z']]
fig = plt.figure()
ax = plt.axes(projection="3d")
ax.plot_wireframe(X, Y, Z, color='green')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
this one run but didn’t show any output
and using this one same error as the first one
fig = plt.figure() ax = plt.axes(projection="3d") num_bars = len(data1.index) x_pos = data1[['x']], num_bars y_pos = data1[['y']], num_bars z_pos = data1[['z']], num_bars x_size = np.ones(num_bars) y_size = np.ones(num_bars) z_size = np.ones(num_bars) ax.bar3d(x_pos, y_pos, z_pos, x_size, y_size, z_size, color='aqua') plt.show()
error: ValueError: shape mismatch: objects cannot be broadcast to a single shape
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Answer
Either use data1['z'] instead of data1[['z']], or you could even use data1[['z']].values.
Why? Because you want to work with either Series or numpy arrays here, not with DataFrames.
See the difference between:
print(type(data1)) # <class 'pandas.core.frame.DataFrame'> print(type(data1['x'])) #<class 'pandas.core.series.Series'> print(type(data1['x'].values)) #<class 'numpy.ndarray'> print(type(data1[['x']])) #<class 'pandas.core.frame.DataFrame'> print(type(data1[['x']].values)) #<class 'numpy.ndarray'>
To be more precise, the underlying issue is that pandas does not know how it is supposed to handle the addition of two differently named columns, as can be seen here:
a = pd.DataFrame({'x':np.arange(0,5)})
b = pd.DataFrame({'y':np.arange(0,5)})
c = pd.DataFrame({'x':np.arange(0,5)})
print(a+b)
### yields:
# x y
# 0 NaN NaN
# 1 NaN NaN
# 2 NaN NaN
# 3 NaN NaN
# 4 NaN NaN
print(a+c)
### yields:
# x
# 0 0
# 1 2
# 2 4
# 3 6
# 4 8