Axes from plt.subplots() is a "numpy.ndarray" object and has no attribute "plot"
The axes are in 2-d, not 1-d so you can't iterate through using one loop. You need one more loop:
fig,axes=plt.subplots(nrows=2,ncols=2)
plt.tight_layout()
for ho in axes:
for i in ho:
i.plot(a,a**2)
This gives no problem but if I try:
for i in axes:
i.plot(a,a**2)
the error occurs.
If you debug your program by simply printing ax
, you'll quickly find out that ax
is a two-dimensional array: one dimension for the rows, one for the columns.
Thus, you need two indices to index ax
to retrieve the actual AxesSubplot
instance, like:
ax[1,1].plot(...)
If you want to iterate through the subplots in the way you do it now, by flattening ax
first:
ax = ax.flatten()
and now ax
is a one dimensional array. I don't know if rows or columns are stepped through first, but if it's the wrong around, use the transpose:
ax = ax.T.flatten()
Of course, by now it makes more sense to simply create each subplot on the fly, because that already has an index, and the other two numbers are fixed:
for x < plots_tot:
ax = plt.subplot(nrows, ncols, x+1)
Note: you have x <= plots_tot
, but with x
starting at 0, you'll get an IndexError
next with your current code (after flattening your array). Matplotlib is (unfortunately) 1-indexed for subplots. I prefer using a 0-indexed variable (Python style), and just add +1
for the subplot index (like above).
The problem here is with how matplotlib handles subplots. Just do the following:
fig, axes = plt.subplots(nrows=1, ncols=2)
for axis in axes:
print(type(axis))
you will get a matplotlib object which is actually a 1D array which can be traversed using single index i.e. axis[0], axis[1]...and so on. But if you do
fig, axes = plt.subplots(nrows=2, ncols=2)
for axis in axes:
print(type(axis))
you will get a numpy ndarray object which is actually a 2D array which can be traversed only using 2 indices i.e. axis[0, 0], axis[1, 0]...and so on. So be mindful how you incorporate your for loop to traverse through axes object.
In case if you use N by 1 graphs, for example if you do like fig, ax = plt.subplots(3, 1)
then please do likeax[plot_count].plot(...)