Adding a y-axis label to secondary y-axis in matplotlib

I can add a y label to the left y-axis using plt.ylabel, but how can I add it to the secondary y-axis?

table = sql.read_frame(query,connection)

table[0].plot(color=colors[0],ylim=(0,100))
table[1].plot(secondary_y=True,color=colors[1])
plt.ylabel('$')

The best way is to interact with the axes object directly

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 10, 0.1)
y1 = 0.05 * x**2
y2 = -1 *y1

fig, ax1 = plt.subplots()

ax2 = ax1.twinx()
ax1.plot(x, y1, 'g-')
ax2.plot(x, y2, 'b-')

ax1.set_xlabel('X data')
ax1.set_ylabel('Y1 data', color='g')
ax2.set_ylabel('Y2 data', color='b')

plt.show()

There is a straightforward solution without messing with matplotlib: just pandas.

Tweaking the original example:

table = sql.read_frame(query,connection)

ax = table[0].plot(color=colors[0],ylim=(0,100))
ax2 = table[1].plot(secondary_y=True,color=colors[1], ax=ax)

ax.set_ylabel('Left axes label')
ax2.set_ylabel('Right axes label')

Basically, when the secondary_y=True option is given (eventhough ax=ax is passed too) pandas.plot returns a different axes which we use to set the labels.

I know this was answered long ago, but I think this approach worths it.

I don't have access to Python right now, but off the top of my head:

fig = plt.figure()

axes1 = fig.add_subplot(111)
# set props for left y-axis here

axes2 = axes1.twinx()   # mirror them
axes2.set_ylabel(...)

For everyone stumbling upon this post because pandas gets mentioned, you now have the very elegant and straightforward option of directly accessing the secondary_y axis in pandas with ax.right_ax

So paraphrasing the example initially posted, you would write:

table = sql.read_frame(query,connection)

ax = table[[0, 1]].plot(ylim=(0,100), secondary_y=table[1])
ax.set_ylabel('$')
ax.right_ax.set_ylabel('Your second Y-Axis Label goes here!')

(this is already mentioned in these posts as well: 1 2)

Simple example with few loc:

plot(y1)
plt.gca().twinx().plot(y2, color = 'r') # default color is same as first ax

Explanation:

ax = plt.gca()    # Get current axis
ax2 = ax.twinx()  # make twin axis based on x
ax2.plot(...)     # ...