PANDAS plot multiple Y axes

Question

I know pandas supports a secondary Y axis, but Im curious if anyone knows a way to put a tertiary Y axis on plots... currently I am achieving this with numpy+pyplot ... but it is slow with large data sets.

this is to plot different measurements with distinct units on the same graph for easy comparison (eg Relative Humidity/Temperature/ and Electrical Conductivity)

so really just curious if anyone knows if this is possible in pandas without too much work.

[Edit] I doubt that there is a way to do this(without too much overhead) however I hope to be proven wrong , this may be a limitation of matplotlib...

Answer 1

I think this might work:

import matplotlib.pyplot as plt import numpy as np from pandas import DataFrame df = DataFrame(np.random.randn(5, 3), columns=['A', 'B', 'C'])  fig, ax = plt.subplots() ax3 = ax.twinx() rspine = ax3.spines['right'] rspine.set_position(('axes', 1.15)) ax3.set_frame_on(True) ax3.patch.set_visible(False) fig.subplots_adjust(right=0.7)  df.A.plot(ax=ax, style='b-') # same ax as above since it's automatically added on the right df.B.plot(ax=ax, style='r-', secondary_y=True) df.C.plot(ax=ax3, style='g-')  # add legend --> take advantage of pandas providing us access # to the line associated with the right part of the axis ax3.legend([ax.get_lines()[0], ax.right_ax.get_lines()[0], ax3.get_lines()[0]],\            ['A','B','C'], bbox_to_anchor=(1.5, 0.5)) 

Output:

Answer 2

A simpler solution without plt:

ax1 = df1.plot()  ax2 = ax1.twinx() ax2.spines['right'].set_position(('axes', 1.0)) df2.plot(ax=ax2)  ax3 = ax1.twinx() ax3.spines['right'].set_position(('axes', 1.1)) df3.plot(ax=ax3)  .... 

Using function to achieve this:

def plot_multi(data, cols=None, spacing=.1, **kwargs):      from pandas.plotting._matplotlib.style import get_standard_colors      # Get default color style from pandas - can be changed to any other color list     if cols is None: cols = data.columns     if len(cols) == 0: return     colors = get_standard_colors(num_colors=len(cols))      # First axis     ax = data.loc[:, cols[0]].plot(label=cols[0], color=colors[0], **kwargs)     ax.set_ylabel(ylabel=cols[0])     lines, labels = ax.get_legend_handles_labels()      for n in range(1, len(cols)):         # Multiple y-axes         ax_new = ax.twinx()         ax_new.spines['right'].set_position(('axes', 1 + spacing * (n - 1)))         data.loc[:, cols[n]].plot(ax=ax_new, label=cols[n], color=colors[n % len(colors)], **kwargs)         ax_new.set_ylabel(ylabel=cols[n])                  # Proper legend position         line, label = ax_new.get_legend_handles_labels()         lines += line         labels += label      ax.legend(lines, labels, loc=0)     return ax 

Example:

from random import randrange  data = pd.DataFrame(dict(     s1=[randrange(-1000, 1000) for _ in range(100)],     s2=[randrange(-100, 100) for _ in range(100)],     s3=[randrange(-10, 10) for _ in range(100)], ))  plot_multi(data.cumsum(), figsize=(10, 5)) 

Output: