Matplotlib axis with two scales shared origin

Question

I need two overlay two datasets with different Y-axis scales in Matplotlib. The data contains both positive and negative values. I want the two axes to share one origin, but Matplotlib does not align the two scales by default.

import numpy as np import matplotlib.pyplot as plt  fig = plt.figure() ax1 = fig.add_subplot(111) ax2 = ax1.twinx()  ax1.bar(range(6), (2, -2, 1, 0, 0, 0)) ax2.plot(range(6), (0, 2, 8, -2, 0, 0)) plt.show() 

I suppose it is possible to perform some computation with .get_ylim() and .set_ylim() two align the two scales. Is there an easier solution?

Answer 1

use the align_yaxis() function:

import numpy as np import matplotlib.pyplot as plt  def align_yaxis(ax1, v1, ax2, v2):     """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1"""     _, y1 = ax1.transData.transform((0, v1))     _, y2 = ax2.transData.transform((0, v2))     inv = ax2.transData.inverted()     _, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))     miny, maxy = ax2.get_ylim()     ax2.set_ylim(miny+dy, maxy+dy)   fig = plt.figure() ax1 = fig.add_subplot(111) ax2 = ax1.twinx()  ax1.bar(range(6), (2, -2, 1, 0, 0, 0)) ax2.plot(range(6), (0, 2, 8, -2, 0, 0))  align_yaxis(ax1, 0, ax2, 0) plt.show() 

Answer 2

In order to ensure that the y-bounds are maintained (so no data points are shifted off the plot), and to balance adjustment of both y-axes, I made some additions to @HYRY's answer:

def align_yaxis(ax1, v1, ax2, v2):     """adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1"""     _, y1 = ax1.transData.transform((0, v1))     _, y2 = ax2.transData.transform((0, v2))     adjust_yaxis(ax2,(y1-y2)/2,v2)     adjust_yaxis(ax1,(y2-y1)/2,v1)  def adjust_yaxis(ax,ydif,v):     """shift axis ax by ydiff, maintaining point v at the same location"""     inv = ax.transData.inverted()     _, dy = inv.transform((0, 0)) - inv.transform((0, ydif))     miny, maxy = ax.get_ylim()     miny, maxy = miny - v, maxy - v     if -miny>maxy or (-miny==maxy and dy > 0):         nminy = miny         nmaxy = miny*(maxy+dy)/(miny+dy)     else:         nmaxy = maxy         nminy = maxy*(miny+dy)/(maxy+dy)     ax.set_ylim(nminy+v, nmaxy+v)