A multi-axis plot with a line on top. Matplotlib
I am trying to use doublex () to create a combined bar / line graph with a line visible on top of the line. It currently looks like this:
I also want a line chart to be plotted on the left vertical axis (ax), and the right (ax2) at the moment. If I draw a line on the second axis it appears on top, but obviously it appears on the wrong axis (right)
Here's my code:
self.ax2=ax.twinx()
df[['Opportunities']].plot(kind='bar', stacked=False, title=get_title, color='grey', ax=self.ax2, grid=False)
ax.plot(ax.get_xticks(),df[['Percentage']].values, linestyle='-', marker='o', color='k', linewidth=1.0)
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = self.ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, loc='lower right')
There are also problems with labels, but only one thing at a time.
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By default, by default, artists are drawn on ax
, then artists on double axes ax2
from above. Since in your code the line graph was drawn on ax
and the hatch graph on ax2
, the stroke graph sits on top of (and hides) the line.
(I thought I could change this by pointing out zorder
, but this attempt doesn't work ...)
Thus, one way to solve the problem is to use a ax
stroke to draw a graph and ax2
to draw a line. This will place the line on top of the bars. It will also, by default, place yticks for ax
(stroke plot) on the left and yticks for ax2
(string) on the right. However, you can use
ax.yaxis.set_ticks_position("right")
ax2.yaxis.set_ticks_position("left")
to swap the labels on the left and right.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
np.random.seed(2015)
N = 16
df = pd.DataFrame({'Opportunities': np.random.randint(0, 30, size=N),
'Percentage': np.random.randint(0, 100, size=N)},
index=pd.date_range('2015-3-15', periods=N, freq='B').date)
fig, ax = plt.subplots()
df[['Opportunities']].plot(kind='bar', stacked=False, title='get_title',
color='grey', ax=ax, grid=False)
ax2 = ax.twinx()
ax2.plot(ax.get_xticks(), df[['Percentage']].values, linestyle='-', marker='o',
color='k', linewidth=1.0, label='percentage')
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, loc='best')
ax.yaxis.set_ticks_position("right")
ax2.yaxis.set_ticks_position("left")
fig.autofmt_xdate()
plt.show()
gives
Alternatively, the axes zorder
for the axes can be set to draw ax
above ax2
. Paul Ivanov shows how :
ax.set_zorder(ax2.get_zorder()+1) # put ax in front of ax2
ax.patch.set_visible(False) # hide the 'canvas'
ax2.patch.set_visible(True) # show the 'canvas'
Thus,
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
np.random.seed(2015)
N = 16
df = pd.DataFrame({'Opportunities': np.random.randint(0, 30, size=N),
'Percentage': np.random.randint(0, 100, size=N)},
index=pd.date_range('2015-3-15', periods=N, freq='B').date)
fig, ax = plt.subplots()
ax2 = ax.twinx()
df[['Opportunities']].plot(kind='bar', stacked=False, title='get_title',
color='grey', ax=ax2, grid=False)
ax.plot(ax.get_xticks(), df[['Percentage']].values, linestyle='-', marker='o',
color='k', linewidth=1.0, label='percentage')
lines, labels = ax.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax.legend(lines + lines2, labels + labels2, loc='best')
ax.set_zorder(ax2.get_zorder()+1) # put ax in front of ax2
ax.patch.set_visible(False) # hide the 'canvas'
ax2.patch.set_visible(True) # show the 'canvas'
fig.autofmt_xdate()
plt.show()
gives the same result without having to change the roles played by ax
and ax2
.
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