Changing the left and right parameters in subplot_adjust automatically?-CodePudding

I'm working with this code using

However, I can see that the numbers on the right hand side x axis are clustered closer together than the numbers on the left hand side, and I would like to make these even.

I know it has to do with the line:

plt.subplots_adjust(wspace=0, top=0.85, bottom=0.1, left=0.2, right=0.8)

I know how to change this manually (i.e. just change the right and left parameters). But I could never be sure that I was being accurate, as I would just be manually changing these parameters until they looked even?

I'm wondering if someone could show me how make the axes evenly spaced automatically, without manually changing the left and right parameters?

CodePudding user response：

A way to solve this problem is to use the set_xlim function from Matplotlib and apply it to your subplots. The code then looks like this:

import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import seaborn as sns
sns.set()
#%matplotlib notebook
#plt.style.use('classic')



Class = ['test1', 'test2', 'test3', 'test4', 'test5', 'test6', 'test7']
Lens = [111, 80, 114, 59, 109.0491744186047, 117, 120]
Nums = [124, 28, 22, 41, 85, 33, 156]
data = pd.DataFrame(data=zip(Class,Lens,Nums),columns=['Class','Lens','Nums'])
data.set_index('Class', inplace=True)

font_color = '#525252'
hfont = {'fontname':'Calibri'}
#facecolor = '#eaeaf2'
color_red = '#fd625e'
color_blue = '#01b8aa'
index = data.index
column0 = data['Lens']
column1 = data['Nums']
title0 = "Title 1"
title1 = 'Title 2'

fig, axes = plt.subplots(figsize=(10,5), ncols=2,sharey=True)
fig.tight_layout()
fig.canvas.set_window_title('test') # Adds new title to the window

axes[0].barh(index, column0, align='center', color=color_red, zorder=10)
axes[0].set_title(title0, fontsize=18, pad=15, color=color_red, **hfont)
axes[1].barh(index, column1, align='center', color=color_blue, zorder=10)
axes[1].set_title(title1, fontsize=18, pad=15, color=color_blue, **hfont)

print(column1)
# If you have positive numbers and want to invert the x-axis of the left plot
axes[0].invert_xaxis() 

# To show data from highest to lowest
plt.gca().invert_yaxis()

axes[0].grid(False)
axes[1].grid(False)
axes[0].set_facecolor('white')
axes[1].set_facecolor('white')


axes[0].set(yticks=data.index, yticklabels=data.index)
axes[0].yaxis.tick_left()
axes[0].tick_params(axis='y', colors='white') # tick color


#Set xlim
max_columns_value=max([max(column0),max(column1)])

axes[0].set_xlim([max_columns_value,0])
axes[1].set_xlim([0,max_columns_value])



for label in (axes[0].get_xticklabels()   axes[0].get_yticklabels()):
    label.set(fontsize=13, color=font_color, **hfont)
for label in (axes[1].get_xticklabels()   axes[1].get_yticklabels()):
    label.set(fontsize=13, color=font_color, **hfont)
    
plt.subplots_adjust(wspace=0, top=0.85, bottom=0.1, left=0.18, right=0.95)

#axes[0].update_xaxes(showline=True, linewidth=2, linecolor='black')
#plt.update_yaxes(showline=True, linewidth=2, linecolor='black')

plt.show()

And the output gives: