调整水平条形图 matplotlib 以适应条形图

Question

I am doing a horizontal bar chart but struggling with adjusting ylim, or maybe another parameter to make my labels clearer and make all the labels fit the y axis. I played around with ylim and the text size can be bigger or smaller but the bars do not fit the y axis. Any idea about the right approach?

My code:

import matplotlib.pyplot as plt #we load the library that contains the plotting capabilities
from operator import itemgetter
D=[]        
for att, befor, after in zip(df_portion['attributes'], df_portion['2005_2011 (%)'], df_portion['2012_2015 (%)']):
    i=(att, befor, after)
    D.append(i)
Dsort = sorted(D, key=itemgetter(1), reverse=False) #sort the list in order of usage
attri = [x[0] for x in Dsort] 
aft  = [x[1] for x in Dsort]
bef  = [x[2] for x in Dsort] 

ind = np.arange(len(attri))
width=3

ax = plt.subplot(111)
ax.barh(ind, aft, width,align='center',alpha=1, color='r', label='from 2012 to 2015') #a horizontal bar chart (use .bar instead of .barh for vertical)
ax.barh(ind - width, bef, width, align='center',  alpha=1, color='b', label='from 2005 to 2008') #a horizontal bar chart (use .bar instead of .barh for vertical)
ax.set(yticks=ind, yticklabels=attri,ylim=[1, len(attri)/2])
plt.xlabel('Frequency distribution (%)')
plt.title('Frequency distribution (%) of common attributes between 2005_2008 and between 2012_2015')
plt.legend()
plt.show()

This is the plot for above code

Answer 1

To make the labels fit, you need to set a smaller fontsize, or use a larger figsize. Changing the ylim will either just show a subset of the bars (in case ylim is set too narrow), or will show more whitespace (when ylim is larger).

The biggest problem in the code is width being too large. Twice the width needs to fit over a distance of 1.0 (the ticks are placed via ind , which is an array 0,1,2,... ). As matplotlib calls the thickness of a horizontal bar plot "height", this name is used in the example code below. Using align='edge' lets you position the bars directly ( align='center' will move them half their "height").

Pandas has simple functions to sort dataframes according to one or more rows.

Code to illustrate the ideas:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# first create some test data
df = pd.DataFrame({'attributes': ["alpha", "beta", "gamma", "delta", "epsilon", "zata", "eta", "theta", "iota",
                                  "kappa", "lambda", "mu", "nu", "xi", "omikron", "pi", "rho", "sigma", "tau",
                                  "upsilon", "phi", "chi", "psi", "omega"]})
totals_2005_2011 = np.random.uniform(100, 10000, len(df))
totals_2012_2015 = totals_2005_2011 * np.random.uniform(0.70, 2, len(df))
df['2005_2011 (%)'] = totals_2005_2011 / totals_2005_2011.sum() * 100
df['2012_2015 (%)'] = totals_2012_2015 / totals_2012_2015.sum() * 100

# sort all rows via the '2005_2011 (%)' column, sort from large to small
df = df.sort_values('2005_2011 (%)', ascending=False)

ind = np.arange(len(df))
height = 0.3  # two times height needs to be at most 1

fig, ax = plt.subplots(figsize=(12, 6))
ax.barh(ind, df['2012_2015 (%)'], height, align='edge', alpha=1, color='crimson', label='from 2012 to 2015')
ax.barh(ind - height, df['2005_2011 (%)'], height, align='edge', alpha=1, color='dodgerblue', label='from 2005 to 2011')
ax.set_yticks(ind)
ax.set_yticklabels(df['attributes'], fontsize=10)
ax.grid(axis='x')

ax.set_xlabel('Frequency distribution (%)')
ax.set_title('Frequency distribution (%) of common attributes between 2005_2011 and between 2012_2015')
ax.legend()
ax.margins(y=0.01)  # use smaller margins in the y-direction
plt.tight_layout()
plt.show()

The seaborn library has some functions to create barplots with multiple bars per attribute, without the need to manually fiddle with bar positions. Seaborn prefers its data in "long form", which can be created via pandas' melt() .

Example code:

import seaborn as sns

df = df.sort_values('2005_2011 (%)', ascending=True)
df_long = df.melt(id_vars='attributes', value_vars=['2005_2011 (%)', '2012_2015 (%)'],
                  var_name='period', value_name='distribution')
fig, ax = plt.subplots(figsize=(12, 6))
sns.barplot(data=df_long, y='attributes', x='distribution', hue='period', palette='turbo', ax=ax)
ax.set_xlabel('Frequency distribution (%)')
ax.set_title('Frequency distribution (%) of common attributes between 2005_2011 and between 2012_2015')
ax.grid(axis='x')
ax.tick_params(axis='y', labelsize=12)
sns.despine()
plt.tight_layout()
plt.show()