带2个y轴的分组箱线图，每x勾选2个变量

Question

我试图绘制一个成本（卢比单位）和装机容量（兆瓦单位）的方框图，其中xaxis作为可再生能源的份额（单位百分比）

也就是说，每个x记号与两个箱线图关联，一个是成本，一个是装机容量。我有3个xtick值(20%, 40%, 60%)

我尝试了this answer，但我得到了附在底部的错误

我需要每个xtick两个箱线图

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
plt.rcParams["font.family"] = "Times New Roman"
plt.style.use('seaborn-ticks')
plt.grid(color='w', linestyle='solid')
data1 = pd.read_csv('RES_cap.csv')
df=pd.DataFrame(data1, columns=['per','cap','cost'])

cost= df['cost']
cap=df['cap']
per_res=df['per']

fig, ax1 = plt.subplots()
xticklabels = 3

ax1.set_xlabel('Percentage of RES integration')
ax1.set_ylabel('Production Capacity (MW)')
res1 = ax1.boxplot(cost, widths=0.4,patch_artist=True)
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
    plt.setp(res1[element])

for patch in res1['boxes']:
    patch.set_facecolor('tab:blue')

ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis
ax2.set_ylabel('Costs', color='tab:orange')
res2 = ax2.boxplot(cap, widths=0.4,patch_artist=True)
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
    plt.setp(res2[element], color='k')
for patch in res2['boxes']:
    patch.set_facecolor('tab:orange')

ax1.set_xticklabels(['20%','40%','60%'])
fig.tight_layout()  
plt.show()

样本数据： data attached

Answer 1

通过测试您的代码并将其与answer by Thomas Kühn in the linked question进行比较，我发现了几个突出的方面：

• 为x参数输入的数据是一维形状，而不是二维形状。输入一个变量，得到一个框，而不是实际需要的三个框
• 尚未定义positions参数，这会导致两个箱线图的框重叠
• 在res1上的第一个for循环中，plt.setp中的color参数丢失
• 您已经设置了x记号标签，但没有首先设置x记号（如警告here），这会导致错误消息

我提供了以下基于this answer by ImportanceOfBeingErnest的解决方案。它解决了正确塑造数据的问题，并利用字典定义绘图中多个对象共享的许多参数。这样可以更容易地根据您的喜好调整格式，并使代码更简洁，因为它避免了使用for循环（在boxplot元素和res对象上）以及在共享相同参数的函数中重复参数

import numpy as np                 # v 1.19.2
import pandas as pd                # v 1.1.3
import matplotlib.pyplot as plt    # v 3.3.2

# Create a random dataset similar to the one in the image you shared
rng = np.random.default_rng(seed=123) # random number generator
data = dict(per = np.repeat([20, 40, 60], [60, 30, 10]),
            cap = rng.choice([70, 90, 220, 240, 320, 330, 340, 360, 410], size=100),
            cost = rng.integers(low=2050, high=2250, size=100))
df = pd.DataFrame(data)

# Pivot table according to the 'per' categories so that the cap and
# cost variables are grouped by them:
df_pivot = df.pivot(columns=['per'])

# Create a list of the cap and cost grouped variables to be plotted 
# in each (twinned) boxplot: note that the NaN values must be removed
# for the plotting function to work.
cap = [df_pivot['cap'][var].dropna() for var in df_pivot['cap']]
cost = [df_pivot['cost'][var].dropna() for var in df_pivot['cost']]

# Create figure and dictionary containing boxplot parameters that are
# common to both boxplots (according to my style preferences):
# note that I define the whis parameter so that values below the 5th
# percentile and above the 95th percentile are shown as outliers
nb_groups = df['per'].nunique()
fig, ax1 = plt.subplots(figsize=(9,6))
box_param = dict(whis=(5, 95), widths=0.2, patch_artist=True,
                 flierprops=dict(marker='.', markeredgecolor='black',
                 fillstyle=None), medianprops=dict(color='black'))

# Create boxplots for 'cap' variable: note the double asterisk used
# to unpack the dictionary of boxplot parameters
space = 0.15
ax1.boxplot(cap, positions=np.arange(nb_groups)-space,
            boxprops=dict(facecolor='tab:blue'), **box_param)

# Create boxplots for 'cost' variable on twin Axes
ax2 = ax1.twinx()
ax2.boxplot(cost, positions=np.arange(nb_groups)+space,
            boxprops=dict(facecolor='tab:orange'), **box_param)

# Format x ticks
labelsize = 12
ax1.set_xticks(np.arange(nb_groups))
ax1.set_xticklabels([f'{label}%' for label in df['per'].unique()])
ax1.tick_params(axis='x', labelsize=labelsize)

# Format y ticks
yticks_fmt = dict(axis='y', labelsize=labelsize)
ax1.tick_params(colors='tab:blue', **yticks_fmt)
ax2.tick_params(colors='tab:orange', **yticks_fmt)

# Format axes labels
label_fmt = dict(size=12, labelpad=15)
ax1.set_xlabel('Percentage of RES integration', **label_fmt)
ax1.set_ylabel('Production Capacity (MW)', color='tab:blue', **label_fmt)
ax2.set_ylabel('Costs (Rupees)', color='tab:orange', **label_fmt)

plt.show()

Matplotlib文档：boxplot demo、boxplot function parameters、marker symbols for fliers、label text formatting parameters

考虑到这是一个相当大的努力来建立这个，如果我是为自己这样做，我会去并排子地块，而不是创建孪生轴。在seaborn中，使用^{}函数可以很容易地完成这项工作，该函数会自动处理许多格式。由于每个变量只有三个类别，因此使用不同颜色对每个百分比类别并排比较箱线图相对容易，如基于相同数据的本例所示：

import seaborn as sns    # v 0.11.0

# Convert dataframe to long format with 'per' set aside as a grouping variable
df_melt = df.melt(id_vars='per')

# Create side-by-side boxplots of each variable: note that the boxes
# are colored by default
g = sns.catplot(kind='box', data=df_melt, x='per', y='value', col='variable',
                height=4, palette='Blues', sharey=False, saturation=1,
                width=0.3, fliersize=2, linewidth=1, whis=(5, 95))

g.fig.subplots_adjust(wspace=0.4)
g.set_titles(col_template='{col_name}', size=12, pad=20)

# Format Axes labels
label_fmt = dict(size=10, labelpad=10)
for ax in g.axes.flatten():
    ax.set_xlabel('Percentage of RES integration', **label_fmt)
g.axes.flatten()[0].set_ylabel('Production Capacity (MW)', **label_fmt)
g.axes.flatten()[1].set_ylabel('Costs (Rupees)', **label_fmt)

plt.show()