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TA贡献1951条经验 获得超3个赞
绘制此图的更好方法是使用面向对象的 matplotlib api。首先,我们必须定义我们的Figure,axes然后为了正确绘制第二个 y,我们将创建一个伪轴对象,该对象链接回我们创建的原始轴。然后我们可以告诉 pandas 直接在我们的轴上绘图,以确保所有内容都到达正确的位置。
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
import pandas as pd
import seaborn as sns
flatui1 = ["#0C6514", "#18AB25"]
flatui2 = ["#0E1D56", "#18AB25"]
colors = sns.color_palette(flatui1)
cmap1 = LinearSegmentedColormap.from_list("my_colormap", colors)
colors = sns.color_palette(flatui2)
cmap2 = LinearSegmentedColormap.from_list("my_colormap", colors)
sns.set_style(style='white') # we don't want the grid coming from seaborn
m1_t = pd.DataFrame({
"A":[0.21,0.05,1.22,0.41,1.28,1.15,0.91,0.63,0.38,1.18],
"B":[13.33,18,23.69,21.46,35.31,16,20.11,15.87,20.53,17.71],
"C":[5.71,2,23.44,9.02,35.39,13.48,14.62,13.17,13.68,14.66]
})
fig, ax = plt.subplots()
twin_x = ax.twinx() # Create a pseudo axes based off of the original
# ax is our main plot with the "primary y-axis"
# twin_x is also our main plot, but plotting on this plots
# our "secondary y" axis
# Put the bar plot on the "primary y" via ax=ax
m1_t['A'].plot(kind='bar',colormap=cmap1, ax=ax, zorder=1)
# Put the line plot on the "secondary y" via ax=twin_x
# don't have pandas place our legend by default, we'll do this manually for more control later
m1_t[['B','C']].plot(kind='line', colormap=cmap2, ax=twin_x, zorder=2, legend=False)
ax.grid(True, zorder=0)
ax.set_axisbelow(True)
ax.set_xticklabels(('P0', 'P1','P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8', 'P9'))
# to keep the line and bar legends separate:
# you can simply draw a legend on each one, since each
# respective Axes holds onto its own data/artists
ax.legend(loc="upper left")
twin_x.legend(loc="upper left", bbox_to_anchor=(0, .85))
# To create 1 all encompassing legend:
# you can use fig.legend with some tweaking
# fig.legend automatically gathers legend information from all Axes on the figure
# we'll need to give it a bounding box, as well as a new coordinate system so
# that it will appear inside of the bounds of the Axes (instead of the bounds of the figure)
fig.legend(bbox_to_anchor=(.9, 1), bbox_transform=ax.transAxes)
# Legends on the left are the legends we made with ax.legend(...) + twin_x.legend(...)
# legend on the right is the all encompassing fig.legend(...)
plt.show()
无论代码行的顺序如何,该解决方案都将起作用,因为我们告诉 pandas 在特定轴上绘制,而不是让它选择在一组现有轴上绘制或创建一个新轴。
编辑:
手动指定 zorder 是控制元素绘制顺序的可靠方法。本质上,具有较高 zorder 的元素将位于具有较低 zorder 的元素之上。在本例中,我们的网格的 zorder 为 0,条形图和线条的 zorder 为 1 和 2,确保它们将放置在网格的顶部(因为它们的 zorder 高于 0)。
编辑2(添加图例):
左边的图例是我们用 ax.legend(...) + twin_x.legend(...) 创建的图例
右侧的图例是无所不包的Fig.legend(...) 有关方法的描述,请参阅代码中的注释
TA贡献1806条经验 获得超5个赞
以下两轴图方法很简单,因为它保留索引并包含图例。
# This two line sequence has the problem
# m1_t['A'].plot(kind='bar',colormap=cmap1)
# m1_t[['B','C']].plot(kind='line',secondary_y=True,colormap=cmap2)
ax = m1_t.plot(y='A', kind='bar',colormap=cmap1)
ax1 = m1_t.plot(y=['B','C'], kind='line',secondary_y=True,colormap=cmap2, ax=ax)
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