matplotlib 中是否可以有给定数量（n>2）的 y 轴？

Question

prices = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),                   columns=['a', 'b', 'c'])我有我的prices数据框，它目前有 3 列。但在其他时候，它可能有更多或更少的列。有没有办法使用某种twinx()循环来创建具有（可能）无限数量的 y 轴的所有不同时间序列的折线图？我尝试了下面的双循环，但我得到了typeError'd:bTypeError: 'AxesSubplot' object does not support item assignment# for i in range(0,len(prices.columns)):#     for column in list(prices.columns):#         fig, ax[i] = plt.subplots()#         ax[i].set_xlabel(prices.index()) #         ax[i].set_ylabel(column[i]) #         ax[i].plot(prices.Date, prices[column]) #         ax[i].tick_params(axis ='y') # #         ax[i+1] = ax[i].twinx() #         ax[i+1].set_ylabel(column[i+1]) #         ax[i+1].plot(prices.Date, column[i+1]) #         ax[i+1].tick_params(axis ='y') # #         fig.suptitle('matplotlib.pyplot.twinx() function \ Example\n\n', fontweight ="bold") #         plt.show() # =============================================================================我相信我明白为什么会收到错误 -ax对象不允许分配变量i。我希望有一些巧妙的方法来实现这一目标。

Answer 1

事实证明，主要问题是您不应该将 pandas 绘图函数与 matplotlib 混合使用，这会导致轴重复。否则，实现是相当直接的，改编自这个matplotlib 示例。

from mpl_toolkits.axes_grid1 import host_subplot

import mpl_toolkits.axisartist as AA

from matplotlib import pyplot as plt

from itertools import cycle

import pandas as pd

#fake data creation with different spread for different axes

#this entire block can be deleted if you import your df

from pandas._testing import rands_array

import numpy as np

fakencol=5

fakenrow=7

np.random.seed(20200916)

df = pd.DataFrame(np.random.randint(1, 10, fakenrow*fakencol).reshape(fakenrow, fakencol), columns=rands_array(2, fakencol))

df = df.multiply(np.power(np.asarray([10]), np.arange(fakencol)))

df.index = pd.date_range("20200916", periods=fakenrow)

#defining a color scheme with unique colors

#if you want to include more than 20 axes, well, what can I say

sc_color = cycle(plt.cm.tab20.colors)

#defining the size of the figure in relation to the number of dataframe columns

#might need adjustment for optimal data presentation

offset = 60

plt.rcParams['figure.figsize'] = 10+df.shape[1], 5

#host figure and first plot

host = host_subplot(111, axes_class=AA.Axes)

h, = host.plot(df.index, df.iloc[:, 0], c=next(sc_color), label=df.columns[0])

host.set_ylabel(df.columns[0])

host.axis["left"].label.set_color(h.get_color())

host.set_xlabel("time")

#plotting the rest of the axes

for i, cols in enumerate(df.columns[1:]):

  

    curr_ax = host.twinx()       

    new_fixed_axis = curr_ax.get_grid_helper().new_fixed_axis

    curr_ax.axis["right"] = new_fixed_axis(loc="right",

                                axes=curr_ax,

                                offset=(offset*i, 0))

    

    curr_p, = curr_ax.plot(df.index, df[cols], c=next(sc_color), label=cols)

    

    curr_ax.axis["right"].label.set_color(curr_p.get_color())

    curr_ax.set_ylabel(cols)

    curr_ax.yaxis.label.set_color(curr_p.get_color())

plt.legend()

plt.tight_layout()

plt.show()

想想看 - 将轴平均分配到图的左侧和右侧可能会更好。那好吧。