我有一个pandas dataframe（df0），从中得到唯一代码并放入df1。然后，我需要创建图表，从df1循环列表检索数据df0。这是一个较大数据集的虚拟版本，将根据某些标准进行过滤。
我需要为代码列中的每个项目创建三重y轴组合图，如下所示代码A和B：

的数据

的
到目前为止，我已经能够使用循环从透视表创建条形图：

import numpy as np
import pandas as pd
from numpy.random import randn
import matplotlib.pyplot as plt
import caffeinate

np.random.seed(100)
df0 = pd.DataFrame([])
df0['Date'] = ['2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10',
              '2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10']
df0['Code'] = ['A','A','A','A','A','A','A','A','A','A','B','B','B','B','B','B','B','B','B','B']
df0['WindSpeed'] = np.random.randint(1,150,size = (20,1))
df0['Precipitation'] = np.random.randint(1,20,size = (20,1))
df0['Temperature'] = np.random.randint(1,50,size = (20,1))

df1 = pd.DataFrame([])
df1 = df0['Code'].unique()
df1.flatten()
#for item in df1:
#    print(df0['Code'])

for item in df1:
    #pd.pivot_table(data=df0, index='Date', columns='Code', values='Close').plot.bar()
    pd.pivot_table(df0[df0.Code==item], index = 'Date',columns = None).plot.bar(title = item)
plt.show()

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理想情况下，我想使用这种方法，但我还不能从它创建三重Y轴图表。
最接近的是下面的，但产生了两个相同的图表：

df0 = pd.DataFrame([])
df0['Date'] = ['2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10',
              '2020-01-01','2020-01-02','2020-01-03','2020-01-04','2020-01-05','2020-01-06','2020-01-07','2020-01-08','2020-01-09','2020-01-10']
df0['Code'] = ['A','A','A','A','A','A','A','A','A','A','B','B','B','B','B','B','B','B','B','B']
df0['Windspeed'] = np.random.randint(1,150,size = (20,1))
df0['Precipitation'] = np.random.randint(1,20,size = (20,1))
df0['Temperature'] = np.random.randint(1,50,size = (20,1))

df1 = pd.DataFrame([])
df1 = df0['Code'].unique()
df1.flatten()

####MATPLOTLIB 3 AXIS - BARS:
def chartFunc(item):
    data = df0[df0.Code==item]
    index = df0.Date
    x = index
    # Create figure and axis #1
    fig, ax1 = plt.subplots()
    # plot line chart on axis #1
    p1, = ax1.plot(x, df0['Windspeed'])
    #invalid syntax:
    #p1, = ax1.plot(x, df0[(df0.Code ==item) & (df0['Close'])
    ax1.set_ylabel('Windspeed')
    ax1.set_ylim(df0['Windspeed'].min(), df0['Windspeed'].max())
    #ax1.legend(['Open'], loc="upper left")
    ax1.legend(['Windspeed'], loc="upper left")
    ax1.yaxis.label.set_color(p1.get_color())
    ax1.yaxis.label.set_fontsize(14)
    ax1.tick_params(axis='y', colors=p1.get_color(), labelsize=14)
    
    
    # set up the 2nd axis
    ax2 = ax1.twinx() 
    # plot bar chart on axis #2
    p2 = ax2.bar(x, df0['Precipitation'], color='orange')
    
    #ax2.grid(False) # turn off grid #2
    ax2.set_ylabel('Precipitation')
    ax2.set_ylim(df0['Precipitation'].min(), df0['Precipitation'].max())
    
    ax2.legend(['Precipitation'], loc="upper center")
    #ax2.yaxis.label.set_color()
    ax2.yaxis.label.set_fontsize(14)
    #ax2.tick_params(axis='y', colors=p2.get_color(), labelsize=14)

    # set up the 3rd axis
    ax3 = ax1.twinx()
    # Offset the right spine of ax3.    
    ax3.spines.right.set_position(("axes", 1.25))
    # Plot line chart on axis #3
    p3, = ax3.plot(x, df0['Temperature'], color='red')
    ax3.grid(False) # turn off grid #3
    ax3.set_ylabel('Temperature')
    ax3.set_ylim(df0['Temperature'].min(), df0['Temperature'].max())
    ax3.legend(['Temperature'], loc="upper right")
    ax3.yaxis.label.set_color(p3.get_color())
    ax3.yaxis.label.set_fontsize(14)
    ax3.tick_params(axis='y', colors=p3.get_color(), labelsize=14)
    

for item in df1:
    chartFunc(item)
    plt.show()

型
输出是相同的两张图表。

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任何帮助都是真诚的感谢。