使用Matplotlib优化绘制图表的脚本

Question

我想绘制一个dataframe，它在这个dataframe中有41个列，所以有41个图表要绘制。我写了一个脚本，但是它加载得太慢了。是否有优化此脚本的解决方案？是否可以使用循环函数来简化zip函数中的列表？

import matplotlib.pyplot as plt
import pandas as pd

fig,((axs),(axs2),(axs3),(axs4),(axs5),(axs6),(axs7),(axs8),(axs9)) = plt.subplots(9,5,figsize=(15,6))

for ax, y in zip(axs,['XPEV','M','MLCO','VIPS','HD']):
    ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
    ax.ticklabel_format(style='plain', axis='y')
    ax.set_title(y)
    
    for ax, y in zip(axs2,['LVS','PTON','SBUX','BLMN','NCLH']):
        ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
        ax.ticklabel_format(style='plain', axis='y')
        ax.set_title(y)
        
        for ax, y in zip(axs3,['NIO','NKE','NKLA','NLS','QS']):
            ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
            ax.ticklabel_format(style='plain', axis='y')
            ax.set_title(y)
            
            for ax, y in zip(axs4,['AYRO','RMO','TSLA','XL','ASO']):
                ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
                ax.ticklabel_format(style='plain', axis='y')
                ax.set_title(y)
                
                for ax, y in zip(axs5,['TOL','VSTO','BABA','FTCH','RIDE']):
                    ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
                    ax.ticklabel_format(style='plain', axis='y')
                    ax.set_title(y)

                    for ax, y in zip(axs6,['EBAY','DS','DKNG','DHI','UAA']):
                        ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
                        ax.ticklabel_format(style='plain', axis='y')
                        ax.set_title(y)
                        
                        for ax, y in zip(axs7,['VFC','TPX','ARVL','GM','GOEV']):
                            ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
                            ax.ticklabel_format(style='plain', axis='y')
                            ax.set_title(y)
                            
                            for ax, y in zip(axs8,['PLBY','CCL','GME','CVNA','LOTZ']):
                                ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
                                ax.ticklabel_format(style='plain', axis='y')
                                ax.set_title(y)
                                
                                for ax, y in zip(axs9,['F']):
                                    ax.plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
                                    ax.ticklabel_format(style='plain', axis='y')
                                    ax.set_title(y)
                                

plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.show()

Answer 1

没有样本数据，所以我从简化steps

• tight_layout()返回的plt.subplots()到1D

• 的轴表开始，在列中，1级索引H 29<代码>H 110简单<

>D11>，所以我已经评论了H 215F 216/code>。

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

tickers_data = pd.DataFrame({("Volume",t):np.random.randint(20,200, 7) for t in ['XPEV','M','MLCO','VIPS','HD']+['LVS','PTON','SBUX','BLMN','NCLH']+['NIO','NKE','NKLA','NLS','QS']+['AYRO','RMO','TSLA','XL','ASO']+['TOL','VSTO','BABA','FTCH','RIDE']+['EBAY','DS','DKNG','DHI','UAA']+
                            ['VFC','TPX','ARVL','GM','GOEV']+['PLBY','CCL','GME','CVNA','LOTZ']+['F']}, index=pd.date_range("1-Jan-2021", periods=7))

tickers_data
fig,ax = plt.subplots(9,5,figsize=(15,6))

ax = np.array(ax).flatten()
for i,y in enumerate(tickers_data.columns.get_level_values(1)):
    ax[i].plot(tickers_data.index.strftime("%d"),tickers_data['Volume',y])
    ax[i].ticklabel_format(style='plain', axis='y')
    ax[i].set_title(y)
    
# plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.show()