Optimize this script of plotting charts using Matplotlib

Question

I would like to plot a dataframe which has 41 columns in this dataframe so there has 41 charts to plot. I write a script but it loads so slowly. Is there has a solution to optimize this script? Is it possible to use loop function to simplify the list in the zip function?

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

• no sample data so I've simulated it
• start by simplifying axis list returned by plt.subplots() to 1D
• iterate over tickers in columns level 1 index
• simple plot() with above steps
• tight_layout() over compresses for me so have commented

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()