Добавление вертикальных линий с помощью matplotlib (РЕШЕНО) ⇐ Python

[Гость]

My aim is to first create two plots (which I correctly created), each of them representing the CDF of the number of Puts and Calls still open in the market. For each plot, I also wanted to draw the vertical line representing the 40% and 80% percentile.

However, the final aim is to merge the two plot and also add the respective vertical lines for both sides (here is the problem). This is what I did:

Creation of the two single plots (FIRST CALL):

plt.figure(figsize=(14, 6)) # Adjusting figure size plt.bar(CALL.index, CALL['CDF'], width=0.5, color='lightgreen') # Using DataFrame index as x-axis ticks plt.xlabel('STRIKE') plt.ylabel('CDF') plt.title('Cumulative Distribution Function (CDF) for CALL') plt.xticks(CALL.index, CALL['STRIKE'], rotation=90) # Setting x-axis ticks to the Strike values with rotation plt.grid(True) # Find the nearest value to 40% in the 'Percentile' column nearest_40_percentile_idx = (CALL['Percentile'] - 0.4).abs().idxmin() nearest_40_percentile_strike = CALL.loc[nearest_40_percentile_idx, 'STRIKE'] # Add a vertical line at the corresponding 'Strike' value for 40th percentile plt.axvline(x=nearest_40_percentile_idx, color='red', linestyle='--', label=f'Nearest to 40% ({nearest_40_percentile_strike})') # Find the nearest value to 80% in the 'Percentile' column nearest_80_percentile_idx = (CALL['Percentile'] - 0.8).abs().idxmin() nearest_80_percentile_strike = CALL.loc[nearest_80_percentile_idx, 'STRIKE'] # Add a vertical line at the corresponding 'Strike' value for 80th percentile plt.axvline(x=nearest_80_percentile_idx, color='blue', linestyle='--', label=f'Nearest to 80% ({nearest_80_percentile_strike})') # Show legend plt.legend() plt.show() The one of the PUT is basically the same, just with the df's name changed.

Now, I tried to merge them and plot the vertical lines in this way, but the graph is a mess:

res = pd.concat([CALL, PUT]) # Set different colors for CALL and PUT bars colors = {1: 'lightgreen', 2: 'orange'} # Plot the bar chart plt.figure(figsize=(16, 6)) sns.barplot(x='Strike', y='CDF', data=res, hue='hue', palette=colors) # Calculate the nearest strike values to the 40th and 80th percentiles for both CALL and PUT nearest_40_percentile_idx_call = (CALL['Percentile'] - 0.4).abs().idxmin() nearest_40_percentile_strike_call = CALL.loc[nearest_40_percentile_idx_call, 'Strike'] nearest_40_percentile_idx_put = (PUT['Percentile'] - 0.4).abs().idxmin() nearest_40_percentile_strike_put = PUT.loc[nearest_40_percentile_idx_put, 'Strike'] # Add vertical lines at the corresponding strike values for the 40th and 80th percentiles plt.axvline(x=nearest_40_percentile_idx_call, color='blue', linestyle='--', label=f'40% for CALL ({nearest_40_percentile_strike_call})') plt.axvline(x=nearest_40_percentile_idx_put, color='green', linestyle='--', label=f'40% for PUT ({nearest_40_percentile_strike_put})') # Set labels and title plt.xlabel('Strike') plt.ylabel('Cumulative Distribution Function (CDF)') plt.title('CDF for CALL and PUT') # Rotate x-axis labels for better readability plt.xticks(rotation=60) # Show legend plt.legend() # Show the plot plt.show() Any suggestion?


Источник: https://stackoverflow.com/questions/781 ... lib-solved