多图只成一个

Question

I'm new into python and cannot solve my problem after googling for hours:

I adjusted an existing Python Script for Kano analysis, but want to plot all “outcome-dots” into only one combined plot instead of generating one for each Feature. (My goal is to plot all Features into one graphic with listing feature names like legends)

Here is the data to run the code: https://wetransfer.com/downloads/f1815b3660dca105b3364085d36a99e420220901195108/512a6d

Thanks a lot for your help!!

# -*- coding: utf-8 -*-
"""
Spyder Editor

This is a temporary script file.
"""
#reading the Data-Export from Unipark
import pandas as pd
data = pd.read_excel (r'C:\Users\xxx\Nextcloud\03_Documents\xxx\data_kano.xlsx',header=0)

data.drop(index=0)
segment=list(range(0,len(data.columns)+1,2))

num=len(data.columns)/2
store=[]
for x in range(1,len(segment)):
    df=data.iloc[:,segment[x-1]:segment[x]].copy()
    store.append(df)

#creating the scoring to rate the answeres to categories
eval_matrix=pd.DataFrame({'#1':['Q','R','R','R','R'],'#2':['A','I','I','I','R'],
             '#3':['A','I','I','I','R'],'#4':['A','I','I','I','R'],
             '#5':['O','M','M','M','Q']})
eval_matrix.index=['#1','#2','#3','#4','#5']


#scoring to the different categories 
result=pd.DataFrame()
for x in range(0,len(store)):
    Kano_score=[]
    for y in range(0,len(list(store[x].iloc[:,0]))):
        Kano_score.append(eval_matrix.loc[store[x].iloc[y,0],store[x].iloc[y,1]])
    pos='Feature '+str(x+1)+"-1"  
    neg='Feature '+str(x+1)+"-2" 
    col_name3='Feature '+str(x+1)+"-Result" 
    result[pos]=store[x].iloc[:,0].copy()
    result[neg]=store[x].iloc[:,1].copy()
    result[col_name3]=Kano_score
    

import matplotlib.pyplot as plt
import numpy as np

# Create a function to calculate the satisfaction and dissatisfaction coefficients
def SI(A: int,O: int,M: int,I: int)->float:
    return float((A+O) / (A+O+M+I))

def DSI(A: int,O: int,M: int,I: int)->float:
    return float((O+M) / (A+O+M+I) * -1)


def showGraph(dsi: float, si:float,title:str,x: int, y: int, n:int)->str:
    ax=plt.axes()
    ax.scatter(si,dsi)

  
    ax.set(xlim=[0,1],ylim=[-1,0],xlabel='Functional (Satisfaction Coefficients CS+)',ylabel='Disfunctional (Dissatisfaction Coefficients CS-)',xticks=np.arange(0,1,0.1),yticks=np.arange(-1,0,0.1)) # set the ranges of x-axis y-axis
    ax.set_title(title,size=16)
    ax.grid(True)
    
    # Adjust the thickness of the two lines in the middle as a cross
    gridlines = ax.yaxis.get_gridlines()
    gridlines[5].set_color('k')
    gridlines[5].set_linewidth(2.5)
    gridlines = ax.xaxis.get_gridlines()
    gridlines[5].set_color('k')
    gridlines[5].set_linewidth(2.5)
    
    #colouring
    
    
    plt.fill([0.0,0.0,0.3,0.3],[-0.0,-0.5,-0.5,-0.0],alpha=0.25, color ="b")
    plt.fill([0.3,0.3,0.5,0.5],[-0.0,-0.5,-0.5,-0.0],alpha=0.25, color ="b")
    
    plt.fill([0.7,0.7,1.0,1.0],[-0.0,-0.5,-0.5,-0.0],alpha=0.25, color ="#036630")
    plt.fill([0.5,0.5,0.7,0.7],[-0.0,-0.5,-0.5,-0.0],alpha=0.25, color ="#036630")
    
    plt.fill([0.5,0.5,0.7,0.7],[-0.5,-1.0,-1.0,-0.5],alpha=0.25, color ="y")
    plt.fill([0.7,0.7,1.0,1.0],[-0.5,-1.0,-1.0,-0.5],alpha=0.25, color ="y")
    
    plt.fill([0.0,0.0,0.3,0.3],[-0.5,-1.0,-1.0,-0.5],alpha=0.25, color ="r")
    plt.fill([0.3,0.3,0.5,0.5],[-0.5,-1.0,-1.0,-0.5],alpha=0.25, color ="r")
    plt.savefig('./figures/'+title+'.jpg')
    
    #add a legend
    plt.plot(x, y, alpha=0.8, color ="b", label="Indifferent")
    plt.plot(x, y, alpha=0.8, color ="r", label="Must Be")
    plt.plot(x, y, alpha=0.8, color ="k", label="Neutral")
    plt.plot(x, y, alpha=0.8, color ="#036630", label="Attractive")
    plt.plot(x, y, alpha=0.8, color ="y", label="One-Dimensional")
    plt.legend(bbox_to_anchor =(0.225,-0.45,0.55, 0.5), loc='lower center', ncol=2, fontsize ="small", framealpha=1, shadow=True, borderpad=1)
    
    plt.show()
    

import collections
import os
os.makedirs('figures',exist_ok=True)
pos=list(range(2,len(result.columns),3))

count=collections.Counter(result.iloc[:,2])
df=pd.DataFrame.from_dict(count,orient='index')
df.columns=['Score']

si=SI(df.loc['A','Score'],df.loc['O','Score'],df.loc['M','Score'],df.loc['I','Score'])
dsi=DSI(df.loc['A','Score'],df.loc['O','Score'],df.loc['M','Score'],df.loc['I','Score'])
df['SI']=np.nan
df.iloc[0,1]=si
df['DSI']=np.nan
df.iloc[0,2]=dsi
df.to_excel('Kano_scoring_Ergebnisse.xlsx',sheet_name='Feature 1')
showGraph(dsi,si,'Feature 1',len(pos)/2,2,1)

with pd.ExcelWriter('Kano_scoring_Ergebnisse.xlsx', engine="openpyxl",mode='a') as writer:
    for x in range(1,len(pos)):
        count=collections.Counter(result.iloc[:,pos[x]])
        df=pd.DataFrame.from_dict(count,orient='index')
        df.columns=['Score']
        si=SI(df.loc['A','Score'],df.loc['O','Score'],df.loc['M','Score'],df.loc['I','Score'])
        dsi=DSI(df.loc['A','Score'],df.loc['O','Score'],df.loc['M','Score'],df.loc['I','Score'])
        df['SI']=np.nan
        df.iloc[0,1]=si
        df['DSI']=np.nan
        df.iloc[0,2]=dsi
        df.to_excel(writer,sheet_name='Feature '+str(x+1))
        showGraph(dsi,si,'Feature '+str(x+1),len(pos)/2,2,x+1)

Answer 1

In your code, I have made some small adjustments to read the dsi and si data into a dataframe called scatter_data . Each time you use ShowGraph() , the date gets read into the dataframe instead. At the end, the same dataframe is passed to ShowGraph() and that will create just one graph. Hope this is what you are looking for.

Note that I have only shown the last part of the code... rest is as you have it. Also, kept your code in comments, so you can see what is being changed. Lastly, I have tried to make the minimal changes to the code, so you may be able to do a more efficient code as you know it better.

## From row 106 onwards...

#showGraph(dsi,si,'Feature 1',len(pos)/2,2,1) ##Don't plot
scatter_data = pd.DataFrame(columns = ['dsi', 'si']) ## Create empty dataframe
scatter_data = scatter_data.append({'dsi':dsi, 'si':si}, ignore_index=True) ##Append feature 1 to dataframe

with pd.ExcelWriter('Kano_scoring_Ergebnisse.xlsx', engine="openpyxl",mode='a') as writer:
    for x in range(1,len(pos)):
        count=collections.Counter(result.iloc[:,pos[x]])
        df=pd.DataFrame.from_dict(count,orient='index')
        df.columns=['Score']
        si=SI(df.loc['A','Score'],df.loc['O','Score'],df.loc['M','Score'],df.loc['I','Score'])
        dsi=DSI(df.loc['A','Score'],df.loc['O','Score'],df.loc['M','Score'],df.loc['I','Score'])
        df['SI']=np.nan
        df.iloc[0,1]=si
        df['DSI']=np.nan
        df.iloc[0,2]=dsi
        df.to_excel(writer,sheet_name='Feature '+str(x+1))
#        showGraph(dsi,si,'Feature '+str(x+1),len(pos)/2,2,x+1) ## Dont plot, read into df instead
        scatter_data = scatter_data.append({'dsi':dsi, 'si':si}, ignore_index=True) 

##Finally plot once (note this is outside the with loop
showGraph(scatter_data.dsi,scatter_data.si,'All Feature',len(pos)/2,2,1)

Plot