问如何将.loc与groupby一起使用，以使基于分组数据创建新列不会被视为副本？

EN

import os.path
import sys
import pandas as pd

filename = "data/cal_data.csv"
df = pd.read_csv(filename, header=0) #one line of headers

df['Test']="Model "+df['Model No'] +", SN "+ df['Serial No'].values.astype(str) +", Test time "+ df['Test time'].values.astype(str) # combining several columns into a single column that makes grouping straight-forward, and simplifies titles of graphs. Not completely necessary.

df = df[df.index <= df.groupby('Test')['Test Point'].transform('idxmax')]#drop rows after each max test point

for title, group in df.groupby('Test'):
    x1, x2 = min(group["Test Reading"]),max(group["Test Reading"])
    x4, x3 = max(group["Test Point"]),min(group["Test Point"]) #min is usually zero
    R=(x2-x1)/(x4-x3) #linearize
    
    group['Test Point Error']=100*(group['Test Reading']- (group['Test Point']*R+x1))
    
    ax=group.plot(x='Test Point', y='Test Point Error', title=title, grid=True)
    ax.set_ylabel("% error (+/-"+str(Error_Limit)+"% limit)")

SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

 Test Point Test Reading    Test
0   0   0.10453 Model LC-500, SN 937618, Test time 17:20:10
1   20  0.17271 Model LC-500, SN 937618, Test time 17:20:10
2   50  0.27838 Model LC-500, SN 937618, Test time 17:20:10
3   100 0.45596 Model LC-500, SN 937618, Test time 17:20:10
4   150 0.63435 Model LC-500, SN 937618, Test time 17:20:10
5   200 0.81323 Model LC-500, SN 937618, Test time 17:20:10
6   250 0.99252 Model LC-500, SN 937618, Test time 17:20:10
7   300 1.17222 Model LC-500, SN 937618, Test time 17:20:10
8   350 1.35219 Model LC-500, SN 937618, Test time 17:20:10
9   400 1.53260 Model LC-500, SN 937618, Test time 17:20:10
10  450 1.71312 Model LC-500, SN 937618, Test time 17:20:10
11  500 1.89382 Model LC-500, SN 937618, Test time 17:20:10
14  0   0.10468 Model LC-500, SN 937618, Test time 17:31:46
15  20  0.17284 Model LC-500, SN 937618, Test time 17:31:46
16  50  0.27856 Model LC-500, SN 937618, Test time 17:31:46
17  100 0.45609 Model LC-500, SN 937618, Test time 17:31:46
18  150 0.63457 Model LC-500, SN 937618, Test time 17:31:46
19  200 0.81341 Model LC-500, SN 937618, Test time 17:31:46
20  250 0.99277 Model LC-500, SN 937618, Test time 17:31:46
21  300 1.17237 Model LC-500, SN 937618, Test time 17:31:46

if os.path.exists("data/temp.csv"):
    os.remove("data/temp.csv")
for title, group in df.groupby('Test'):

    x1 = min(group["Test Reading"].head(1))
    x2 = max(group["Test Reading"].tail(1))
    x3 = min(group["Test Point"].head(1))
    x4 = max(group["Test Point"].tail(1))
    R=(x2-x1)/(x4-x3) #linearization scalar
    group['Test Point Error'] =100*(group['Test Reading']- (group['Test Point']*R+x1))/(x2-x1)
    file = open('data/temp.csv','a')
    group.to_csv('data/temp.csv', mode="a", index=False, columns=columns, header=False)#, header=True, index=True, index_label=None, mode='w', encoding=None, compression='infer', quoting=None, quotechar='"', line_terminator=None, chunksize=None, date_format=None, doublequote=True, escapechar=None, decimal='.'))
    file.close()

df = pd.read_csv('data/temp.csv', header=0)
df['Model/SN']=df['Model No']+" / "+df['Serial No'].values.astype(str)
g = sns.FacetGrid(df, col='Model/SN', hue='Test', col_wrap=4, sharey=False, sharex=False)

g.map(plt.axhline, y=Error_Limit, ls='--', c='red')
g.map(plt.axhline, y=-Error_Limit, ls='--', c='red')

g = g.map(sns.lineplot, 'Test Point', 'Test Point Error', ci=None)