panda中如何把append()换成pd.concat()

问题遇到的现象和发生背景

刷到一个题是把 append 换成 concat 来去掉警告，不太清楚怎么处理，想请教一下！ 非常感谢！

问题相关代码，请勿粘贴截图

import pandas as pd
import random

df_norms = pd.DataFrame()
df = pd.DataFrame({'Individual_Scale' : ['Scale A','Scale B','Scale C','Scale D','Scale E','Scale F'],
'Score' : [random.random()*100 for _ in range(6)]})

for scale in ['Scale A','Scale B','Scale C','Scale D','Scale E','Scale F']:
df_norms[scale] = sorted([random.random()*100 for _ in range(100)])
df = df.append(pd.Series([scale,random.random()*100], index = df.columns), ignore_index=True)

append相当于追加数据，这个函数在新版本中弃用了，concat是把两个相似的df组合起来，你直接新建一个列相同的df就行：

for scale in ['Scale A','Scale B','Scale C','Scale D','Scale E','Scale F']:
    df_norms[scale] = sorted([random.random()*100 for _ in range(100)])
    data = pd.DataFrame({'Individual_Scale' : [scale],'Score':random.random()*100})
    df = pd.concat([df, data], ignore_index = True)

for scale in ['Scale A','Scale B','Scale C','Scale D','Scale E','Scale F']:
    df_norms[scale] = sorted([random.random()*100 for _ in range(100)])
    d = pd.DataFrame([[scale,random.random()*100]], columns = df.columns)  
    df = pd.concat([df, d], ignore_index = True)

df.append会有警告吗？至少在我的环境中运行没有警告。append 换成 concat，大约是这样的吧？

>>> import numpy as np
>>> import random
>>> df_norms = pd.DataFrame()
>>> df = pd.DataFrame({'Individual_Scale' : ['Scale A','Scale B','Scale C','Scale D','Scale E','Scale F'],
'Score' : [random.random()*100 for _ in range(6)]})
>>> d = {'Individual_Scale': list(), 'Score': list()}
>>> for scale in ['Scale A','Scale B','Scale C','Scale D','Scale E','Scale F']:
    df_norms[scale] = sorted([random.random()*100 for _ in range(100)])
    d['Individual_Scale'].append(scale)
    d['Score'].append(random.random()*100)

    
>>> df = pd.concat([df, pd.DataFrame(d)], ignore_index=True)
>>> df
   Individual_Scale      Score
0           Scale A  11.172429
1           Scale B  60.556072
2           Scale C  83.491132
3           Scale D  29.711069
4           Scale E   3.881941
5           Scale F  37.477554
6           Scale A  33.567009
7           Scale B  19.329035
8           Scale C  23.453353
9           Scale D  74.918504
10          Scale E  48.306329
11          Scale F  79.210148
>>> df_norms
      Scale A    Scale B    Scale C    Scale D    Scale E    Scale F
0    0.081019   1.051657   0.097027   0.290851   1.913932   0.068956
1    0.145290   2.009734   0.249679   1.844872   3.686597   0.397679
2    1.183374   2.135974   0.715719   1.985853   5.343396   0.464777
3    2.681695   4.154178   0.979934   4.583160   5.428240   0.568354
4    2.704504   7.787778   1.785705   4.763334   7.310502   2.048466
..        ...        ...        ...        ...        ...        ...
95  97.530636  97.993456  95.150912  94.789002  95.547736  93.160846
96  98.519154  98.129680  95.495692  95.644331  97.053258  93.254725
97  98.880739  98.895117  95.604106  97.643440  97.625797  95.610542
98  99.083063  99.516753  96.752881  99.645669  99.104014  95.693973
99  99.999940  99.529845  97.521243  99.863202  99.353511  97.750327

[100 rows x 6 columns]

pd.concat((df,pd.DataFrame({'Individual_Scale':['Scale A','Scale B','Scale C','Scale D','Scale E','Scale F'],
                           'Score':sorted([random.random()*100 for _ in range(6)]) }))).reset_index()