搜了一圈，都是这种答案，不用估计，就是弃用了。

DataFrame.append在1.4版本中弃用，2.0版本中完全删除。

为啥？

因为它有严重的性能问题，有时候还会出现性能警告

DataFrame is highly fragmented.  
This is usually the result of calling `frame.insert` many times, 
which has poor performance. 
Consider joining all columns at once using pd.concat(axis=1) instead.  
To get a de-fragmented frame, use `newframe = frame.copy()`

在此前的pandas文档中有这么一段话

It is worth noting however, that concat (and therefore append) makes a full copy of the data, 
and that constantly reusing this function can create a significant performance hit.
 If you need to use the operation over several datasets, use a list comprehension.

翻译过来就是说append操作会生成数据的完整副本，反复重用此函数可能会对性能造成重大影响。

一般来说，append都是在迭代中使用较多。在官方讨论看到说DataFrame.append is around the 10th most visited page in our API docs.

可见，这个方法使用量是相当大的。

比如Brideau这位老哥有一个几百万记录的数据集，使用如下脚本进行处理，肯定是贼慢的

city = ["SomeCity"]
df = DataFrame({}, columns=['Date', 'HouseID', 'Price'])
for city in cities:
    for dateRun in data[city]:
        for record in data[city][dateRun]:
            recSeries = Series([record['Timestamp'], 
                                record['Id'], 
                                record['Price']],
                                index = ['Date', 'HouseID', 'Price'])
            FredDF = FredDF.append(recSeries, ignore_index=True)

也有人做过基准测试，使用append和loc都挺慢的。

官方推荐：Deprecated since version 1.4.0: Use concat() instead. 

但更好的做法是