pandas.DataFrame.copy(deep = True)实际上没有创建深层副本

问题描述

我已经尝试了一段时间pd.Series和pd.DataFrame，并且遇到了一些奇怪的问题.假设我有以下pd.DataFrame:

I've been experimenting for a while with pd.Series and pd.DataFrame and faced some strange problem. Let's say I have the following pd.DataFrame:

df = pd.DataFrame({'col':[[1,2,3]]})

请注意，此数据框包括包含列表的列.我想修改此数据框的副本并返回其修改后的版本，以使初始版本保持不变.为了简单起见，假设我要在其单元格中添加整数"4".

Notice, that this dataframe includes column containing list. I want to modify this dataframe's copy and return its modified version so that the initial one will remain unchanged. For the sake of simplicity, let's say I want to add integer '4' in its cell.

我尝试了以下代码:

def modify(df):
    dfc = df.copy(deep=True)
    dfc['col'].iloc[0].append(4)
    return dfc

modify(df)
print(df)

问题在于，除了新的副本 dfc 外，初始DataFrame df 也被修改了.为什么?我应该怎么做才能防止初始数据帧被修改?我的熊猫版本是0.25.0

The problem is that, besides the new copy dfc, the initial DataFrame df is also modified. Why? What should I do to prevent initial dataframes from modifying? My pandas version is 0.25.0

推荐答案

从文档此处，在注释"部分中:

From the docs here, in the Notes section:

此问题在GitHub上的此问题中再次引用，其中开发人员指出:

This is referenced again in this issue on GitHub, where the devs state that:

因此，此功能按开发人员的意图工作-可变对象(如列表)不应嵌入到DataFrames中.

So this function is working as the devs intend - mutable objects such as lists should not be embedded in DataFrames.

我找不到使 copy.deepcopy 在DataFrame上按预期方式工作的方法，但是我确实找到了使用挑剔:

I couldn't find a way to get copy.deepcopy to work as intended on a DataFrame, but I did find a fairly awful workaround using pickle:

import pandas as pd
import pickle

df = pd.DataFrame({'col':[[1,2,3]]})

def modify(df):
    dfc = pickle.loads(pickle.dumps(df))
    print(dfc['col'].iloc[0] is df['col'].iloc[0]) #Check if we've succeeded in deepcopying
    dfc['col'].iloc[0].append(4)
    print(dfc)
    return dfc

modify(df)
print(df)

输出:

False
            col
0  [1, 2, 3, 4]
         col
0  [1, 2, 3]

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