DataFrame复制的奇怪行为 [英] Strange behavior with DataFrame copy

问题描述

考虑以下代码：

In [16]: data = [['Alex',10],['Bob',12],['Clarke',13]]
In [17]: df = pd.DataFrame(data,columns=['Name','Age'])
Out[18]: 
     Name  Age
0    Alex   10
1     Bob   12
2  Clarke   13

In [19]: df_new = df
In [20]: df_new['Age'] = df_new['Age'] * 90 / 100

In [21]: df_new
     Name   Age
0    Alex   9.0
1     Bob  10.8
2  Clarke  11.7

In [22]: df
     Name   Age
0    Alex   9.0
1     Bob  10.8
2  Clarke  11.7

当我为新的 Age 列分配新值时DataFrame（ df_new ），原始DataFrame（ df ）的 Age 列也已更改。

When I assigned new values to the Age columns of the new DataFrame (df_new), the Age column of the original DataFrame (df) changed as well.

为什么会发生？它与创建原始DataFrame副本的方式有关吗？似乎它们被链接在一起。

Why does it happen? Does it have something to do with the way I create a copy of the original DataFrame? Seem like they are chained together.

推荐答案

使用-

df_new = df.copy()

OR

df_new = df.copy(deep=True)

这是复制 pandas 对象的索引和数据的标准方法。

This is the standard way of making a copy of a pandas object’s indices and data.

从 pandas文档

当deep = True（默认）时，将使用
的副本，即调用对象的数据和索引。副本的数据或
索引的修改不会反映在原始对象中

When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object

说明

如果看到创建的各种DataFrame的对象ID，则可以清楚地看到正在发生的事情。

If you see the object IDs of the various DataFrames you create, you can clearly see what is happening.

当编写df_new = df时，您将创建一个名为 new_df 的变量，并将其绑定到与 df具有相同ID的对象。

When you write df_new = df, you are creating a variable named new_df, and binding it with an object with same id as that of df.

示例

data = [['Alex',10],['Bob',12],['Clarke',13]]
df = pd.DataFrame(data,columns=['Name','Age'])

df_new = df
df_copy = df.copy()
print("ID of old df: {}".format(id(df)))
print("ID of new df: {}".format(id(df_new)))
print("ID of copy df: {}".format(id(df_copy)))

输出

ID of old df: 113414664
ID of new df: 113414664
ID of copy df: 113414832

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