在 Pandas 中，.iloc 方法是否提供副本或视图？
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In Pandas, does .iloc method give a copy or view?
提问by Qiyu
I find the result is a little bit random. Sometimes it's a copy sometimes it's a view. For example:
我发现结果有点随机。有时是副本有时是视图。例如：
df = pd.DataFrame([{'name':'Marry', 'age':21},{'name':'John','age':24}],index=['student1','student2'])
df
age name
student1 21 Marry
student2 24 John
Now, Let me try to modify it a little bit.
现在，让我试着稍微修改一下。
df2= df.loc['student1']
df2 [0] = 23
df
age name
student1 21 Marry
student2 24 John
As you can see, nothing changed. df2 is a copy. However, if I add another student into the dataframe...
如您所见，什么都没有改变。df2 是一个副本。但是，如果我将另一个学生添加到数据框中...
df.loc['student3'] = ['old','Tom']
df
age name
student1 21 Marry
student2 24 John
student3 old Tom
Try to change the age again..
再换个年龄试试。。
df3=df.loc['student1']
df3[0]=33
df
age name
student1 33 Marry
student2 24 John
student3 old Tom
Now df3 suddenly became a view. What is going on? I guess the value 'old' is the key?
现在df3突然变成了一个视图。到底是怎么回事？我猜“旧”的价值是关键？
采纳答案by juanpa.arrivillaga
In general, you can get a view if the data-frame has a single dtype, which is notthe case with your original data-frame:
一般来说，如果数据框有单个 ，您可以获得一个视图dtype，而您的原始数据框不是这种情况：
In [4]: df
Out[4]:
age name
student1 21 Marry
student2 24 John
In [5]: df.dtypes
Out[5]:
age int64
name object
dtype: object
However, when you do:
但是，当您这样做时：
In [6]: df.loc['student3'] = ['old','Tom']
...:
The first column get's coerced to object, since columns cannot have mixed dtypes:
第一列被强制为object，因为列不能有混合数据类型：
In [7]: df.dtypes
Out[7]:
age object
name object
dtype: object
In this case, the underlying .valueswill always return an array with the same underlying buffer, and changes to that array will be reflected in the data-frame:
在这种情况下，底层.values将始终返回一个具有相同底层缓冲区的数组，对该数组的更改将反映在数据帧中：
In [11]: vals = df.values
In [12]: vals
Out[12]:
array([[21, 'Marry'],
[24, 'John'],
['old', 'Tom']], dtype=object)
In [13]: vals[0,0] = 'foo'
In [14]: vals
Out[14]:
array([['foo', 'Marry'],
[24, 'John'],
['old', 'Tom']], dtype=object)
In [15]: df
Out[15]:
age name
student1 foo Marry
student2 24 John
student3 old Tom
On the other hand, with mixed types like with your original data-frame:
另一方面，对于像原始数据框这样的混合类型：
In [26]: df = pd.DataFrame([{'name':'Marry', 'age':21},{'name':'John','age':24}]
...: ,index=['student1','student2'])
...:
In [27]: vals = df.values
In [28]: vals
Out[28]:
array([[21, 'Marry'],
[24, 'John']], dtype=object)
In [29]: vals[0,0] = 'foo'
In [30]: vals
Out[30]:
array([['foo', 'Marry'],
[24, 'John']], dtype=object)
In [31]: df
Out[31]:
age name
student1 21 Marry
student2 24 John
Note, however, that a view will only be returned if it is possible to be a view, i.e. if it is a proper slice, otherwise, a copy will be made regardless of the dtypes:
但是请注意，只有当它可能是一个视图时才会返回一个视图，即如果它是一个适当的切片，否则，无论 dtypes 是什么，都将进行复制：
In [39]: df.loc['student3'] = ['old','Tom']
In [40]: df2
Out[40]:
name
student3 Tom
student2 John
In [41]: df2.loc[:] = 'foo'
In [42]: df2
Out[42]:
name
student3 foo
student2 foo
In [43]: df
Out[43]:
age name
student1 21 Marry
student2 24 John
student3 old Tom
回答by ayhan
You are starting with a DataFrame that has two columns with two different dtypes:
您从一个 DataFrame 开始，它有两列具有两种不同的数据类型：
df.dtypes
Out:
age int64
name object
dtype: object
Since different dtypes are stored in different numpy arrays under the hood, you have two different blocks for them:
由于不同的 dtype 存储在不同的 numpy 数组中，因此您有两个不同的块：
df.blocks
Out:
{'int64': age
student1 21
student2 24, 'object': name
student1 Marry
student2 John}
If you attempt to slice the first row of this DataFrame, it has to get one value from each different block which makes it necessary to create a copy.
如果您尝试对该 DataFrame 的第一行进行切片，则它必须从每个不同的块中获取一个值，这使得创建副本成为必要。
df2.is_copy
Out[40]: <weakref at 0x7fc4487a9228; to 'DataFrame' at 0x7fc4488f9dd8>
In the second attempt, you are changing the dtypes. Since 'old' cannot be stored in an integer array, it casts the Series as an object Series.
在第二次尝试中，您正在更改 dtype。由于 'old' 不能存储在整数数组中，因此它将系列转换为对象系列。
df.loc['student3'] = ['old','Tom']
df.dtypes
Out:
age object
name object
dtype: object
Now all data for this DataFrame is stored in a single block (and in a single numpy array):
现在，此 DataFrame 的所有数据都存储在单个块中（和单个 numpy 数组中）：
df.blocks
Out:
{'object': age name
student1 21 Marry
student2 24 John
student3 old Tom}
At this step, slicing the first row can be done on the numpy array without creating a copy, so it returns a view.
在这一步，可以在 numpy 数组上完成第一行的切片，而无需创建副本，因此它返回一个视图。
df3._is_view
Out: True
