为什么需要将地图类型转换为列表以将其分配给 pandas 系列? [英] Why is it required to typecast a map into a list to assign it to a pandas series?

问题描述

我刚刚开始学习熊猫的基础知识，有一件事让我思考.

I have just started learning the basics of pandas, and there is one thing which made me think.

import pandas as pd
data = pd.DataFrame({'Column1': ['A', 'B', 'C']})
data['Column2'] = map(str.lower, data['Column1'])
print(data)

该程序的输出为:

   Column1                             Column2
 0       A  <map object at 0x00000205D80BCF98>
 1       B  <map object at 0x00000205D80BCF98>
 2       C  <map object at 0x00000205D80BCF98>

获得所需输出的一种可能解决方案是将地图对象转换成列表.

One possible solution to get the desired output is to typecast the map object into a list.

import pandas as pd
data = pd.DataFrame({'Column1': ['A', 'B', 'C']})
data['Column2'] = list(map(str.lower, data['Column1']))
print(data)

输出:

   Column1 Column2
 0       A       a
 1       B       b
 2       C       c

但是，如果我使用range()并在Python 3中返回其自身的类型，则无需将对象类型转换为列表.

However if I use range(), which also returns its own type in Python 3, there is no need to typecast the object to a list.

import pandas as pd
data = pd.DataFrame({'Column1': ['A', 'B', 'C']})
data['Column2'] = range(3)
print(data)

输出:

   Column1  Column2
 0       A        0
 1       B        1
 2       C        2

有什么理由不要求强制转换范围对象，而要求映射对象?

Is there any reason why range object is not required to be typecasted but map object is?

推荐答案

TL; DR::range具有__getitem__和__len__，而map没有

我假设对于

I'm assuming that the syntax of creating a new dataframe column is in some way syntactic sugar for Pandas.DataFrame.insert, which takes as an argument for value a

标量，系列或类似数组的

scalar, Series, or array-like

鉴于此，问题似乎简化为:为什么熊猫将列表和范围视为数组而不是地图?"

Given that, it seems the question reduces to "Why does pandas treat a list and a range as array-like, but not a map?"

请参阅: numpy:"array_like"的正式定义；对象?.

如果您尝试使数组超出范围，则可以正常工作，因为范围足够接近类似于数组的数组，但是您无法使用地图做到这一点.

If you try making an array out of a range, it works fine, because range is close enough to array-like, but you can't do so with a map.

>>>将numpy导入为np
>>> foo = np.array(range(10))
>>> bar = np.array(map(lambda x:x + 1，范围(10))
>>> foo
array([0，1，2，3，4，5，6，7，8，9])
>>>栏
array(<在0x7f7e553219e8处映射对象，dtype = object)

>>> import numpy as np
>>> foo = np.array(range(10))
>>> bar = np.array(map(lambda x: x + 1, range(10))
>>> foo
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> bar
array(<map object at 0x7f7e553219e8>, dtype=object)

map不是类似数组的"，而range是.

map is not "array-like", while range is.

进一步查看 PyArray_GetArrayParamsFromObject ，在链接的答案中引用，该函数的末尾调用PySequence_Check.该代码是python代码，并且在Stack Overflow上对此进行了很好的讨论:什么是Python的序列协议?

Looking further into PyArray_GetArrayParamsFromObject, referred to in the linked answer, the end of the function calls PySequence_Check. That code is python code, and there's a good discussion of it on Stack Overflow: What is Python's sequence protocol? .

更早之前，在相同文件，它说:

   /*
     * PySequence_Check detects whether an old type object is a
     * sequence by the presence of the __getitem__ attribute, and
     * for new type objects that aren't dictionaries by the
     * presence of the __len__ attribute as well. In either case it
     * is possible to have an object that tests as a sequence but
     * doesn't behave as a sequence and consequently, the
     * PySequence_GetItem call can fail. When that happens and the
     * object looks like a dictionary, we truncate the dimensions
     * and set the object creation flag, otherwise we pass the
     * error back up the call chain.
     */

这似乎是类数组"的主要部分-具有 getitem 和 len 的任何项目都类似于数组. range都有，而map都没有.

This seems to be a major part of "array-like" - any item that has getitem and len is array like. range has both, while map has neither.

__getitem__和__len__是创建序列所必需的，因此足以使该列按您希望的方式显示，而不是作为单个对象显示.

__getitem__ and __len__ are necessary and sufficient to make a sequence, and therefore get the column to display as you wish instead of as a single object.

尝试一下:

class Column(object):
    def __len__(self):
        return 5
    def __getitem__(self, index):
        if 0 <= index < 5:
            return index+5
        else:
            raise IndexError

col = Column()
a_col = np.array(col)

• 如果您没有__getitem__()或__len()__，numpy将为您创建一个数组，但是它将与其中的对象一起使用，并且不会为您迭代.
• 如果同时具有这两种功能，它将显示您想要的方式.
• If you don't have either __getitem__() or __len()__, numpy will create an array for you, but it will be with the object in it, and it won't iterate through for you.
• If you have both functions, it displays the way you want.

(感谢user2357112纠正我.在一个稍微简单的示例中，我认为需要__iter__.不是.__getitem__函数的确需要确保索引在范围内.)

(Thanks to user2357112 for correcting me. In a slightly simpler example, I thought __iter__ was required. It's not. The __getitem__ function does need to make sure the index is in range, though.)

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