从 pandas 数据框中获取索引列表 [英] Getting lists of indices from pandas dataframe

问题描述

我正在尝试从熊猫数据框中获取索引列表.

I'm trying to get a list of indices out of a pandas dataframe.

首先执行导入.

import pandas as pd

构造一个熊猫数据框.

# Create dataframe
data = {'name': ['Jason', 'Jason', 'Tina', 'Tina', 'Tina', 'Jason', 'Tina'],
        'reports': [4, 24, 31, 2, 3, 5, 10],
        'coverage': [True, False, False, False, True, True, False]}
df = pd.DataFrame(data)
print(df)

输出:

  coverage   name  reports
0     True  Jason        4
1    False  Jason       24
2    False   Tina       31
3    False   Tina        2
4     True   Tina        3
5     True  Jason        5
6    False   Tina       10

当coverage设置为True时，我希望在数据框的左侧具有索引，但是我希望每个名称分别具有此索引.最好在没有显式for循环的情况下执行此操作.

I would like to have the indices on the left of the dataframe when the coverage is set to True, but I would like to have this for every name separately. Preferably do this without an explicit for-loop.

所需的输出是这样的.

Desired output is something like this.

list_Jason = [0, 5]
list_Tina = [4]

尝试的解决方案:我认为我应该使用"groupby"，然后访问coverage列.从那里我不知道如何进行.感谢所有帮助.

Attempted solution: I thought I should use 'groupby' and then access the coverage column. From there I don't know how to proceed. All help is appreciated.

df.groupby('name')['coverage']

推荐答案

您要获取每个组的索引.

You want to get the index out for each group.

它存储在groupbydataframe的'groups'属性中.

this is stored in the 'groups' attribute of a groupbydataframe.

#filter for coverage==True
#group by 'name'
#access the 'groups' attribute
by_person = df[df.coverage].groupby('name').groups

将返回:

{'Jason': Int64Index([0, 5], dtype='int64'),
 'Tina': Int64Index([4], dtype='int64')}

您可以像常规词典一样从中访问个人:

From which you can access the individuals as you would a regular dictionary:

by_person['Jason']

返回:

Int64Index([0, 5], dtype='int64')

您可以将其视为常规列表.

Which you can treat like a regular list.

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