Pandas数据帧获得每个组的第一行

Question

我有一只DataFrame像熊猫一样的熊猫.

df = pd.DataFrame({'id' : [1,1,1,2,2,3,3,3,3,4,4,5,6,6,6,7,7],
                'value'  : ["first","second","second","first",
                            "second","first","third","fourth",
                            "fifth","second","fifth","first",
                            "first","second","third","fourth","fifth"]})

我想通过["id","value"]对此进行分组,并得到每个组的第一行.

        id   value
0        1   first
1        1  second
2        1  second
3        2   first
4        2  second
5        3   first
6        3   third
7        3  fourth
8        3   fifth
9        4  second
10       4   fifth
11       5   first
12       6   first
13       6  second
14       6   third
15       7  fourth
16       7   fifth

预期结果

    id   value
     1   first
     2   first
     3   first
     4  second
     5  first
     6  first
     7  fourth

我试过以下只给出了第一行DataFrame.对此有任何帮助表示赞赏.

In [25]: for index, row in df.iterrows():
   ....:     df2 = pd.DataFrame(df.groupby(['id','value']).reset_index().ix[0])

Answer 1

>>> df.groupby('id').first()
     value
id        
1    first
2    first
3    first
4   second
5    first
6    first
7   fourth

如果您需要id列:

>>> df.groupby('id').first().reset_index()
   id   value
0   1   first
1   2   first
2   3   first
3   4  second
4   5   first
5   6   first
6   7  fourth

要获得n个第一个记录,可以使用head():

>>> df.groupby('id').head(2).reset_index(drop=True)
    id   value
0    1   first
1    1  second
2    2   first
3    2  second
4    3   first
5    3   third
6    4  second
7    4   fifth
8    5   first
9    6   first
10   6  second
11   7  fourth
12   7   fifth

Answer 2

这将为您提供每组的第二行(零索引,nth(0)与first()相同):

df.groupby('id').nth(1) 

文档:http://pandas.pydata.org/pandas-docs/stable/groupby.html#taking-the-nth-row-of-each-group

Answer 3

我建议使用.nth(0)而不是.first()你需要获得第一行.

它们之间的区别在于它们如何处理NaN,因此.nth(0)无论此行中的值是什么,都将返回组的第一行,而.first()最终将返回每列中的第一个非 NaN值.

例如,如果您的数据集是:

df = pd.DataFrame({'id' : [1,1,1,2,2,3,3,3,3,4,4],
            'value'  : ["first","second","third", np.NaN,
                        "second","first","second","third",
                        "fourth","first","second"]})

>>> df.groupby('id').nth(0)
    value
id        
1    first
2    NaN
3    first
4    first

和

>>> df.groupby('id').first()
    value
id        
1    first
2    second
3    first
4    first

Answer 4

如果您只需要我们可以使用的每个组的第一行drop_duplicates，请注意函数默认方法keep='first'。

df.drop_duplicates('id')
Out[1027]: 
    id   value
0    1   first
3    2   first
5    3   first
9    4  second
11   5   first
12   6   first
15   7  fourth

Answer 5

也许这就是你想要的

import pandas as pd
idx = pd.MultiIndex.from_product([['state1','state2'],   ['county1','county2','county3','county4']])
df = pd.DataFrame({'pop': [12,15,65,42,78,67,55,31]}, index=idx)

                pop
state1 county1   12
       county2   15
       county3   65
       county4   42
state2 county1   78
       county2   67
       county3   55
       county4   31

df.groupby(level=0, group_keys=False).apply(lambda x: x.sort_values('pop', ascending=False)).groupby(level=0).head(3)

> Out[29]: 
                pop
state1 county3   65
       county4   42
       county2   15
state2 county1   78
       county2   67
       county3   55