Question

我想根据来自多列的条件创建一个值为1或0的新列。

使用了下面的数据集。

我尝试了以下操作：

df['passed'] = lambda x: '1' if df[(df['name']=='a') & (df['month'] <8) & (df['score']> 3.5)] else '0'

    'name'  : ['a', 'a', 'a','a',' a','a','a', 's', 's','s','l','a','c','a', 'e','a','g', 'd','c','d','a','f','a','a','a'],
    'month' : [5, 12, 3, 12, 3, 6,7,8,9,10,11,12,4,5,2,6,7,8,3, 4, 7, 6,7,8,8],
    'score' : [2.5, 5, 3.5, 2.5, 5, 3.5,2,3.5,4,2,1.5,1,1.5,4,5.5,2,3,1,2,3.5,4,2,3.5,3,4]})

这是我得到的输出：


    name    month   score   passed
0   a   5   2.5 <function <lambda> at 0x1a2050c158>
1   a   12  5.0 <function <lambda> at 0x1a2050c158>

我需要一个1或0的值，而不是“函数lambda，在0x1a2050c158>”。

Answer 1

尝试使用np.where

df['passed'] = np.where( (df['name']=='a') & (df['month'] <8) & (df['score']> 3.5),1,0)

或

df['passed'] = ( (df['name']=='a') & (df['month'] <8) & (df['score']> 3.5)).astype(int)

Answer 2

尝试：

df['passed'] = df.apply(lambda row: 1 if (row['name']=='a') and (row.month < 8) and (row.score > 3.5) else 0, axis = 1)

Answer 3

改用pandas.Series：

import pandas as pd
input_dict = {'name'  : ['a', 'a', 'a','a',' a','a','a', 's', 's','s','l','a','c','a', 'e','a','g', 'd','c','d','a','f','a','a','a'],
    'month' : [5, 12, 3, 12, 3, 6,7,8,9,10,11,12,4,5,2,6,7,8,3, 4, 7, 6,7,8,8],
    'score' : [2.5, 5, 3.5, 2.5, 5, 3.5,2,3.5,4,2,1.5,1,1.5,4,5.5,2,3,1,2,3.5,4,2,3.5,3,4]}
df = pd.DataFrame(input_dict)
df['passed'] = pd.Series(['1' if x=='a' and y<8 and z>3.5 else '0' for (x, y, z) in zip(df['name'].values, 
                                                                                          df['month'].values,
                                                                                          df['score'].values)
                           ])

输出：

name    month   score   passed
0   a   5   2.5 0
1   a   12  5.0 0
2   a   3   3.5 0
3   a   12  2.5 0
4   a   3   5.0 0
5   a   6   3.5 0
6   a   7   2.0 0
7   s   8   3.5 0
8   s   9   4.0 0
9   s   10  2.0 0
10  l   11  1.5 0
11  a   12  1.0 0
12  c   4   1.5 0
13  a   5   4.0 1
14  e   2   5.5 0
15  a   6   2.0 0
16  g   7   3.0 0
17  d   8   1.0 0
18  c   3   2.0 0
19  d   4   3.5 0
20  a   7   4.0 1
21  f   6   2.0 0
22  a   7   3.5 0
23  a   8   3.0 0
24  a   8   4.0 0

应用lambda后未显示任何值

3 个答案: