根据另一个数据框中的其他列创建新列

Question

我有 2 个数据帧：

df1

Name   Apples   Pears   Grapes   Peachs
James    3       5        5        2
Harry    1       0        2        9
Will     20      2        7        3

df2

Class   User   Factor  
A       Harry  3
A       Will   2
A       James  5
B       NaN    4

我想在 df2 中创建一个名为 Total 的新列，它是 df1 中每个用户的所有列的列表，乘以该用户的因子 - 只有当他们是在 Class A.

最终的 df 应该是这样的

df2

Class   User   Factor   Total 
A       Harry  3        [3,0,6,27]
A       Will   2        [40,4,14,6]
A       James  5        [15,25,25,10]
B       NaN    4

这是我试过的：

df2['Total'] = list(df1.Name.isin((df2.User) and (df2.Class==A)) * df2.Factor)

Answer 1

您可以使用：

# First lookup
factor = df2[df2['Class'] == 'A'].set_index('User')['Factor']
df1['Total'] = df1[cols].mul(df1['Name'].map(factor), axis=0).agg(list, axis=1)

# Second lookup
df2['Total'] = df2['User'].map(df1.set_index('Name')['Total'])

输出：

>>> df2
  Class   User  Factor             Total
0     A  Harry       3     [3, 0, 6, 27]
1     A   Will       2    [40, 4, 14, 6]
2     A  James       5  [15, 25, 25, 10]
3     B    NaN       4               NaN

>>> df1
    Name  Apples  Pears  Grapes  Peachs             Total
0  James       3      5       5       2  [15, 25, 25, 10]
1  Harry       1      0       2       9     [3, 0, 6, 27]
2   Will      20      2       7       3    [40, 4, 14, 6]

Answer 2

这将解决您的问题：

df2 = df2[df2.Class=='A'].join(df.set_index('Name'), on='User').set_index(['Class','User'])
df2['Total'] = df2.apply(lambda x: list(x * x.Factor)[1:], axis=1)
df2 = df2.reset_index()[['Class','User','Factor','Total']]

完整测试代码：

import pandas as pd
import numpy as np
df = pd.DataFrame(columns=[
x.strip() for x in 'Name   Apples   Pears   Grapes   Peachs'.split()], data =[
['James',    3,       5,        5,        2],
['Harry',   1,       0,        2,        9],
['Will',     20,      2,        7,        3]])
print(df)

df2 = pd.DataFrame(columns=[
x.strip() for x in 'Class   User   Factor'.split()], data =[
['A',       'Harry',  3],
['A',       'Will',   2],
['A',       'James',  5],
['B',       np.nan,    4]])
print(df2)

df2 = df2[df2.Class=='A'].join(df.set_index('Name'), on='User').set_index(['Class','User'])
df2['Total'] = df2.apply(lambda x: list(x * x.Factor)[1:], axis=1)
df2 = df2.reset_index()[['Class','User','Factor','Total']]
print(df2)

输入：

    Name  Apples  Pears  Grapes  Peachs
0  James       3      5       5       2
1  Harry       1      0       2       9
2   Will      20      2       7       3
  Class   User  Factor
0     A  Harry       3
1     A   Will       2
2     A  James       5
3     B    NaN       4

输出

  Class   User  Factor             Total
0     A  Harry       3     [3, 0, 6, 27]
1     A   Will       2    [40, 4, 14, 6]
2     A  James       5  [15, 25, 25, 10]

Answer 3

On-liner 受虐狂，问候 ;)

df2['Total'] = pd.Series(df1.sort_values(by='Name').reset_index(drop=True).iloc[:,1:5]\
                         .mul(df2[df2.Class == 'A'].sort_values(by='User')['Factor'].reset_index(drop=True), axis=0)\
                         .values.tolist())
df2

输出：

index	Class	User	Factor	Total
0	A	Harry	3	3,0,6,27
1	A	Will	2	15,25,25,10
2	A	James	5	40,4,14,6
3	B	NaN	4	NaN

根据另一个数据框中的其他列创建新列

Creating a new column based on other columns from another dataframe

python

pandas

dataframe

data-wrangling

data-munging