python - 使用多个标准 pandas python 查找和替换

Question

我有以下数据框 (df):

loc pop_1   source_1    pop_2   source_2
a   99  group_a 77  group_b
b   93  group_a 90  group_b
c   58  group_a 59  group_b
d   47  group_a 62  group_b

我创建了一个附加列“upper_limit”:

df['upper_limit'] = df[['pop_1','pop_2']].max(axis=1)

我现在想添加另一列来查看“upper_limit”中的值，将它们与 pop_1 和 pop_2 进行比较，然后在它们匹配时从 source_1 或 source_2 中选择文本。即:

loc pop_1   source_1    pop_2   source_2 upper_limit source
a   99  group_a 77  group_b 99 group_a
b   93  group_a 90  group_b 93 group_a
c   58  group_a 59  group_b 59 group_b
d   47  group_a 62  group_b 62 group_b

我尝试通过以下方式从 pop_1 和 source_1 创建字典:

table_dict = df[['pop_1','source_1']]
z = table_dict.to_dict

然后使用以下方式映射:

df['source'] = 'n/a'
df['source'].replace(z,inplace=True)

这将返回数据框，但“源”列仅显示 n/a 结果。

Answer 1

I now want to add another column that looks at the values in 'upper_limit', compares them to pop_1 and pop_2 and then selects the text from source_1 or source_2 when they match.

您可以使用 np.where 更简单地做到这一点:

In [19]: import numpy as np

In [20]: df['upper_limit source'] = np.where(df.upper_limit == df.pop_1, df.source_1, df.source_2)

In [20]: df
Out[20]: 
  loc  pop_1  pop_2 source_1 source_2  upper_limit upper_limit source
0   a     99     77  group_a  group_b           99            group_a
1   b     93     90  group_a  group_b           93            group_a
2   c     58     59  group_a  group_b           59            group_b
3   d     47     62  group_a  group_b           62            group_b