问题描述
问题设置
熊猫数据框
df = pd.DataFrame({'Group': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A'], 'Subgroup': ['Group 1', 'Group 1', 'Group 1', 'Group 1', 'Group 1', 'Group 1', 'Group 2', 'Group 2', 'Group 2'], 'Keyword': ['kw 1', 'kw 1', 'kw 1', 'kw 2', '+kw +2', 'kw 2', 'kw 3', 'kw 3', 'kw 3'], 'Normalized': ['kw 1', 'kw 1', 'kw 1', 'kw 2', 'kw 2', 'kw 2', 'kw 3', 'kw 3', 'kw 3'], 'Criterion Type': ['Exact', 'Phrase', 'Broad', 'Phrase', 'Broadified', 'Exact', 'Broad', 'Exact', 'Phrase'], 'Max CPC': [1.62, 1.73, 0.87, 1.70, 0.85, 1.60, 0.99, 1.58, 1.68], 'CPC Rank': [2, 1, 3, 1, 3, 2, 3, 2, 1], 'Type Rank': [1, 2, 3, 2, 3, 1, 3, 1, 2]})
这样可以得到列中的列正确点：
This to get the columns in the right spot:
df = df[['Group', 'Subgroup', 'Keyword', 'Normalized', 'Criterion Type', 'Max CPC', 'CPC Rank', 'Type Rank']]
目标
groupby ['Group'，'Subgroup' ，'Normalized'] ，然后排名 最高CPC s。接下来，我想将与 CPC Rank 相关联的最高CPC 映射到类型排名根据条件类型和我自己的自定义排名确定：
{'Exact'：1， 'Phrase'：2，'Broadified'：3，'Broad'：4}
groupby ['Group', 'Subgroup', 'Normalized'], then rank the Max CPCs. Next, I want to map the Max CPC associated to the CPC Rank to the Type Rank which is determined based on Criterion Type and my own custom rank:{'Exact':1, 'Phrase':2, 'Broadified':3, 'Broad':4}
结果将是新CPC 列及其相应的最高每次点击费用。
The result would be the New CPC column with its appropriate Max CPC.
推荐答案
import pandas as pd
import numpy as np
df = pd.DataFrame({'Group': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A'], 'Subgroup': ['Group 1', 'Group 1', 'Group 1', 'Group 1', 'Group 1', 'Group 1', 'Group 2', 'Group 2', 'Group 2'], 'Keyword': ['kw 1', 'kw 1', 'kw 1', 'kw 2', '+kw +2', 'kw 2', 'kw 3', 'kw 3', 'kw 3'], 'Normalized': ['kw 1', 'kw 1', 'kw 1', 'kw 2', 'kw 2', 'kw 2', 'kw 3', 'kw 3', 'kw 3'], 'Criterion Type': ['Exact', 'Phrase', 'Broad', 'Phrase', 'Broadified', 'Exact', 'Broad', 'Exact', 'Phrase'], 'Max CPC': [1.62, 1.73, 0.87, 1.70, 0.85, 1.60, 0.99, 1.58, 1.68], 'CPC Rank': [2, 1, 3, 1, 3, 2, 3, 2, 1], 'Type Rank': [1, 2, 3, 2, 3, 1, 3, 1, 2]})
df = df[['Group', 'Subgroup', 'Keyword', 'Normalized', 'Criterion Type', 'Max CPC', 'CPC Rank', 'Type Rank']]
#Sort by custom priority based on their Criterion Type
df = df.sort(['Group', 'Subgroup', 'Normalized', 'Type Rank'])
#Reset index and drop old one
df = df.reset_index(drop=True)
print(df)
#Create df1 which is a Series of the Max CPC column in its correctly ranked order
df1 = df.sort(['Group', 'Subgroup', 'Normalized', 'CPC Rank'])['Max CPC']
#Reset index and drop old one
df1 = df1.reset_index(drop=True)
print(df1)
#Add the df1 Series to df and name the column New CPC
df['New CPC'] = df1
print(df)
这是迄今为止这个问题的最有效的解决方案。很难的部分是意识到我可以通过类型等级 sort df $ c>所以条件类型行按排名排序。这意味着我希望最高的最高每次点击费用适用于第一个，第二高最高每次点击费用到第二个，
This is by far the most efficient solution to this problem. The hard part was realizing that I could sort df by the Type Rank so the Criterion Type rows were ordered by their rank. This meant I wanted the highest Max CPC to apply to the first, the second highest Max CPC to the second, and so on.
然后我只需要创建一个最高CPC 系列按 CPC排名排序
Then all I had to do was create a Max CPC Series sorted by CPC Rank.
最后，添加这个系列到现有的 df 。
Lastly, add this Series to the existing df.
这篇关于Python Pandas groupby，排名，然后根据自定义排名分配值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！