pandas DataFrame窗口功能 [英] Pandas DataFrame Window Function

问题描述

我正在尝试像使用SQL窗口函数一样操作我的数据框.考虑以下样本集:

I'm trying to manipulate my data frame similar to how you would using SQL window functions. Consider the following sample set:

import pandas as pd

df = pd.DataFrame({'fruit' : ['apple', 'apple', 'apple', 'orange', 'orange', 'orange', 'grape', 'grape', 'grape'],
               'test' : [1, 2, 1, 1, 2, 1, 1, 2, 1],
               'analysis' : ['full', 'full', 'partial', 'full', 'full', 'partial', 'full', 'full', 'partial'],
               'first_pass' : [12.1, 7.1, 14.3, 19.1, 17.1, 23.4, 23.1, 17.2, 19.1],
               'second_pass' : [20.1, 12.0, 13.1, 20.1, 18.5, 22.7, 14.1, 17.1, 19.4],
               'units' : ['g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g'],
               'order' : [2, 1, 3, 2, 1, 3, 3, 2, 1]})

+--------+------+----------+------------+-------------+-------+-------+
| fruit  | test | analysis | first_pass | second_pass | order | units |
+--------+------+----------+------------+-------------+-------+-------+
| apple  |    1 | full     | 12.1       | 20.1        |     2 | g     |
| apple  |    2 | full     | 7.1        | 12.0        |     1 | g     |
| apple  |    1 | partial  | 14.3       | 13.1        |     3 | g     |
| orange |    1 | full     | 19.1       | 20.1        |     2 | g     |
| orange |    2 | full     | 17.1       | 18.5        |     1 | g     |
| orange |    1 | partial  | 23.4       | 22.7        |     3 | g     |
| grape  |    1 | full     | 23.1       | 14.1        |     3 | g     |
| grape  |    2 | full     | 17.2       | 17.1        |     2 | g     |
| grape  |    1 | partial  | 19.1       | 19.4        |     1 | g     |
+--------+------+----------+------------+-------------+-------+-------+

我想添加几列:

• 一个布尔列，指示该测试和分析的second_pass值在所有水果类型中是否最高.
• 另一列，列出了每种测试和分析组合的哪些水果的second_pass值最高.

使用此逻辑，我想获得下表:

Using this logic, I'd like to get the following table:

+--------+------+----------+------------+-------------+-------+-------+---------+---------------------+
| fruit  | test | analysis | first_pass | second_pass | order | units | highest |   highest_fruits    |
+--------+------+----------+------------+-------------+-------+-------+---------+---------------------+
| apple  |    1 | full     | 12.1       | 20.1        |     2 | g     | true    | ["apple", "orange"] |
| apple  |    2 | full     | 7.1        | 12.0        |     1 | g     | false   | ["orange"]          |
| apple  |    1 | partial  | 14.3       | 13.1        |     3 | g     | false   | ["orange"]          |
| orange |    1 | full     | 19.1       | 20.1        |     2 | g     | true    | ["apple", "orange"] |
| orange |    2 | full     | 17.1       | 18.5        |     1 | g     | true    | ["orange"]          |
| orange |    1 | partial  | 23.4       | 22.7        |     3 | g     | true    | ["orange"]          |
| grape  |    1 | full     | 23.1       | 22.1        |     3 | g     | false   | ["orange"]          |
| grape  |    2 | full     | 17.2       | 17.1        |     2 | g     | false   | ["orange"]          |
| grape  |    1 | partial  | 19.1       | 19.4        |     1 | g     | false   | ["orange"]          |
+--------+------+----------+------------+-------------+-------+-------+---------+---------------------+

我是熊猫的新手，所以我确定我缺少一些简单的东西.

I'm new to pandas, so I'm sure I'm missing something very simple.

推荐答案

您可以返回boolean值，其中second_pass等于group max，因为idxmax仅返回:

You could return boolean values where second_pass equals the group max, as idxmax only returns the first occurrence of the max:

df['highest'] = df.groupby(['test', 'analysis'])['second_pass'].transform(lambda x: x == np.amax(x)).astype(bool)

，然后使用np.where捕获具有group max的所有fruit值，并将结果merge捕获到您的DataFrame中，如下所示:

and then use np.where to capture all fruit values that have a group max, and merge the result into your DataFrame like so:

highest_fruits = df.groupby(['test', 'analysis']).apply(lambda x: [f for f in np.where(x.second_pass == np.amax(x.second_pass), x.fruit.tolist(), '').tolist() if f!='']).reset_index()
df =df.merge(highest_fruits, on=['test', 'analysis'], how='left').rename(columns={0: 'highest_fruit'})

最后，请您跟进:

first_pass = df.groupby(['test', 'analysis']).apply(lambda x: {fruit: x.loc[x.fruit==fruit, 'first_pass'] for fruit in x.highest_fruit.iloc[0]}).reset_index()
df =df.merge(first_pass, on=['test', 'analysis'], how='left').rename(columns={0: 'first_pass_highest_fruit'})

获得:

  analysis  first_pass   fruit  order  second_pass  test units highest  \
0     full        12.1   apple      2         20.1     1     g    True   
1     full         7.1   apple      1         12.0     2     g   False   
2  partial        14.3   apple      3         13.1     1     g   False   
3     full        19.1  orange      2         20.1     1     g    True   
4     full        17.1  orange      1         18.5     2     g    True   
5  partial        23.4  orange      3         22.7     1     g    True   
6     full        23.1   grape      3         14.1     1     g   False   
7     full        17.2   grape      2         17.1     2     g   False   
8  partial        19.1   grape      1         19.4     1     g   False   

     highest_fruit             first_pass_highest_fruit  
0  [apple, orange]  {'orange': [19.1], 'apple': [12.1]}  
1         [orange]                   {'orange': [17.1]}  
2         [orange]                   {'orange': [23.4]}  
3  [apple, orange]  {'orange': [19.1], 'apple': [12.1]}  
4         [orange]                   {'orange': [17.1]}  
5         [orange]                   {'orange': [23.4]}  
6  [apple, orange]  {'orange': [19.1], 'apple': [12.1]}  
7         [orange]                   {'orange': [17.1]}  
8         [orange]                   {'orange': [23.4]} 

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