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问题:
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 | +--------+------+----------+------------+-------------+-------+-------+
I'd like to add a few columns:
- a boolean column to indicate whether the second_pass value for that test and analysis is the highest amongst all fruit types.
- another column that lists which fruits had the highest second_pass values for each test and analysis combination.
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"] | +--------+------+----------+------------+-------------+-------+-------+---------+---------------------+
I'm new to pandas, so I'm sure I'm missing something very simple.
回答1:
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)
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'})
finally, for your follow up:
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'})
to get:
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]}
回答2:
I'm going to assume you meant
'test' : [1, 2, 3, 1, 2, 3, 1, 2, 3]
To generate your first column, you can group by the test number, and compare each second pass score to the max score:
df['highest'] = df['second_pass'] == df.groupby('test')['second_pass'].transform('max')
For the second part, I don't have a clean solution, but here's a bit of an ugly one, first set the index to fruit:
df = df.set_index('fruit')
Next, find which rows have 'highest' set to True for each test, and return the a list of the indices that those rows have (which are the names of the fruits):
test1_max_fruits = df[df['test']==1&df['highest']].index.values.tolist() test2_max_fruits = df[df['test']==2&df['highest']].index.values.tolist() test3_max_fruits = df[df['test']==3&df['highest']].index.values.tolist()
Define a function to look at the test number then return the corresponding max_fruits that we just generated:
def max_fruits(test_num): if test_num == 1: return test1_max_fruits if test_num == 2: return test2_max_fruits if test_num == 3: return test3_max_fruits
Create a column and apply this function over your 'test' column:
df['highest_fruits'] = df['test'].apply(max_fruits)
