Python 数据帧分组按多级选择

我正试图用pandas来解决我用纯python解决的问题，但不知道DataFrameGroupBy的最佳实践

我想为每个邮政编码选择最多处方药物的百分比（占该邮政编码中所有药物的百分比）。 如果两种药物的处方量相同，我想选择“按字母顺序第一”的药物：

我已经按邮政编码进行了分组，但在选择行时遇到了问题（应用lambda）

所有按邮政编码销售的药品，我打算将此药品与之前的数据集一起使用：

all_by_post_code = drugs_prescriptions.groupby(['PostCode'])['Quantity'].sum()

我不知道如何选择每个邮政编码中药品最大数量的行，如果两种药品的数量相同，则应选择第一个字母顺序的药品（D1表示邮政编码P2）

我一直想做这样的事情：

durg_qualtity_per_post_code [durg_qualtity_per_post_code .apply(lambda x: int(x['Quantity']) == max_items_by_post_code[x['post_code']], axis=1, reduce=True)]

更新：

# sort by PostCode, Drug
df = drugs_prescriptions.groupby(['PostCode', 'Drug']).agg('sum')
df = df.groupby(['PostCode']).apply(lambda x: x.sort_values(['Quantity', 'Drug'], ascending=[False, True]))

# select first value by PostCode
# reset index in order to have drug in the output as well
df.reset_index(level=[1], inplace=True)
df = df.groupby(['PostCode']).first()

# calculate percentage of total by PostCode
allQuantities = drugs_prescriptions.groupby(['PostCode']).agg('sum')
df['Quantity'] = df.apply(lambda row: row['Quantity']/allQuantities.loc[row.name], axis=1)

---

这里有一个可能的解决方案，但它让人觉得很尴尬，也不符合python。但它是有效的，注释在代码中

# setting string to integer
df.Quantity = df.Quantity.astype('int')

# create a mulitiindex
df.set_index(['PostCode', 'Drug'], inplace=True)

# use transform to divide the sum of the 'Drug' level by the 'PostCode' level
df = df.groupby(level=[0,1]).transform('sum') / df.groupby(level=0).transform('sum')

# move 'Drug' out of the multi index to allow for sorting
df.reset_index(level=[1], inplace=True)

# Sort the 'Quantity' descending order, and the 'Drug' in ascending order,
# then we can select the first 'PostCode' for our result
df.sort_values(['Quantity','Drug'], ascending=[False, True], inplace=True)

df.groupby('PostCode').first()

           Drug Quantity
PostCode        
P1          D1  0.571429
P2          D1  0.500000
P3          D2  1.000000

---

谢谢，有很多资料要研究。真的很感激。@user007这是个好问题，我自己也学到了一些东西。谢谢

# sort by PostCode, Drug
df = drugs_prescriptions.groupby(['PostCode', 'Drug']).agg('sum')
df = df.groupby(['PostCode']).apply(lambda x: x.sort_values(['Quantity', 'Drug'], ascending=[False, True]))

# select first value by PostCode
# reset index in order to have drug in the output as well
df.reset_index(level=[1], inplace=True)
df = df.groupby(['PostCode']).first()

# calculate percentage of total by PostCode
allQuantities = drugs_prescriptions.groupby(['PostCode']).agg('sum')
df['Quantity'] = df.apply(lambda row: row['Quantity']/allQuantities.loc[row.name], axis=1)

# setting string to integer
df.Quantity = df.Quantity.astype('int')

# create a mulitiindex
df.set_index(['PostCode', 'Drug'], inplace=True)

# use transform to divide the sum of the 'Drug' level by the 'PostCode' level
df = df.groupby(level=[0,1]).transform('sum') / df.groupby(level=0).transform('sum')

# move 'Drug' out of the multi index to allow for sorting
df.reset_index(level=[1], inplace=True)

# Sort the 'Quantity' descending order, and the 'Drug' in ascending order,
# then we can select the first 'PostCode' for our result
df.sort_values(['Quantity','Drug'], ascending=[False, True], inplace=True)

df.groupby('PostCode').first()

           Drug Quantity
PostCode        
P1          D1  0.571429
P2          D1  0.500000
P3          D2  1.000000