执行groupy后从数据中删除一些行

问题描述

我有这个数据集：

df = pd.DataFrame({'scientist':["Wendelaar Bonga"," Sjoerd E.","Grätzel"," Michael","Willett","Walter C.","Kessler","Ronald C.","Witten,Edward","Wang,Zhong Lin"],'SubjectField': ["Biomedical Engineering","Inorganic & Nuclear Chemistry","Organic Chemistry","Biomedical Engineering","Developmental Biology","Mechanical Engineering & Transports","Microbiology","Cardiovascular System & Hematology","Biomedical Engineering"]})

我想计算每个学科领域的科学家人数，并从我的数据中删除少于 2 个科学家的学科领域。

x= df.groupby('SubjectField')['scientist'].count()
ans = x[x > 2]

这是我的代码，但我不知道如何删除提到的行：

解决方法

你已经在正确的轨道上，我刚刚添加了删除不满足条件的行的代码

import pandas as pd

df = pd.DataFrame({'scientist':["Wendelaar Bonga"," Sjoerd E.","Grätzel"," Michael","Willett","Walter C.","Kessler","Ronald C.","Witten,Edward","Wang,Zhong Lin"],'SubjectField': ["Biomedical Engineering","Inorganic & Nuclear Chemistry","Organic Chemistry","Biomedical Engineering","Developmental Biology","Mechanical Engineering & Transports","Microbiology","Cardiovascular System & Hematology","Biomedical Engineering"]})


x = df.groupby('SubjectField')['scientist'].count()

您可以使用带有参数 drop 的 index 删除不符合条件的行

波浪号 ~ 用作否定以获取条件的反面

drop_idx = x[~(x > 2)].index.values
x = x.drop(index=drop_idx)

x 将只包含计数大于 2 的行

,

试试这个：

mask = df.groupby('SubjectField')['SubjectField'].transform('count') > 2
filtered = df[mask]