问题描述
pandas drop_duplicates 函数非常适合唯一化"数据帧.但是,要传递的关键字参数之一是 take_last=True 或 take_last=False,而我想删除在列子集中重复的所有行.这可能吗?
The pandas drop_duplicates function is great for "uniquifying" a dataframe. However, one of the keyword arguments to pass is take_last=True or take_last=False, while I would like to drop all rows which are duplicates across a subset of columns. Is this possible?
A B C 0 foo 0 A 1 foo 1 A 2 foo 1 B 3 bar 1 A
例如,我想删除与列 A 和 C 匹配的行,所以这应该删除第 0 行和第 1 行.
As an example, I would like to drop rows which match on columns A and C so this should drop rows 0 and 1.
推荐答案
现在有了 drop_duplicates 和 keep 参数.
This is much easier in pandas now with drop_duplicates and the keep parameter.
import pandas as pd df = pd.DataFrame({"A":["foo", "foo", "foo", "bar"], "B":[0,1,1,1], "C":["A","A","B","A"]}) df.drop_duplicates(subset=['A', 'C'], keep=False)