本文介绍了在 Python Pandas 中删除多列中的所有重复行的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着跟版网的小编来一起学习吧！
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)
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