[英]How to delete rows with multiple conditions in pandas dataframe
import pandas as pd
import numpy as np
print df
I'm a newbie, I used pandas to process an excel file. 我是新手,我用熊猫来处理Excel文件。 I have a data frame like bellow
我有一个像波纹管这样的数据框
DAT_KEY IP DATA
01-04-19 10.0.0.1 3298329
01-04-19 10.0.0.1 0
02-04-19 10.0.0.1 3298339
02-04-19 10.0.0.1 0
01-04-19 10.0.0.2 3233233
01-04-19 10.0.0.2 0
01-04-19 10.0.0.3 0
I only want to delete the row when having same IP and DAT_KEY
and DATA=0
. 我只想删除具有相同IP且
DAT_KEY
和DATA=0
。 Don't want to delete row have DATA=0
, but DAT_KEY and IP unique. 不想删除具有
DATA=0
行,但DAT_KEY和IP唯一。
My expected outcome: 我的预期结果:
DAT_KEY IP DATA
01-04-19 10.0.0.1 3298329
02-04-19 10.0.0.1 3298339
01-04-19 10.0.0.2 3233233
01-04-19 10.0.0.3 0
I try with drop duplicates but it not suitable with my case 我尝试放置重复副本,但不适合我的情况
df = df.drop_duplicates()
Use 采用
groupby
- function is used to split the data into groups based on some criteria. groupby
函数用于根据某些条件将数据分为几组。 .first()
- Compute first of group values. .first()
-首先计算组值。 Ex. 防爆。
df = df.groupby(['DAT_KEY','IP'],as_index=False,sort=False).first()
print(df)
O/P: O / P:
DAT_KEY IP DATA
0 01-04-19 10.0.0.1 3298329
1 02-04-19 10.0.0.1 3298339
2 01-04-19 10.0.0.2 3233233
3 01-04-19 10.0.0.3 0
Maybe that's what you need: 也许这就是您需要的:
DAT_KEY IP DATA
0 01-04-19 10.0.0.1 3298329
1 01-04-19 10.0.0.1 0
2 02-04-19 10.0.0.1 3298339
3 02-04-19 10.0.0.1 0
4 01-04-19 10.0.0.2 3233233
5 01-04-19 10.0.0.2 0
6 01-04-19 10.0.0.3 0
7 01-04-19 10.0.0.1 99999
df.groupby(["DAT_KEY","IP"], as_index=False,sort=False).apply(lambda g: g if len(g)==1 else g[g["DATA"]!=0] ).reset_index(drop=True)
Out[94]:
DAT_KEY IP DATA
0 01-04-19 10.0.0.1 3298329
1 01-04-19 10.0.0.1 99999
2 02-04-19 10.0.0.1 3298339
3 01-04-19 10.0.0.2 3233233
4 01-04-19 10.0.0.3 0
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