Python 根据熊猫中的条件删除行
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delete rows based on a condition in pandas
提问by Shiva Krishna Bavandla
I have the below dataframe
我有以下数据框
In [62]: df
Out[62]:
coverage name reports year
Cochice 45 Jason 4 2012
Pima 214 Molly 24 2012
Santa Cruz 212 Tina 31 2013
Maricopa 72 Jake 2 2014
Yuma 85 Amy 3 2014
Basically i can filter the rows as below
基本上我可以过滤如下行
df[df["coverage"] > 30
and i can drop/delete a single row as below
我可以删除/删除单行，如下所示
df.drop(['Cochice', 'Pima'])
But i want to delete a certain number of rows based on a condition, how can i do so?
但是我想根据条件删除一定数量的行，我该怎么做？
回答by jezrael
The best is boolean indexingbut need invert condition - get all values equal and higher as 72:
最好的是boolean indexing但需要反转条件 - 使所有值相等且更高72：
print (df[df["coverage"] >= 72])
coverage name reports year
Pima 214 Molly 24 2012
Santa Cruz 212 Tina 31 2013
Maricopa 72 Jake 2 2014
Yuma 85 Amy 3 2014
It is same as gefunction:
它与ge功能相同：
print (df[df["coverage"].ge(72)])
coverage name reports year
Pima 214 Molly 24 2012
Santa Cruz 212 Tina 31 2013
Maricopa 72 Jake 2 2014
Yuma 85 Amy 3 2014
Another possible solution is invert mask by ~:
另一种可能的解决方案是通过~以下方式反转掩码：
print (df["coverage"] < 72)
Cochice True
Pima False
Santa Cruz False
Maricopa False
Yuma False
Name: coverage, dtype: bool
print (~(df["coverage"] < 72))
Cochice False
Pima True
Santa Cruz True
Maricopa True
Yuma True
Name: coverage, dtype: bool
print (df[~(df["coverage"] < 72)])
coverage name reports year
Pima 214 Molly 24 2012
Santa Cruz 212 Tina 31 2013
Maricopa 72 Jake 2 2014
Yuma 85 Amy 3 2014
回答by qaiser
we can use pandas.query() functionality as well
我们也可以使用 pandas.query() 功能
import pandas as pd
dict_ = {'coverage':[45,214,212,72,85], 'name': ['jason','Molly','Tina','Jake','Amy']}
df = pd.DataFrame(dict_)
print(df.query('coverage > 72'))
