Python dataframe drop negative values in multiple columns-CodePudding

I want drop negative values in some columns of the dataframe. My code:

ad = pd.DataFrame({'A':[-1,2,3,4],'B':[5,-6,7,8],'C':[1,-2,0,1]})

    A   B   C
0   -1  5   1
1   2   -6  -2
2   3   7   0
3   4   8   -1

I want to drop negative value rows in A and B columns

Expected result:

ad = 
    A   B   C
1   3   7   0
2   4   8   -1

Present solution:

ad = ad[ad[['A','B']]>0]
     A       B       C
0   NaN     5.0     NaN
1   2.0     NaN     NaN
2   3.0     7.0     NaN
3   4.0     8.0     NaN

ad.dropna(how='any',inplace=True)
ad = []

CodePudding user response：

With all you can check whether all elements in a row or column are true. You can use this in a filter on a subset of columns:

import pandas as pd
ad = pd.DataFrame({'A':[-1,2,3,4],'B':[5,-6,7,8],'C':[1,-2,0,-1]})
ad[(ad[['A','B']] > 0).all(1)]

Output:

	A	B	C
2	3	7	0
3	4	8	-1

CodePudding user response：

For an exclusively Pandas solution, use .loc to filter out by boolean expressions.

The line of code:

ad.loc[(ad['A'] > 0) & (ad['B'] > 0)]

should filter out all rows corresponding to columns with negative A and negative B. If you want to reset the index as you did in your expected output, then just put .reset_index(drop=True) at the end of the above expression.

Using this on your example, here's what I get:

ad = pd.DataFrame({'A':[-1,2,3,4],'B':[5,-6,7,8],'C':[1,-2,0,1]})

ad.loc[(ad['A'] > 0) & (ad['B'] > 0)]

results in:

    A   B   C
2   3   7   0
3   4   8   1

Then doing

ad.loc[(ad['A'] > 0) & (ad['B'] > 0)].reset_index(drop=True)

results in:

    A   B   C
1   3   7   0
2   4   8   1