To calculate the number of times the two dataframe columns are equal to -1 at the same time-CodePudding

I have two dataframe columns containing sequences of 0 and -1. Using Python command “count” I can calculate the number of times the 1st column equal '-1' ( =3 times) and the number of times the 2nd column equals '-1' ( =2 times). Actually, I would like to calculate the number of times that both columns x and y are equal to '-1' simultaneously ( = it should be equal to 1 in the given example)(something like calculating: count = df1['x'][df1['x'] == df1['y'] == -1]. count() but I cannot put 2 conditions directly in command 'count'..). Is there a simpe way to do it (using count or some other workaround)? Thanks in advance!

import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
df1 = pd.DataFrame({
"x":     [0, 0, 0, -1 , 0, -1,  0, 0, 0, 0 , 0, 0,  0, -1, 0],
"y":     [0, 0, 0,  0 , 0, 0,  -1, 0, 0, 0 , 0, 0,  0, -1, 0],   
})
df1
    x   y
0   0   0
1   0   0
2   0   0
3   -1  0
4   0   0
5   -1  0
6   0   -1
7   0   0
8   0   0
9   0   0
10  0   0
11  0   0
12  0   0
13  -1  -1
14  0   0

count = df1['x'][df1['x'] == -1]. count()
count

3

count = df1['y'][df1['y'] == -1]. count()
count

2

CodePudding user response：

You can use eq all to get a boolean Series that returns True if both columns are equal to -1 at the same time. Then sum fetches the total:

out = df1[['x','y']].eq(-1).all(axis=1).sum()

Output:

CodePudding user response：

Sum x and y, and count the ones where they add to -2. ie both are -1

 (df1.x   df1.y).eq(-2).sum()
 1