Finding the the first column with zero value for each row in python

Question

I have a dataframe which has Student IDs and their respective stage score.

I want to find the stage where student got dropped i.e. in which stage did the first zero score appeared. And then update the corresponding stage flag = 1 .Below is the sample data :

StuID | Stage1 | Stage2 | Stage3 | Stage4  | S1Flag |S2Flag |S3Flag | S4Flag
Ak    | 80.1   |  23.3  |    0   |    0    |   0    |  0    |  1    | 0 
XF    |   0    |  0     |    0   |    0    |   1    |  0    |  0    | 0
WE    |  23    |  34    |    43  |    34   |   0    |  0    |  0    | 0

For above Data , for StuID = 'AK' , the first zero appeared in stage 3 so S3 Flag is changed to 1 .For StuID = 'XF' , the first zero appeared in stage 1 , so S1 flag is updated as 1 . And goes o for other rows as well.

Hi Surya , I need help in writing the code . I sense some loops and updates but doesn't no how to implement them in python — Mighty
– Mighty, Commented May 31, 2018 at 8:08

jezrael · Accepted Answer · 2018-05-31 08:28:17Z

1

First filter only Stage columns, compare by 0, get cumulative sum, so if compare by 1 get mask for first 0:

m = df.filter(like='Stage').eq(0).cumsum(axis=1).eq(1)
print (m)
   Stage1  Stage2  Stage3  Stage4
0   False   False    True   False
1    True   False   False   False
2   False   False   False   False

Then filter Flag columns and set 1 by mask:

cols = df.filter(like='Flag').columns
df[cols] = df[cols].mask(m.values, 1)
print (df)
  StuID  Stage1  Stage2  Stage3  Stage4  S1Flag  S2Flag  S3Flag  S4Flag
0    Ak    80.1    23.3       0       0       0       0       1       0
1    XF     0.0     0.0       0       0       1       0       0       0
2    WE    23.0    34.0      43      34       0       0       0       0

Details:

  print (df.filter(like='Stage'))
   Stage1  Stage2  Stage3  Stage4
0    80.1    23.3       0       0
1     0.0     0.0       0       0
2    23.0    34.0      43      34

print (df.filter(like='Stage').eq(0))
   Stage1  Stage2  Stage3  Stage4
0   False   False    True    True
1    True    True    True    True
2   False   False   False   False

print (df.filter(like='Stage').eq(0).cumsum(1))
   Stage1  Stage2  Stage3  Stage4
0       0       0       1       2
1       1       2       3       4
2       0       0       0       0

print (df.filter(like='Stage').eq(0).cumsum(1).eq(1))
   Stage1  Stage2  Stage3  Stage4
0   False   False    True   False
1    True   False   False   False
2   False   False   False   False

edited May 31, 2018 at 8:28

answered May 31, 2018 at 8:08

jezrael

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4 Comments

Mighty Over a year ago

Thanks Jez, it worked. However , I need it dig in as to what .eq, .cumsum and mask functions are doing .

jezrael Over a year ago

@Mighty - added details for better explanation it.

Mighty Over a year ago

Thank you so much :) @jezrael

jezrael Over a year ago

@Mighty - Glad can help!

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