how to iterate over rows in pandas and get specific but different column name

Question

The data is:

name day1 day2 day3 day4
anshu  1   .     1    1
Yash   1   1     .    1
Natasha 1  1     1    .

_1st_absent_on : showing which day the person was 1st absent on.

For example, for Anshu , this variable will take the value 2, and for Natasha this variable will take the value 4.

Any help would be greatly appreciated

Answer 1

import pandas as pd
data = pd.read_csv(filePath,header=None)

data['_1st_absent_on'] = None
for row in range(1, data.shape[0]):
   for col in range(data.shape[1]-1):
      #print(data[col][row])
      if data[col][row] == '.':
          print('{} 1st absent on: {}'.format(data[0][row], data[col][0]))
          data.iloc[row, data.shape[1]-1] = col+1
          break

      

Answer 2

One way to do it is to iterate over the rows, then use numpy to fetch the rows where they are absent on. Let's say that that absent is a 0

import pandas as pd import numpy as np

d = {'fname': ['anshu', 'arth', 'natasha', 'saurav'], 'day1': [1, 1, 1, 1], 'day2': [0, 1, 1, 1], 'day3': [1, 0, 1, 1], 'day4': [1, 1, 0, 1]}
df = pd.DataFrame(data=d)

for i, row in df.iterrows():
    print(row['fname'], np.where(row[1:] == 0))

This will print a list of indexes where the user has been absent.

Disclaimer Im not an expert on pandas so there is probably a better way of doing this but this is what I could think of atop of my head.

Answer 3

as you can can possible have multiple absences per row we can melt the df and take the last day.

df['lastabscence'] = df["name"].map(
    pd.melt(df, id_vars="name")
    .query('value == "."')
    .groupby("name")["variable"].last()
)



      name  day1 day2 day3 day4 lastabscence
0    anshu     1    .    .    1         day3
1     Yash     1    1    .    1         day3
2  Natasha     1    1    1    .         day4