looping through rows of pandas when the equation also changes-CodePudding

I need to ignore timestamps and loop through rows this way.

import pandas as pd
import numpy as np

time = ['11:50', '12:50', '13:50']
data_1 = {'time': time,
          'n1': [1, 5, 8],
          'n2': [2, 6 ,7],
          'n3': [3, 7 ,6],
          'n4': [4, 8, 5],
        }

df1 = pd.DataFrame(data = data_1)
df1

I am trying to multiply:

row 1 * (10^0)
row 2 * (10^1)
row 3 * (10^2)
...
row n * (10^(n-1))

Before:

	time	n1	n2	n3	n4
0	11:50	1	2	3	4
1	12:50	`5`	`6`	`7`	`8`
2	13:50	8	7	6	5

Expected result:

	time	n1	n2	n3	n4
0	11:50	1	2	3	4
1	12:50	50	60	70	80
2	13:50	800	700	600	500

CodePudding user response：

You can use mul on index axis:

df1.iloc[:, 1:] = df1.iloc[:, 1:].mul(10**df1.index, axis=0)
print(df1)

# Output
    time   n1   n2   n3   n4
0  11:50    1    2    3    4
1  12:50   50   60   70   80
2  13:50  800  700  600  500

You can replace df1.index by np.arange(len(df1)) if your index is not a RangeIndex.

CodePudding user response：

You can try using this also numpy broadcasting and get_indexer,

df1.iloc[:, 1:] *= 10**df1.index.get_indexer(df1.index)[:, None]
df1

Output

    time   n1   n2   n3   n4
a  11:50    1    2    3    4
b  12:50   50   60   70   80
c  13:50  800  700  600  500

Works with none standard unique indexing:

Given df,

time = ['11:50', '12:50', '13:50']
data_1 = {'time': time,
          'n1': [1, 5, 8],
          'n2': [2, 6 ,7],
          'n3': [3, 7 ,6],
          'n4': [4, 8, 5],
        }

df1 = pd.DataFrame(data = data_1, index=[*'abc'])
df1