Write a program in Python to select any random odd index rows in a given DataFrame

In data analysis, you often need to randomly select rows from a DataFrame based on certain criteria. This tutorial shows how to select a random row from odd-indexed positions (1, 3, 5, etc.) in a Pandas DataFrame.

Sample DataFrame

Let's start with a sample DataFrame to demonstrate the concept ?

import pandas as pd
import random

df = pd.DataFrame({
    'id': [1, 2, 3, 4, 5, 6, 7],
    'mark': [70, 60, 40, 50, 80, 90, 60],
    'age': [12, 13, 12, 13, 12, 13, 12]
})

print("DataFrame is:")
print(df)

DataFrame is:
   id  mark  age
0   1    70   12
1   2    60   13
2   3    40   12
3   4    50   13
4   5    80   12
5   6    90   13
6   7    60   12

Method 1: Using Loop to Find Odd Indices

This approach iterates through all indices and collects odd-indexed positions ?

import pandas as pd
import random

df = pd.DataFrame({
    'id': [1, 2, 3, 4, 5, 6, 7],
    'mark': [70, 60, 40, 50, 80, 90, 60],
    'age': [12, 13, 12, 13, 12, 13, 12]
})

# Collect odd indices
odd_indices = []
for i in df.index.values:
    if i % 2 == 1:
        odd_indices.append(i)

# Select random odd index
random_index = random.choice(odd_indices)
print("Random odd index row is:")
print(df.iloc[random_index])

Random odd index row is:
id      4
mark    50
age     13
Name: 3, dtype: int64

Method 2: Using List Comprehension

A more concise approach using list comprehension to filter odd indices ?

import pandas as pd
import random

df = pd.DataFrame({
    'id': [1, 2, 3, 4, 5, 6, 7],
    'mark': [70, 60, 40, 50, 80, 90, 60],
    'age': [12, 13, 12, 13, 12, 13, 12]
})

# Get odd indices using list comprehension
odd_indices = [i for i in df.index if i % 2 == 1]
print("Odd indices:", odd_indices)

# Select random odd index
random_index = random.choice(odd_indices)
print("Selected index:", random_index)
print("Random odd index row:")
print(df.iloc[random_index])

Odd indices: [1, 3, 5]
Selected index: 5
Random odd index row:
id      6
mark    90
age     13
Name: 5, dtype: int64

Method 3: Direct Slicing with Random Selection

Use DataFrame slicing to get odd rows directly, then randomly select one ?

import pandas as pd
import random

df = pd.DataFrame({
    'id': [1, 2, 3, 4, 5, 6, 7],
    'mark': [70, 60, 40, 50, 80, 90, 60],
    'age': [12, 13, 12, 13, 12, 13, 12]
})

# Get all odd-indexed rows
odd_rows = df.iloc[1::2]  # Start from index 1, step by 2
print("All odd-indexed rows:")
print(odd_rows)

# Select random row from odd rows
random_row = odd_rows.sample(n=1)
print("\nRandom odd index row:")
print(random_row.iloc[0])

All odd-indexed rows:
   id  mark  age
1   2    60   13
3   4    50   13
5   6    90   13

Random odd index row:
id      2
mark    60
age     13
Name: 1, dtype: int64

Comparison

Conclusion

Use direct slicing with sample() for the most efficient approach. List comprehension offers a good balance of readability and performance. The loop method helps understand the logic but is less efficient for large datasets.