# Assign same random value to a b b a pairs in python dataframe

When working with a Python dataframe, there may be instances where you need to assign the same random value to pairs of columns. This can be achieved in different ways, depending on your specific requirements. In this article, we will explore three different solutions to solve this problem.

## Solution 1: Using a Loop

``````import random
import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [5, 6, 7, 8]})

# Assign the same random value to a b b a pairs
for i in range(len(df)):
value = random.randint(1, 10)
df.at[i, 'a'] = value
df.at[i, 'b'] = value``````

In this solution, we use a loop to iterate over each row in the dataframe. We generate a random value using the `random.randint()` function and assign it to both ‘a’ and ‘b’ columns for each row. This ensures that the pairs have the same random value.

## Solution 2: Using the Apply Function

``````import random
import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [5, 6, 7, 8]})

# Define a function to assign the same random value to a b b a pairs
def assign_random_value(row):
value = random.randint(1, 10)
row['a'] = value
row['b'] = value
return row

# Apply the function to each row in the dataframe
df = df.apply(assign_random_value, axis=1)``````

In this solution, we define a function `assign_random_value()` that takes a row as input. Inside the function, we generate a random value and assign it to both ‘a’ and ‘b’ columns of the row. We then use the `apply()` function to apply this function to each row in the dataframe, effectively assigning the same random value to the pairs.

## Solution 3: Using Numpy

``````import numpy as np
import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [5, 6, 7, 8]})

# Generate an array of random values
values = np.random.randint(1, 10, len(df))

# Assign the same random value to a b b a pairs
df['a'] = values
df['b'] = values``````

In this solution, we use the `numpy.random.randint()` function to generate an array of random values. We specify the range of values and the length of the array, which is equal to the number of rows in the dataframe. We then assign this array of random values to both ‘a’ and ‘b’ columns of the dataframe, ensuring that the pairs have the same random value.

After exploring these three solutions, it is evident that Solution 3 using Numpy is the most efficient and concise approach. It leverages the power of Numpy to generate an array of random values in a single line of code, and then assigns this array to the desired columns of the dataframe. This eliminates the need for loops or applying functions to each row, resulting in improved performance.

In conclusion, if you need to assign the same random value to pairs of columns in a Python dataframe, Solution 3 using Numpy is the recommended approach.

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### 3 Responses

1. Anika Contreras says:

Solution 3: Using Numpy? More like Solution 3: Using Nump-yay for simple and efficient coding!

2. Manuel Peralta says:

Solution 2 using the Apply Function seems more elegant and efficient. Who needs loops? 🤷‍♂️

3. Naomi Berger says:

Solution 2 with the Apply Function seems more efficient here. Who knew? 😄