Unlock the Power of Random Sampling in Pandas

Understanding the sample() Method

The sample() method in Pandas is a powerful tool for extracting meaningful insights from large datasets. It allows you to select a representative subset of data from your entire dataset, giving you a new DataFrame containing the randomly selected rows or columns.

The sample() method takes several optional arguments that give you fine-grained control over the sampling process:

• n: specifies the number of random samples to select
• frac: specifies the fraction of the DataFrame to sample (between 0 and 1)
• replace: a boolean that determines if sampling should be with replacement or not
• weights: allows assigning different probabilities to rows for weighted sampling
• random_state: an integer for controlling randomness

Selecting Random Rows

Let’s start with a simple example. Suppose we have a DataFrame df and we want to randomly select 3 rows from it. We can use the sample() method with n=3 to achieve this.

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]})

# select 3 random rows
sampled_rows = df.sample(n=3)

print(sampled_rows)

Selecting a Fraction of Rows

What if we want to select a fraction of the rows instead? We can use the frac parameter to achieve this.

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]})

# select 30% of the rows
sampled_fraction = df.sample(frac=0.3)

print(sampled_fraction)

Sampling with Replacement

Sometimes, you might want to allow the same row to be selected multiple times. This is known as sampling with replacement. By setting replace=True, you can enable this behavior.

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]})

# select 5 rows with replacement
sampled_with_replacement = df.sample(n=5, replace=True)

print(sampled_with_replacement)

Controlling Randomness

When working with random sampling, it’s essential to have control over the randomness. The random_state argument allows you to set a specific random seed, ensuring that the same random sample is generated whenever you use this seed.

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]})

# select 3 random rows with controlled randomness
sampled_with_control = df.sample(n=3, random_state=42)

print(sampled_with_control)

Weighted Sampling for Biased Data Selection

In some cases, you might have biased data, where certain rows have a higher probability of being selected. Weighted sampling allows you to assign different probabilities to each row.

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]})

# create a list of weights
weights_list = [0.1, 0.2, 0.3, 0.2, 0.2]

# select 2 random rows with weighted sampling
sampled_with_weights = df.sample(n=2, weights=weights_list)

print(sampled_with_weights)