Data Sampling with Pandas: A Flexible Approach

In data analysis and machine learning, it’s often necessary to randomly select a subset of rows from a dataset. This can be useful for generating training datasets, testing models, or creating mock datasets for research purposes. In this article, we’ll explore how to use pandas, a popular Python library for data manipulation and analysis, to achieve this task.

Understanding the Problem

The problem statement requires us to randomly select n rows from a DataFrame with certain constraints:

Select at least 3 and maximum 5 values from each category.
For a specific category (Type), select at least 2 and maximum 3 values, while the remaining categories should have at least 1 value.
The sum of credits remaining should not exceed a specified threshold X.

We’ll also need to generate multiple samples based on these conditions.

Setting Up the Problem

To tackle this problem, we’ll use pandas, numpy, and the scipy library for random number generation. We’ll assume that you have a DataFrame df containing your data.

import pandas as pd
import numpy as np
from scipy import stats

Step 1: Define Constraints and Thresholds

First, let’s define the minimum and maximum counts for each category, as well as the threshold for credits remaining.

# Define constraints
min_counts = {'Category A': 3, 'Category B': 5}
max_counts = {'Category C': 10, 'Category D': 15}

# Set threshold
credits_threshold = 1000

Step 2: Filter Data Based on Constraints

Next, we’ll filter our DataFrame to only include rows where the counts meet the minimum and maximum requirements.

# Initialize an empty list to store valid data
valid_data = []

for index, row in df.iterrows():
    # Initialize a flag to indicate whether this row is valid
    is_valid = True

    # Check if each category meets its count constraints
    for column, counts in min_counts.items():
        if row[column] < counts:
            is_valid = False
            break

    if not is_valid:
        continue

    for column, counts in max_counts.items():
        if row[column] > counts:
            is_valid = False
            break

    # If the row meets all constraints, add it to valid data
    if is_valid:
        valid_data.append(row)

Step 3: Apply Type-Specific Constraints and Calculate Credits Remaining

Now, let’s apply the type-specific constraints and calculate the credits remaining for each valid row.

# Initialize a list to store rows after applying type-specific constraints
type_constrained_data = []

for index, row in valid_data:
    # Check if this row meets Type=2 & 3 constraints
    if (row['Type'] == 2) and (row['Category A'] + row['Category B']) < min_counts['Category C']:
        continue

    if (row['Type'] == 3) and (row['Category A'] + row['Category B']) < min_counts['Category D']:
        continue

    # If the row meets Type-specific constraints, add it to type-constrained data
    type_constrained_data.append(row)

# Calculate credits remaining for each valid row
df['Credits Remaining'] = df.apply(lambda row: max(0, 100 * (row['Credit Value'] / row['Total Credit']), 1), axis=1)

Step 4: Randomly Select n Rows and Generate Multiple Samples

Finally, we can randomly select n rows from our type-constrained data. To generate multiple samples based on these selected rows, you could use techniques such as bootstrapping or resampling.

import random

# Set seed for reproducibility
random.seed(42)

# Randomly select n rows
n = 10  # change this value to set the desired sample size
sample_rows = random.sample(type_constrained_data, n)

# Generate multiple samples based on selected rows (e.g., using bootstrapping)
def generate_samples(sample_rows):
    # Initialize an empty list to store generated samples
    samples = []

    for _ in range(10):  # change this value to set the desired sample size
        # Randomly select a subset of columns from the DataFrame
        selected_columns = random.sample(df.columns, n)

        # Select rows randomly from the sample_rows and create new DataFrame
        df_sample = pd.DataFrame(
            {column: [random.choice(row[column]) for column in selected_columns] for row in sample_rows},
            index=range(len(sample_rows))
        )

        # Add generated sample to list of samples
        samples.append(df_sample)

    return samples

# Generate multiple samples based on selected rows
generated_samples = generate_samples(sample_rows)

Putting it All Together: Saving Samples as CSV Files

Now that we’ve generated multiple samples, let’s save them as CSV files.

import pandas as pd

for i, sample in enumerate(generated_samples):
    # Save the sample to a CSV file
    sample.to_csv(f"sample_{i+1}.csv", index=False)

By following these steps and using these techniques, you can effectively randomize rows from your DataFrame based on specific constraints and generate multiple samples for further analysis or use in machine learning models.

Last modified on 2023-12-21