Sampling from a DataFrame with Variable Sample Sizes per Customer

Sampling from a DataFrame with Variable Sample Sizes per Customer

When working with data, it’s not uncommon to encounter scenarios where the sample size varies for each customer or group. In this post, we’ll explore how to achieve this in Python using the pandas and NumPy libraries.

Introduction

Suppose you have a dataset containing information about customers, including their IDs, names, and other relevant details. You also have another DataFrame that stores the sample sizes for each customer. Your goal is to create a new DataFrame with randomly sampled rows from the original dataset, ensuring that the sample size for each customer matches the corresponding value in the sample size DataFrame.

The Problem

The provided code snippet demonstrates how to achieve this using the groupby and apply functions. However, it relies on setting a global constant or hardcoding the values, which may not be ideal if you want to use different sample sizes for each customer.

Step 1: Define the Sample Size Mapper

To overcome this limitation, we can create a dictionary that maps each customer ID to their corresponding sample size. This will allow us to dynamically set the sample size for each row based on the customer ID.

# Create a sample size mapper from df2 with sample size and use it as a reference
df2 = pd.DataFrame({'Customer': ['A', 'B', 'A'], 'Day': [1, 3, 2]})
sample_size_mapper = df2.set_index('Customer')['Day'].to_dict()

Step 2: Apply the Sampling Function

We can now use this sample size mapper to create a new DataFrame with randomly sampled rows. We’ll apply a custom function that uses the groupby and apply functions to achieve this.

# Define the sampling function using the sample size mapper
def sampling(frame):
    return np.random.choice(frame['Id'], size=sample_size_mapper[frame.name])

# Create a new DataFrame with randomly sampled rows
df = pd.DataFrame({'Customer': ['A', 'B', 'A', 'A', 'B', 'B'], 
                   'Day': [1, 3, 2, 1, 4, 1],
                   'Id': [0, 1, 2, 0, 5, 6]})
grouped = df.groupby(['Customer', 'Day'])
sampled_df = grouped.apply(sampling).reset_index()

Step 3: Verify the Results

The resulting sampled_df should have the same customer IDs and days as the original DataFrame, but with randomly sampled rows.

# Print the first few rows of the sampled DataFrame
print(sampled_df.head())

Step 4: Handle Multi-Indexing

If you’re working with a multi-indexed DataFrame (i.e., one with multiple levels in its index), you may encounter issues when trying to use the sample function. In this case, you’ll need to reset the index after sampling.

# Reset the index of the sampled DataFrame if it's multi-indexed
if len(sampled_df.index.names) > 1:
    sampled_df = sampled_df.reset_index(drop=True)

Conclusion

In this post, we demonstrated how to sample from a DataFrame with variable sample sizes per customer using pandas and NumPy. By creating a sample size mapper and applying a custom function using groupby and apply, you can achieve flexible and dynamic sampling in your data analysis tasks.

Additional Considerations

Handling Missing Values: If there are missing values in the sample size DataFrame, make sure to handle them properly when creating the sample size mapper.
Using Different Sampling Strategies: Depending on your specific use case, you may want to explore other sampling strategies, such as stratified sampling or resampling with replacement.

Step 1: Define the Sample Size Mapper

Step 4: Handle Multi-Indexing

Additional Considerations

Further Reading