Pandas DataFrame: Expanding Existing Dataset to Finer Timestamps

Introduction

When working with large datasets, it’s essential to optimize performance and efficiency. In this article, we’ll explore a technique for expanding an existing dataset in Pandas by creating finer timestamps.

Background

The itertuples() method is used to iterate over the rows of a DataFrame. It returns an iterator yielding tuple objects, which are more memory-efficient than Series or DataFrames. However, it’s not the most efficient way to perform this operation, especially when dealing with large datasets.

Why IterTuples Isn’t Ideal

When using itertuples(), the performance is affected by several factors:

• Memory usage: The tuples themselves require more memory than Series or DataFrames.
• Iteration overhead: Creating and iterating over tuple objects can be slower than working with Pandas objects directly.

Optimizing Timestamp Expansion

To improve performance when expanding existing datasets to finer timestamps, we’ll employ a different strategy. We’ll leverage the power of vectorized operations in Pandas to create new columns without requiring an explicit loop.

Step 1: Prepare Your Data

Before diving into the solution, ensure that your DataFrame is in a suitable format:

• Ensure all datetime-related columns are in the correct format.
• Check for any missing or NaN values in these columns.

Sample Data

For demonstration purposes, let’s create a sample DataFrame with our desired structure:

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {
    'PayPeriodEnding': ['2022-01-31', '2022-02-28', '2022-03-31'],
    'Hours': [10, 12, 14],
    'AmountPaid': [100, 200, 300]
}
df = pd.DataFrame(data)

# Convert datetime to datetime64[D] format
df['PayPeriodEnding'] = pd.to_datetime(df['PayPeriodEnding'], format='%Y-%m-%d')

print(df)

Step 2: Calculate Time Deltas and Create New Timestamps

To create new timestamps, we’ll calculate time deltas between each row. We can then use these differences to create a new timestamp for each day preceding the PayPeriodEnding date:

# Calculate time delta in days (D) format
df['TimeDelta'] = df.groupby('PayPeriodEnding')['PayPeriodEnding'].apply(lambda x: pd.to_timedelta(x).days)

# Create new timestamps by adding time deltas to PayPeriodEnding
df['NewTimestamp'] = df.apply(lambda row: row['PayPeriodEnding'] - pd.Timedelta(days=row['TimeDelta']), axis=1)

Step 3: Expand the DataFrame and Adjust Calculations

Now, we’ll create a new DataFrame with our desired structure. We can use Pandas’ join function to concatenate rows:

# Create a new DataFrame for expansion
new_df = pd.DataFrame({
    'Day': np.arange(len(df['PayPeriodEnding'])),  # Day index
    'Hours': df['Hours'].repeat(14),  # Hours repeated 14 times
    'AmountPaid': df['AmountPaid'].repeat(14),   # AmountPaid repeated 14 times
    'PayPeriodEnding': df['NewTimestamp']
})

print(new_df)

In the new_df DataFrame, we’ve created a new column for each day preceding the PayPeriodEnding date. We’ve also adjusted our calculations to reflect this change.

Step 4: Optimize Performance with Vectorized Operations

Now that we have our desired structure, let’s optimize performance by using vectorized operations:

# Create an array of time deltas (in days) for each row
time_deltas = np.arange(0, len(df['PayPeriodEnding'])).reshape(-1, 1)

# Calculate hours and amount paid divided by 14
new_df['HoursDivided'] = new_df['Hours'].div(14)
new_df['AmountPaidDivided'] = new_df['AmountPaid'].div(14)

# Create a new column for PayPeriodEnding in datetime64[D] format
new_df['PayPeriodEnding'] = pd.to_datetime(new_df['PayPeriodEnding'], format='%Y-%m-%d')

print(new_df)

In this optimized version, we’ve used NumPy arrays to perform calculations and vectorized operations to improve performance.

Conclusion

Expanding existing datasets to finer timestamps can be achieved using Pandas. By leveraging the power of vectorized operations, we can optimize performance and efficiency. This approach eliminates the need for explicit loops and provides a more scalable solution.

Last modified on 2025-02-26