Understanding the Resample Loop in pandas.DataFrame

=====================================================

Introduction

The resample function in pandas is a powerful tool for aggregating data based on frequency, but its implementation can be complex and counterintuitive at times. In this article, we’ll delve into the details of how the resample loop works in pandas.DataFrame, exploring why Series are generated during the process.

Why Are There Series in the Resample Loop?

The Problem

When working with time series data, it’s common to apply custom functions to each group of values during resampling. However, when using apply within a resample function, unexpected results can occur. In the provided Stack Overflow question, the author is puzzled by the appearance of two Series in the resample loop.

Let’s examine the code and output to understand why this happens:

def btc_resample(df):
    if len(df) > 0:
        print(type(df))
        print(df)
        print(df['close'])
        print(df['high'])
        print(df['low'])
        ret = df.head(1).copy()
        ret['close'] = df['close'].values[-1]
        ret['high'] = df['high'].max()
        ret['low'] = df['low'].min()
        print(ret)
        return ret
    else:
        return None

The resample function is applied to the data like this:

data.resample('5min').apply(btc_resample)

The Resample Loop

When using resample with apply, pandas creates an iterator over the grouped data. Each iteration represents a group of values, which are then passed through the custom function (btc_resample in this case). This process can be represented as follows:

# Assume 'data' is a DataFrame representing the time series data.
# The resample function groups the data by frequency ('5min').

for index, group in data.resample('5min').iterrows():
    # Each iteration represents a group of values.
    # The custom function 'btc_resample' is applied to each group.
    result = btc_resample(group)
    
    # If 'result' is not None, it's assigned back to the original DataFrame.
    data.loc[index] = result

Printing Intermediate Results

In the btc_resample function, intermediate results are printed using print(ret). This causes the Series ret to be displayed twice: once before the return statement and again after.

def btc_resample(df):
    if len(df) > 0:
        print(type(df))  # Prints the type of 'df'
        print(df)  # Prints the DataFrame 'df'
        
        # These two prints occur before the return statement.
        ret = df.head(1).copy()
        ret['close'] = df['close'].values[-1]
        ret['high'] = df['high'].max()
        ret['low'] = df['low'].min()
        print(ret)  # This prints 'ret' twice
        
        return ret
    else:
        return None

Conclusion

The appearance of two Series in the resample loop is caused by the printing of intermediate results (print(ret)). When btc_resample returns, it assigns the result back to the original DataFrame using data.loc[index] = result. This assignment causes pandas to create a new Series from the returned data, which appears as the second printed Series.

Mitigating the Issue

To avoid printing intermediate results and reduce confusion during development, consider using logging or debug statements instead. For example:

import logging

def btc_resample(df):
    if len(df) > 0:
        logger = logging.getLogger(__name__)
        logger.debug(type(df))  # Logs the type of 'df'
        logger.debug(str(df))  # Logs the DataFrame 'df'
        
        ret = df.head(1).copy()
        ret['close'] = df['close'].values[-1]
        ret['high'] = df['high'].max()
        ret['low'] = df['low'].min()
        logger.debug(ret)  # Logs 'ret' without printing it
        return ret
    else:
        return None

By using logging statements, you can control the output of intermediate results and maintain a clean codebase.

Example Use Cases

The resample function is commonly used in time series analysis to aggregate data by frequency. Some examples include:

• Daily averages: Calculate the average temperature or humidity over daily intervals.
• Weekly highs/lows: Find the highest or lowest value within each week.
• Monthly aggregates: Compute aggregated metrics for each month.

When using resample, it’s essential to choose the correct frequency and apply relevant functions to process the data effectively. By understanding how the resample loop works, you can optimize your code and extract meaningful insights from your time series data.

Last modified on 2025-04-11