5 Best Ways to Change Frequency from Seconds to Hours in Python pandas

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💡 Problem Formulation: In data analysis, time series data is often recorded in varying granularities such as seconds, minutes, or hours. Transforming this data into a uniform frequency is crucial for meaningful analysis. This article explores how to change the frequency of a given pandas Period object from seconds to an hourly frequency. For instance, transforming an input Period object defined per second ‘2023-03-01 13:01:05’ into an output Period object with an hourly frequency ‘2023-03-01 13:00’.

Method 1: Using the asfreq() Method

This method utilizes pandas’ own asfreq() function to resample the Period object to the desired frequency. The function is specifically designed to convert Period objects from one frequency to another, ensuring preservation of the period’s anchor point while adjusting its granularity.

Here’s an example:

import pandas as pd

# Create a Period object with secondly frequency
secondly_period = pd.Period('2023-03-01 13:01:05', freq='S')

# Change the frequency to hourly
hourly_period = secondly_period.asfreq('H')
print(hourly_period)

Output:

2023-03-01 13:00

This snippet creates a Period object representing a point in time with a secondly frequency. It then uses the asfreq() method to change the frequency to hourly. The resulting output shows the period beginning at hour 13 on March 1st, 2023.

Method 2: Resampling with a Custom Function

By creating a custom function, one can manage more complex scenarios and perform additional operations when changing the frequency. The custom function can be applied to a pandas Series of Period objects, enabling batch conversion.

Here’s an example:

import pandas as pd

# Custom function to change period frequency
def to_hourly(period):
    return period.asfreq('H')

# Create a Period object with secondly frequency
secondly_period = pd.Period('2023-03-01 13:01:05', freq='S')

# Apply custom function to change to hourly frequency
hourly_period = to_hourly(secondly_period)
print(hourly_period)

Output:

2023-03-01 13:00

This code defines a function that calls asfreq() for changing the frequency of a given period object to hourly. The function is demonstrated on a single Period object but can be applied to a pandas Series using the apply() method for multiple conversions.

Method 3: Period Arithmetic

Period objects in pandas support arithmetic operations. Using division or floor division by the number of seconds in an hour can provide a simple way to convert the frequency from seconds to hours.

Here’s an example:

import pandas as pd

# Constants
SECONDS_IN_HOUR = 3600

# Create a Period object with secondly frequency
secondly_period = pd.Period('2023-03-01 13:01:05', freq='S')

# Convert to hourly frequency using division
hourly_period = secondly_period // SECONDS_IN_HOUR * SECONDS_IN_HOUR
hourly_period = pd.Period(hourly_period, freq='S').asfreq('H')

print(hourly_period)

Output:

2023-03-01 13:00

The code creates a Period object with secondly frequency, then divides and multiplies by the number of seconds in an hour to round down to the nearest hour. The result is then converted back into a Period object at hourly frequency using asfreq().

Method 4: The to_timestamp() and to_period() Combo

Transforming a Period object to a Timestamp with to_timestamp() and then back to a Period with to_period() allows for flexible frequency adjustments, accommodating edge cases where direct conversion is not possible.

Here’s an example:

import pandas as pd

# Create a Period object with secondly frequency
secondly_period = pd.Period('2023-03-01 13:01:05', freq='S')

# Convert to Timestamp and then to Period with hourly frequency
hourly_timestamp = secondly_period.to_timestamp()
hourly_period = hourly_timestamp.to_period('H')

print(hourly_period)

Output:

2023-03-01 13:00

This snippet first converts the Period object into a Timestamp to remove frequency information. It then converts the Timestamp back into a Period, specifying hourly frequency during the conversion.

Bonus One-Liner Method 5: Lambda Function with asfreq()

A lambda function provides a quick, one-line solution for applying the frequency change to a pandas Series or DataFrame column of Period objects, without needing to define a separate function.

Here’s an example:

import pandas as pd

# Create a pandas Series of Period objects with secondly frequency
secondly_periods = pd.Series([pd.Period('2023-03-01 13:01:05', freq='S'), 
                              pd.Period('2023-03-01 13:02:10', freq='S')])

# Convert all periods to hourly frequency using a lambda function
hourly_periods = secondly_periods.apply(lambda p: p.asfreq('H'))

print(hourly_periods)

Output:

0   2023-03-01 13:00
1   2023-03-01 13:00
dtype: period[H]

The one-liner uses a lambda function to apply the asfreq() method to each element in a pandas Series of Period objects, changing the frequency to hourly for all of them.

Summary/Discussion

  • Method 1: asfreq() Method. Straightforward and built-in. May not handle complex conversion scenarios.
  • Method 2: Custom Function. Allows for flexibility and additional operations. Requires extra coding compared to direct methods.
  • Method 3: Period Arithmetic. Low-level control over the conversion process. Less intuitive and may be prone to errors with complex time calculations.
  • Method 4: Combo of to_timestamp() and to_period(). Versatile for unusual cases. Involves two conversion steps, which could be less efficient.
  • Method 5: Lambda Function. Quick and efficient for applying frequency changes to pandas Series. Limited to simple use cases without additional logic.