Alternative Approaches to Align Time Stamps with Microsecond Intervals in Pandas

• is_on_offset(dt)
  This method takes a datetime object (dt) as input and returns a boolean value. It checks if the microseconds component of the dt timestamp falls exactly on a microsecond boundary (i.e., if the microseconds are zero).
• Micro
  This specific offset represents a microsecond (one-millionth of a second).
• Data offsets
  These are tools in pandas for representing and manipulating time series data. They define how you move along a date or time index. Examples include days, months, years, hours, minutes, etc.

In simpler terms

Imagine a ruler where each tick mark represents a microsecond. is_on_offset checks if a specific point in time (represented by the datetime object) falls exactly on a tick mark (a microsecond boundary) on that ruler.

Here are some key points to remember:

• For Micro, it only considers the microseconds part of the timestamp.
• It's not specific to the Micro class. The is_on_offset method is available for most offset classes in pandas (e.g., Day, MonthEnd, etc.) to check against their respective frequencies.
• This method is useful for checking if a timestamp aligns with microsecond intervals.

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import pandas as pd

# Create timestamps
dt1 = pd.Timestamp('2024-06-21 07:32:10.123456')  # Microseconds not on a boundary
dt2 = pd.Timestamp('2024-06-21 07:32:10.000000')  # Microseconds on a boundary (zero)

# Create a Micro offset
micro_offset = pd.Micro()

# Check if timestamps are on a microsecond boundary
result1 = micro_offset.is_on_offset(dt1)  # False (not on boundary)
result2 = micro_offset.is_on_offset(dt2)  # True (on boundary)

print(f"dt1 (123456 microseconds): {result1}")
print(f"dt2 (000000 microseconds): {result2}")

This code defines two timestamps:

• dt2 has microseconds set to zero.
• dt1 has microseconds set to a non-zero value (123456).

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1. Using modulo operator (%)
   

This approach works for offsets with a fixed frequency (e.g., days, hours, minutes). You can calculate the remainder after dividing the desired part of the timestamp (e.g., microseconds for Micro) by the offset frequency. If the remainder is zero, the timestamp aligns with the offset.

import pandas as pd

dt = pd.Timestamp('2024-06-21 07:32:10.123456')
micro_freq = 1000000  # One million microseconds (one second)

# Check if microseconds are a multiple of the frequency
is_on_boundary = (dt.microsecond % micro_freq) == 0

print(f"dt microseconds on a one-second boundary: {is_on_boundary}")

1. Datetime attribute comparisons
   

This approach utilizes built-in datetime object attributes to compare specific parts (e.g., microsecond, minute, hour) with zero. It's suitable for checking if a specific part is zero.

import pandas as pd

dt = pd.Timestamp('2024-06-21 07:32:10.000000')

# Check if microseconds are zero
is_on_boundary = dt.microsecond == 0

print(f"dt microseconds are zero: {is_on_boundary}")

• If you specifically want to check if a particular part (like microseconds) is zero, datetime attribute comparisons are simpler.
• If you need to check for offsets with a fixed frequency (like days, hours), the modulo operator approach is efficient.