NumPy Timedelta Arrays – Mastering Python

NumPy’s timedelta64 dtype offers a powerful way to handle time differences within your Python code, especially when working with large datasets. Unlike standard Python’s datetime.timedelta objects, timedelta64 arrays offer the speed and efficiency of NumPy’s vectorized operations, making them ideal for time series analysis and manipulation. This post dives deep into working with NumPy Timedelta arrays, demonstrating their capabilities with clear examples.

Creating Timedelta Arrays

Creating timedelta64 arrays is straightforward. You can specify the unit (e.g., ‘D’ for days, ‘h’ for hours, ‘m’ for minutes, ‘s’ for seconds, ‘ms’ for milliseconds, ‘us’ for microseconds, ‘ns’ for nanoseconds) directly within the array creation:

import numpy as np

timedeltas = np.array([1, 2, 3], dtype='timedelta64[D]')  #Days
print(timedeltas)

timedeltas_hours = np.array([1, 2, 3], dtype='timedelta64[h]') #Hours
print(timedeltas_hours)


#Creating from a list of strings
timedeltas_strings = np.array(['1D', '2D', '3D'], dtype='timedelta64')
print(timedeltas_strings)

#From a list of existing timedelta64 objects

import datetime
td_list = [datetime.timedelta(days=1), datetime.timedelta(days=2), datetime.timedelta(days=3)]
timedeltas_from_list = np.array(td_list)
print(timedeltas_from_list)

Performing Arithmetic Operations

The real power of timedelta64 shines through its ability to perform arithmetic operations efficiently on entire arrays:

result_add = timedeltas + np.array([4, 5, 6], dtype='timedelta64[D]')
print(result_add)

result_sub = timedeltas - np.array([1, 1, 1], dtype='timedelta64[D]')
print(result_sub)


result_mul = timedeltas * 2
print(result_mul)

result_div = timedeltas / 2 #this will result in a float
print(result_div)

Converting Units

You can easily convert between different time units using NumPy’s casting abilities:

days_to_hours = timedeltas.astype('timedelta64[h]')
print(days_to_hours)

hours_to_seconds = days_to_hours.astype('timedelta64[s]')
print(hours_to_seconds)

Combining with datetime64

timedelta64 arrays work seamlessly with datetime64 arrays, enabling powerful date and time calculations:

dates = np.array(['2024-03-10', '2024-03-11', '2024-03-12'], dtype='datetime64')
future_dates = dates + timedeltas
print(future_dates)

Handling Missing Values

NumPy’s timedelta64 also gracefully handles missing values represented by NaT (Not a Time):

timedeltas_with_nan = np.array([1, 2, np.nan], dtype='timedelta64[D]')
print(timedeltas_with_nan)

result_nan = timedeltas_with_nan + np.array([4, 5, 6], dtype='timedelta64[D]')
print(result_nan)

Beyond the Basics: Advanced Usage

NumPy’s timedelta64 arrays integrate well with other NumPy functions and features, allowing for complex time series analysis, including aggregations, filtering, and more. Explore NumPy’s documentation for a complete understanding of its capabilities. This only scratches the surface of what you can achieve with timedelta64 for efficient time-based computations.