Python Pandas - Return a numpy timedelta64 array scalar view in nanoseconds

To return a numpy timedelta64 array scalar view in nanoseconds, use the timedelta.asm8 property in Pandas. This property provides a direct numpy representation of the timedelta object.

At first, import the required libraries ?

import pandas as pd

Understanding Timedelta and asm8

The asm8 property returns a numpy.timedelta64 scalar, which represents time duration in nanoseconds internally. This is useful when you need to work with the underlying numpy representation.

Example

Following is the code ?

import pandas as pd

# TimeDeltas is Python's standard datetime library uses a different representation timedelta's
# create a Timedelta object
timedelta = pd.Timedelta('10 min 20 s')

# display the Timedelta
print("Timedelta...\n", timedelta)

# getting the timedelta64 in nanoseconds
res = timedelta.asm8

# display the timedelta64
print("\nTimedelta64 array scalar view...\n", res)

# display the type to confirm it's numpy.timedelta64
print("\nType:", type(res))

Output

This will produce the following output ?

Timedelta...
 0 days 00:10:20

Timedelta64 array scalar view...
 620000000000

Type: <class 'numpy.timedelta64'>

Working with Different Time Units

You can create timedeltas with various time units and convert them to nanoseconds ?

import pandas as pd

# Create timedeltas with different units
td1 = pd.Timedelta('1 hour')
td2 = pd.Timedelta('30 seconds')
td3 = pd.Timedelta('2 days 3 hours 15 minutes')

print("1 hour in nanoseconds:", td1.asm8)
print("30 seconds in nanoseconds:", td2.asm8)
print("2 days 3 hours 15 minutes in nanoseconds:", td3.asm8)

1 hour in nanoseconds: 3600000000000
30 seconds in nanoseconds: 30000000000
2 days 3 hours 15 minutes in nanoseconds: 183900000000000

Conclusion

The asm8 property provides a convenient way to access the underlying numpy.timedelta64 representation of Pandas Timedelta objects. This is particularly useful for numerical computations and interfacing with numpy arrays that work with time durations.