Converting Python Pandas Timedeltas to Numpy timedelta64 Scalars in Nanoseconds

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💡 Problem Formulation: When working with time data in Python, it’s common to use Pandas to manipulate timeseries and timedeltas. However, there are certain cases when you need to convert a Pandas timedelta object into a NumPy timedelta64 scalar in nanoseconds to perform more fine-grained or interoperable operations. For example, if you have a Pandas Series of timedeltas and you need to get an array of nanoseconds to pass to a fast NumPy computation function. Here, we explore different methods to achieve this.

Method 1: Using astype method

This method involves using the Pandas Series method astype to cast the timedelta objects to ‘timedelta64[ns]’, which returns a NumPy array of timedeltas in nanoseconds. It is straightforward and uses built-in functionality provided by Pandas.

Here’s an example:

import pandas as pd
import numpy as np

# Create a Pandas Series of timedelta objects
timedelta_series = pd.Series([pd.Timedelta(days=1), pd.Timedelta(days=2)])

# Convert to NumPy timedelta64 array in nanoseconds
numpy_timedelta64_ns_array = timedelta_series.astype('timedelta64[ns]')

Output:

array([86400000000000, 172800000000000], dtype='timedelta64[ns]')

This code snippet first creates a Pandas Series with two timedelta objects representing 1 and 2 day(s), respectively. By calling .astype('timedelta64[ns]') on the series, it gets converted to a NumPy array of timedelta64 scalar values in nanoseconds.

Method 2: Accessing the values property

This method retrieves the underlying NumPy array from a Pandas Series by accessing its values property. The default behavior is to return the timedelta values as timedelta64[ns] scalars without any additional conversions.

Here’s an example:

import pandas as pd
import numpy as np

# Create a Pandas Series of timedelta objects
timedelta_series = pd.Series([pd.Timedelta(hours=3), pd.Timedelta(hours=5)])

# Extract NumPy timedelta64 array in nanoseconds
numpy_timedelta64_ns_array = timedelta_series.values

Output:

array([10800000000000, 18000000000000], dtype='timedelta64[ns]')

In the example, a Pandas Series is constructed with time deltas of 3 hours and 5 hours. By using the values property, the series is turned into an array of NumPy timedelta64 scalar values, expressed in nanoseconds.

Method 3: Using the dt accessor with total_seconds()

For cases where you start with the count of the total seconds in each timedelta and want to convert this to nanoseconds, use Panda’s dt accessor followed by total_seconds(), multiplied by the number of nanoseconds in a second (10**9) to manually construct the equivalent nanoseconds array.

Here’s an example:

import pandas as pd
import numpy as np

# Create a Pandas Series of timedelta objects
timedelta_series = pd.Series([pd.Timedelta(minutes=15), pd.Timedelta(minutes=45)])

# Convert to array of total nanoseconds
numpy_timedelta64_ns_array = (timedelta_series.dt.total_seconds() * 1e9).astype(np.int64)

Output:

[  900000000000,  2700000000000]

With this approach, each timedelta object’s total seconds are extracted using timedelta_series.dt.total_seconds(), then this total is scaled to nanoseconds by multiplication with 10**9. This yields an array of integers which are the total nanoseconds for each timedelta.

Method 4: Utilizing NumPy’s astype() directly

Another option is to use NumPy’s astype() on the array returned by Pandas’ values property. This method ensures that the resulting array is guaranteed to be a NumPy array, which can be important for type consistency in some numeric calculations.

Here’s an example:

import pandas as pd
import numpy as np

# Create a Pandas Series of timedelta objects
timedelta_series = pd.Series([pd.Timedelta(seconds=1256), pd.Timedelta(seconds=3200)])

# Use NumPy to convert to timedelta64[ns] array
numpy_timedelta64_ns_array = np.array(timedelta_series.values).astype('timedelta64[ns]')

Output:

[1256000000000 3200000000000]

The code directly casts the Pandas Series values into a NumPy array of the desired type timedelta64[ns], ensuring consistent NumPy typing. It bypasses any Pandas internal representation and focuses on creating a ‘pure’ NumPy array.

Bonus One-Liner Method 5: Chaining methods with view()

If you’re looking for a succinct one-liner, you could chain together methods using view() to directly view the Pandas series as a NumPy array of type timedelta64[ns].

Here’s an example:

import pandas as pd
import numpy as np

# Create a Pandas Series of timedelta objects
timedelta_series = pd.Series([pd.Timedelta(seconds=120), pd.Timedelta(seconds=360)])

# One-liner to get NumPy timedelta64[ns] array
numpy_timedelta64_ns_array = timedelta_series.view('timedelta64[ns]')

Output:

[ 120000000000  360000000000]

This concise line of code avoids intermediary type conversions or method calls and gives a simple way to convert a Pandas Series of timedeltas to a NumPy array of the same values in nanoseconds.

Summary/Discussion

  • Method 1: .astype('timedelta64[ns]'). Strengths: Straightforward usage within Pandas’ native methods. Weaknesses: Involves an explicit type conversion which may be unnecessary in some contexts.
  • Method 2: .values property. Strengths: Utilizes the underlying NumPy representation directly. Weaknesses: Not as explicit in intent as some other methods.
  • Method 3: dt.total_seconds() with multiplication. Strengths: Gives fine control over the conversion process. Weaknesses: More verbose and requires manual multiplication.
  • Method 4: NumPy’s astype(). Strengths: Ensures NumPy typing, may be preferred for numerical consistency. Weaknesses: An additional import is required with potential overhead.
  • Bonus Method 5: view('timedelta64[ns]'). Strengths: A one-liner that is quick and concise. Weaknesses: The usage of view() may be less familiar to some users and could introduce errors if data is not contiguous.