NumPy ediff1d()
The numpy.ediff1d() function calculates the differences between consecutive elements of an array.
If the input array is multidimensional, it is flattened before computing the differences.
Syntax
numpy.ediff1d(ary, to_end=None, to_begin=None)Parameters
| Parameter | Type | Description |
|---|---|---|
ary | array_like | Input array whose consecutive differences are computed. It is flattened if it is multidimensional. |
to_end | array_like, optional | Value(s) to append at the end of the difference result. |
to_begin | array_like, optional | Value(s) to prepend at the beginning of the difference result. |
Return Value
Returns a NumPy array containing the differences between consecutive elements of ary.
If to_end or to_begin parameters are specified, the additional values are included in the output.
Examples
1. Basic Usage of numpy.ediff1d()
Computing differences between consecutive elements in a one-dimensional array.
import numpy as np
# Define an input array
arr = np.array([1, 2, 4, 7, 11])
# Compute the consecutive differences
differences = np.ediff1d(arr)
# Print the result
print("Differences:", differences)Output:
Differences: [1 2 3 4]The differences are computed as [2-1, 4-2, 7-4, 11-7] → [1, 2, 3, 4].
2. Applying numpy.ediff1d() on a Multidimensional Array
When applied to a multidimensional array, it is first flattened before computing the differences.
import numpy as np
# Define a 2D array
arr = np.array([[1, 2, 3], [4, 5, 6]])
# Compute consecutive differences
differences = np.ediff1d(arr)
# Print the result
print("Differences:", differences)Output:
Differences: [1 1 1 1 1]The 2D array is flattened to [1, 2, 3, 4, 5, 6], then differences are computed as [2-1, 3-2, 4-3, 5-4, 6-5].
3. Using the to_begin and to_end Parameters
Prepending and appending values to the result.
import numpy as np
# Define an input array
arr = np.array([3, 8, 15, 24])
# Compute differences with additional values at the start and end
differences = np.ediff1d(arr, to_begin=0, to_end=99)
# Print the result
print("Differences with to_begin and to_end:", differences)Output:
Differences with to_begin and to_end: [ 0 5 7 9 99]The differences are [5, 7, 9], with 0 prepended and 99 appended to the result.
4. Using numpy.ediff1d() on a Large Sequence
Computing differences in a large sequence of numbers.
import numpy as np
# Generate a sequence of numbers from 1 to 10
arr = np.arange(1, 11)
# Compute consecutive differences
differences = np.ediff1d(arr)
# Print the result
print("Differences:", differences)Output:
Differences: [1 1 1 1 1 1 1 1 1]The differences are [1, 1, 1, 1, 1, 1, 1, 1, 1] since the sequence increases by 1 at each step.
