numpy.strings.slice#

strings.slice(a, start=None, stop=<no value>, step=None, /)[source]#

Slice the strings in a by slices specified by start, stop, step. Like in the regular Python slice object, if only start is specified then it is interpreted as the stop.

Parameters:

aarray-like, with StringDType, bytes_, or str_ dtype

Input array

startNone, an integer or an array of integers

The start of the slice, broadcasted to as shape

stopNone, an integer or an array of integers

The end of the slice, broadcasted to as shape

stepNone, an integer or an array of integers

The step for the slice, broadcasted to as shape

Returns:

outndarray

Output array of StringDType, bytes_ or str_ dtype, depending on input type

Examples

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>>> import numpy as np
>>> a = np.array(['hello', 'world'])
>>> np.strings.slice(a, 2)
array(['he', 'wo'], dtype='<U5')

>>> np.strings.slice(a, 2, None)
array(['llo', 'rld'], dtype='<U5')

>>> np.strings.slice(a, 1, 5, 2)
array(['el', 'ol'], dtype='<U5')

One can specify different start/stop/step for different array entries:

>>> np.strings.slice(a, np.array([1, 2]), np.array([4, 5]))
array(['ell', 'rld'], dtype='<U5')

Negative slices have the same meaning as in regular Python:

>>> b = np.array(['hello world', '  ', '', ' '],
...              dtype=np.dtypes.StringDType())
>>> np.strings.slice(b, -2)
array(['hello wor', '  ', '', ''], dtype=StringDType())

>>> np.strings.slice(b, -2, None)
array(['ld', '', '', ' '], dtype=StringDType())

>>> np.strings.slice(b, [3, -10, 2, -3], [-1, -2, -1, 3])
array(['lo worl', '  ', '', ' '], dtype=StringDType())

>>> np.strings.slice(b, None, None, -1)
array(['dlrow olleh', '  ', '', ' '],
      dtype=StringDType())

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