numpy.strings.slice#

strings.slice(a, start=None, stop=None, 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 a’s shape
stopNone, an integer or an array of integers: The end of the slice, broadcasted to a’s shape
stepNone, an integer or an array of integers: The step for the slice, broadcasted to a’s shape

Returns:

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

Examples

>>> import numpy as np
>>> a = np.array(['hello', 'world'])
>>> np.strings.slice(a, 2)
array(['he', 'wo'], 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', 'γεια σου κόσμε', '你好世界', '👋 🌍'],
...              dtype=np.dtypes.StringDType())
>>> np.strings.slice(b, -2)
array(['hello wor', 'γεια σου κόσ', '你好', '👋'], dtype=StringDType())

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

>>> np.strings.slice(b, None, None, -1)
array(['dlrow olleh', 'εμσόκ υοσ αιεγ', '界世好你', '🌍 👋'],
      dtype=StringDType())