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Numpy Articles
Page 50 of 81
Left-justify elements of an array and set the characters to use for padding in Numpy
To left-justify elements of an array and set the characters to use for padding, use the numpy.char.ljust() method in Python Numpy. The "width" parameter is the length of the resulting strings. The "fillchar" parameter is the character to use for padding.The function returns an output array of str or unicode, depending on input type. The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of string −arr = np.array(['Tom', 'John', 'Kate', 'Amy', 'Brad'])Displaying our array −print("Array...", arr)Get the datatype: −print("Array datatype...", ...
Read MoreReturn an array with the elements of an array left-justified in a string of length width in Numpy
To return an array with the elements of an array left-justified in a string of length width, use the numpy.char.ljust() method in Python Numpy. The "width" parameter is the length of the resulting strings. The function returns an output array of str or unicode, depending on input type.The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of string −arr = np.array(['Tom', 'John', 'Kate', 'Amy', 'Brad'])Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the Array ...
Read MoreCompare and return True if two string Numpy arrays are not equal
To compare and return True if two string arrays are not equal, use the numpy.char.not_equal() method in Python Numpy. The arr1 and arr2 are the two input string arrays of the same shape.Unlike numpy.not_equal, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray.The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate two One-Dimensional arrays of string −arr1 = np.array(['Bella', 'Tom', 'John', 'Kate', 'Amy', 'Brad']) arr2 = np.array(['Bella', 'Tom', 'Cena', ...
Read MoreReturn a list of the words in the string using separator as the delimiter string in Numpy
To return a list of the words in the string using separator as the delimiter string, use the numpy.char.split() method in Python Numpy −The 1st parameter is the input arrayThe 2nd parameter is the separatorIf maxsplit parameter is given, at most maxsplit splits are done. The function split() returns an array of list objects.The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate an array −arr = np.array(["Bella-Cio", "Brad-Pitt", "Katie-Perry"])Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the ...
Read MoreReturn a copy of the string with all occurrences of substring old replaced by new in Numpy
To return a copy of the string with all occurrences of substring old replaced by new, use the numpy.char.replace() method in Python Numpy −The 1st parameter is the input arrayThe 2nd parameter is the old string to be replacedThe 3rd parameter is the new string to be replaced with the oldIf the optional parameter count is given, only the first count occurrences are replacedThe numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate an array −arr = np.array(["Welcome to the Jungle", "Jungle Safari"])Displaying our ...
Read MoreReturn True if two Numpy arrays are element-wise equal within a tolerance
To return True if two arrays are element-wise equal within a tolerance, use the ma.allclose() method in Python Numpy. This function is equivalent to allclose except that masked values are treated as equal (default) or unequal, depending on the masked_equal argument. The "masked_values" parameter is used to set the masked values in both the arrays are considered equal (True) or not (False).Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned.A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either ...
Read MoreMultiply each element of a masked Array by a scalar value in-place in Numpy
To multiply each element of a masked Array by a scalar value in-place, use the ma.MaskedArray.__imul__() method in Python Numpy. A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and ...
Read MoreSubtract a scalar value from each element of a Masked Array in-place in Numpy
To subtract a scalar value from each element of a masked Array in-place, use the ma.MaskedArray.__isub__() method in Python Numpy.A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse ...
Read MoreCompute the bit-wise NOT of an array element-wise in Numpy
To compute the bit-wise NOT of an array element-wise, use the numpy.bitwise_not() method in Python Numpy. Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ˜.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.StepsAt first, import ...
Read MoreRight-justify elements of an array and set the characters to use for padding in Numpy
To right-justify elements of an array and set the characters to use for padding, use the numpy.char.rjust() method in Python Numpy. The "width" parameter is the length of the resulting strings. The "fillchar" parameter is the character to use for padding.The function returns an output array of str or unicode, depending on input type. The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of string −arr = np.array(['Tom', 'John', 'Kate', 'Amy', 'Brad']) Displaying our array −print("Array...", arr)Get the datatype −print("Array ...
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