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To return the lowest index in the string where substring sub is found, use the numpy.char.find() method in Python Numpy. The method returns the output array of ints. Returns -1 if sub is not found. The first parameter is the input array. The second parameter is the substring to be searchedStepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of strings −arr = np.array(['KATIE', 'JOHN', 'KATE', 'AmY', 'BRADley'])Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the Array −print("Array Dimensions...", arr.ndim)Get the shape of the Array −print("Our Array Shape...", arr.shape)Get the ... Read More

To return a boolean array which is True where the string element in array ends with suffix, use the numpy.char.endswith() method in Python Numpy. The method outputs an array of bools. The first parameter is the input array. The second parameter is the suffix. With optional start parameter, test beginning at that position. With optional end parameter, stop comparing at that position.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of strings −arr = np.array(['KATIE', 'JOHN', 'KATE', 'AmY', 'CRADlE'])Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the Array −print("Array Dimensions...", ... Read More

To test whether similar int type of different sizes are subdtypes of integer class, use the numpy.issubdtype() method in Python Numpy. The parameters are the dtype or object coercible to one.StepsAt first, import the required library −import numpy as npUsing the issubdtype() method in Numpy. Checking for integer datatype with different sizes −print("Result...", np.issubdtype(np.int16, np.signedinteger)) print("Result...", np.issubdtype(np.int32, np.signedinteger)) print("Result...", np.issubdtype(np.int64, np.signedinteger)) print("Result...", np.issubdtype(np.int16, np.integer)) print("Result...", np.issubdtype(np.int32, np.integer)) print("Result...", np.issubdtype(np.int64, np.integer))Exampleimport numpy as np # To test whether similar int type of different sizes are subdtypes of integer class, use the numpy.issubdtype() method in Python Numpy. # The parameters are ... Read More

The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. The 1st parameter, f is an Ndimensional array containing samples of a scalar function. The 2nd parameter is the varargs i.e. the spacing between f values. Default unitary spacing for all dimensions.The 3rd parameter is the edge_order{1, 2} i.e. the Gradient is calculated using N-th order accurate differences at the boundaries. Default: 1. The 4th parameter is the Gradient, ... Read More

To calculate the n-th discrete difference, use the numpy.diff() method. The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff recursively. The diff() method returns the n-th differences. The shape of the output is the same as a except along axis where the dimension is smaller by n. The type of the output is the same as the type of the difference between any two elements of a. This is the same as the type of a in most cases. A notable exception is datetime64, which results in ... Read More

The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in base e. The method returns the natural logarithm of x, elementwise. This is a scalar if x is a scalar. The 1st parameter is the input value, array-like. The 2nd parameter is out, a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number ... Read More

To convert angles from degrees to radians, use the numpy.radians() method in Python Numpy. The method returns the corresponding radian values. This is a scalar if x is a scalar. The 1st parameter is an input array in degrees. The 2nd and 3rd parameters are optional.The 2nd parameter is an ndarray, A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.The 3rd parameter ... Read More

Suppose, we are making a cleaning robot that works on a grid. The grid is of dimensions h x w. There are m dirty cells that need to be cleaned that are given in an array of integer pairs 'dirt'. The cleaning robot, if placed in a particular cell; can clean every cell in that particular row and column. So, our task is to clean the maximum number of dirty cells. We have to find out the count and display it as output.So, if the input is like h = 3, w = 3, m = 3, dirt = {{0, ... Read More

To return True if all entries of two arrays are equal, use the ma.allequal() method in Python Numpy. Returns True if the two arrays are equal within the given tolerance, False otherwise. If either array contains NaN, then False is returned.The fill_value sets whether masked values in a or b are considered equal (True) or not (False). 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 ... Read More

To clip (limit) the values in an array, use the np.ma.clip() method in Python Numpy. The "out" parameter is where results will be placed in this array. It may be the input array for in-place clipping. out must be of the right shape to hold the output. Its type is preserved. . Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1. Equivalent to but faster than np.minimum(a_max, np.maximum(a, a_min)).The function returns an array ... Read More