Python Articles - Page 295 of 1048

To compute the square root of input, use the scimath.sqrt() method in Python Numpy. The method returns the square root of x. If x was a scalar, so is out, otherwise an array is returned. The parameter x is the input value. For negative input elements, a complex value is returnedStepsAt first, import the required libraries −import numpy as npCreating a numpy array using the array() method −arr = np.array([1, 4, 9, 16, 25, 36]) Display the array −print("Our Array...", arr)Check the Dimensions −print("Dimensions of our Array...", arr.ndim) Get the Datatype −print("Datatype of our Array object...", arr.dtype)Get the Shape −print("Shape ... Read More

To return real parts if input is complex with all imaginary parts close to zero, use the numpy.real_if_close in Python. “Close to zero” is defined as tol * (machine epsilon of the type for a). If a is real, the type of a is used for the output. If a has complex elements, the returned type is float. The 1st parameter is a, the input array. The 2nd parameter is tol, Tolerance in machine epsilons for the complex part of the elements in the array.StepsAt first, import the required libraries −import numpy as npCreating a numpy array using the array() ... Read More

To replace NaN with zero and infinity with large finite numbers, use the numpy.nan_to_num() method. The method returns, x, with the non-finite values replaced. If copy is False, this may be x itself. The 1st parameter is the input data. The 2nd parameter is copy, whether to create a copy of x (True) or to replace values in-place. The in-place operation only occurs if casting to an array does not require a copy. Default is True.The 3rd parameter is nan, the value to be used to fill NaN values. If no value is passed then NaN values will be replaced ... Read More

To replace NaN with zero and infinity with large finite numbers, use the numpy.nan_to_num() method in Python. The method returns, x, with the non-finite values replaced. If copy is False, this may be x itself. The 1st parameter is the input data. The 2nd parameter is copy, whether to create a copy of x (True) or to replace values in-place (False). The in-place operation only occurs if casting to an array does not require a copy. Default is True.The 3rd parameter is nan, the value to be used to fill NaN values. If no value is passed then NaN values ... Read More

To replace NaN with zero and infinity with large finite numbers, use the numpy.nan_to_num() method in Python. The method returns, x, with the non-finite values replaced. If copy is False, this may be x itself. The 1st parameter is the input data. The 2nd parameter is copy, whether to create a copy of x (True) or to replace values in-place (False). The in-place operation only occurs if casting to an array does not require a copy. Default is True.The 3rd parameter is nan, the value to be used to fill NaN values. If no value is passed then NaN values ... Read More

To replace NaN with zero and infinity with large finite numbers, use the numpy.nan_to_num() method in Python. The method returns, x, with the non-finite values replaced. If copy is False, this may be x itself. The 1st parameter is the input data. The 2nd parameter is copy, whether to create a copy of x (True) or to replace values in-place (False). The in-place operation only occurs if casting to an array does not require a copy. Default is True.The 3rd parameter is nan, the value to be used to fill NaN values. If no value is passed then NaN values ... Read More

To return the discrete linear convolution of two one-dimensional sequences, use the numpy.convolve() method in Python Numpy. The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal. In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions.If v is longer than a, the arrays are swapped before computation. The method returns the Discrete, linear convolution of a and v. The 1st parameter, a is the first one-dimensional input array. The 2nd parameter, v is the second one-dimensional input ... Read More

To return the discrete linear convolution of two one-dimensional sequences, use the numpy.convolve() method in Python Numpy.The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal. In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions. If v is longer than a, the arrays are swapped before computation. The method returns the Discrete, linear convolution of a and v. The 1st parameter, a is the first one-dimensional input array. The 2nd parameter, v is the second one-dimensional input ... Read More

To return the minimum of an array or minimum ignoring any NaNs, use the numpy.nanmin() method in Python. The method returns an array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned.The 1st parameter, a is an array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.The 2nd parameter, axis is an axis or axes along which the minimum is computed. The default is to compute the minimum ... Read More

To return the minimum of an array or minimum ignoring any NaNs, use the numpy.nanmin() method in Python. The method returns an array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, an ndarray scalar is returned. The same dtype as a is returned.The 1st parameter, a is an array containing numbers whose minimum is desired. If a is not an array, a conversion is attempted.The 2nd parameter, axis is an axis or axes along which the minimum is computed. The default is to compute the minimum ... Read More