NumPy Square Root: np.sqrt(), Arrays, Dtypes, and Domains
Calculate element-wise square roots with NumPy, while choosing a domain policy for negative values, complex results, dtypes, and broadcasting.
Numpy
Learn NumPy with array operations, random sampling, math functions, shapes, dtypes, plotting examples, and fixes for common NumPy errors.
NumPy any(): Check Whether Array Values Are True
Use NumPy any() to test whether array values are true with axis, keepdims, where, out, NaN behavior, and boolean masks.
NumPy gradient(): Calculate Numerical Derivatives
Use NumPy gradient to estimate derivatives on evenly or unevenly spaced data, control edge handling, and avoid axis and spacing mistakes.
NumPy Count Substrings with strings.count() and char.count()
Count non-overlapping substrings in NumPy string arrays with strings.count(), legacy char.count(), start and end ranges, and masks.
NumPy divide: Element-Wise Division, Zeros, and Dtypes
Use NumPy divide for element-wise division with broadcasting, zero and invalid-value handling, dtype control, where masks, and numerical tests.
NumPy multiply: Elementwise Products, Broadcasting, and Dtypes
Use numpy.multiply for elementwise products with broadcasting, masks, output arrays, integer and floating-point dtypes, and validation.
NumPy add: Element-Wise Addition, Broadcasting, and Dtypes
Use NumPy add for element-wise array addition with broadcasting, dtype promotion, output control, masks, overflow, and reliable tests.
NumPy subtract(): Element-Wise Difference and Broadcasting
Use NumPy subtract() for element-wise differences, broadcasting, dtype and out controls, masked updates, and predictable operand order.
NumPy clip(): Limit Array Values with Bounds
Limit NumPy arrays with scalar or per-element bounds, one-sided clipping, in-place output, dtype checks, and validation guidance.
NumPy power(): Element-Wise Exponents, Broadcasting, and Dtypes
Use np.power() deliberately by checking broadcasted shapes, negative and fractional domains, dtype, and masked output behavior.