>>> import numpy as np >>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]]) array([1, 2, 3, ..., 7, 8, 9])

When axis is specified, values must have the correct shape. >>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0) array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0) …

The + operator can be used as a shorthand for np.add on ndarrays. >>> x1 = np.arange(9.0).reshape((3, 3)) >>> x2 = np.arange(3.0) >>> x1 + x2 array([[ 0., 2., 4.], [ 3., 5., 7.], [ …

numpy.append # numpy.append(arr, values, axis=None) [source] # Append values to the end of an array. Parameters arrarray_like Values are appended to a copy of this array. valuesarray_like These …

numpy.insert # numpy.insert(arr, obj, values, axis=None) [source] # Insert values along the given axis before the given indices. Parameters: arrarray_like Input array. objslice, int, array-like of ints or bools …

When it comes to the data science ecosystem, Python and NumPy are built with the user in mind. One of the best examples of this is the built-in access to documentation.

numpy.concatenate # numpy.concatenate(arrays, /, axis=0, out=None, *, dtype=None, casting='same_kind') # Join a sequence of arrays along an existing axis. Parameters: a1, a2, …

The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes Python, …

The following lists the ones with known Python libraries to read them and return NumPy arrays (there may be others for which it is possible to read and convert to NumPy arrays so check the last section …

numpy.stack # numpy.stack(arrays, axis=0, out=None, *, dtype=None, casting='same_kind') [source] # Join a sequence of arrays along a new axis. The axis parameter specifies the index of the new axis …