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How to create structured arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. We know Numpy arrays are data objects that store same type of data in one object. However, this statement is not strictly correct. Numpy provides also availability to create array that allows different data types in it. This kind of array is called structured array. Let us see an example. We have seen, A1 Read more…

By wilsonzhang746, ago

Broadcasting of Numpy arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. If two Numpy arrays may having different shapes take operations, e.g. addition or subtraction, and their shapes are compatible for such operation. This is called broadcasting. The prerequisite of broadcasting is that either two arrays have the same shapes, or at least one axis of an array is 1. In the latter case, this Read more…

By wilsonzhang746, ago

Views and copy of Numpy array in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. Same as with Python lists, simply using equal symbol to assign an array to a new object will make two arrays referring to the same array. Unlike lists, in which slicing of a list returns an independent list, slicing a Numpy array is also a view of the original array. If you really want Read more…

By wilsonzhang746, ago

How to split Numpy array in python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. A Numpy array can be divided into several arryas in Pyhton, and these kinds of operation are called splitting. In terms of row-wise or coloum-wise splitting, Numpy provide two operations in this respect,hsplit() and vsplit(). hsplit() will divide an original array into parts, with the option for how many parts to be splitted to. Read more…

By wilsonzhang746, ago

Joining Numpy arrays using stacking operations in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. Two or more arrays with at least one dimension has the same shape, can be joined together to formulate a new array. These kinds of operations are called stacking. Stacking can be performed either row-wise or column-wise. vstack() is used to stack arrays vertically, such that the resulting array combines all the inputting arrays Read more…

By wilsonzhang746, ago

Numpy array shape manipulation, using reshape(),ravel() and transpose() in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. Python Numpy module provides a series functions that can reshape Numpy arrays. When an array is created via Numpy, it has a shape and dimension. reshape() is used to rearrange all elements of the array with another specified shape. Comparatively, ravel() will reshape the object to an one-dimensional array, If the original array has Read more…

By wilsonzhang746, ago

Using conditional statements to select parts of NumPy arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. In addition to simply using indexing to select elements from a Numpy array, Python lets you easily use conditional tests to systematically select parts of a Numpy array. In the following example, elements less than 0.8 from a two-dimensional array is selected via a conditional test inside the brackets, and the returning result is Read more…

By wilsonzhang746, ago

Using apply_along_axis() to apply an aggregate function upon an Numpy array in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. As an alternative to for looping, Numpy provides an aggregate function, apply_along_axis(), that applies various summary functions upon an array’s column or rows. The function has an option paraemter ‘axis’, which stands for columns when it equals to 0 and for rows when it is 1. In addition to functions that come from Python Read more…

By wilsonzhang746, ago

How to iterate over an Numpy array in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. If we want to get the contents of an Numpy array element by element, a for loop can be performed. The usage of a for loop upon an Numpy array is quite similar as for a Python list, which is show in the following code example. For two-dimensional array, for loop will first looping Read more…

By wilsonzhang746, ago

Indexing and slicing Numpy arrays in Python

We provide affordable online training course(via ZOOM meeting) for Python and R programming at fundamental level, click here for more details. When an Numpy array is created, its elements can be returned by indexing. Indexing of array elements is implemented by using brackets. If there are several non contiguous elements to be indexed, a list of indices should be appended. Similar as for Python list, negative indexing represents indexing number from the end of an Read more…

By wilsonzhang746, ago