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Operations on Numpy arrays-Part 2

This post is the continuation of the Part 1 of the operations on the numpy arrays.In this post we will see some more functions and operations in detail.So without any further a due we will move forward with the post.


Hello All,

This post is the continuation of the first part on the operations on numpy arrays. If you have not read the first part of this post ,I request you to go through the first part using the following link.Operations on Numpy arrays-Part 1 

Let us move forward with the operations in this post. In the last post we had seen various functions like copy(), arange()  random.rand().

In this post we are going to see some of the operations related to managing the data type and details about the size of the numpy arrays.

First we are going to some commonly used data types within numpy.

Some of the most common data types used are np.float32, np.float64. np.int32, np.int64 and many more specialized data types which can be viewed in detail on the numpy website. Refer the references section for the links to the specific pages.

In the following code snippets we will create numpy arrays containing heterogeneous data and homogeneous data. Also we will check ways to change the data type of the array.

Firstly we will create an array of type integer.

Next we will create array of type float.

Moving on we will create an array containing heterogenous data(with strings and numbers).

Changing the data type of integer array to float.

One critical this to be observed is that when we created an integer array the data type of the array was int. When we created an array with same number and just one string, the data type changes.This is because there is a priority order for deciding the data type when certain type of data is available. The priority order is given as follows with the first data type having the highest priority and the last having the least priority: String > Float > Integer .


Next we are going to see how to check total bytes occupied by the numpy array using the nbytes attribute of the ndim array.

We can also check the size of each item inside the array using the itemsize attribute of the ndim array.

Thank you.
That's all for this post!!
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References:
1. https://docs.scipy.org/doc/numpy/user/basics.types.html
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