Numpy Basics

Numpy

Data comes in various forms and needs to be stored and processed in Python programs. Numpy is meant for such purposes and enables a person to operate on Data in an optimized and fast way. It handles matrices and multi-dimensional arrays in a very good way. It is used extensively for Linear Algebra tasks. It comes under the umbrella of modules that are meant for scientific purposes in Python. In this lesson, we will be going through it, using various hands-on exercises, and seeing how it works.

ndarray data structure

“ndarray” is short for N-dimensional array. It has a fixed size in memory, and contains elements of the same type. We will be looking into the operations that are extensively used in the Industry on Numpy arrays.

Difference between Python List and Numpy array

All the elements in a Numpy array are homogeneous, meaning of the same type. A Numpy provides a vast variety of ways to create arrays. Elements in a Python List can be of different types. This gives the following advantages to Numpy arrays over Python Lists:

• Mathematical Functions over the homogeneous numpy arrays are extremely fast.

• Numpy arrays consume less memory. Their data types are defined which makes them more optimized than standard Python List.

Basic operations and manipulations with Numpy arrays.

Now, we will be jumping into extensive hands-on work with Numpy arrays and looking into the operations used extensively in the industry and day-to-day lives of data scientist.

Using different attributes.

In the below code snippet, we will look at the most commonly used attributes of Numpy arrays.

• ndarray.ndim: This is used to get the number of dimensions of a ndarray.

• ndarray.shape: This gives us the tuple showing us the size of each dimension of a Numpy array.

• ndarray.size: This gives us the total size of the ndarray and is the multiplication of elements in the tuple obtained by ndarray.shape.

• ndarray.dtype: This gives the datatype of the ndarray.

• ndarray.itemsize: This gives us the size in bytes of each element of the ndarray.

• numpy.reshape(a, newshape, order='C'): It gives us the ndarray with the same data but with a new shape. Parameter a is the ndarray that is to be reshaped.

• numpy.amin(a, axis=0), numpy.sum(a, axis=0), numpy.amax(a, axis=0) : As the names suggests, these give us the minimum value, sum of values and maximum value of ndarray respectively. Parameter a is the ndarray on which the respective operation is to be applied. Specifying the parameter axis gives us the control to either perform the operation row or column. axis=0 performs the operation column-wise, and axis=1 performs the operation row wise.

• numpy.cumsum(a, axis=0): It gives us the cumulative sum along the specified axis. Parameter a is the ndarray on which respective operation is to be applied.

• numpy.ravel(a, order='C') : It gives us the flatten(1-D) version of the ndarray. Parameter a is the ndarray on which the respective operation is to be applied.

• ndarray.astype(dtype) : It casts the copy of the array to a specified type.

Let’s jump into practical stuff, incorporating the above attributes and functions and many more used extensively in the field.

Coding Exercise