NumPy has the np.reshape and np.resize methods. reshape ( 5, 4 ) > A 'Output' : array (,, ,, ]) # The vector a has been reshaped into a 5 by 4 matrix A shape 'Output' : ( 20 ,) # reshape into a 5 x 4 matrix linspace ( 0, 5, 20 ) > a 'Output' : array () # observe that a is a 1-D array # make 20 elements evenly spaced between 0 and 5 Let’s go through a few of them.įirst let’s create random integers using the method np.random.randint(low, high=None, size=None,) which returns random integers from low (inclusive) to high (exclusive). NumPy has vectorized routines for various matrix operations. Linear algebra is a crucial component of machine learning, and deep learning research and implementation of learning algorithms. Linear Algebra is a convenient and powerful system for manipulating a set of data features and is one of the strong points of NumPy. ![]() > my_3D 'Output' : array (,, ]) # slice the first 2 rows and columns > my_3D # In this case, the cell at the 2nd row and column randn ( 3, 3 ) 'Output' : array (,, ]) # select a particular cell (or element) from a 2-D array. # create a 3x3 array contain random normal numbers A colored image is composed of pixels intensity values with a color depth of three for the red, green and blue (RGB) color profiles. Each column represents a feature or attribute and each row an observation.Īlso, other data forms like images are adequately represented using 3-D arrays. And even when data is not necessarily represented in this format, it is often transformed into a tabular form before doing any data analytics or machine learning. Structured data is usually represented in as a grid of rows and columns. 2-D arrays are ideal for storing data for analysis. This section will now consider working with 2-D and 3-D arrays. Previously, we covered the creation of 1-D arrays (or vectors) in NumPy to get a feel of how NumPy works. ![]() exp ( my_array ) 'Output' : array ()Īs we’ve seen earlier, the strength of NumPy is its ability to construct and manipulate n-dimensional arrays with highly optimized (i.e., vectorized) operations. log ( my_array ) 'Output' : array () # exponent sqrt ( my_array ) 'Output' : array () # log arange ( 2, 11, 2 ) 'Output' : array () # sum of array elements # create an array of even numbers between 2 and 10 Let’s explore a bit with NumPy datatypes: Also, Numpy infers contiguous arrays of the same type. The parameter dtype is used to assign a datatype to a NumPy function. The datatypes with a _ appended are base Python datatypes converted to NumPy datatypes. This extended datatype support is useful for dealing with different kinds of signed and unsigned integer and floating-point numbers and well as booleans and complex numbers for scientific computation. NumPy boasts a broad range of numerical datatypes in comparison with vanilla Python. ones ( 5 ) 'Output' : array () # create an array of zeros linspace ( 2, 10, 5 ) 'Output' : array () # create an array of ones ![]() arange ( 2, 10, 2 ) 'Output' : # create a range of points between two numbers ![]() arange ( 10 ) 'Output' : # create an array from start to end (exclusive) via a step size - (start, stop, step) # create an array from a range of numbers To begin using NumPy, we’ll start by importing the NumPy module: This understanding forms a foundation from which one can extend and seek solutions from the NumPy reference documentation when a specific functionality is needed. This tutorial will cover the basics of NumPy to get you very comfortable working with the package and also get you to appreciate the thinking behind how NumPy works. Hence, NumPy’s 2-Dimensional arrays is a natural fit for storing and manipulating datasets. Data is often represented in a matrix-like grid of rows and columns, where each row represents an observation and each column a variable or feature. This is particularly critical for building Machine learning and Deep learning models. NumPy core strength lies in its ability to create and manipulate -dimensional arrays. It bears close semblance with MATLAB and is equally as powerful when used in conjunction with other packages such as SciPy for various scientific functions, Matplotlib for visualization and Pandas for data analysis. NumPy is a Python library optimized for numerical computing. Basic Math Operations on Arrays: Universal Functions.
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