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## Python Matrices and NumPy Arrays

A matrix is a two-dimensional data structure where numbers are arranged into rows and columns. For example:

This matrix is a 3x4 (pronounced "three by four") matrix because it has 3 rows and 4 columns.

- Python Matrix

Python doesn't have a built-in type for matrices. However, we can treat a list of a list as a matrix. For example:

We can treat this list of a list as a matrix having 2 rows and 3 columns.

Be sure to learn about Python lists before proceed this article.

Let's see how to work with a nested list.

When we run the program, the output will be:

Here are few more examples related to Python matrices using nested lists.

- Add two matrices
- Transpose a Matrix
- Multiply two matrices

Using nested lists as a matrix works for simple computational tasks, however, there is a better way of working with matrices in Python using NumPy package.

- NumPy Array

NumPy is a package for scientific computing which has support for a powerful N-dimensional array object. Before you can use NumPy, you need to install it. For more info,

- Visit: How to install NumPy?
- If you are on Windows, download and install anaconda distribution of Python. It comes with NumPy and other several packages related to data science and machine learning.

Once NumPy is installed, you can import and use it.

NumPy provides multidimensional array of numbers (which is actually an object). Let's take an example:

As you can see, NumPy's array class is called ndarray .

## How to create a NumPy array?

There are several ways to create NumPy arrays.

1. Array of integers, floats and complex Numbers

When you run the program, the output will be:

2. Array of zeros and ones

Here, we have specified dtype to 32 bits (4 bytes). Hence, this array can take values from -2 -31 to 2 -31 -1 .

3. Using arange() and shape()

Learn more about other ways of creating a NumPy array .

## Matrix Operations

Above, we gave you 3 examples: addition of two matrices, multiplication of two matrices and transpose of a matrix. We used nested lists before to write those programs. Let's see how we can do the same task using NumPy array.

Addition of Two Matrices

We use + operator to add corresponding elements of two NumPy matrices.

Multiplication of Two Matrices

To multiply two matrices, we use dot() method. Learn more about how numpy.dot works.

Note: * is used for array multiplication (multiplication of corresponding elements of two arrays) not matrix multiplication.

Transpose of a Matrix

We use numpy.transpose to compute transpose of a matrix.

As you can see, NumPy made our task much easier.

## Access matrix elements, rows and columns

Access matrix elements

Similar like lists, we can access matrix elements using index. Let's start with a one-dimensional NumPy array.

Now, let's see how we can access elements of a two-dimensional array (which is basically a matrix).

Access rows of a Matrix

Access columns of a Matrix

If you don't know how this above code works, read slicing of a matrix section of this article.

## Slicing of a Matrix

Slicing of a one-dimensional NumPy array is similar to a list. If you don't know how slicing for a list works, visit Understanding Python's slice notation .

Let's take an example:

Now, let's see how we can slice a matrix.

As you can see, using NumPy (instead of nested lists) makes it a lot easier to work with matrices, and we haven't even scratched the basics. We suggest you to explore NumPy package in detail especially if you trying to use Python for data science/analytics.

NumPy Resources you might find helpful:

- NumPy Tutorial
- NumPy Reference

## Table of Contents

- What is a matrix?
- Numbers(integers, float, complex etc.) Array
- Zeros and Ones Array
- Array Using arange() and shape()
- Multiplication
- Access elements
- Access rows
- Access columns
- Slicing of Matrix
- Useful Resources

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## Take Matrix input from user in Python

Matrix is nothing but a rectangular arrangement of data or numbers. In other words, it is a rectangular array of data or numbers. The horizontal entries in a matrix are called as ‘rows’ while the vertical entries are called as ‘columns’. If a matrix has r number of rows and c number of columns then the order of matrix is given by r x c . Each entries in a matrix can be integer values, or floating values, or even it can be complex numbers.

In Python, we can take a user input matrix in different ways. Some of the methods for user input matrix in Python are shown below:

Code #1:

Output:

Time complexity: O(RC) Auxiliary space: O(RC)

One liner:

Code #2: Using map() function and Numpy. In Python, there exists a popular library called NumPy . This library is a fundamental library for any scientific computation. It is also used for multidimensional arrays and as we know matrix is a rectangular array, we will use this library for user input matrix.

Time complexity: O(RC), as the code iterates through RC elements to create the matrix. Auxiliary space: O(RC), as the code creates an RC sized matrix to store the entries.

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## numpy.matrix #

It is no longer recommended to use this class, even for linear algebra. Instead use regular arrays. The class may be removed in the future.

Returns a matrix from an array-like object, or from a string of data. A matrix is a specialized 2-D array that retains its 2-D nature through operations. It has certain special operators, such as * (matrix multiplication) and ** (matrix power).

If data is a string, it is interpreted as a matrix with commas or spaces separating columns, and semicolons separating rows.

Data-type of the output matrix.

If data is already an ndarray , then this flag determines whether the data is copied (the default), or whether a view is constructed.

Return self as an ndarray object.

Return self as a flattened ndarray .

Returns the (complex) conjugate transpose of self .

Returns the (multiplicative) inverse of invertible self .

Returns the transpose of the matrix.

Base object if memory is from some other object.

An object to simplify the interaction of the array with the ctypes module.

Python buffer object pointing to the start of the array’s data.

Data-type of the array’s elements.

Information about the memory layout of the array.

A 1-D iterator over the array.

The imaginary part of the array.

Length of one array element in bytes.

Total bytes consumed by the elements of the array.

Number of array dimensions.

The real part of the array.

Tuple of array dimensions.

Number of elements in the array.

Tuple of bytes to step in each dimension when traversing an array.

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Matrix is a special case of two dimensional array where each data element is of strictly same size. So every matrix is also a two dimensional array but not vice versa.

Matrices are very important data structures for many mathematical and scientific calculations. As we have already discussed two dimnsional array data structure in the previous chapter we will be focusing on data structure operations specific to matrices in this chapter.

We also be using the numpy package for matrix data manipulation.

## Matrix Example

Consider the case of recording temprature for 1 week measured in the morning, mid-day, evening and mid-night. It can be presented as a 7X5 matrix using an array and the reshape method available in numpy.

The above data can be represented as a two dimensional array as below −

## Accessing Values

The data elements in a matrix can be accessed by using the indexes. The access method is same as the way data is accessed in Two dimensional array.

When the above code is executed, it produces the following result −

## Adding a row

Use the below mentioned code to add a row in a matrix.

## Adding a column

We can add column to a matrix using the insert() method. here we have to mention the index where we want to add the column and a array containing the new values of the columns added.In the below example we add t a new column at the fifth position from the beginning.

## Delete a row

We can delete a row from a matrix using the delete() method. We have to specify the index of the row and also the axis value which is 0 for a row and 1 for a column.

## Delete a column

We can delete a column from a matrix using the delete() method. We have to specify the index of the column and also the axis value which is 0 for a row and 1 for a column.

## Update a row

To update the values in the row of a matrix we simply re-assign the values at the index of the row. In the below example all the values for thrusday's data is marked as zero. The index for this row is 3.

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## NumPy Indexing and Assignment

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In this lesson, we will explore indexing and assignment in NumPy arrays.

## The Array I'll Be Using In This Lesson

As before, I will be using a specific array through this lesson. This time it will be generated using the np.random.rand method. Here's how I generated the array:

Here is the actual array:

To make this array easier to look at, I will round every element of the array to 2 decimal places using NumPy's round method:

Here's the new array:

## How To Return A Specific Element From A NumPy Array

We can select (and return) a specific element from a NumPy array in the same way that we could using a normal Python list: using square brackets.

An example is below:

We can also reference multiple elements of a NumPy array using the colon operator. For example, the index [2:] selects every element from index 2 onwards. The index [:3] selects every element up to and excluding index 3. The index [2:4] returns every element from index 2 to index 4, excluding index 4. The higher endpoint is always excluded.

A few example of indexing using the colon operator are below.

## Element Assignment in NumPy Arrays

We can assign new values to an element of a NumPy array using the = operator, just like regular python lists. A few examples are below (note that this is all one code block, which means that the element assignments are carried forward from step to step).

arr[2:5] = 0.5

## Returns array([0. , 0. , 0.5, 0.5, 0.5])

As you can see, modifying second_new_array also changed the value of new_array .

Why is this?

By default, NumPy does not create a copy of an array when you reference the original array variable using the = assignment operator. Instead, it simply points the new variable to the old variable, which allows the second variable to make modification to the original variable - even if this is not your intention.

This may seem bizarre, but it does have a logical explanation. The purpose of array referencing is to conserve computing power. When working with large data sets, you would quickly run out of RAM if you created a new array every time you wanted to work with a slice of the array.

Fortunately, there is a workaround to array referencing. You can use the copy method to explicitly copy a NumPy array.

An example of this is below.

As you can see below, making modifications to the copied array does not alter the original.

So far in the lesson, we have only explored how to reference one-dimensional NumPy arrays. We will now explore the indexing of two-dimensional arrays.

## Indexing Two-Dimensional NumPy Arrays

To start, let's create a two-dimensional NumPy array named mat :

There are two ways to index a two-dimensional NumPy array:

- mat[row, col]
- mat[row][col]

I personally prefer to index using the mat[row][col] nomenclature because it is easier to visualize in a step-by-step fashion. For example:

You can also generate sub-matrices from a two-dimensional NumPy array using this notation:

Array referencing also applies to two-dimensional arrays in NumPy, so be sure to use the copy method if you want to avoid inadvertently modifying an original array after saving a slice of it into a new variable name.

## Conditional Selection Using NumPy Arrays

NumPy arrays support a feature called conditional selection , which allows you to generate a new array of boolean values that state whether each element within the array satisfies a particular if statement.

An example of this is below (I also re-created our original arr variable since its been awhile since we've seen it):

You can also generate a new array of values that satisfy this condition by passing the condition into the square brackets (just like we do for indexing).

An example of this is below:

Conditional selection can become significantly more complex than this. We will explore more examples in this section's associated practice problems.

In this lesson, we explored NumPy array indexing and assignment in thorough detail. We will solidify your knowledge of these concepts further by working through a batch of practice problems in the next section.

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## Chapter 1. Vectors, Matrices, and Arrays

1.0 introduction.

NumPy is the foundation of the Python machine learning stack. NumPy allows for efficient operations on the data structures often used in machine learning: vectors, matrices, and tensors. While NumPy is not the focus of this book, it will show up frequently throughout the following chapters. This chapter covers the most common NumPy operations we are likely to run into while working on machine learning workflows.

## 1.1 Creating a Vector

You need to create a vector.

Use NumPy to create a one-dimensional array:

NumPy’s main data structure is the multidimensional array. To create a vector, we simply create a one-dimensional array. Just like vectors, these arrays can be represented horizontally (i.e., rows) or vertically (i.e., columns).

Vectors, Math Is Fun

Euclidean vector, Wikipedia

## 1.2 Creating a Matrix

You need to create a matrix.

Use NumPy to create a two-dimensional array:

To create a matrix we can use a NumPy two-dimensional array. In our solution, the matrix contains three rows and two columns (a column of 1s and a column of 2s).

NumPy actually has a dedicated matrix data structure:

However, the matrix data structure is not recommended for two reasons. First, arrays are the de facto standard data structure of NumPy. Second, the vast majority of NumPy operations return arrays, not matrix objects.

Matrix, Wikipedia

Matrix, Wolfram MathWorld

## 1.3 Creating a Sparse Matrix

Given data with very few nonzero values, you want to efficiently represent it.

Create a sparse matrix:

A frequent situation in machine learning is having a huge amount of data; however, most of the elements in the data are zeros. For example, imagine a matrix where the columns are every movie on Netflix, the rows are every Netflix user, and the values are how many times a user has watched that particular movie. This matrix would have tens of thousands of columns and millions of rows! However, since most users do not watch most movies, the vast majority of elements would be zero.

Sparse matrices only store nonzero elements and assume all other values will be zero, leading to significant computational savings. In our solution, we created a NumPy array with two nonzero values, then converted it into a sparse matrix. If we view the sparse matrix we can see that only the nonzero values are stored:

There are a number of types of sparse matrices. However, in compressed sparse row (CSR) matrices, (1, 1) and (2, 0) represent the (zero-indexed) indices of the non-zero values 1 and 3 , respectively. For example, the element 1 is in the second row and second column. We can see the advantage of sparse matrices if we create a much larger matrix with many more zero elements and then compare this larger matrix with our original sparse matrix:

As we can see, despite the fact that we added many more zero elements in the larger matrix, its sparse representation is exactly the same as our original sparse matrix. That is, the addition of zero elements did not change the size of the sparse matrix.

As mentioned, there are many different types of sparse matrices, such as compressed sparse column, list of lists, and dictionary of keys. While an explanation of the different types and their implications is outside the scope of this book, it is worth noting that while there is no “best” sparse matrix type, there are meaningful differences between them and we should be conscious about why we are choosing one type over another.

Sparse matrices, SciPy documentation

101 Ways to Store a Sparse Matrix

## 1.4 Selecting Elements

You need to select one or more elements in a vector or matrix.

NumPy’s arrays make that easy:

Like most things in Python, NumPy arrays are zero-indexed, meaning that the index of the first element is 0, not 1. With that caveat, NumPy offers a wide variety of methods for selecting (i.e., indexing and slicing) elements or groups of elements in arrays:

## 1.5 Describing a Matrix

You want to describe the shape, size, and dimensions of the matrix.

Use shape , size , and ndim :

This might seem basic (and it is); however, time and again it will be valuable to check the shape and size of an array both for further calculations and simply as a gut check after some operation.

## 1.6 Applying Operations to Elements

You want to apply some function to multiple elements in an array.

Use NumPy’s vectorize :

NumPy’s vectorize class converts a function into a function that can apply to all elements in an array or slice of an array. It’s worth noting that vectorize is essentially a for loop over the elements and does not increase performance. Furthermore, NumPy arrays allow us to perform operations between arrays even if their dimensions are not the same (a process called broadcasting ). For example, we can create a much simpler version of our solution using broadcasting:

## 1.7 Finding the Maximum and Minimum Values

You need to find the maximum or minimum value in an array.

Use NumPy’s max and min :

Often we want to know the maximum and minimum value in an array or subset of an array. This can be accomplished with the max and min methods. Using the axis parameter we can also apply the operation along a certain axis:

## 1.8 Calculating the Average, Variance, and Standard Deviation

You want to calculate some descriptive statistics about an array.

Use NumPy’s mean , var , and std :

Just like with max and min , we can easily get descriptive statistics about the whole matrix or do calculations along a single axis:

## 1.9 Reshaping Arrays

You want to change the shape (number of rows and columns) of an array without changing the element values.

Use NumPy’s reshape :

reshape allows us to restructure an array so that we maintain the same data but it is organized as a different number of rows and columns. The only requirement is that the shape of the original and new matrix contain the same number of elements (i.e., the same size). We can see the size of a matrix using size :

One useful argument in reshape is -1 , which effectively means “as many as needed,” so reshape(1, -1) means one row and as many columns as needed:

Finally, if we provide one integer, reshape will return a 1D array of that length:

## 1.10 Transposing a Vector or Matrix

You need to transpose a vector or matrix.

Use the T method:

Transposing is a common operation in linear algebra where the column and row indices of each element are swapped. One nuanced point that is typically overlooked outside of a linear algebra class is that, technically, a vector cannot be transposed because it is just a collection of values:

However, it is common to refer to transposing a vector as converting a row vector to a column vector (notice the second pair of brackets) or vice versa:

## 1.11 Flattening a Matrix

You need to transform a matrix into a one-dimensional array.

Use flatten :

flatten is a simple method to transform a matrix into a one-dimensional array. Alternatively, we can use reshape to create a row vector:

## 1.12 Finding the Rank of a Matrix

You need to know the rank of a matrix.

Use NumPy’s linear algebra method matrix_rank :

The rank of a matrix is the dimensions of the vector space spanned by its columns or rows. Finding the rank of a matrix is easy in NumPy thanks to matrix_rank .

The Rank of a Matrix, CliffsNotes

## 1.13 Calculating the Determinant

You need to know the determinant of a matrix.

Use NumPy’s linear algebra method det :

It can sometimes be useful to calculate the determinant of a matrix. NumPy makes this easy with det .

The determinant | Essence of linear algebra, chapter 5, 3Blue1Brown

Determinant, Wolfram MathWorld

## 1.14 Getting the Diagonal of a Matrix

You need to get the diagonal elements of a matrix.

Use diagonal :

NumPy makes getting the diagonal elements of a matrix easy with diagonal . It is also possible to get a diagonal off from the main diagonal by using the offset parameter:

## 1.15 Calculating the Trace of a Matrix

You need to calculate the trace of a matrix.

Use trace :

The trace of a matrix is the sum of the diagonal elements and is often used under the hood in machine learning methods. Given a NumPy multidimensional array, we can calculate the trace using trace . We can also return the diagonal of a matrix and calculate its sum:

The Trace of a Square Matrix

## 1.16 Finding Eigenvalues and Eigenvectors

You need to find the eigenvalues and eigenvectors of a square matrix.

Use NumPy’s linalg.eig :

Eigenvectors are widely used in machine learning libraries. Intuitively, given a linear transformation represented by a matrix, A , eigenvectors are vectors that, when that transformation is applied, change only in scale (not direction). More formally:

where A is a square matrix, λ contains the eigenvalues and v contains the eigenvectors. In NumPy’s linear algebra toolset, eig lets us calculate the eigenvalues, and eigenvectors of any square matrix.

Eigenvectors and Eigenvalues Explained Visually, Setosa.io

Eigenvectors and eigenvalues | Essence of linear algebra, Chapter 10, 3Blue1Brown

## 1.17 Calculating Dot Products

You need to calculate the dot product of two vectors.

Use NumPy’s dot :

The dot product of two vectors, a and b , is defined as:

where a i is the i th element of vector a . We can use NumPy’s dot function to calculate the dot product. Alternatively, in Python 3.5+ we can use the new @ operator:

Vector dot product and vector length, Khan Academy

Dot Product, Paul’s Online Math Notes

## 1.18 Adding and Subtracting Matrices

You want to add or subtract two matrices.

Use NumPy’s add and subtract :

Alternatively, we can simply use the + and - operators:

## 1.19 Multiplying Matrices

You want to multiply two matrices.

Alternatively, in Python 3.5+ we can use the @ operator:

If we want to do element-wise multiplication, we can use the * operator:

Array vs. Matrix Operations, MathWorks

## 1.20 Inverting a Matrix

You want to calculate the inverse of a square matrix.

Use NumPy’s linear algebra inv method:

The inverse of a square matrix, A , is a second matrix A –1 , such that:

where I is the identity matrix. In NumPy we can use linalg.inv to calculate A –1 if it exists. To see this in action, we can multiply a matrix by its inverse and the result is the identity matrix:

Inverse of a Matrix

## 1.21 Generating Random Values

You want to generate pseudorandom values.

Use NumPy’s random :

NumPy offers a wide variety of means to generate random numbers, many more than can be covered here. In our solution we generated floats; however, it is also common to generate integers:

Alternatively, we can generate numbers by drawing them from a distribution:

Finally, it can sometimes be useful to return the same random numbers multiple times to get predictable, repeatable results. We can do this by setting the “seed” (an integer) of the pseudorandom generator. Random processes with the same seed will always produce the same output. We will use seeds throughout this book so that the code you see in the book and the code you run on your computer produces the same results.

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Are you looking to become a Python developer? With its versatility and widespread use in the tech industry, Python has become one of the most popular programming languages today. One factor to consider is whether you prefer self-paced learn...

A matrix work environment is a structure where people or workers have more than one reporting line. Typically, it’s a situation where people have more than one boss within the workplace.

An orthogonal matrix is a square matrix with real entries whose columns and rows are orthogonal unit vectors or orthonormal vectors. Similarly, a matrix Q is orthogonal if its transpose is equal to its inverse.

4 Answers 4 ... Each row points to the same sublist. This is the result of appending the same sublist repeatedly. So when you modify one row, you

Python Matrices and NumPy Arrays. A matrix is a two-dimensional data structure where numbers are arranged into rows and columns. For example:.

Method 1: Assign value to an individual cell in Matrix. Here we are replacing and assigning value to an individual cell (1 row and 1 column = 11)

In other words, it is a rectangular array of data or numbers. The horizontal entries in a matrix are called as 'rows' while the vertical entries

ctypes. An object to simplify the interaction of the array with the ctypes module. data. Python buffer object

To update the values in the row of a matrix we simply re-assign the values at the index of

Element Assignment in NumPy Arrays. We can assign new values to an element of a NumPy array using the = operator, just like regular python lists. A few

To create a matrix we can use a NumPy two-dimensional array. In our solution, the matrix contains three rows and two columns (a column of 1s and a column of 2s)

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A matrix in Python is a rectangular Numpy array. This array has to be two-dimensional. It contains data stored in the array's rows and columns. In a Python