DATA MUNGING
DATA CLEANING PYTHON
MACHINE LEARNING RECIPES
PANDAS CHEATSHEET
ALL TAGS
# How to create a sparse Matrix in Python?

# How to create a sparse Matrix in Python?

This recipe helps you create a sparse Matrix in Python

Many a times we work on matrices in python and making Sparse Matrix manually is quite a hectic process but we know how to use python and using we can do this very well for us.

There are two popular kinds of matrices: dense and sparse. Sparse matrices have lots of 'zero' values. In machine learning projects, the learning algorithms require the data to be in-memory. If the data needed for the learning (dataframe) is not in the RAM, then the algorithm does not work. By converting a dense matrix into a sparse matrix it can be made to fit in the RAM.

There are many data structures that can be used to construct a sparse matrix in python. Python Scipy provides the following ways to represent a sparse matrix:

- Block Sparse Row matrix (BSR)

- Coordinate list matrix (COO)

- Compressed Sparse Column matrix (CSC)

- Compressed Sparse Row matrix (CSR)

- Sparse matrix with DIAgonal storage (DIA)

- Dictionary Of Keys based sparse matrix (DOK)

- Row-based linked list sparse matrix (LIL)

The recipe above takes a dense matrix and displays the various formats of sparse matrix that scipy supports.

References: https://docs.scipy.org/doc/scipy/reference/sparse.html

So this is the recipe on how we can create a sparse Matrix in Python.

```
import numpy as np
from scipy import sparse
```

We have imported numpy and sparse modules which will be requied.

We have created a matrix of which we will calculate sparse Matrix.
```
matrix = np.array([[9, 8, 7],
[6, 5, 4],
[3, 2, 1]])
print()
print("Original Matrix: \n", matrix)
```

We have created various sparse matrices by passing the original matix from the required functions.

- Creating Dictionary Of Keys based sparse matrix (DOK).
- Creating Block Sparse Row matrix (BSR).
- Creating Coordinate list matrix (COO)
- Creating Compressed Sparse Column matrix (CSC)
- Creating Compressed Sparse Row matrix (CSR)
- Creating Sparse matrix with DIAgonal storage (DIA)
- Creating Row-based linked list sparse matrix (LIL)

```
print(sparse.dok_matrix(matrix))
```

```
print(sparse.bsr_matrix(matrix))
```

```
print(sparse.coo_matrix(matrix))
```

```
print(sparse.csc_matrix(matrix))
```

```
print(sparse.csr_matrix(matrix))
```

```
print(sparse.dia_matrix(matrix))
```

```
print(sparse.lil_matrix(matrix))
```

Original Matrix: [[9 8 7] [6 5 4] [3 2 1]] Sparse Matrices: (0, 0) 9 (0, 1) 8 (0, 2) 7 (1, 0) 6 (1, 1) 5 (1, 2) 4 (2, 0) 3 (2, 1) 2 (2, 2) 1 (0, 0) 9 (0, 1) 8 (0, 2) 7 (1, 0) 6 (1, 1) 5 (1, 2) 4 (2, 0) 3 (2, 1) 2 (2, 2) 1 (0, 0) 9 (0, 1) 8 (0, 2) 7 (1, 0) 6 (1, 1) 5 (1, 2) 4 (2, 0) 3 (2, 1) 2 (2, 2) 1 (0, 0) 9 (1, 0) 6 (2, 0) 3 (0, 1) 8 (1, 1) 5 (2, 1) 2 (0, 2) 7 (1, 2) 4 (2, 2) 1 (0, 0) 9 (0, 1) 8 (0, 2) 7 (1, 0) 6 (1, 1) 5 (1, 2) 4 (2, 0) 3 (2, 1) 2 (2, 2) 1 (2, 0) 3 (1, 0) 6 (2, 1) 2 (0, 0) 9 (1, 1) 5 (2, 2) 1 (0, 1) 8 (1, 2) 4 (0, 2) 7 (0, 0) 9 (0, 1) 8 (0, 2) 7 (1, 0) 6 (1, 1) 5 (1, 2) 4 (2, 0) 3 (2, 1) 2 (2, 2) 1

In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Use the Adult Income dataset to predict whether income exceeds 50K yr based on
census data.

In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.

In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.