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 machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

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.

In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

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.