How to convert a sparse dataframe matrix to a dense matrix dataframe?

How to convert a sparse dataframe matrix to a dense matrix dataframe?

How to convert a sparse dataframe matrix to a dense matrix dataframe?

This recipe helps you convert a sparse dataframe matrix to a dense matrix dataframe


Recipe Objective

Sparse objects are 'compressed' when any data matching a specific value (NaN / missing value, though any value can be chosen) is omitted. A special SparseIndex object tracks where data has been 'sparsifie'.

So this recipe is a short example on How to convert a sparse dataframe/matrix to a dense matrix/dataframe. Let's get started.

Step 1 - Import the library

import pandas as pd

Let's pause and look at these imports. Pandas is generally used for performing mathematical operation and preferably over arrays.

Step 2 - Setup the Data

df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])})

Here we have setup a random dataframe.

Now our dataset is ready.

Step 3 - Sparse to dense

df.sparse.to_dense() print(df)

Simply set sparse.to_dense for coverstion.

Step 4 - Let's look at our dataset now

Once we run the above code snippet, we will see:

Scroll down to the ipython file to look at the results.

Relevant Projects

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
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.

Build an Image Classifier for Plant Species Identification
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.

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

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

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Topic modelling using Kmeans clustering to group customer reviews
In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
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.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.