MACHINE LEARNING RECIPES
DATA CLEANING PYTHON
DATA MUNGING
PANDAS CHEATSHEET
ALL TAGS
# 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

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.

```
import pandas as pd
```

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

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

Here we have setup a random dataframe.

Now our dataset is ready.

```
df.sparse.to_dense()
print(df)
```

Simply set sparse.to_dense for coverstion.

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

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

In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.

In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.

In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

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

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.

In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.

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.