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# How to convert a sparse dataframe matrix to a dense matrix dataframe using pandas

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

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.

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