# How to visualize (plot) a numpy array in python using seaborn ?

Published: April 12, 2021

Examples of how to visualize (plot) a numpy array in python using seaborn

### Create an a numpy array

Let's first create a random numpy array:

````import numpy as np`

`data = np.random.randint(10, size=(10,8))`

`print(data)`
```

returns for example

````[[9 6 7 8 6 4 4 9]`
` [1 1 4 0 4 6 0 1]`
` [6 9 2 2 8 6 8 0]`
` [9 8 9 1 4 2 2 3]`
` [3 3 4 8 9 9 5 4]`
` [5 4 2 8 7 3 4 7]`
` [0 1 0 0 0 3 0 2]`
` [7 2 6 5 4 4 5 2]`
` [5 2 6 5 6 2 2 2]`
` [3 1 0 5 9 2 2 2]]`
```

### Plotting an array with seaborn

Note: If you want to quickly visualize a not too large numpy array, a solution is to use seaborn with heatmap, example

````import seaborn as sns; sns.set()`
`import matplotlib.pyplot as plt`

`ax = sns.heatmap(data, annot=True, fmt="d")`

`plt.title("How to visualize (plot) \n a numpy array in python using seaborn ?",fontsize=12)`

`plt.savefig("visualize_numpy_array_01.png", bbox_inches='tight', dpi=100)`

`plt.show()`
```

returns

### Removing the colorbar

````ax = sns.heatmap(data, annot=True, fmt="d", cbar=None)`

`plt.title("How to visualize (plot) \n a numpy array in python using seaborn ?",fontsize=12)`

`plt.savefig("visualize_numpy_array_02.png", bbox_inches='tight', dpi=100, )`

`plt.show()`
```

### Removing axis labels

````ax = sns.heatmap(data, annot=True, fmt="d", cbar=None, xticklabels=False, yticklabels=False)`

`plt.title("How to visualize (plot) \n a numpy array in python using seaborn ?",fontsize=12)`

`plt.savefig("visualize_numpy_array_03.png", bbox_inches='tight', dpi=100, )`

`plt.show()`
```

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