# Set Size of Seaborn Heatmap

The heatmap is used to produce a graphical representation of a matrix. It plots a matrix on the graph and uses different color shades for different values.

We can use the `seaborn.heatmap()` function to create heatmap plots in the seaborn module.

While representing a large matrix, the default size of the plot may not provide a clear representation of the data.

In this tutorial, we will tackle this problem and learn how to alter the size of seaborn heatmaps.

Since the `heatmap()` returns a matplotlib-axes object, we can use functions from that library, too.

## Use the `seaborn.set()` function to set the seaborn heatmap size

The `set()` function defines the configuration and theme of the seaborn plots. We can mention the size of the plot in the `rc` parameter.

For example,

``````import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame({"Day 1": [7,1,5,6,3,10,5,8],
"Day 2" : [1,2,8,4,3,9,5,2],
"Day 3" : [4,6,5,8,6,1,2,3],
"Day 4" : [5,8,9,5,1,7,8,9]})

sns.set(rc = {'figure.figsize':(15,8)})
sns.heatmap(df.corr())
`````` Note that the value for the `rc` parameter is specified as a dictionary. The final height and width are passed as a tuple.

## Use the `matplotlib.pyplot.figure()` function to set the seaborn heatmap size

The `figure()` function is used to initiate or customize the current figure in Python. The heatmap is plotted in this figure. The size can be altered using the `figsize` parameter in the function.

For example,

``````import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame({"Day 1": [7,1,5,6,3,10,5,8],
"Day 2" : [1,2,8,4,3,9,5,2],
"Day 3" : [4,6,5,8,6,1,2,3],
"Day 4" : [5,8,9,5,1,7,8,9]})

plt.figure(figsize = (15,8))
sns.heatmap(df.corr())
`````` Note that the function is used before the `heatmap()` function.

## Use the `matplotlib.pyplot.gcf()` function to set the size of a seaborn plot

The `gcf()` function returns a view instance object of the figure. The size of this object can be altered using the `set_size_inches()` method. In this way, we can set the size of the heatmap plot on this object.

For example,

``````import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame({"Day 1": [7,1,5,6,3,10,5,8],
"Day 2" : [1,2,8,4,3,9,5,2],
"Day 3" : [4,6,5,8,6,1,2,3],
"Day 4" : [5,8,9,5,1,7,8,9]})

sns.heatmap(df.corr())
plt.gcf().set_size_inches(15, 8)
`````` Note that this method is used after the `heatmap()` function.

Additionally, it should be noted that in all the above-used methods, the size of annotations in the heatmap is not affected much.

To increase the size of annotations, we need to set the `annot` parameter to True in the `heatmap()` function. Then we can specify the font size as a key-value pair in the `annot_kws` parameter like `annot_kws = {'size':15}`.

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## Related Article - Seaborn Heatmap

• Correlation Heatmap in Seaborn