You can customize a heatmap in several ways. Following examples will demonstrate these ways.
Annotate each cell with value
The heatmap can show the exact value behind the color. To add a label to each cell, annot parameter of the heatmap() function should be set to True.
# libraries
import seaborn as sns
import pandas as pd
import numpy as np
# Create a dataset
df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
# plot a heatmap with annotation
sns.heatmap(df, annot=True, annot_kws={"size": 7})<AxesSubplot:>
Custom grid lines
The following parameters will make customizations to the heatmap plot:
linewidth: the thickness of the lineslinecolor: the color of the lines
# libraries
import seaborn as sns
import pandas as pd
import numpy as np
# Create a dataset
df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
# plot a heatmap with custom grid lines
sns.heatmap(df, linewidths=2, linecolor='yellow')<AxesSubplot:>
Remove X or Y labels
yticklabels and xticklabels control the presence / abscence of labels for the Y and X axis respectively.
# libraries
import seaborn as sns
import pandas as pd
import numpy as np
# Create a dataset
df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
# plot a heatmap
sns.heatmap(df, yticklabels=False)<AxesSubplot:>
Remove color bar
You can remove the color bar from a heatmap plot by giving False to the parameter cbar.
# libraries
import seaborn as sns
import pandas as pd
import numpy as np
# Create a dataset
df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
# plot a heatmap
sns.heatmap(df, cbar=False) <AxesSubplot:>
Hide a few axis labels to avoid overlapping
As you can remove x or y labels by setting xticklabels or yticklabels as False, you can also give an integer to plot only every n label.
# libraries
import seaborn as sns
import pandas as pd
import numpy as np
# Create a dataset
df = pd.DataFrame(np.random.random((10,10)), columns=["a","b","c","d","e","f","g","h","i","j"])
# plot a heatmap
sns.heatmap(df, xticklabels=4)<AxesSubplot:>
