Is it possible to adjust the size of squares (cells) in Seaborn heatmap?
Question:
Suppose I have a heatmap plot like this:
Using this data:
import numpy as np
import pandas as pd
arr = np.array([[ 2, 2, 2, 8, 7, 7, 6, 5, 2, 7, 7, 8, 7, 5, 6, 6, 6],
[ 8, 7, 5, 4, 4, 3, 9, 6, 7, 4, 3, 2, 8, 9, 3, 3, 3],
[ 1, 3, 2, 2, 2, 3, 5, 3, 3, 2, 3, 3, 4, 1, 10, 10, 10],
[ 3, 2, 4, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 9, 9, 9],
[ 7, 6, 7, 6, 6, 6, 2, 2, 5, 6, 5, 4, 7, 9, 9, 9, 9],
[ 6, 7, 8, 4, 3, 4, 4, 8, 7, 3, 4, 5, 6, 3, 4, 4, 4],
[ 3, 1, 1, 9, 9, 9, 3, 1, 8, 9, 9, 9, 1, 6, 1, 1, 1],
[ 3, 3, 3, 5, 5, 5, 5, 1, 2, 5, 6, 5, 10, 8, 8, 8, 8],
[ 1, 1, 1, 2, 3, 2, 7, 3, 1, 3, 2, 2, 10, 8, 7, 7, 7],
[ 5, 5, 2, 2, 2, 1, 1, 3, 3, 2, 1, 1, 5, 2, 7, 7, 7],
[ 7, 9, 10, 3, 4, 4, 8, 9, 9, 3, 4, 6, 2, 3, 2, 2, 2],
[ 5, 6, 7, 3, 3, 3, 3, 1, 4, 4, 3, 4, 9, 10, 2, 2, 2],
[ 4, 4, 3, 4, 4, 4, 3, 4, 3, 4, 4, 3, 2, 7, 10, 10, 10],
[ 2, 1, 1, 8, 8, 8, 1, 4, 2, 8, 8, 8, 4, 1, 5, 5, 5],
[ 9, 9, 8, 8, 8, 8, 5, 6, 8, 8, 8, 5, 1, 5, 2, 2, 2],
[ 5, 5, 5, 5, 5, 5, 4, 2, 1, 5, 5, 4, 6, 5, 5, 5, 5],
[ 8, 8, 9, 10, 10, 10, 6, 7, 6, 10, 10, 10, 3, 7, 4, 4, 4],
[ 9, 8, 10, 5, 7, 7, 10, 10, 9, 6, 5, 6, 5, 6, 3, 3, 3],
[10, 9, 9, 7, 6, 5, 10, 10, 9, 8, 7, 8, 3, 10, 8, 8, 8],
[10, 10, 8, 10, 10, 10, 2, 5, 10, 10, 10, 9, 7, 9, 3, 3, 3],
[ 4, 4, 5, 3, 2, 2, 9, 8, 4, 2, 2, 3, 4, 4, 5, 5, 5],
[ 4, 4, 4, 7, 5, 6, 4, 4, 4, 5, 6, 7, 10, 2, 8, 8, 8],
[ 7, 8, 6, 6, 8, 8, 7, 9, 8, 7, 8, 7, 9, 8, 6, 6, 6],
[ 8, 7, 7, 7, 7, 7, 8, 9, 5, 7, 7, 7, 5, 7, 1, 1, 1],
[ 1, 2, 3, 1, 1, 1, 9, 7, 7, 1, 1, 1, 9, 3, 4, 4, 4],
[ 2, 5, 6, 1, 1, 2, 7, 5, 6, 1, 2, 2, 8, 4, 1, 1, 1],
[10, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 10, 7, 7, 7],
[ 6, 3, 4, 9, 9, 9, 8, 7, 5, 9, 9, 10, 1, 2, 10, 10, 10],
[ 9, 10, 10, 9, 9, 9, 1, 8, 10, 9, 9, 9, 8, 4, 9, 9, 9]])
columns = ["feature1", "feature2", "feature3", "feature4", "feature5", "feature6", "feature7", "feature8", "feature9", "feature10", "feature11", "feature12", "feature13", "feature14", "feature15", "feature16", "feature17"]
indexes = ['AAPL', 'AMGN', 'AXP', 'BA', 'CAT', 'CRM', 'CSCO', 'CVX', 'DIS', 'GS',
'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK',
'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'V', 'VZ', 'WBA', 'WMT']
df = pd.DataFrame(arr, columns=columns, index=indexes)
Using this code:
import seaborn as sns
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,10), dpi=600)
a = sns.heatmap(df, annot=True, cmap="RdBu_r", square=True, ax=ax)
plt.show()
I want to adjust each cell’s size based on its value! I mean, the square cells with the value of 1 should be smaller than those with higher values!
Example:
Note that this example is not strictly related to the values of the previous heatmap plot! I just provided an example to show what I mean by adjusting each square cell size based on its value.
Answers:
This is something you can accomplish with scatterplot or relplot:
flights = sns.load_dataset("flights")
g = sns.relplot(
data=flights,
x="year", y="month", size="passengers", hue="passengers",
marker="s", sizes=(40, 400), palette="blend:b,r",
)
(This post elaborates on @mwaskom’s excellent solution, adapted to the given dataframe.)
For most seaborn functions, it helps to have the dataframe in "long form".
Here is an example of how your dataframe could be transformed to long form to get to the format used by e.g. sns.relplot or sns.scatterplot. Probably, it will be easier to start from the original dataframe used to create the pivot table.
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
arr = np.array([[2, 2, 2, 8, 7, 7, 6, 5, 2, 7, 7, 8, 7, 5, 6, 6, 6], [8, 7, 5, 4, 4, 3, 9, 6, 7, 4, 3, 2, 8, 9, 3, 3, 3], [1, 3, 2, 2, 2, 3, 5, 3, 3, 2, 3, 3, 4, 1, 10, 10, 10], [3, 2, 4, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 9, 9, 9], [7, 6, 7, 6, 6, 6, 2, 2, 5, 6, 5, 4, 7, 9, 9, 9, 9], [6, 7, 8, 4, 3, 4, 4, 8, 7, 3, 4, 5, 6, 3, 4, 4, 4], [3, 1, 1, 9, 9, 9, 3, 1, 8, 9, 9, 9, 1, 6, 1, 1, 1], [3, 3, 3, 5, 5, 5, 5, 1, 2, 5, 6, 5, 10, 8, 8, 8, 8], [1, 1, 1, 2, 3, 2, 7, 3, 1, 3, 2, 2, 10, 8, 7, 7, 7], [5, 5, 2, 2, 2, 1, 1, 3, 3, 2, 1, 1, 5, 2, 7, 7, 7], [7, 9, 10, 3, 4, 4, 8, 9, 9, 3, 4, 6, 2, 3, 2, 2, 2], [5, 6, 7, 3, 3, 3, 3, 1, 4, 4, 3, 4, 9, 10, 2, 2, 2], [4, 4, 3, 4, 4, 4, 3, 4, 3, 4, 4, 3, 2, 7, 10, 10, 10], [2, 1, 1, 8, 8, 8, 1, 4, 2, 8, 8, 8, 4, 1, 5, 5, 5], [9, 9, 8, 8, 8, 8, 5, 6, 8, 8, 8, 5, 1, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 4, 2, 1, 5, 5, 4, 6, 5, 5, 5, 5], [8, 8, 9, 10, 10, 10, 6, 7, 6, 10, 10, 10, 3, 7, 4, 4, 4], [9, 8, 10, 5, 7, 7, 10, 10, 9, 6, 5, 6, 5, 6, 3, 3, 3], [10, 9, 9, 7, 6, 5, 10, 10, 9, 8, 7, 8, 3, 10, 8, 8, 8], [10, 10, 8, 10, 10, 10, 2, 5, 10, 10, 10, 9, 7, 9, 3, 3, 3], [4, 4, 5, 3, 2, 2, 9, 8, 4, 2, 2, 3, 4, 4, 5, 5, 5], [4, 4, 4, 7, 5, 6, 4, 4, 4, 5, 6, 7, 10, 2, 8, 8, 8], [7, 8, 6, 6, 8, 8, 7, 9, 8, 7, 8, 7, 9, 8, 6, 6, 6], [8, 7, 7, 7, 7, 7, 8, 9, 5, 7, 7, 7, 5, 7, 1, 1, 1], [1, 2, 3, 1, 1, 1, 9, 7, 7, 1, 1, 1, 9, 3, 4, 4, 4], [2, 5, 6, 1, 1, 2, 7, 5, 6, 1, 2, 2, 8, 4, 1, 1, 1], [10, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 10, 7, 7, 7], [6, 3, 4, 9, 9, 9, 8, 7, 5, 9, 9, 10, 1, 2, 10, 10, 10], [9, 10, 10, 9, 9, 9, 1, 8, 10, 9, 9, 9, 8, 4, 9, 9, 9]])
columns = [f"feature{i}" for i in range(1, 18)]
indexes = ['AAPL', 'AMGN', 'AXP', 'BA', 'CAT', 'CRM', 'CSCO', 'CVX', 'DIS', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'V', 'VZ', 'WBA', 'WMT']
df = pd.DataFrame(arr, columns=columns, index=indexes)
df.index.name = 'Ticker'
df_long = df.reset_index().melt(id_vars='Ticker', var_name='Feature', value_name='Value')
sns.set_style('darkgrid')
g = sns.relplot(data=df_long, x="Feature", y="Ticker", size="Value", hue="Value",
marker="s", sizes=(20, 200), palette="blend:limegreen,orange", height=8, aspect=1.1)
g.ax.tick_params(axis='x', labelrotation=45)
g.ax.set_facecolor('aliceblue')
g.ax.grid(color='red', lw=1)
g.fig.subplots_adjust(left=0.1, bottom=0.15)
plt.show()
Suppose I have a heatmap plot like this:
Using this data:
import numpy as np
import pandas as pd
arr = np.array([[ 2, 2, 2, 8, 7, 7, 6, 5, 2, 7, 7, 8, 7, 5, 6, 6, 6],
[ 8, 7, 5, 4, 4, 3, 9, 6, 7, 4, 3, 2, 8, 9, 3, 3, 3],
[ 1, 3, 2, 2, 2, 3, 5, 3, 3, 2, 3, 3, 4, 1, 10, 10, 10],
[ 3, 2, 4, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 9, 9, 9],
[ 7, 6, 7, 6, 6, 6, 2, 2, 5, 6, 5, 4, 7, 9, 9, 9, 9],
[ 6, 7, 8, 4, 3, 4, 4, 8, 7, 3, 4, 5, 6, 3, 4, 4, 4],
[ 3, 1, 1, 9, 9, 9, 3, 1, 8, 9, 9, 9, 1, 6, 1, 1, 1],
[ 3, 3, 3, 5, 5, 5, 5, 1, 2, 5, 6, 5, 10, 8, 8, 8, 8],
[ 1, 1, 1, 2, 3, 2, 7, 3, 1, 3, 2, 2, 10, 8, 7, 7, 7],
[ 5, 5, 2, 2, 2, 1, 1, 3, 3, 2, 1, 1, 5, 2, 7, 7, 7],
[ 7, 9, 10, 3, 4, 4, 8, 9, 9, 3, 4, 6, 2, 3, 2, 2, 2],
[ 5, 6, 7, 3, 3, 3, 3, 1, 4, 4, 3, 4, 9, 10, 2, 2, 2],
[ 4, 4, 3, 4, 4, 4, 3, 4, 3, 4, 4, 3, 2, 7, 10, 10, 10],
[ 2, 1, 1, 8, 8, 8, 1, 4, 2, 8, 8, 8, 4, 1, 5, 5, 5],
[ 9, 9, 8, 8, 8, 8, 5, 6, 8, 8, 8, 5, 1, 5, 2, 2, 2],
[ 5, 5, 5, 5, 5, 5, 4, 2, 1, 5, 5, 4, 6, 5, 5, 5, 5],
[ 8, 8, 9, 10, 10, 10, 6, 7, 6, 10, 10, 10, 3, 7, 4, 4, 4],
[ 9, 8, 10, 5, 7, 7, 10, 10, 9, 6, 5, 6, 5, 6, 3, 3, 3],
[10, 9, 9, 7, 6, 5, 10, 10, 9, 8, 7, 8, 3, 10, 8, 8, 8],
[10, 10, 8, 10, 10, 10, 2, 5, 10, 10, 10, 9, 7, 9, 3, 3, 3],
[ 4, 4, 5, 3, 2, 2, 9, 8, 4, 2, 2, 3, 4, 4, 5, 5, 5],
[ 4, 4, 4, 7, 5, 6, 4, 4, 4, 5, 6, 7, 10, 2, 8, 8, 8],
[ 7, 8, 6, 6, 8, 8, 7, 9, 8, 7, 8, 7, 9, 8, 6, 6, 6],
[ 8, 7, 7, 7, 7, 7, 8, 9, 5, 7, 7, 7, 5, 7, 1, 1, 1],
[ 1, 2, 3, 1, 1, 1, 9, 7, 7, 1, 1, 1, 9, 3, 4, 4, 4],
[ 2, 5, 6, 1, 1, 2, 7, 5, 6, 1, 2, 2, 8, 4, 1, 1, 1],
[10, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 10, 7, 7, 7],
[ 6, 3, 4, 9, 9, 9, 8, 7, 5, 9, 9, 10, 1, 2, 10, 10, 10],
[ 9, 10, 10, 9, 9, 9, 1, 8, 10, 9, 9, 9, 8, 4, 9, 9, 9]])
columns = ["feature1", "feature2", "feature3", "feature4", "feature5", "feature6", "feature7", "feature8", "feature9", "feature10", "feature11", "feature12", "feature13", "feature14", "feature15", "feature16", "feature17"]
indexes = ['AAPL', 'AMGN', 'AXP', 'BA', 'CAT', 'CRM', 'CSCO', 'CVX', 'DIS', 'GS',
'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK',
'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'V', 'VZ', 'WBA', 'WMT']
df = pd.DataFrame(arr, columns=columns, index=indexes)
Using this code:
import seaborn as sns
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,10), dpi=600)
a = sns.heatmap(df, annot=True, cmap="RdBu_r", square=True, ax=ax)
plt.show()
I want to adjust each cell’s size based on its value! I mean, the square cells with the value of 1 should be smaller than those with higher values!
Example:
Note that this example is not strictly related to the values of the previous heatmap plot! I just provided an example to show what I mean by adjusting each square cell size based on its value.
This is something you can accomplish with scatterplot or relplot:
flights = sns.load_dataset("flights")
g = sns.relplot(
data=flights,
x="year", y="month", size="passengers", hue="passengers",
marker="s", sizes=(40, 400), palette="blend:b,r",
)
(This post elaborates on @mwaskom’s excellent solution, adapted to the given dataframe.)
For most seaborn functions, it helps to have the dataframe in "long form".
Here is an example of how your dataframe could be transformed to long form to get to the format used by e.g. sns.relplot or sns.scatterplot. Probably, it will be easier to start from the original dataframe used to create the pivot table.
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
arr = np.array([[2, 2, 2, 8, 7, 7, 6, 5, 2, 7, 7, 8, 7, 5, 6, 6, 6], [8, 7, 5, 4, 4, 3, 9, 6, 7, 4, 3, 2, 8, 9, 3, 3, 3], [1, 3, 2, 2, 2, 3, 5, 3, 3, 2, 3, 3, 4, 1, 10, 10, 10], [3, 2, 4, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 9, 9, 9], [7, 6, 7, 6, 6, 6, 2, 2, 5, 6, 5, 4, 7, 9, 9, 9, 9], [6, 7, 8, 4, 3, 4, 4, 8, 7, 3, 4, 5, 6, 3, 4, 4, 4], [3, 1, 1, 9, 9, 9, 3, 1, 8, 9, 9, 9, 1, 6, 1, 1, 1], [3, 3, 3, 5, 5, 5, 5, 1, 2, 5, 6, 5, 10, 8, 8, 8, 8], [1, 1, 1, 2, 3, 2, 7, 3, 1, 3, 2, 2, 10, 8, 7, 7, 7], [5, 5, 2, 2, 2, 1, 1, 3, 3, 2, 1, 1, 5, 2, 7, 7, 7], [7, 9, 10, 3, 4, 4, 8, 9, 9, 3, 4, 6, 2, 3, 2, 2, 2], [5, 6, 7, 3, 3, 3, 3, 1, 4, 4, 3, 4, 9, 10, 2, 2, 2], [4, 4, 3, 4, 4, 4, 3, 4, 3, 4, 4, 3, 2, 7, 10, 10, 10], [2, 1, 1, 8, 8, 8, 1, 4, 2, 8, 8, 8, 4, 1, 5, 5, 5], [9, 9, 8, 8, 8, 8, 5, 6, 8, 8, 8, 5, 1, 5, 2, 2, 2], [5, 5, 5, 5, 5, 5, 4, 2, 1, 5, 5, 4, 6, 5, 5, 5, 5], [8, 8, 9, 10, 10, 10, 6, 7, 6, 10, 10, 10, 3, 7, 4, 4, 4], [9, 8, 10, 5, 7, 7, 10, 10, 9, 6, 5, 6, 5, 6, 3, 3, 3], [10, 9, 9, 7, 6, 5, 10, 10, 9, 8, 7, 8, 3, 10, 8, 8, 8], [10, 10, 8, 10, 10, 10, 2, 5, 10, 10, 10, 9, 7, 9, 3, 3, 3], [4, 4, 5, 3, 2, 2, 9, 8, 4, 2, 2, 3, 4, 4, 5, 5, 5], [4, 4, 4, 7, 5, 6, 4, 4, 4, 5, 6, 7, 10, 2, 8, 8, 8], [7, 8, 6, 6, 8, 8, 7, 9, 8, 7, 8, 7, 9, 8, 6, 6, 6], [8, 7, 7, 7, 7, 7, 8, 9, 5, 7, 7, 7, 5, 7, 1, 1, 1], [1, 2, 3, 1, 1, 1, 9, 7, 7, 1, 1, 1, 9, 3, 4, 4, 4], [2, 5, 6, 1, 1, 2, 7, 5, 6, 1, 2, 2, 8, 4, 1, 1, 1], [10, 10, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 3, 10, 7, 7, 7], [6, 3, 4, 9, 9, 9, 8, 7, 5, 9, 9, 10, 1, 2, 10, 10, 10], [9, 10, 10, 9, 9, 9, 1, 8, 10, 9, 9, 9, 8, 4, 9, 9, 9]])
columns = [f"feature{i}" for i in range(1, 18)]
indexes = ['AAPL', 'AMGN', 'AXP', 'BA', 'CAT', 'CRM', 'CSCO', 'CVX', 'DIS', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'V', 'VZ', 'WBA', 'WMT']
df = pd.DataFrame(arr, columns=columns, index=indexes)
df.index.name = 'Ticker'
df_long = df.reset_index().melt(id_vars='Ticker', var_name='Feature', value_name='Value')
sns.set_style('darkgrid')
g = sns.relplot(data=df_long, x="Feature", y="Ticker", size="Value", hue="Value",
marker="s", sizes=(20, 200), palette="blend:limegreen,orange", height=8, aspect=1.1)
g.ax.tick_params(axis='x', labelrotation=45)
g.ax.set_facecolor('aliceblue')
g.ax.grid(color='red', lw=1)
g.fig.subplots_adjust(left=0.1, bottom=0.15)
plt.show()