Seaborn Heatmap Size | Code Underscored

A heatmap is used to create a visual description of a matrix. It draws a matrix on the graph, with different shades of color representing different values. We can use seaborn to generate heatmap plots in the seaborn module.heatmap() function. The default plot size may not provide a good picture of the data when plotting a huge matrix.

Annotations are lines of text that appear in a heatmap cell to describe what the cell represents.

The font size of the notes is set by default. However, it can be changed using the annot kws option of the heatmap() method of the heatmap method. The annot kws option is of dictionary type and requires a value for the size key. The value assigned to this key determines the size of the notes. However, various requirements must be met to increase the size of the annotations, such as setting the heatmap() function’s annot argument to True and selecting the required length for the annot kws option.

This guide will address this issue and teach you how to resize sea-born heatmaps. We can use functions from the matplotlib-axes library because the heatmap() returns a matplotlib-axes object.

The heatmap syntax in Seaborn is:

seaborn.heatmap(data,  vmin=None, vmax=None,  annot=None,annot_kws=True, linewidths=0,cbar=None, cbar_kws=None,square=False, xticklabels="auto", yticklabels="auto", mask=None, ax=None, kwargs)

• Data: Convert a 2D dataset to an ndarray. The column and row names will be obtained using index/column information from the Pandas DataFrame.
• vmin, vmax: values ​​to be subtracted from the dataset and other term receivers to anchor the colormap.
• annot: Fill each cell with the data value if True. If it’s an array-like object with the same format as data, use it to annotate the heatmap rather than the data. Instead of using an index, DataFrames will be adjusted based on their location.
• annot_kws: When annot is True, the keyword parameters are provided to matplotlib.axes.Axes.text().
• Latitude: The distance between the lines used to divide each cell.
• cbar: bool argument determines whether a color bar is drawn or not.
• cbar_ax: the axes from which the color bar will be created; Otherwise, the place in the main routes will be occupied.
• Square: If true, change the axes property to “equals” so that each cell is square.
• fmt: Use this string formatting code to add comments.
• kwargs: All other keyword options are passed to Matplotlib.axes.Axes.pcolormesh() as kwargs.
• xticklabels, yticklabels: If true, graph the column names of the data frame with xticklabels and yticklabels. The column names are not formatted if this is False. If the alternate labels are xticklabels, list them. If the number is an integer, use the field names but only the first in the labels. If you use “Auto”, try to draw as many non-overlapping labels as possible.
• Mask: If this option is set to True, no data will be displayed in the cells when the mask is True.
• Masked cells are cells with empty values.
• ax: the axes on which the slide will be built; Otherwise, use the currently active axes

How to resize the Seaborn Heatmap using the seaborn.set() function

The definition and theme of the plots born in the sea are defined using the set() function. The plot size can be specified using the rc parameter.

For example,

import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd


rs_df = pd.DataFrame({"First Month": [7,1,5,6,3,10,5,8],
                    "Second Month" : [1,2,8,4,3,9,5,2],
                    "Third Month" : [4,6,5,8,6,1,2,3],
                    "Fourth Month" : [5,8,9,5,1,7,8,9]})

sns.set(rc = {'figure.figsize':(15,8)})
sns.heatmap(rs_df.corr())

It is worth noting that the value of the rc parameter is provided as a dictionary. The final width and height are provided as a tuple.

To resize the Seaborn Heatmap, use the matplotlib.pyplot.figure() function.

In Python, the figure() method is used to initialize or modify the current figure. This diagram depicts the heat map. The figsize parameter can be used in the function to change the size. For example,

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

rs_df = pd.DataFrame({"First Month": [7,1,5,6,3,10,5,8],
                    "Second Month" : [1,2,8,4,3,9,5,2],
                    "Third Month" : [4,6,5,8,6,1,2,3],
                    "Fourth Month" : [5,8,9,5,1,7,8,9]})



plt.figure(figsize = (15,8))
sns.heatmap(rs_df.corr())

It is worth noting that the function is called before the heatmap() method. To resize a Seaborn plot, use the matplotlib.pyplot.gcf() function.

The gcf() function returns the character’s display object. The set_size_inches() method can be used to resize this object. Now we can resize the heatmap visualization in this object. For example,

import matplotlib.pyplot as plt

import pandas as pd
import seaborn as sns

rs_df = pd.DataFrame({"First Month": [7,1,5,6,3,10,5,8],
                    "Second Month" : [1,2,8,4,3,9,5,2],
                    "Third Month" : [4,6,5,8,6,1,2,3],
                    "Fourth Month" : [5,8,9,5,1,7,8,9]})



sns.heatmap(rs_df.corr())
plt.gcf().set_size_inches(15, 8)

This method is called after the heatmap() function. It should also be noted that the amount of comments in the heat map is not significantly affected by any of the methods mentioned above.

To increase the number of annotations, set the annot parameter in the heatmap() function to True. The font size can then be specified as a key-value pair in the annot kws parameter, such as annot kws =’size’:15.

Example 1: Examining the figure() function

The figure() function in Python initializes or modifies the current figure. The heat map is depicted in this diagram. The figsize parameter of the function can be used to adjust the size. We must first create data to create the plot with the selected character size. We created a data frame with four columns, List1, List2, List3 and List4, and filled them with random values. So we have a figure() method where we set the size of the figure. Finally, the heatmap function is used to apply the correct technique to the data frame.

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

lst_dt = pd.DataFrame({"First List": [5,8,9,5,1,7,8,9],

"Second List" : [4,6,5,8,6,1,2,3],

"Third List" : [1,2,8,4,3,9,5,2],

"Fourth List" : [7,1,5,6,3,10,5,8]})

plt.figure(figsize = (15,7))

sns.heatmap(lst_dt.corr())

plt.show()

Example 2: What to consider before determining the size

Consideration should be given to determining the size. When you enter a large number, the notes will be significantly enlarged, making them impossible to read and decipher. They may even collapse on top of each other. As a result, the heatmap no longer functions.

We selected the iris data frame and loaded it into the load_dataset method. Call the heatmap function with the annot argument set to true and annot_kws set to 20.

import matplotlib.pyplot as plt

Import seaborn as sns

var_data = sns.load_dataset("iris")

sns-heatmap(var_data.corr(), annot=Truc, annot_kws={'size': 20})

plt.show()

Example: Using set() to set the layout and theme

The set() function sets the layout and theme of Seaborn plots. The RC option can be used to determine the plot size. In the following example, we have defined the modules used in the Python script. Next, we created data in the kpis variable and ran the dataframe function. We recorded the kpis of the employees in four student columns in the data frame function. We have already entered the plot data.

The set function is now defined, and the plot size is indicated by the size of the figs. The Seaborn heatmap function is then called, and the corr function is applied to the kpis. The corr() function returns all the columns of the data frame, and displays a pairwise correlation.

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

kpis = pd.DataFrame({"Employee One": [6,3,1,7,3,10,5,4],

"Employee Two" : [3,7,2,1,8,2,4,2],

"Employee Three" : [1,6,9,8,6,4,9,3],

"Employee Four" : [5,5,1,9,4,7,8,3]})

sns.set(rc = {'figure.figsize':(10,5)})

sns .heatmap(kpis.corr())

plt.show()

Example 4: In Seaborn, how can heatmaps be resized?

We’ll use the Seaborn data flights for this example, which contains the number of airline passengers flown each month from 1949 to 1960:

import seaborn as sns

import matplotlib.pyplot as plt


#loading the dataset "flights."
var_data = sns.load_dataset("flights")
var_data = var_data.pivot("month", "year", "passengers")

#view first five rows of dataset
print(var_data.head())

Example 5: Using annot_kws and annot for heatmap sizing

For heatmap size, we use the annot and annot_kws options. We used the Seaborn load_dataset option to load the “tips” sample dataset, which is saved in the variable data. The heatmap function was then called, and the corr function was provided for the dataset. Then we added the comment option and set it to true. The size of the annot kws parameter is set to 12.

import numpy as np

import seaborn as sns

import matplotlib.pyplot as plt

var_data = sns.load_dataset("tips")

sns-heatmap(var_data .corr(), annot=True, annot_kws={'size': 12})

plt.show()

Summary

Seaborn is a Python data visualization package based on the matplotlib library. The latter represents the data in a relevant and statistically attractive graphical format. A heatmap uses a color palette to visualize variation in linked data, one of Seaborn’s capabilities. To create heatmap charts in the Seaborn module, use the seaborn.heatmap() method.

Annotations are pieces of text that appear in heatmap cells to represent what the cell represents. Additionally, the font size of the annotations is set by default, but it can be changed, which is done by the annot_kws parameter of the heatmap() function. annot_kws is a dictionary type parameter that allows the value named size values. The value assigned to this key determines the size of the notes. However, certain conditions must be met to increase the size of notes.