Listado de la etiqueta: Seaborn

Heatmaps are colored maps that display data in a two-dimensional format. Color variation is achieved by using hue, saturation, or brightness to portray the varied information on the color maps. This color variation provides the readers with visual information about the size of quantitative values. Heatmaps substitute numbers with colors since the human mind understands views better than the textual data. Considering that humans are primarily visual, it makes sense to present the data in any manner. Heatmaps are simple-to-understand visual representations of data. As a result, data visualization tools like Heatmaps are becoming increasingly popular.

Heatmaps are used to display patterns, variance, and anomalies, as well as to depict the saturation or intensity of the variables. Relationships between variables are depicted via heatmaps. Both axes are used to plot these variables. By observing the color shift in the cell, we can look for the patterns. It only takes numerical input and shows it on the grid, with different data values displayed by the varying color intensity.

Many various color schemes can be used to depict the heat map, each with its own set of perceptual advantages and disadvantages. Colors in the Heatmap indicate patterns in the data, thus the color palette decisions are more than just cosmetic. The finding of patterns can be facilitated by the appropriate color palettes but can also be hindered by the poor color choices.

Colormaps are used to visualize heatmaps since they are a simple and effective way to see data. Diverse colormaps could be utilized for different sorts of heatmaps. In this article, we’ll explore how to interact with Seaborn heatmaps using the colormaps.

Example 1: Set the Sequential Colormaps Plot

When the data values (numeric) change from high to low and just one of them is significant for the analysis, we utilize the sequential colormaps. Note that we created a colormap with sns.color palette() and displayed the colors in the colormap with sns.palplot(). The following instance explains how to generate a sequential colormap heatmap with the Seaborn module.

In the following python script, we provide the three modules that are necessary for the code to work. Then, we insert the seed value zero into the random function for generating random numbers. We create the field data where the rand function is called that generates a random number in a specified interval for the x-axis and y-axis.  Then, we create a colormap variable where the color_palette created the color “Reds”. In the end, the cmap color is utilized for the heatmap.

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
data = np.random.rand(10, 10)
colormap = sns.color_palette(«Reds»)
ax = sns.heatmap(data, cmap=colormap)

The sequential color heatmap is represented like this from the previous script.  

Example 2: Set the Sequential Colormaps with the Cmap Argument Plot

As “Reds” is a built-in colormap in seaborn, it can also be passed straight to the cmap argument.

It’s worth noting that our colormap has a continuous color intensity, as opposed to the previous one, which had a discrete green intensity for a range of possible values. Here’s a deeper look at the colormaps obtained in the heatmaps mentioned in the following illustration:

We passed a zero for the rand seed and generated the random number by using the rand function inside the variable data. We set the interval (15,15) for both the x-axis and the y-axis. Then, we passed an argument cmap which has the color “Blues” inside the heatmap function. This creates the “Blue” color variations of the heatmap.

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
data = np.random.rand(15, 15)
ax = sns.heatmap(data, cmap=«Blues»)

The Blues’ sequential color intensity plot is shown inside the figure along with the color bar of the specified color.

Example 3: Set the Diverging Colormaps Plot

They’re utilized to depict the numeric values that range from high to low (and vice versa), with both the maximum and minimum values being important. On a Seaborn heatmap, the following example explains how to use a diverging colormap.

Here, we import the Seaborn library which is installed in our python language. The Matplotlib library is also used for the visualization of the plot. We have another module which is NumPy for the NumPy features. Then, by utilizing the NumPy module, we have the np.random.seed function which passes a value of zero used to initialize the random numbers.

Inside the variable data, we call a NumPy function rand which set the number limit for both the axes in the plot. Then, we have a Seaborn heatmap function, which takes the argument cmap. The cmap is set with the default color scheme which is the coolwarm colors.

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
data = np.random.rand(10, 12)
ax = sns.heatmap(data, cmap=«coolwarm»)

In the following figure, we have a customized heatmap using cmap:

Example 4: Set the Cbar Parameter Plot

The heatmap’s cbar attribute is a Boolean value that implies whether it should be plotted. The color bar is featured in the graph by default if the cbar parameter isn’t specified. Switch the cbar to False to disable the color bar. The cbar=False parameter in the heatmap() method can be used to disable the colorbar of the heatmap in Seaborn.

We required the four libraries; the additional library is the pandas for the data frame which we utilize in the code. With the Seaborn, we call a set function here. Then, with the loaded dataset function, the sample dataset flights are added and stored in the df variable.

In the next line, we have a pivot function that takes the column-wise data and groups the data accordingly. We pass the three columns: months, year, and passengers from the flight’s dataset.  Now, invoking the seaborn heatmap function sets the cbar argument to a false value. With the plt show function, the plot is rendered.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset(«flights»)
df = df.pivot(«month», «year», «passengers»)
ax = sns.heatmap(df, cbar=False)

The cbar is removed from the heatmap plot in the given figure:


It’s simple to work with the Seaborn heatmaps. We discussed the two types of color maps which include the sequential and diverging color maps. We explained them briefly along with the running example with the Python Seaborn compiler inside the Ubuntu 20.04.

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Seaborn is a Matplotlib-based visual analytics library. It has a high-level framework for defining the visually appealing analytical graphs. Matplotlib package is the foundation of the Seaborn module. To visualize the statistics and regression analysis, we use the regplot() function.

To evaluate the regression model, there are many other interrelated contradictory approaches. Whenever the predicted output is a continuous as well as a cumulative value, it is referred to as a prediction model. Numerous other approaches can be employed. The most basic of which is the linear model. It integrates the values to the optimal higher dimensional space that passes through all of the vertices. The regplot() function is used to create the regression plots.

Regression Analysis is a technique used for evaluating the associations between one or more independent factors or predictors and the dependent attributes or covariates. The variations in the requirements in correlation to modifications in specific determinants are analyzed through the Regression Analysis. The criteria’s declarative requirement is dependent on the indicators, which give the new value of the dependent attributes whenever the data points are updated. Evaluating the intensity of covariates, anticipating an outcome, and estimating are the three important applications of a regression model.

Example 1

In this step, we use the regplot() method to draw the regression plot of the data frame “mpg”.

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset(«mpg»)

sns.regplot(x = «mpg»,
            y = «acceleration»,
            data = data)

At the start of the program, we imported the required frameworks, Seaborn and matplotlib.pyplot. Seaborn is a Python module for creating numerical visuals. It is effectively correlated to the matplotlib library. Seaborn library assists users in accessing and evaluating the data. Among the most widely utilized modules for data analysis is Matplotlib. This library is a cross-platform package that creates two-dimensional charts using a range of data. It includes an Interface for integrating graphs in Python Graphical framework based on applications.

Here, we get a dataset of “mpg” by applying the load_dataset() method. This method is taken from the Seaborn library. The regplot() function is employed to draw the regression plots. The Seaborn module contains the regplot() function. This method contains three parameters. The x-axis of the histogram holds the values of mpg. Whereas the y-axis of the regression plot holds the values of acceleration. In the end, we use the function to represent the plot.

Example 2

Another visualization method of plotting the regression plot is by applying the regplot() method. Here, we employ this method on the “titanic” data set.

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset(«titanic»)

sns.regplot(x = «age»,
            y = «fare»,
            data = data,
            dropna = True)

First of all, we integrate the header files. The Seaborn library is integrated as sns and matplotlib.pyplot is integrated as plt. In the next step, we load the required data frame so, we apply the load_dataset() method. This function contains the “titanic” parameter as we want the dataset of the titanic. The Seaborn package holds the function of load_dataset(). In the following step, we utilize the regplot() function. This function creates the regression visual of the titanic dataset. The function contains different arguments including the data, the value of the x-axis, y-axis, data, and dropna.

Here, we provide the value of the “dropna” attribute. By specifying the “dropna” parameter to True, we may insert a curvature to a plot. The x-axis of the regression map is labeled as “age” and the y-axis is labeled as “fare”. The method is applied to illustrate the resultant graph.

Example 3

The regplot() method of the Seaborn library can also be used to create a regression plot. In this case, we create a regression plot of the data set “exercise”.

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset(«exercise»)

sns.regplot(x = «id»,
            y = «pulse»,
            data = data)

Here, we introduce the essential libraries, Seaborn as sns and matplotlib.pyplot as plt. We apply the load_dataset() function of the Seaborn module to acquire the “exercise”  data. The gathered data is saved in the “data” attribute. The regression plot is created by using the regplot() method. This method is found in the Seaborn package. This method has a variable that represents the id, pulse, and data of the graph. Lastly, to depict the plot, we employ the method.

Example 4

In this instance, the regplot() method specifies a data set of “attention” and values of both the x-axis and y-axis.

import seaborn as sns
import matplotlib.pyplot as plt

data = sns.load_dataset(«attention»)

sns.regplot(x = «solutions»,
            y = «score»,
            data = data)

We start by integrating the packages sns and plt. The seaborn library is incorporated as sns. Matplotlib is used to integrate plt. We now retrieve the appropriate data set. As a result, we use the load_dataset() function. If we want a database of attention, this method has an “attention” argument. The load_dataset() method is part of the Seaborn package.

After this, the regplot() method of the Seaborn module is applied. This module creates the regression plot. The function takes the several parameters such as data, x-axis value, and y-axis value. The regression map’s x-axis is marked as “solutions” and the y-axis is marked as “score”. The obtained regression plot is then visualized by using the function.


In this article, we talked about the numerous methods of creating the regression plots in Seaborn. We utilized the regplot() method to draw the regression plots. Furthermore, we drew regression graphs of the different inbuilt data sets of Seaborn. The regression visualizations in the Seaborn package are designed exclusively to provide a visual aid for highlighting the features from the set of data during the data exploration. As the name implies, a regression map draws a regression boundary between two variables and aids in the depiction of the underlying correlation coefficients.

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“Seaborn is a Python module for creating numerical visualizations. It is based on the matplotlib library and extensively interacts with pandas header files. Seaborn assists users in analyzing and comprehending the data. Its visualizing functions work with data structure and arrays, including entire records, providing the required semantic mapping and set of associations internally to generate useful graphs. Its data source, explicit API, allows the users to concentrate on interpreting the charts instead of the technicalities of presenting them.

Seaborn’s plotting interoperability allows the user to access it in various scenarios, such as exploratory analysis, flagrante interactivity in Graphical apps, and archived outcome in a variety of graphic and vector representations.

A box plot tends to keep statistical data organized so that analyses within parameters or even across a set of attributes are clearer. If provided, the percentiles and critical values for the median are shown in the colchoneta frame of the box plot. The data points are horizontal lines that run through the middle of every box, whereas whiskers represent parallel lines that expand to its most excessive data sets, and caps represent lines drawn that run through the whiskers’ edges.

Boxplot can also be utilized to find outliers in a given data frame. We have learned the methods of drawing boxplots by the seaborn module in this article.”

Use of boxplot() Method

The boxplot() function is used to draw a boxplot plot. The iris inflorescence set of data is imported in the instance below. The boxplot eventually displays the lowest, highest, 1st percentile, and 3rd percentile values.

At the beginning of the program, we have to import the libraries. The “seaborn” library is imported as sns, and the library “matplotlib.pyplot” has been imported as plt. Then we declare a variable named “df.” We want to load the data, so we have used the load dataset() function. The data set is stored in the “df” variable. The head() function is being used. To obtain the first n entries, we will be using this function. Dependent on the object’s position, this method contains only the first n records. It’s capable of effectively determining whether the object contains the appropriate sort of data. The series of entries from which to select.

Now we employ the boxplot() function, and we have provided the y-parameter to this function. A boxplot is a standard technique for depicting multidimensional data consisting of five analyses: “minimal,” “first percentile,” “mean,” “third percentile,” and “highest.” To show the final graph, the method is applied.

Boxplot Horizontal

A horizontal plot could be used as a boxplot. We are going to draw the boxplot in the horizontal plane as presented in the figure. We will be using the iris data frame merienda more. The hues displayed are the standard tints; however, they can be changed.

First of all, we have integrated two header files. The header file seaborn and matplotlib.pyplot. The load dataset() is being used to load the dataset in the plot. In the subsequent step, we have added the head() method. This function returns the very first five frames of the dataset by configuration. There is merely one input parameter: the series of rows. This argument allows us to indicate the number of values we require.

Let’s use the boxplot() function as it can indicate further the anomalies and associated values. Here we have given the x-parameter and y-parameter to this function. The label of the y-axis of the plot is taken as the name of the species, and the label of the x-axis of the graph is taken from sepal_length. We have applied the show() function to represent the resultant plot.

Customize the Colors of the Boxplot

Shades for the boxplot plot can be customized. By specifying the “palette” attribute, we may accomplish this. There have been a variety of palettes, and the palette “magma” includes a wide range of magma.

We specify a variant called “df” after introducing the libraries seaborn and matplotlib into the program. Then we have been using the load dataset() method to retrieve the data. In the “df” variable, the data set is saved. The method head() will be applied. This function would be used to acquire the first n items.

Now we will employ the boxplot() method, to which we have assigned the x and y-parameters. Along with these parameters, we have specified the color of box plots. We have set the “magma” color to the parameter “palette” in this code. The method is used to illustrate the molded graph.

Adjust the Size of the Boxplot

The width option will be used to change the size of the different boxplots. The standard width is 1; therefore, somewhat less than that makes the boxes shorter.

The required libraries must be loaded at the start of the code. The dataset in the graphic is loaded by using the load dataset() method. The head() method has been used in the subsequent step. By default, this method will provide the first five segments of the set. We’ll utilize the boxplot() method to draw the box plots. This function has been assigned the x-parameter and the already quoted y-parameter. The width of the box plots is also specified.

So, we have provided this function with the “width” argument. The y-axis of the plot is labeled with the name of the species, while the graph’s x-axis is labeled with sepal length. We have been utilizing the show() method to represent the output plot.


In this artifact, we have looked at how to draw boxplots using the seaborn library. We have seen how to change the width and colors of the box plots. The visual depiction of presenting sets of statistical information by their percentile is referred to as a box plot. It effectively encapsulates the collected data using a box and bars and enables us all to evaluate sets directly.

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