How to use seaborn for statistical data visualization

How to use Seaborn for statistical data visualization

Introduction:
Statistical data visualization is a very important part of data analysis. It can help us better understand the data and discover the patterns hidden in it. . Seaborn is a Python data visualization library based on Matplotlib. It provides some advanced statistical drawing functions to make the data visualization process more concise and beautiful.

This article will introduce how to use Seaborn for statistical data visualization and demonstrate its usage through sample code.

1. Install the Seaborn library
Before we begin, we first need to install the Seaborn library. It can be installed through the pip command:

pip install seaborn

2. Import the Seaborn library and other necessary libraries
After the installation is completed, we need to import the Seaborn library and other necessary libraries into the code. Typically, we also import the NumPy and Pandas libraries for data processing, and the Matplotlib library for custom plotting.

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

3. Load sample data sets
The Seaborn library provides some sample data sets to demonstrate various drawing functions. In this article, we will use the "tips" data set that comes with Seaborn. You can use the following code to load this data set:

tips = sns.load_dataset("tips")

The Tips data set is a data set about restaurant consumption, including consumption amount, consumption time, gender, smoking status and other information.

4. Draw statistical charts
Next, we can start drawing statistical charts. The Seaborn library provides a variety of plotting functions, including the display of one- and two-dimensional discrete and continuous data.

1. Drawing a histogram
   Histograms can be used to show the distribution of one-dimensional data. The distplot() function in Seaborn can draw histograms and kernel density estimation maps at the same time.

sns.distplot(tips['total_bill'], bins=10, kde=True)
plt.show()

With the above code, we can draw a histogram of the total amount spent in the restaurant. Among them, total_bill is a field in the Tips data set, the bins parameter specifies the number of columns of the histogram, and the kde parameter can control whether to draw the kernel density estimation map.

2. Drawing a scatter plot
   A scatter plot can be used to show the relationship between two continuous variables. The scatterplot() function in Seaborn can draw scatter plots.

sns.scatterplot(x='total_bill', y='tip', data=tips)
plt.show()

With the above code, we can draw a scatter plot between the total amount spent in the restaurant and the tip. Among them, the x parameter specifies the variable on the x-axis, the y parameter specifies the variable on the y-axis, and the data parameter specifies the data set to be used.

3. Drawing histograms
   Histograms can be used to display the frequency distribution of discrete variables. The countplot() function in Seaborn can draw a histogram.

sns.countplot(x='day', data=tips)
plt.show()

Through the above code, we can draw a histogram of the number of consumption on different days. Among them, the x parameter specifies the variable on the x-axis, and the data parameter specifies the data set to be used.

4. Drawing box plots
   Box plots can be used to display summary statistical information of a set of data, including median, quartiles, outliers, etc. The boxplot() function in Seaborn can draw box plots.

sns.boxplot(x='day', y='total_bill', hue='smoker', data=tips)
plt.show()

Through the above code, we can draw a box plot of the consumption amount on different days and classify it according to smoking status. Among them, the x parameter specifies the variable on the x-axis, the y parameter specifies the variable on the y-axis, the hue parameter specifies the variable used for classification, # The ##data parameter specifies the data set to be used.

The Seaborn library also provides many functions for customizing chart styles, which can help us create more beautiful charts.

Set chart style
1. The Seaborn library provides a variety of built-in chart styles, which can be set using the
   set_style() function before drawing.

sns.set_style("ticks")

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Adjust the palette
2. The Seaborn library provides a variety of preset palettes, which can be set using the
   set_palette() function.

sns.set_palette("husl", 4)

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With the above code, we can set the palette to "husl" and use 4 colors.
6. Summary

This article introduces how to use Seaborn for statistical data visualization. First, we installed the Seaborn library and imported the required libraries. Then, the sample data set was loaded. Next, Seaborn's drawing functions are demonstrated by drawing histograms, scatter plots, bar charts, and box plots. Finally, it also explains how to set the chart style and color palette.


Through the rich drawing functions and customization options provided by the Seaborn library, we can easily create beautiful and informative statistical charts, providing more powerful tools and support for data analysis. Hope this article helps you!

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