How to do data visualization and exploration in Python

How to perform data visualization and exploration in Python

Data visualization and exploration is one of the important aspects of data analysis. In Python, with the help of various powerful libraries and Tools allow us to easily visualize and explore data. This article will introduce commonly used data visualization libraries and techniques in Python, and give specific code examples.

1. Introduction
   Data visualization is a method of displaying abstract data in an intuitive and easy-to-understand way. Through visualization, we can better understand the distribution, relationships and characteristics of data. There are many libraries and tools for data visualization in Python, such as Matplotlib, Seaborn, Plotly, etc.
2. Data preparation
   Before performing data visualization, you first need to prepare the data to be analyzed. This article takes the Iris data set as an example. The Iris data set is a classic data set in the UCI machine learning library. It contains 150 samples of three varieties of iris flowers (Setosa, Versicolor, and Virginica). Each sample contains Four characteristics (Sepal length, Sepal width, Petal length, Petal width) are included.

First, you need to install the pandas library for data processing and analysis. Then, use the following code to read the Iris data set and prepare for simple data visualization:

import pandas as pd

Read the Iris data set

iris_data = pd.read_csv ('iris.csv')

View the first few rows of the data set

print(iris_data.head())

View the basic information of the data set

print(iris_data.info())

3. Single variable data visualization
   Single variable data visualization refers to visualizing the distribution of a single variable. Commonly used methods include histograms, histograms, and boxplots.

Taking Sepal length (calyx length) as an example, the code example of using the Matplotlib library to draw a histogram is as follows:

import matplotlib.pyplot as plt

Draw a column Figure

plt.bar(iris_data['Species'], iris_data['Sepal length'])
plt.xlabel('Species') # Set the x-axis label
plt.ylabel(' Sepal length') # Set the y-axis label
plt.title('Distribution of Sepal length') # Set the chart title
plt.show()

In addition, you can also use the Seaborn library to draw the histogram Figures and boxplots. The following is a code example for drawing a histogram:

import seaborn as sns

Drawing a histogram

sns.histplot(data=iris_data, x='Sepal length', kde =True)
plt.xlabel('Sepal length') # Set the x-axis label
plt.ylabel('Count') # Set the y-axis label
plt.title('Distribution of Sepal length') #Set chart title
plt.show()

4. Double-variable data visualization
   Double-variable data visualization refers to visualizing the relationship between two variables. Commonly used methods include scatter plots and heat maps.

Taking Sepal length and Petal length as an example, the code example for using the Matplotlib library to draw a scatter plot is as follows:

Draw a scatter plot

plt.scatter( iris_data['Sepal length'], iris_data['Petal length'])
plt.xlabel('Sepal length') # Set the x-axis label
plt.ylabel('Petal length') # Set the y-axis label
plt.title('Relationship between Sepal length and Petal length') #Set the chart title
plt.show()

In addition, you can also use the Seaborn library to draw a heat map to show the relationship between variables correlation. The following is a code example for drawing a heat map:

Calculate the correlation coefficient matrix between variables

correlation_matrix = iris_data[['Sepal length', 'Sepal width', 'Petal length', ' Petal width']].corr()

Draw a heat map

sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix ')
plt.show()

5. Multivariable data visualization
   Multivariable data visualization refers to visualizing the relationship between multiple variables. Commonly used methods include scatter matrices and parallel coordinate plots.

Taking the four features of the Iris data set as an example, the code example of using the Seaborn library to draw the scatter matrix is ​​as follows:

Draw the scatter matrix

sns. pairplot(iris_data, hue='Species')
plt.show()

In addition, you can also use the Plotly library to draw parallel coordinate plots. The following is a code example for drawing parallel coordinate plots:

import plotly.express as px

Draw parallel coordinates graph

fig = px.parallel_coordinates(iris_data, color='Species')
fig.show()

Summary
This article introduces methods for data visualization and exploration in Python and gives specific code examples. Through data visualization and exploration, we can better understand the distribution, relationships, and characteristics of data, thereby providing a foundation and guidance for subsequent data analysis and modeling. In practical applications, appropriate visualization methods and technologies can also be selected based on specific needs and data characteristics to further explore the value of data.

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