Time series data visualization techniques in Python

As data continues to grow and change, time series data analysis becomes more and more important. As an open source, flexible, and easy-to-use programming language, Python is also widely used in the field of time series data analysis. In this article, we will explore time series data visualization techniques in Python to help you better understand and analyze time series data.

1. Introduction to Matplotlib library

Matplotlib is a widely used visualization library in Python, supporting various types of charts, such as line charts, bar charts, pie charts, scatter charts, etc. . Its use is simple and easy to understand, and it can be included in the standard Python installation package. Below we will introduce how to use Matplotlib to draw time series data graphs.

2. Line chart

The line chart is the most common way to present time series data charts. Through the line chart, the trends and changes of the data can be clearly displayed, and the differences between the data at different times can be more intuitively expressed. Next, we first construct a set of test data:

import numpy as np

# 生成随机正弦函数
x = np.linspace(0, 10, 1000)
y = np.sin(x) + np.random.randn(len(x)) * 0.1

# 绘制折线图
import matplotlib.pyplot as plt

plt.plot(x, y)
plt.show()

As shown above, we generated an arithmetic sequence x containing 1000 numbers through the linspace function, and calculated the sine function on it, plus a Random noise term, thus obtaining a sample data y. Then we call the plot function in Matplotlib, pass in x and y, and call the show function to display the line chart.

3. Area chart

The area chart can be used to present the changing trend of time series data and show the relationship between a certain indicator and the overall total. In Matplotlib, you can easily create area plots using the fill_between function. Next we construct another set of test data and draw the area chart:

# 生成随机的正态分布函数
x = np.linspace(0, 10, 1000)
y = np.random.randn(len(x)).cumsum()

# 绘制区域图
plt.fill_between(x, y, color='skyblue', alpha=0.2)
plt.plot(x, y, color='skyblue', alpha=0.6)
plt.show()

In this example, we first use the cumsum function to integrate the random normal distribution function, get the cumulative sum, and then use the fill_between function A blue area map is drawn and its transparency is adjusted. Use the plot function to draw a line chart of the cumulative sum, and specify the color and transparency of the line type.

4. Heat map

Heat map can be used to present data changes within a certain time range, and represents the change trend of the data through changes in color. In Matplotlib, you only need to call the imshow function to create a heat map. Below we draw a heat map through the book_data data set:

from matplotlib import cm

# 导入book_data数据集
from vega_datasets import data
book_data = data.books()

# 绘制热度图
fig, ax = plt.subplots(figsize=(12, 6))
im = ax.imshow(book_data.pivot(index='year', columns='category', values='books'),
               cmap=cm.Reds, interpolation='nearest')

# 设置图表元素
ax.set_xticks(np.arange(len(book_data['category'].unique())))
ax.set_yticks(np.arange(len(book_data['year'].unique())))
ax.set_xticklabels(book_data['category'].unique())
ax.set_yticklabels(book_data['year'].unique())
ax.set_title('书籍类别和年份图书数量热度图')

# 添加坐标轴的标签
ax.set_xlabel('书籍类别')
ax.set_ylabel('年份')

# 添加颜色条
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel('图书数量', rotation=-90, va="bottom")

# 调整布局
plt.tight_layout()
plt.show()

By importing the book_data data set, we obtained a data table containing the number of books of various categories in each year. Then we use the imshow function to map the values ​​in the data table to the red palette, and add some chart elements, such as axis labels, titles, color bars, etc. By adjusting the layout, you can make the chart more beautiful and easier to understand.

5. Summary

This article introduces the main function of the Matplotlib library and several common time series data visualization techniques: line charts, area charts, and heat maps. These techniques can help us better understand and analyze time series data to make more accurate and valuable decisions. At the same time, in actual use, you can also adjust the chart elements to make the chart more beautiful and understandable.

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