Uncovering the magic of Python data visualization

Matplotlib: basic drawing library

Matplotlib is a flexible and powerful 2D plotting library that provides a series of functions to create various types of charts.

import matplotlib.pyplot as plt

# 创建一个简单的折线图
plt.plot([1, 2, 3, 4], [5, 6, 7, 8])
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("折线图示例")
plt.show()

Seaborn: Statistical Graphics

Seaborn is built on top of Matplotlib and provides a high-level interface specifically designed to create beautiful and informative statistical graphics.

import seaborn as sns

# 创建一个直方图
sns.distplot(data["age"])
plt.xlabel("年龄")
plt.ylabel("频率")
plt.title("年龄分布图")
plt.show()

Plotly: Interactive Charts

Plotly allows the creation of interactive charts that can be zoomed, panned and rotated in the browser.

import plotly.express as px

# 创建一个 3D 散点图
fig = px.scatter_3d(data, x="x", y="y", z="z")
fig.show()

Custom chart

Using Matplotlib and Seaborn, we can easily customize the appearance and functionality of the chart.

# 更改图表样式
plt.style.use("ggplot")

# 添加图例
plt.legend(["series1", "series2"])

# 调整字体大小
plt.rcParams["font.size"] = 14

Data Preparation and Exploration

Before visualizing, it is crucial to prepare and explore your data. python Provides libraries such as NumPy and pandas to process and analyze data.

import numpy as np
import pandas as pd

# 导入数据
data = pd.read_csv("data.csv")

# 清洗数据
data["age"] = data["age"].fillna(data["age"].mean())

# 探索数据
print(data.describe())

in conclusion

Python Data Visualization is a powerful tool that transforms complex data into intuitive and actionable insights. With libraries like Matplotlib, Seaborn, and Plotly, we can create various types of charts, customize their appearance, and explore the data to discover meaningful patterns. Using Python's data visualization capabilities, we can effectively communicate and understand data to make informed decisions.

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