Python图表数据可视化Seaborn：4结构化图表可视化，,1.基本设置impo

1.基本设置

import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns% matplotlib inlinesns.set_style("ticks")sns.set_context("paper")# 设置风格、尺度import warningswarnings.filterwarnings(‘ignore‘) # 不发出警告

# 1、基本设置# 绘制直方图tips = sns.load_dataset("tips")print(tips.head())# 导入数据g = sns.FacetGrid(tips, col="time", row="smoker")# 创建一个绘图表格区域，设置好row、col并分组g.map(plt.hist, "total_bill",alpha = 0.5,color = ‘k‘,bins = 10)# 以total_bill字段数据分别做直方图统计

# 1、基本设置# 绘制直方图g = sns.FacetGrid(tips, col="day",                   size=4,    # 图表大小                  aspect=.5) # 图表长宽比g.map(plt.hist, "total_bill", bins=10,      histtype = ‘step‘,   #‘bar‘, ‘barstacked‘, ‘step‘, ‘stepfilled‘      color = ‘k‘)

# 1、基本设置# 绘制散点图g = sns.FacetGrid(tips, col="time",  row="smoker")# 创建一个绘图表格区域，设置好row、col并分组g.map(plt.scatter,       "total_bill", "tip",    # share{x,y} → 设置x、y数据      edgecolor="w", s = 40, linewidth = 1)   # 设置点大小，描边宽度及颜色g.add_legend()# 添加图例

# 1、基本设置# 分类g = sns.FacetGrid(tips, col="time",  hue="smoker")# 创建一个绘图表格区域，设置好col并分组，按hue分类g.map(plt.scatter,       "total_bill", "tip",    # share{x,y} → 设置x、y数据      edgecolor="w", s = 40, linewidth = 1)   # 设置点大小，描边宽度及颜色g.add_legend()# 添加图例

2. 图表矩阵

# 2、图表矩阵attend = sns.load_dataset("attention")print(attend.head())# 加载数据g = sns.FacetGrid(attend, col="subject", col_wrap=5,   # 设置每行的图表数量                  size=1.5)g.map(plt.plot, "solutions", "score",       marker="o",color = ‘gray‘,linewidth = 2)# 绘制图表矩阵g.set(xlim = (0,4),      ylim = (0,10),      xticks = [0,1,2,3,4],      yticks = [0,2,4,6,8,10]      )# 设置x，y轴刻度

Python图表数据可视化Seaborn：4结构化图表可视化