Python图表数据可视化Seaborn：4结构化图表可视化

Posted 2021-01-09 kris12

 1.基本设置

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

sns.set_style("ticks")
sns.set_context("paper")
# 设置风格、尺度

import warnings
warnings.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