分类数据可视化 - 统计图
barplot() / countplot() / pointplot()
1. barplot()
#柱状图 - 置信区间估计
#置信区间：样本均值 + 抽样误差
示例1：
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set_context('paper') #加载数据 titanic = sns.load_dataset('titanic') print(titanic.head()) sns.barplot(x = 'sex', y = 'survived', hue = 'class', data = titanic, palette = 'hls', order = ['male', 'female'], #筛选类别 capsize = 0.05, #误差线横向延申宽度 saturation = 8, #颜色饱和度 errcolor = 'gray', errwidth = 2, #误差线颜色、宽度 ci = 'sd' #置信区间误差 --> 0-100内值、 'sd' 、None ) #计算数据 print(titanic.groupby(['sex', 'class']).mean()['survived']) print(titanic.groupby(['sex', 'class']).std()['survived'])
示例2：
tips = sns.load_dataset('tips') sns.barplot(x = 'day', y = 'total_bill', hue = 'sex', data = tips, palette = 'Blues', edgecolor = 'w') tips.groupby(['day','sex']).mean()
示例3:
#加载数据 crashes = sns.load_dataset('car_crashes').sort_values('total', ascending = False) #创建图表 f, ax = plt.subplots(figsize = (6,15)) #设置第一个柱状图 sns.set_color_codes('pastel') sns.barplot(x = 'total', y = 'abbrev', data = crashes, label = 'Total', color = 'b', edgecolor = 'w') #设置第二个柱状图 sns.set_color_codes('muted') sns.barplot(x = 'alcohol', y = 'abbrev', data = crashes, label = 'Alcohol-involved', color = 'b', edgecolor = 'w') ax.legend(ncol = 2, loc = 'lower right') sns.despine(left = True, bottom = True)
2、countplot()
#计数柱状图
#x/y --> 以x或者y轴绘图（横向，竖向）
#用法和barplot相似
sns.countplot(x = 'class', hue = 'who', data = titanic, palette = 'magma')
sns.pointplot(x = 'time', y = 'total_bill', hue = 'smoker', data=tips, palette = 'hls', dodge = True, #设置点是否分开 join = True, #是否连线 markers = ['o','x'],linestyles = ['-','--'],#设置点样式、线型 ) #计算数据 tips.groupby(['time','smoker']).mean()['total_bill']
3.pointplot（）
#折线图 - 置信区间估计
