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