import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid", color_codes=True)
np.random.seed(sum(map(ord, "categorical")))
titanic = sns.load_dataset("titanic") #加载内置的数据 库
tips = sns.load_dataset("tips")
iris = sns.load_dataset("iris")
sns.stripplot(x="day", y="total_bill", data=tips);
重叠是很常见的现象,但是重叠影响我观察数据的量了
sns.stripplot(x="day", y="total_bill", data=tips, jitter=True) #jitter在x y两个方向上偏移
sns.swarmplot(x="day", y="total_bill", data=tips)
sns.swarmplot(x="day", y="total_bill", hue="sex",data=tips)
sns.swarmplot(x="day", y="total_bill", hue="sex",data=tips)
sns.boxplot(x="day", y="total_bill", hue="time", data=tips);
sns.violinplot(x="total_bill", y="day", hue="time", data=tips);
sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True);# split代表左右分开 代表的是sex的Male和Female
sns.violinplot(x="day", y="total_bill", data=tips, inner=None)
sns.swarmplot(x="day", y="total_bill", data=tips, color="w", alpha=0.1) #alpha 代表的是里面小圈的透明度
显示值的集中趋势可以用条形图
sns.barplot(x="sex", y="survived", hue="class", data=titanic); #查看不同的仓级别 获救的情况
sns.pointplot(x="sex", y="survived", hue="class", data=titanic);
sns.pointplot(x="class", y="survived", hue="sex", data=titanic,
palette={"male": "g", "female": "m"},
markers=["^", "o"], linestyles=["-", "--"]); #maekers代表点的标记 linestyles代表线的标记
sns.boxplot(data=iris,orient="h");
sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips)
sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips, kind="bar")
sns.factorplot(x="day", y="total_bill", hue="smoker",
col="time", data=tips, kind="swarm")
sns.factorplot(x="time", y="total_bill", hue="smoker",
col="day", data=tips, kind="box", size=4, aspect=.5)
seaborn.factorplot(x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=, ci=95, n_boot=1000, units=None, order=None, hue_order=None, row_order=None, col_order=None, kind='point', size=4, aspect=1, orient=None, color=None, palette=None, legend=True, legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None, **kwargs)