seaborn可视化库分析库基础02

%matplotlib inlineimport pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltfrom scipy import stats, integratesns.set(color_codes=True)np.random.seed(sum(map(ord, "discributions")))x = np.random.normal(size=100)#sns.distplot(x, kde=False)sns.distplot(x, bins=20, kde=False, fit=stats.gamma)

%matplotlib inlineimport pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltfrom scipy import stats, integratemean,cov = [0,1], [(1,0.5), (0.5,1)]data = np.random.multivariate_normal(mean, cov, 200)df = pd.DataFrame(data, columns=["x", "y"]) #将数据转化成pandas的Dataframe格式sns.jointplot(x="x", y="y", data=df)x,y = np.random.multivariate_normal(mean, cov, 1000).Twith sns.axes_style("white"):    sns.jointplot(x=x, y=y, kind="hex", color="k")

iris = sns.load_dataset("iris")sns.pairplot(iris)

np.random.seed(sum(map(ord, "regression")))tips = sns.load_dataset("tips")print (tips.head())# 使用regplot回归分析sns.regplot(x="total_bill", y="tip", data=tips)# 使用rlmplot回归分析sns.lmplot(x="total_bill", y="tip", data=tips)

%matplotlib inlineimport pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltfrom scipy import stats, integratenp.random.seed(sum(map(ord, "regression")))tips = sns.load_dataset("tips")sns.regplot(data=tips, x="size", x_jitter=0.05, y="tip")

%matplotlib inlineimport pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltfrom scipy import stats, integratesns.set(style="whitegrid", color_codes=True)tips = sns.load_dataset("tips")# 对于分类的数据绘制散点图# sns.stripplot(data=tips, x="day", y="total_bill", jitter=True)sns.swarmplot(data=tips, x="day", y="total_bill", hue="sex")

# 盒图# IQR即统计学概念的四分位距，第一四分位 与 第三四分位之间的距离# N=1.5IQR 如果一个值>Q3+N 或 <Q1-N，则为离群点%matplotlib inlineimport pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltfrom scipy import stats, integratetips = sns.load_dataset("tips")sns.boxplot(data=tips, x="day", y="total_bill", hue="time")

# 小提琴图（功能类似于盒图）tips = sns.load_dataset("tips")sns.violinplot(data=tips, x="day", y="total_bill", hue="sex", split=True)

sns.violinplot(data=tips, x="day", y="total_bill", inner=None)sns.swarmplot(data=tips, x="day", y="total_bill", color="w", alpha=0.5)

# 柱形图titanic = sns.load_dataset("titanic")sns.barplot(data=titanic, x="sex", y="survived", hue="class")

# 点图 - 描述差异性titanic = sns.load_dataset("titanic")sns.pointplot(data=titanic, x="class", y="survived", hue="sex",               palette={"male":"g", "female":"m"}, markers=["^", "o"], linestyles=["-", "--"])

# 多面板分类图(hue是分组绘图参数)tips = sns.load_dataset("tips")sns.factorplot(data=tips, x="day", y="total_bill", hue="smoker", kind="bar")

sns.factorplot(data=tips, x="day", y="total_bill", hue="smoker", col="time", kind="swarm")

# x,y,hue： xy数据、分组统计字段# row,col：更多分类变量进行平铺显示# col_wrap：每行的最高平铺数（整数）# estimator：在每个分类中进行矢量到标量的映射（矢量）# ci：置信区间（浮点数或None）# n_boot：计算置信区间时使用的引导迭代次数（整数）# utils：采样单元的标识符，用于执行多级引导和重复测量设计（数据变量或者向量数据）# order,hue_order：对应排序列表（字符串列表）# row_order,col_order：对应排序列表（字符串列表）# kind：可选（默认为point）。bar柱形图，count频次，box箱体，violin提琴图，strip散点图，swarm分散点，# size：每个面的高度[英寸]# aspect：横纵比（标量）# orient：方向 v/h# color：颜色# matplotlib：颜色# palette：调色板# seaborn：颜色色板或字典# legend：hue的信息面板 (True/False)# legent_out：是否扩展图形，并将信息框绘制在中心右边（True/False）# share{x,y}：共享轴线 True/Falsesns.factorplot(data=tips, x="time", y="total_bill", hue="smoker", col="day", kind="box", size=4, aspect=0.5)

tips = sns.load_dataset("tips")pal = {'Lunch': 'seagreen', 'Dinner': 'gray'}g = sns.FacetGrid(tips, hue="time", hue_kws={"marker":["o","+"]}, palette=pal, size=5)g.map(plt.scatter, "total_bill", "tip", s=50, alpha=0.7, linewidth=0.5, edgecolor="white")g.add_legend()

with sns.axes_style("white"):    g = sns.FacetGrid(tips, row="sex", col="smoker", margin_titles=True, size=2.5)g.map(plt.scatter, "total_bill", "tip", color="#334488", edgecolor="white", lw=0.5)g.set_axis_labels("Total bill (US Dollars)", "Tip")g.set(xticks=[10,30,50], yticks=[2,6,10])g.fig.subplots_adjust(wspace=0.02, hspace=0.02)

iris = sns.load_dataset("iris")print (iris.head())g = sns.PairGrid(iris, hue="species")g.map_diag(plt.hist)g.map_offdiag(plt.scatter)g.add_legend()#g.map(plt.scatter)

# 取数据的子集(vars)g = sns.PairGrid(iris, vars=["sepal_length", "sepal_width"], hue="species")g.map(plt.scatter)

# 热度图%matplotlib inlineimport pandas as pdimport numpy as npimport seaborn as snsimport matplotlib.pyplot as pltsns.set()uniform_data = np.random.rand(3,3)print (uniform_data)heatmap = sns.heatmap(uniform_data)

# 指定显示的数据区间print (uniform_data)ax = sns.heatmap(uniform_data, vmin=0.2, vmax=0.5)

normal_data = np.random.randn(3,3)print (normal_data)ax = sns.heatmap(normal_data, center=0)

# seaborn航班数据flights = sns.load_dataset("flights")print (flights.head())flights = flights.pivot("month", "year", "passengers")print (flights.head())#linewidths表示元素间的间距（默认没有间距）#annot表示是否显示数值，d表示数值以数字显示，e表示数值以科学计数法显示ax = sns.heatmap(flights, linewidths=0.5, annot=True, fmt="d")

# 指定热图的调色板 cmapax = sns.heatmap(flights, linewidths=0.5, annot=True, fmt="d", cmap="YlGnBu")