010.python科学计算库seaborn(下)

你的名字 2021-10-06 15:20 349阅读 0赞

### category ###

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() print(tips) # stripplot：画一个散点图，其中一个变量是类别的 sns.stripplot(x="day", y="total_bill", data=tips) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() print(tips) # stripplot：画一个散点图，其中一个变量是类别的 # 重叠是很常见的现象，但是重叠影响我观察数据的量了 # jitter:抖动 sns.stripplot(x="day", y="total_bill", data=tips, jitter=True) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() print(tips) # 画一个无重叠点的分类散点图 sns.swarmplot(x="day", y="total_bill", data=tips) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() print(tips) # 画一个无重叠点的分类散点图 sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() print(tips) # 盒图 # IQR即统计学概念四分位距，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 sns.boxplot(x="day", y="total_bill", hue="time", data=tips) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # 结合箱线图和核密度估算 sns.violinplot(x="total_bill", y="day", hue="time", data=tips) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # 结合箱线图和核密度估算 sns.violinplot(x="total_bill", y="day", hue="sex", data=tips, split=True) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # 结合箱线图和核密度估算 sns.violinplot(x="total_bill", y="day", data=tips, inner=None) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") print(tips) # color：颜色 alpha:透明度 sns.swarmplot(x="day", y="total_bill", data=tips, color="red", alpha=.3) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns titanic = sns.load_dataset("titanic") print(titanic) # 显示值的集中趋势可以用条形图 sns.barplot(x="sex", y="survived", hue="class", data=titanic) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns titanic = sns.load_dataset("titanic") # 点图可以更好的描述变化差异 sns.pointplot(x="sex", y="survived", hue="class", data=titanic) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns titanic = sns.load_dataset("titanic") # 点图可以更好的描述变化差异 sns.pointplot(x="class", y="survived", hue="sex", data=titanic, palette={ "male": "g", "female": "m"}, markers=["^", "o"], linestyles=["-", "--"]) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns iris = sns.load_dataset("iris") # 宽形数据 sns.boxplot(data=iris, orient="h") plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # 多层面板分类图 sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # 多层面板分类图 sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips, kind="bar") plt.show()

![20180926_19_03_58.png][]

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # 多层面板分类图 sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips,col="time", kind="swarm") plt.show()

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### FacetGrid ###

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # FacetGrid:用于绘制条件关系的多图网格 g = sns.FacetGrid(tips, col="time") # map:对每个方面的数据子集应用一个绘图函数 g.map(plt.hist, "tip") plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # FacetGrid:用于绘制条件关系的多图网格 g = sns.FacetGrid(tips, col="sex", hue="smoker") # map:对每个方面的数据子集应用一个绘图函数 g.map(plt.scatter, "total_bill", "tip", alpha=.7) # add_legend:画一个图例，可能把它放在坐标轴外面，调整图形的大小 g.add_legend() plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # FacetGrid:用于绘制条件关系的多图网格 g = sns.FacetGrid(tips, row="smoker", col="time", margin_titles=True) # map:对每个方面的数据子集应用一个绘图函数 # regplot:图数据与线性回归模型拟合 # fit_reg:是否添加线性拟合 g.map(sns.regplot, "size", "total_bill", color="green", fit_reg=False, x_jitter=.1) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # FacetGrid:用于绘制条件关系的多图网格 g = sns.FacetGrid(tips, col="day", size=4, aspect=.5) # map:对每个方面的数据子集应用一个绘图函数 # barplot:以矩形条显示点估计和置信区间 g.map(sns.barplot, "sex", "total_bill") plt.show()

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import matplotlib.pyplot as plt import seaborn as sns from pandas import Categorical tips = sns.load_dataset("tips") ordered_days = tips.day.value_counts().index print (ordered_days) ordered_days = Categorical(['Thur', 'Fri', 'Sat', 'Sun']) # FacetGrid:用于绘制条件关系的多图网格 g = sns.FacetGrid(tips, row="day", row_order=ordered_days, size=1.5, aspect=5,) # map:对每个方面的数据子集应用一个绘图函数 # boxplot:绘制一个框图来显示与类别相关的分布 g.map(sns.boxplot, "total_bill") plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") pal = dict(Lunch="seagreen", Dinner="gray") # FacetGrid:用于绘制条件关系的多图网格 # palette:调色板 g = sns.FacetGrid(tips, hue="time", palette=pal, size=5) # map:对每个方面的数据子集应用一个绘图函数 # scatter:散射 g.map(plt.scatter, "total_bill", "tip", s=50, alpha=.7, linewidth=.5, edgecolor="white") g.add_legend() plt.show()

![20180927_09_52_07.png][]

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") # FacetGrid:用于绘制条件关系的多图网格 # palette:调色板 g = sns.FacetGrid(tips, hue="sex", palette="Set1", size=5, hue_kws={ "marker": ["^", "v"]}) # map:对每个方面的数据子集应用一个绘图函数 # scatter:散射 g.map(plt.scatter, "total_bill", "tip", s=100, linewidth=.5, edgecolor="white") g.add_legend() plt.show()

![20180927_09_54_52.png][]

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") 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=.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=.02, hspace=.02) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns iris = sns.load_dataset("iris") g = sns.PairGrid(iris) g.map(plt.scatter) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns iris = sns.load_dataset("iris") g = sns.PairGrid(iris) g.map_diag(plt.hist) g.map_offdiag(plt.scatter) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns iris = sns.load_dataset("iris") print(iris) g = sns.PairGrid(iris, hue="species") g.map_diag(plt.hist) g.map_offdiag(plt.scatter) g.add_legend() plt.show()

![20180927_10_09_45.png][]

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import matplotlib.pyplot as plt import seaborn as sns iris = sns.load_dataset("iris") g = sns.PairGrid(iris, vars=["sepal_length", "sepal_width"], hue="species") g.map(plt.scatter) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") g = sns.PairGrid(tips, hue="size", palette="GnBu_d") g.map(plt.scatter, s=50, edgecolor="white") g.add_legend() plt.show()

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### Heatmap ###

import matplotlib.pyplot as plt import seaborn as sns import numpy as np uniform_data = np.random.rand(3, 3) print(uniform_data) # heatmap:将矩形数据绘制成彩色编码矩阵 heatmap = sns.heatmap(uniform_data) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns import numpy as np uniform_data = np.random.rand(3, 3) # heatmap:将矩形数据绘制成彩色编码矩阵 # 小于vmin的显示为vmin;大于max的显示为vmax heatmap = sns.heatmap(uniform_data, vmin=0.2, vmax=0.5) plt.show()

![20180927_10_22_39.png][]

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import matplotlib.pyplot as plt import seaborn as sns import numpy as np uniform_data = np.random.rand(3, 3) # heatmap:将矩形数据绘制成彩色编码矩阵 heatmap = sns.heatmap(uniform_data, center=0) plt.show()

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import matplotlib.pyplot as plt import seaborn as sns flights = sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers") # heatmap:将矩形数据绘制成彩色编码矩阵 ax = sns.heatmap(flights) plt.show()

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