Python数据可视化库seaborn

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Python数据可视化库seaborn

2024-06-26 07:46| 来源: 网络整理| 查看: 265

seaborn官方文档：http://seaborn.pydata.org/api.html

绘制多变量的分布图

　　先绘制两个变量的分布图，其中X变量为分类变量，Y为数值变量。

1 import pandas as pd 2 import numpy as np 3 import seaborn as sns 4 import matplotlib.pyplot as plt 5 import matplotlib as mpl 6 tips = sns.load_dataset("tips") 7 sns.set(style="whitegrid", color_codes=True) 8 sns.stripplot(x="day", y="total_bill", data=tips) 9 plt.show()

　　运行结果：

　　注意：观察上图不难发现，带图默认是有抖动的，即 jitter=True 。下面用 swarmplot 绘制带分布的散点图。并且将展示在图中分割多个分类变量，以不同的颜色展示。

1 plt.subplot(121) 2 sns.swarmplot(x="day", y="total_bill", data=tips) 3 plt.subplot(122) 4 sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips) 5 plt.show() 6 sns.swarmplot(x="total_bill", y="day", hue="time", data=tips) 7 plt.show()

　　运行结果：

　　通过上面的图像我们很容易观察到 day 与 time 、sex 之间的一些关系。

箱线图与小提琴图

　　下面我们将绘制箱线图以及小提琴图展示变量间的关系

　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　 scalar, optional

Statistical function to estimate within each categorical bin.

ci : float or “sd” or None, optional

Size of confidence intervals to draw around estimated values. If “sd”, skip bootstrapping and draw the standard deviation of the observations. If None, no bootstrapping will be performed, and error bars will not be drawn.

n_boot : int, optional

Number of bootstrap iterations to use when computing confidence intervals.

units : name of variable in data or vector data, optional

Identifier of sampling units, which will be used to perform a multilevel bootstrap and account for repeated measures design.

order, hue_order : lists of strings, optional

Order to plot the categorical levels in, otherwise the levels are inferred from the data objects.

row_order, col_order : lists of strings, optional

Order to organize the rows and/or columns of the grid in, otherwise the orders are inferred from the data objects.

kind : string, optional

The kind of plot to draw (corresponds to the name of a categorical plotting function. Options are: “point”, “bar”, “strip”, “swarm”, “box”, “violin”, or “boxen”.

height : scalar, optional

Height (in inches) of each facet. See also: aspect.

aspect : scalar, optional

Aspect ratio of each facet, so that aspect * height gives the width of each facet in inches.

orient : “v” | “h”, optional

Orientation of the plot (vertical or horizontal). This is usually inferred from the dtype of the input variables, but can be used to specify when the “categorical” variable is a numeric or when plotting wide-form data.

color : matplotlib color, optional

Color for all of the elements, or seed for a gradient palette.

palette : palette name, list, or dict, optional

Colors to use for the different levels of the hue variable. Should be something that can be interpreted by color_palette(), or a dictionary mapping hue levels to matplotlib colors.

legend : bool, optional

If True and there is a hue variable, draw a legend on the plot.

legend_out : bool, optional

If True, the figure size will be extended, and the legend will be drawn outside the plot on the center right.

share{x,y} : bool, ‘col’, or ‘row’ optional

If true, the facets will share y axes across columns and/or x axes across rows.

margin_titles : bool, optional

If True, the titles for the row variable are drawn to the right of the last column. This option is experimental and may not work in all cases.

facet_kws : dict, optional

Dictionary of other keyword arguments to pass to FacetGrid.

kwargs : key, value pairings

Other keyword arguments are passed through to the underlying plotting function.

Returns:

g : FacetGrid

Returns the FacetGrid object with the plot on it for further tweaking.

　　Parameters：

　　x,y,hue 数据集变量变量名

　　date 数据集数据集名

　　row,col 更多分类变量进行平铺显示变量名

　　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

　　units 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：point 默认, bar 柱形图, count 频次, box 箱体, violin 提琴, strip 散点，swarm 分散点

　　size 每个面的高度（英寸）标量已经不用了，现在使用height

　　aspect 纵横比标量

　　orient 方向 "v"/"h"

　　color 颜色 matplotlib颜色

　　palette 调色板名称 seaborn颜色色板

　　legend_hue 布尔值：如果是真的，图形大小将被扩展，并且图画将绘制在中心右侧的图外。

　　share{x,y} 共享轴线 True/False：如果为真，则刻面将通过列和/或X轴在行之间共享Y轴。

　　下面将是常用图像的展示：

1 sns.catplot(x="day", y="total_bill", hue="smoker", data=tips) 2 plt.show()

1 sns.catplot(x="day", y="total_bill", hue="smoker", data=tips, kind="bar") 2 plt.show()

1 sns.catplot(x="day", y="total_bill", hue="smoker", 2 col="time", data=tips, kind="swarm") 3 plt.show()

1 sns.catplot(x="time", y="total_bill", hue="smoker", 2 col="day", data=tips, kind="box", height=4, aspect=.5) 3 plt.show()

　用 FacetGrid 这个类来展示数据

　　更多内容请点击上面的链接，下面将简单展示

1 g = sns.FacetGrid(tips, col="time") # 占位 2 g.map(plt.hist, "tip") # 画图;第一个参数是func 3 plt.show()

1 g = sns.FacetGrid(tips, col="sex", hue="smoker") 2 g.map(plt.scatter, "total_bill", "tip", alpha=.7) 3 g.add_legend() 4 plt.show()

1 sns.set_style("ticks") 2 g = sns.FacetGrid(tips, row="smoker", col="time", margin_titles=True) # 变量标题右侧，实验性并不总是有效 3 g.map(sns.regplot, "size", "total_bill", color=".1", fit_reg=False, x_jitter=.1) # color 颜色深浅 fit_reg 回归的线 x_jitter 浮动 4 plt.show()

1 g = sns.FacetGrid(tips, col="day", height=4, aspect=.5) 2 g.map(sns.barplot, "sex", "total_bill", order=["Male", "Female"]) 3 plt.show()

1 from pandas import Categorical 2 ordered_days = tips.day.value_counts().index 3 print(ordered_days) 4 ordered_days = Categorical(['Thur', 'Fri', 'Sat', 'Sun']) 5 g = sns.FacetGrid(tips, row="day", row_order=ordered_days, 6 height=1.7, aspect=4) 7 g.map(sns.boxplot, "total_bill", order=["Male","Female"]) 8 plt.show()

1 pal = dict(Lunch="seagreen", Dinner="gray") 2 g = sns.FacetGrid(tips, hue="time", palette=pal, height=5) 3 g.map(plt.scatter, "total_bill", "tip", s=50, alpha=.7, linewidth=.5, edgecolors="red") # edgecolors 元素边界颜色 4 g.add_legend() 5 plt.show()

1 g = sns.FacetGrid(tips, hue="sex", palette="Set1", height=5, hue_kws={"marker": ["^", "v"]}) 2 g.map(plt.scatter, "total_bill", "tip", s=100, linewidth=.5, edgecolor="white") 3 g.add_legend() 4 plt.show()

1 with sns.axes_style("white"): 2 g = sns.FacetGrid(tips, row="sex", col="smoker", margin_titles=True, height=2.5) 3 g.map(plt.scatter, "total_bill", "tip", color="#334488", edgecolor="white", lw=.5) 4 g.set_axis_labels("Total bill (US Dollars)", "Tip") 5 g.set(xticks=[10, 30, 50], yticks=[2, 6, 10]) 6 g.fig.subplots_adjust(wspace=.02, hspace=.02) # 子图与子图 7 # g.fig.subplots_adjust(left = 0.125,right = 0.5,bottom = 0.1,top = 0.9, wspace=.02, hspace=.02) 8 plt.show()

PairGrid 的简单展示 1 iris = sns.load_dataset("iris") 2 g = sns.PairGrid(iris) 3 g.map(plt.scatter) 4 plt.show()

1 g = sns.PairGrid(iris) 2 g.map_diag(plt.hist) # 对角线 3 g.map_offdiag(plt.scatter) # 非对角线 4 plt.show()

1 g = sns.PairGrid(iris, hue="species") 2 g.map_diag(plt.hist) 3 g.map_offdiag(plt.scatter) 4 g.add_legend() 5 plt.show()

1 g = sns.PairGrid(iris, vars=["sepal_length", "sepal_width"], hue="species") # vars 取一部分 2 g.map(plt.scatter) 3 plt.show()

1 g = sns.PairGrid(tips, hue="size", palette="GnBu_d") 2 g.map(plt.scatter, s=50, edgecolor="white") 3 g.add_legend() 4 plt.show()

热力图

　　用颜色的深浅、亮度等来显示数据的分布

1 uniform_data = np.random.rand(3, 3) 2 print(uniform_data) 3 heatmap = sns.heatmap(uniform_data) 4 plt.show()

1 ax = sns.heatmap(uniform_data, vmin=0.2, vmax=0.5) # 最大最小取值 2 plt.show()

　　注意上图的随机数发生了变化。

1 normal_data = np.random.randn(3, 3) 2 print(normal_data) 3 ax = sns.heatmap(normal_data, center=0) # 中心值 4 plt.show()

1 flights = sns.load_dataset("flights") 2 print(flights.head()) 3 flights = flights.pivot("month", "year", "passengers") # 根据列值重塑数据 4 print(flights) 5 sns.heatmap(flights) 6 plt.show()

1 # fmt参数在这里是必须的，不然会乱码 2 sns.heatmap(flights, annot=True, fmt="d") 3 plt.show()

1 sns.heatmap(flights, linewidths=.4) 2 plt.show()

1 sns.heatmap(flights, cmap="YlGnBu") # 指定数据值到颜色空间的映射;如果不提供，默认将取决于是否设置了中心 2 plt.show()

1 sns.heatmap(flights, cbar=False) # 隐藏bar 2 plt.show()

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