[seaborn] seaborn学习笔记11-绘图实例(3) Drawing example(3)
11 绘图实例(3) Drawing example(3)(代码下载)
本文主要讲述seaborn官网相关函数绘图实例。具体内容有:
- Plotting a diagonal correlation matrix(heatmap)
- Scatterplot with marginal ticks(JointGrid)
- Multiple bivariate KDE plots(kdeplot)
- Multiple linear regression(lmplot)
- Paired density and scatterplot matrix(PairGrid)
- Paired categorical plots(PairGrid)
- Dot plot with several variables(PairGrid)
- Plotting a three-way ANOVA(catplot)
- Linear regression with marginal distributions(jointplot)
- Plotting model residuals(residplot)
# import packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
1. Plotting a diagonal correlation matrix(heatmap)
# 读取字母表
from string import ascii_letters
# Generate a large random dataset 生成数据集
rs = np.random.RandomState(33)
d = pd.DataFrame(data=rs.normal(size=(100, 26)),
columns=list(ascii_letters[26:]))
# Compute the correlation matrix 计算相关系数
corr = d.corr()
# Generate a mask for the upper triangle 生成掩模
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
d# Set up the matplotlib figure 设置图大小
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap 设置颜色
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
# square表都是正方形
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5});
2. Scatterplot with marginal ticks(JointGrid)
sns.set(style="white", color_codes=True)
# Generate a random bivariate dataset
rs = np.random.RandomState(9)
mean = [0, 0]
cov = [(1, 0), (0, 2)]
x, y = rs.multivariate_normal(mean, cov, 100).T
# Use JointGrid directly to draw a custom plot
# 创建一个绘图表格区域,设置好x,y对应数据
grid = sns.JointGrid(x, y, space=0, height=6, ratio=50)
# 在联合分布上画出散点图
grid.plot_joint(plt.scatter, color="g")
# 在边缘分布上再作图
grid.plot_marginals(sns.rugplot, height=1, color="g");
C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\tight_layout.py:199: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations
warnings.warn('Tight layout not applied. '
3. Multiple bivariate KDE plots(kdeplot)
sns.set(style="darkgrid")
iris = sns.load_dataset("iris")
# Subset the iris dataset by species
# 单独筛选对应类的数据
setosa = iris.query("species == 'setosa'")
virginica = iris.query("species == 'virginica'")
# Set up the figure
f, ax = plt.subplots(figsize=(8, 8))
# 设置轴的缩放比例,equal表示x,y轴同等缩放比例
ax.set_aspect("equal")
# Draw the two density plots
# 画核密度图
# shade表示添加阴影,shade_lowest表示两个核密度图相叠时,核密度小的部分不画出来
ax = sns.kdeplot(setosa.sepal_width, setosa.sepal_length,
cmap="Reds", shade=True, shade_lowest=False)
ax = sns.kdeplot(virginica.sepal_width, virginica.sepal_length,
cmap="Blues", shade=True, shade_lowest=False)
# Add labels to the plot
# 添加颜色
red = sns.color_palette("Reds")[-2]
blue = sns.color_palette("Blues")[-2]
ax.text(2.5, 8.2, "virginica", size=16, color=blue)
ax.text(3.8, 4.5, "setosa", size=16, color=red);
C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
4. Multiple linear regression(lmplot)
# Load the iris dataset 读取数据
iris = sns.load_dataset("iris")
# Plot sepal with as a function of sepal_length across days
# 画散点图,lmplot默认参数,以hue设定不同种类
# truncate为true表示现植回归拟合曲线绘图时只画出有数据的部分
g = sns.lmplot(x="sepal_length", y="sepal_width", hue="species",
truncate=True, height=5, data=iris);
# Use more informative axis labels than are provided by default
# 设置横竖坐标轴label
g.set_axis_labels("Sepal length (mm)", "Sepal width (mm)");
5. Paired density and scatterplot matrix(PairGrid)
sns.set(style="white")
df = sns.load_dataset("iris")
# 制作散点图矩阵
# diag_sharey是否共享y轴
g = sns.PairGrid(df, diag_sharey=False)
# 下三角绘多变量核密度图
g.map_lower(sns.kdeplot)
# 上三角绘散点图
g.map_upper(sns.scatterplot)
# 对角线绘单变量核密度图,lw表示线条粗细
g.map_diag(sns.kdeplot, lw=3);
6. Paired categorical plots(PairGrid)
sns.set(style="whitegrid")
# Load the example Titanic dataset
titanic = sns.load_dataset("titanic")
# Set up a grid to plot survival probability against several variables
# 制作散点图矩阵
# y轴为survived值,x_vars设定x轴
g = sns.PairGrid(titanic, y_vars="survived",
x_vars=["class", "sex", "who", "alone"],
height=5, aspect=.5)
# Draw a seaborn pointplot onto each Axes
# 制作折线图, errwidth表示上下标准注的长度,其中各点代表平均值
g.map(sns.pointplot, scale=1.3, errwidth=4, color="xkcd:plum")
g.set(ylim=(0, 1))
sns.despine(fig=g.fig, left=True);
7. Dot plot with several variables(PairGrid)
sns.set(style="whitegrid")
# Load the dataset
crashes = sns.load_dataset("car_crashes")
# Make the PairGrid
# 按crash排序的值绘图,x_vars,y_vars表示x轴或者y轴
g = sns.PairGrid(crashes.sort_values("total", ascending=False),
x_vars=crashes.columns[:-3], y_vars=["abbrev"],
height=10, aspect=.25)
# Draw a dot plot using the stripplot function
g.map(sns.stripplot, size=10, orient="h",
palette="ch:s=1,r=-.1,h=1_r", linewidth=1, edgecolor="w")
# Use the same x axis limits on all columns and add better labels
# 设置x轴,x标签
g.set(xlim=(0, 25), xlabel="Crashes", ylabel="")
# Use semantically meaningful titles for the columns
titles = ["Total crashes", "Speeding crashes", "Alcohol crashes",
"Not distracted crashes", "No previous crashes"]
#去除轴线
for ax, title in zip(g.axes.flat, titles):
# Set a different title for each axes
ax.set(title=title)
# Make the grid horizontal instead of vertical
ax.xaxis.grid(False)
ax.yaxis.grid(True)
sns.despine(left=True, bottom=True);
8. Plotting a three-way ANOVA(catplot)
# Load the example exercise dataset
df = sns.load_dataset("exercise")
# Draw a pointplot to show pulse as a function of three categorical factors
# 分类型数据作坐标轴画图catplot,
# col表示用什么变量对图像在横坐标方向分列
# hue表示在单个维度上用某个变量区分;
# capsize表示延伸线的长度
g = sns.catplot(x="time", y="pulse", hue="kind", col="diet",
capsize=0.2, palette="YlGnBu_d", height=6, aspect=.75,
kind="point", data=df)
g.despine(left=True);
9. Linear regression with marginal distributions(jointplot)
sns.set(style="darkgrid")
tips = sns.load_dataset("tips")
# 设置联合图像,类型是"reg"回归图
g = sns.jointplot("total_bill", "tip", data=tips, kind="reg",
xlim=(0, 60), ylim=(0, 12), color="m", height=7)
10. Plotting model residuals(residplot)
sns.set(style="whitegrid")
# Make an example dataset with y ~ x
rs = np.random.RandomState(7)
x = rs.normal(2, 1, 75)
y = 2 + 1.5 * x + rs.normal(0, 2, 75)
# Plot the residuals after fitting a linear model 残差图
# 中间曲线为残差曲线((对比一阶拟合直线的残差)),lowess曲线平滑
sns.residplot(x, y, lowess=True, color="g");
本文来自博客园,作者:落痕的寒假,转载请注明原文链接:https://www.cnblogs.com/luohenyueji/p/16991255.html
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