[seaborn] seaborn学习笔记9-绘图实例(1) Drawing example(1)
文章目录
- 9 绘图实例(1) Drawing example(1)
- 1. Anscombe’s quartet(lmplot)
- 2. Color palette choices(barplot)
- 3. Different cubehelix palettes(kdeplot)
- 4. Distribution plot options(distplot)
- 5. Timeseries plot with error bands(lineplot)
- 6. FacetGrid with custom projection(FacetGrid)
- 7. FacetGrid with custom projection(FacetGrid)
- 8. Line plots on multiple facets(relplot)
- 9. Grouped barplots(catplot)
- 10. Grouped boxplots(boxplot)
9 绘图实例(1) Drawing example(1)
(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:
- Anscombe’s quartet(lmplot)
- Color palette choices(barplot)
- Different cubehelix palettes(kdeplot)
- Distribution plot options(distplot)
- Timeseries plot with error bands(lineplot)
- FacetGrid with custom projection(FacetGrid)
- Facetting histograms by subsets of data(FacetGrid)
- Line plots on multiple facets(relplot)
- Grouped barplots(catplot)
- Grouped boxplots(boxplot)
# import packages
# from IPython.core.interactiveshell import InteractiveShell
# InteractiveShell.ast_node_interactivity = "all"
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set(style="ticks")
1. Anscombe’s quartet(lmplot)
# Load the example dataset for Anscombe's quartet
df = sns.load_dataset("anscombe")
df.head()
# Show the results of a linear regression within each dataset 显示各组的回归拟合结果
# col确认分组类别,hue设置绘图颜色类别.col_wrap设置每行多少图像
# ci置信区间大小,palette颜色设置,height设置每张图的尺寸
sns.lmplot(x="x", y="y", col="dataset", hue="dataset", data=df,
col_wrap=2, ci=None, palette="muted", height=4,
scatter_kws={"s": 50, "alpha": 1});
2. Color palette choices(barplot)
#context控制上下文本参数
sns.set(style="white", context="talk")
rs = np.random.RandomState(8)
# Set up the matplotlib figure 设置matplotlib绘图参数
f, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(7, 5), sharex=True)
# Generate some sequential data 建立数据集
x = np.array(list("ABCDEFGHIJ"))
y1 = np.arange(1, 11)
# 第一个条形图
sns.barplot(x=x, y=y1, palette="rocket", ax=ax1);
# 绘制平行于x轴的水平参考线
ax1.axhline(0, color="k", clip_on=False)
# 添加标签
ax1.set_ylabel("Sequential")
# Center the data to make it diverging
y2 = y1 - 5.5
sns.barplot(x=x, y=y2, palette="vlag", ax=ax2)
ax2.axhline(0, color="k", clip_on=False)
ax2.set_ylabel("Diverging")
# Randomly reorder the data to make it qualitative 生成数据
y3 = rs.choice(y1, len(y1), replace=False)
sns.barplot(x=x, y=y3, palette="deep", ax=ax3)
ax3.axhline(0, color="k", clip_on=False)
ax3.set_ylabel("Qualitative")
# Finalize the plot 绘图最后参数调整
# 去掉了上、右和下边界。注意,这里despine()函数默认会去除上方和右侧的边界,
# 如果想要去掉左边和下方的边界,就需要额外指定left=True, bottom=True
sns.despine(bottom=True)
# 去除了所有的纵轴刻度尺
plt.setp(f.axes, yticks=[])
# ight_layout设置了每行坐标轴(子图)之间的间隔,h_pad确定上下间隔
plt.tight_layout(h_pad=2)
3. Different cubehelix palettes(kdeplot)
sns.set(style="dark")
rs = np.random.RandomState(50)
# Set up the matplotlib figure 设置图像
f, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True)
# Rotate the starting point around the cubehelix hue circle
for ax, s in zip(axes.flat, np.linspace(0, 3, 10)):
# Create a cubehelix colormap to use with kdeplot 颜色设置
cmap = sns.cubehelix_palette(start=s, light=1, as_cmap=True)
# Generate and plot a random bivariate dataset
x, y = rs.randn(2, 50)
# 核函数估计图制作
sns.kdeplot(x, y, cmap=cmap, shade=True, cut=5, ax=ax)
ax.set(xlim=(-3, 3), ylim=(-3, 3))
f.tight_layout()
4. Distribution plot options(distplot)
sns.set(style="white", palette="muted", color_codes=True)
rs = np.random.RandomState(10)
# Set up the matplotlib figure 设置matplolitlib参数
f, axes = plt.subplots(2, 2, figsize=(7, 7), sharex=True)
# 去除边框
sns.despine(left=True)
# Generate a random univariate dataset
d = rs.normal(size=100)
# Plot a simple histogram with binsize determined automatically 直方图
sns.distplot(d, kde=False, color="b", ax=axes[0, 0])
# Plot a kernel density estimate and rug plot 画核密度曲线
sns.distplot(d, hist=False, rug=True, color="r", ax=axes[0, 1])
# Plot a filled kernel density estimate
sns.distplot(d, hist=False, color="g", kde_kws={"shade": True}, ax=axes[1, 0])
# Plot a historgram and kernel density estimate
sns.distplot(d, color="m", ax=axes[1, 1])
# 隐藏y轴
plt.setp(axes, yticks=[])
plt.tight_layout()
5. Timeseries plot with error bands(lineplot)
sns.set(style="darkgrid")
# Load an example dataset with long-form data
fmri = sns.load_dataset("fmri")
# Plot the responses for different events and regions 线条图
sns.lineplot(x="timepoint", y="signal",
hue="region", style="event",
#置信区间设置
#ci=None,
data=fmri);
6. FacetGrid with custom projection(FacetGrid)
sns.set()
# Generate an example radial datast 随机生成数据
r = np.linspace(0, 10, num=100)
df = pd.DataFrame({'r': r, 'slow': r, 'medium': 2 * r, 'fast': 4 * r})
# Convert the dataframe to long-form or "tidy" format
df = pd.melt(df, id_vars=['r'], var_name='speed', value_name='theta')
# Set up a grid of axes with a polar projection 建立坐标轴
# col表示使用什么分图,这里是speed,并用hue设置不同类对应不同颜色
# subplot_kws确定坐标类型,这里是极坐标
g = sns.FacetGrid(df, col="speed", hue="speed",
subplot_kws=dict(projection='polar'), height=4.5,
sharex=False, sharey=False, despine=False)
# Draw a scatterplot onto each axes in the grid
# 在实例化后的坐标轴上画图
g.map(sns.scatterplot, "theta", "r");
7. FacetGrid with custom projection(FacetGrid)
sns.set(style="darkgrid")
tips = sns.load_dataset("tips")
# 初始化坐标轴,margin_titles行变量的标题被绘制到最后一列的右侧
g = sns.FacetGrid(tips, row="sex", col="time", margin_titles=True)
bins = np.linspace(0, 60, 13)
g.map(plt.hist, "total_bill", color="steelblue", bins=bins);
8. Line plots on multiple facets(relplot)
sns.set(style="ticks")
dots = sns.load_dataset("dots")
# Define a palette to ensure that colors will be
# shared across the facets
# 设置颜色
palette = dict(zip(dots.coherence.unique(),
sns.color_palette("rocket_r", 6)))
# Plot the lines on two facets
sns.relplot(x="time", y="firing_rate",
hue="coherence", size="choice", col="align",
# size_order画图顺序
size_order=["T1", "T2"], palette=palette,
# aspect图的比例,facet_kws每个图参数设置
height=5, aspect=0.75, facet_kws=dict(sharex=False),
# kind画图类型,legend图例类型
kind="line", legend="brief", data=dots);
9. Grouped barplots(catplot)
sns.set(style="whitegrid")
# Load the example Titanic dataset 读取数据
titanic = sns.load_dataset("titanic")
# Draw a nested barplot to show survival for class and sex
# catplot分类数据作图
g = sns.catplot(x="class", y="survived", hue="sex", data=titanic,
height=6, kind="bar", palette="muted")
# 设置轴框
g.despine(left=True)
# 设置标签
g.set_ylabels("survival probability");
10. Grouped boxplots(boxplot)
sns.set(style="ticks", palette="pastel")
# Load the example tips dataset
tips = sns.load_dataset("tips")
# Draw a nested boxplot to show bills by day and time
# 画箱形图 hue颜色
sns.boxplot(x="day", y="total_bill",
hue="smoker", palette=["m", "g"],
data=tips)
# offset 两轴偏移绝对距离,trim只显示主要刻度
sns.despine(offset=10,trim=True)
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/16970300.html
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