[seaborn] seaborn学习笔记12-绘图实例(4) Drawing example(4)
文章目录
- 12 绘图实例(4) Drawing example(4)
- 1. Scatterplot with varying point sizes and hues(relplot)
- 2. Scatterplot with categorical variables(swarmplot)
- 3. Scatterplot Matrix(pairplot)
- 4. Scatterplot with continuous hues and sizes(scatterplot)
- 5. Violinplots with observations(violinplot)
- 6. Discovering structure in heatmap data(clustermap)
- 7. Lineplot from a wide-form dataset(lineplot)
- 8. Violinplot from a wide-form dataset(violinplot)
12 绘图实例(4) Drawing example(4)
(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:
- Scatterplot with varying point sizes and hues(relplot)
- Scatterplot with categorical variables(swarmplot)
- Scatterplot Matrix(pairplot)
- Scatterplot with continuous hues and sizes(scatterplot)
- Violinplots with observations(violinplot)
- Discovering structure in heatmap data(clustermap)
- Lineplot from a wide-form dataset(lineplot)
- Violinplot from a wide-form dataset(violinplot)
# import packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
1. Scatterplot with varying point sizes and hues(relplot)
sns.set(style="white")
# Load the example mpg dataset
mpg = sns.load_dataset("mpg")
# Plot miles per gallon against horsepower with other semantics
# 其中x,y为横轴坐标变量,hue表示分类类别,size表示点的大小
sns.relplot(x="horsepower", y="mpg", hue="origin", size="weight",
sizes=(40, 400), alpha=.5, palette="muted",
height=6, data=mpg);
2. Scatterplot with categorical variables(swarmplot)
# Load the example iris dataset
iris = sns.load_dataset("iris")
# "Melt" the dataset to "long-form" or "tidy" representation
# 合并数据集
iris = pd.melt(iris, "species", var_name="measurement")
# Draw a categorical scatterplot to show each observation
# swarmplot将不同类别散点图用树状表示
sns.swarmplot(x="measurement", y="value", hue="species",
palette=["r", "c", "y"], data=iris);
3. Scatterplot Matrix(pairplot)
df = sns.load_dataset("iris")
#制作多变量图,hue为使用指定变量为分类变量画图
sns.pairplot(df, hue="species");
4. Scatterplot with continuous hues and sizes(scatterplot)
# Load the example iris dataset
planets = sns.load_dataset("planets")
# 设定颜色
#cubehelix_palette表示从cubehelix中制作顺序调色板
cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
ax = sns.scatterplot(x="distance", y="orbital_period",
hue="year", size="mass",
palette=cmap, sizes=(10, 200),
data=planets)
5. Violinplots with observations(violinplot)
# Create a random dataset across several variables
rs = np.random.RandomState(0)
n, p = 40, 8
d = rs.normal(0, 2, (n, p))
d += np.log(np.arange(1, p + 1)) * -5 + 10
# Use cubehelix to get a custom sequential palette
pal = sns.cubehelix_palette(p, rot=-.5, dark=.3)
# Show each distribution with both violins and points
# 制作小提琴图,pal表示颜色
sns.violinplot(data=d, palette=pal, inner="points");
6. Discovering structure in heatmap data(clustermap)
# Load the brain networks example dataset
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
# Select a subset of the networks
used_networks = [1, 5, 6, 7, 8, 12, 13, 17]
used_columns = (df.columns.get_level_values("network")
.astype(int)
.isin(used_networks))
# 建立矩阵类数据集
df = df.loc[:, used_columns]
# Create a categorical palette to identify the networks
#创建调色盘
network_pal = sns.husl_palette(8, s=.45)
network_lut = dict(zip(map(str, used_networks), network_pal))
# Convert the palette to vectors that will be drawn on the side of the matrix
networks = df.columns.get_level_values("network")
network_colors = pd.Series(networks, index=df.columns).map(network_lut)
# Draw the full plot
# 将矩阵数据集绘制为分层聚类热图
# row_colors,col_color行或列标记的颜色列表
sns.clustermap(df.corr(), center=0, cmap="vlag",
row_colors=network_colors, col_colors=network_colors,
linewidths=.75, figsize=(13, 13));
7. Lineplot from a wide-form dataset(lineplot)
sns.set(style="whitegrid")
rs = np.random.RandomState(365)
values = rs.randn(365, 4).cumsum(axis=0)
dates = pd.date_range("1 1 2016", periods=365, freq="D")
data = pd.DataFrame(values, dates, columns=["A", "B", "C", "D"])
data = data.rolling(7).mean()
# 创建折线图
sns.lineplot(data=data, palette="tab10", linewidth=2.5);
8. Violinplot from a wide-form dataset(violinplot)
# Load the example dataset of brain network correlations
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
# Pull out a specific subset of networks
used_networks = [1, 3, 4, 5, 6, 7, 8, 11, 12, 13, 16, 17]
used_columns = (df.columns.get_level_values("network")
.astype(int)
.isin(used_networks))
#创建矩阵
df = df.loc[:, used_columns]
# Compute the correlation matrix and average over networks
# 计算相对系数和均值
corr_df = df.corr().groupby(level="network").mean()
corr_df.index = corr_df.index.astype(int)
corr_df = corr_df.sort_index().T
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 6))
# Draw a violinplot with a narrower bandwidth than the default
sns.violinplot(data=corr_df, palette="Set3", bw=.2, cut=1, linewidth=1)
# Finalize the figure
ax.set(ylim=(-.7, 1.05))
sns.despine(left=True, bottom=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/16970297.html
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