[seaborn] seaborn学习笔记5-小提琴图VIOLINPLOT
文章目录
5 小提琴图Violinplot
(代码下载)
小提琴图允许可视化一个或多个组的数字变量的分布。它与箱形图非常接近,但可以更深入地了解密度。小提琴图特别适用于数据量巨大且无法显示个别观察结果的情况。在seaborn中使用violinplot函数绘制小提琴图,该章节主要内容有:
- 基础小提琴图绘制 Basic violinplot
- 小提琴图样式自定义 Custom seaborn violinplot
- 小提琴图颜色自定义 Control color of seaborn violinplot
- 分组小提琴图 Grouped violinplot
- 小提琴图组的顺序设置 Control order of groups in violinplot
- 显示小提琴图上的观察次数 Show number of observation on violinplot
#调用seaborn
import seaborn as sns
#调用seaborn自带数据集
df = sns.load_dataset('iris')
#显示数据集
df.head()
sepal_length | sepal_width | petal_length | petal_width | species | |
---|---|---|---|---|---|
0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
1. 基础小提琴图绘制 Basic violinplot
- 单个变量 One numerical variable only
- 包含多个分组的单个变量 One variable and several groups
- 多个变量 Several variables
- 水平小提琴图 Horizontal violinplot
# 单个变量 One numerical variable only
# 如果只有一个数值变量,则最好制作直方图或密度图,但是仍然可以用小提琴图来表示
# Make boxplot for one group only
sns.violinplot( y=df["sepal_length"] );
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
# 包含多个分组的单个变量 One variable and several groups
# x为种类名,y为花萼长度
sns.violinplot( x=df["species"], y=df["sepal_length"] );
# 多个变量 Several variables
# 单独拿出sepal_length和sepal_width绘制
sns.violinplot(data=df.iloc[:,0:2]);
# 水平小提琴图 Horizontal violinplot
# 可以通过orient设定方向,但是交换x,y画水平小提琴图更好
# Just switch x and y
sns.violinplot( y=df["species"], x=df["sepal_length"] );
2. 小提琴图样式自定义 Custom seaborn violinplot
- 线宽自定义 Change line width
- 图像一般宽度自定义 Change width
# 线宽自定义 Change line width
sns.violinplot( x=df["species"], y=df["sepal_length"], linewidth=5);
# 图像一般宽度自定义 Change width
sns.violinplot( x=df["species"], y=df["sepal_length"], width=0.3);
3. 小提琴图颜色自定义 Control color of seaborn violinplot
- 使用调色板 Use a color palette
- 单种颜色 Uniform color
- 指定每个组的颜色 Specify color of each group
- 突出显示一个组 Highlight a group
# 使用调色板 Use a color palette
sns.violinplot( x=df["species"], y=df["sepal_length"], palette="Blues");
# 单种颜色 Uniform color
sns.violinplot( x=df["species"], y=df["sepal_length"], color="skyblue");
# 指定每个组的颜色 Specify color of each group
# Make a dictionary with one specific color per group:
my_pal = {"versicolor": "g", "setosa": "b", "virginica":"m"}
#plot it
sns.violinplot( x=df["species"], y=df["sepal_length"], palette=my_pal);
# 突出显示一个组 Highlight a group
# make a vector of color: red for the interesting group, blue for others:
my_pal = {species: "r" if species == "versicolor" else "b" for species in df.species.unique()}
# make the plot
sns.violinplot( x=df["species"], y=df["sepal_length"], palette=my_pal);
4. 分组小提琴图 Grouped violinplot
# 如果您有一个变量,变量有几个组和子组,您可能需要制作一个分组的小提琴图。
df_test = sns.load_dataset('tips')
# Grouped violinplot 分组
sns.violinplot(x="day", y="total_bill", hue="smoker", data=df_test, palette="Pastel1");
5. 小提琴图组的顺序设置 Control order of groups in violinplot
# plot order设置顺序就行
sns.violinplot(x='species', y='sepal_length', data=df, order=[ "versicolor", "virginica", "setosa"]);
# Find the order 或者通过设置一定的规则排序
my_order = df.groupby(by=["species"])["sepal_length"].median().iloc[::-1].index
# Give it to the violinplot
sns.violinplot(x='species', y='sepal_length', data=df, order=my_order);
6. 显示小提琴图上的观察次数 Show number of observation on violinplot
# Basic violinplot 基础小提琴图像绘制
ax = sns.violinplot(x="species", y="sepal_length", data=df)
# Calculate number of obs per group & median to position labels 计算各个样本数量
medians = df.groupby(['species'])['sepal_length'].median().values
nobs = df['species'].value_counts().values
nobs = [str(x) for x in nobs.tolist()]
nobs = ["n: " + i for i in nobs]
# Add it to the plot 加入图像
pos = range(len(nobs))
for tick,label in zip(pos,ax.get_xticklabels()):
ax.text(pos[tick], medians[tick] + 0.03, nobs[tick], horizontalalignment='center', size='x-small', color='w', weight='semibold');
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/16970303.html
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