使用seaborn包绘制箱线图
# libraries & dataset
import seaborn as sns
import matplotlib.pyplot as plt
# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above)
sns.set(style="darkgrid")
# 加载示例数据集
df = sns.load_dataset('iris')
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 |
# 绘制基础箱线图
sns.boxplot(y=df["sepal_length"])
plt.show()
image.png
# 绘制多个变量的箱线图
sns.boxplot(data=df.loc[:, ['sepal_length', 'sepal_width']])
plt.show()
image.png
# 绘制分组箱线图
sns.boxplot(x=df["species"], y=df["sepal_length"])
plt.show()
image.png
# 添加扰动点
# boxplot
ax = sns.boxplot(x='species', y='sepal_length', data=df)
# add stripplot
ax = sns.stripplot(x='species', y='sepal_length', data=df, color="orange", jitter=0.2, size=4)
# add title
plt.title("Boxplot with jitter", loc="left")
# show the graph
plt.show()
image.png
# 设置linewidth参数自定义边框线的宽度
sns.boxplot(x=df["species"], y=df["sepal_length"], linewidth=5)
plt.show()
image.png
# 设置notch=True参数添加缺口
sns.boxplot(x=df["species"], y=df["sepal_length"], notch=True)
plt.show()
image.png
# 设置width参数之定义箱型的宽度
sns.boxplot(x=df["species"], y=df["sepal_length"], width=0.3)
plt.show()
image.png
# 自定义颜色
# 设置palette参数自定义颜色画板
sns.boxplot(x=df["species"], y=df["sepal_length"], palette="Blues")
plt.show()
image.png
# 设置color参数自定义颜色
sns.boxplot(x=df["species"], y=df["sepal_length"], color='skyblue')
plt.show()
image.png
# 对每组设置不同的颜色
my_pal = {"versicolor": "g", "setosa": "b", "virginica":"m"}
sns.boxplot(x=df["species"], y=df["sepal_length"], palette=my_pal)
plt.show()
image.png
# 设置order参数自定义分组的排序
sns.boxplot(x='species', y='sepal_length', data=df, order=["versicolor", "virginica", "setosa"])
plt.show()
image.png
# 根据每组的中位数进行降序排序
# Find the order
my_order = df.groupby(by=["species"])["sepal_length"].median().iloc[::-1].index
# Give it to the boxplot
sns.boxplot(x='species', y='sepal_length', data=df, order=my_order)
plt.show()
image.png
# 添加文本注释标签
ax = sns.boxplot(x="species", y="sepal_length", data=df)
# Calculate number of obs per group & median to position labels
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
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')
plt.show()
image.png
# 按分组变量填充颜色
df = sns.load_dataset('tips')
df.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
sns.boxplot(x="day", y="total_bill", hue="smoker", data=df, palette="Set1", width=0.5)
plt.show()
image.png
# Grouped violinplot
sns.violinplot(x="day", y="total_bill", hue="smoker", data=df, palette="Pastel1")
plt.show()
image.png