绘图利器 seaborn

seaborn 是一个基于 matplotlib的绘图工具库， 提供比较高层的接口来绘制精美的统计图表

看看官方文档上给的一个例子， 泰坦尼克上的乘客数据分析

import seaborn as sns
sns.set(style="darkgrid")
titanic = sns.load_dataset("titanic")

print(titanic.info())


ax = sns.countplot(x="class", hue="who", data=titanic)

g = sns.factorplot(x="class", hue="who", col="survived",
                    data=titanic, kind="count",
                    size=4, aspect=.7);

数据集装载为一个数据框

RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
survived       891 non-null int64
pclass         891 non-null int64
sex            891 non-null object
age            714 non-null float64
sibsp          891 non-null int64
parch          891 non-null int64
fare           891 non-null float64
embarked       889 non-null object
class          891 non-null category
who            891 non-null object
adult_male     891 non-null bool
deck           203 non-null category
embark_town    889 non-null object
alive          891 non-null object
alone          891 non-null bool
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 63.0+ KB
None

图一为一二三等舱的乘客分布， 在传统直方图中可以在增加类型

图一为生还的乘客的分布， 在其中可以看出点什么

再用鸢尾花数据集为例， 我们来绘制一个箱体图

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets
from pandas.plotting import scatter_matrix

iris = datasets.load_iris()

# Create box plot with Seaborn's default settings
_ = sns.boxplot(x='species', y='petal length (cm)', data=iris.data)

# Label the axes
_ = plt.xlabel('species')
_ = plt.ylabel('petal length (cm)')


# Show the plot
plt.show()

参考资料

• http://seaborn.pydata.org/generated/seaborn.countplot.html#seaborn.countplot
• http://seaborn.pydata.org/generated/seaborn.factorplot.html#seaborn.factorplot
• http://seaborn.pydata.org/generated/seaborn.heatmap.html