各种各样的图
有时候，我们想画某一种图，就到处找代码，现学现卖。这里，笔者就做一个收集，使用python的matplotlib加上seaborn来美化的各种各样的图。
1.曲线图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
def sinplot(flip=1):
x = np.linspace(0, 14, 100)
for i in range(1, 7):
plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)
plt.show()
sns.set_style("whitegrid")
sinplot()
2.箱体图
sns.set_style("whitegrid")
data = np.random.normal(size=(20, 6)) + np.arange(6) / 2
sns.boxplot(data=data);
plt.show()
3.小提琴箱体图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
data = np.random.normal(size=(20, 6)) + np.arange(6) / 2
sns.violinplot(data=data)
sns.despine(offset=10, trim=True)#形成坐标裂开的样子
plt.show()
4.密度图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T
cmap = sns.cubehelix_palette(light=1, as_cmap=True)
sns.kdeplot(x, y, cmap=cmap, shade=True)
plt.show()
5.分布图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = np.random.normal(size=100)
sns.distplot(x)
plt.show()
6.柱状图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
x = np.random.normal(size=100)
sns.distplot(x, kde=False, rug=True)
plt.show()rug设为False就是我们最常见的柱状图
同样的，如果是
sns.distplot(x, hist=False, rug=True)那么就是柱状没了。
7.联合散点图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
mean, cov = [0, 1], [(1, .5), (.5, 1)]
data = np.random.multivariate_normal(mean, cov, 200)
df = pd.DataFrame(data, columns=["x", "y"])
sns.jointplot(x="x", y="y", data=df)
plt.show()
上面这个jointplot中间增加一些设置有更好的效果。
例如
sns.jointplot(x="x", y="y", data=df, kind="hex")
sns.jointplot(x="x", y="y", data=df, kind="kde")
8，叠加
mport numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
mean, cov = [0, 1], [(1, .5), (.5, 1)]
data = np.random.multivariate_normal(mean, cov, 200)
df = pd.DataFrame(data, columns=["x", "y"])
g = sns.jointplot(x="x", y="y", data=df, kind="kde", color='m')
g.plot_joint(plt.scatter, marker="+")
plt.show()散点图和上面的联合密度分布图叠加
9.配对关系矩阵图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
iris = sns.load_dataset("iris")
print iris
sns.pairplot(iris)
plt.show()
这里，我们来看一下我们的数据集：
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 5 5.4 3.9 1.7 0.4 setosa 6 4.6 3.4 1.4 0.3 setosa 7 5.0 3.4 1.5 0.2 setosa 8 4.4 2.9 1.4 0.2 setosa 9 4.9 3.1 1.5 0.1 setosa 10 5.4 3.7 1.5 0.2 setosa 11 4.8 3.4 1.6 0.2 setosa 12 4.8 3.0 1.4 0.1 setosa 13 4.3 3.0 1.1 0.1 setosa 14 5.8 4.0 1.2 0.2 setosa 15 5.7 4.4 1.5 0.4 setosa 16 5.4 3.9 1.3 0.4 setosa 17 5.1 3.5 1.4 0.3 setosa 18 5.7 3.8 1.7 0.3 setosa 19 5.1 3.8 1.5 0.3 setosa 20 5.4 3.4 1.7 0.2 setosa 21 5.1 3.7 1.5 0.4 setosa 22 4.6 3.6 1.0 0.2 setosa 23 5.1 3.3 1.7 0.5 setosa 24 4.8 3.4 1.9 0.2 setosa
这是一个dataframe的结构，我们绘制的就是四列一其他三列分别的散点图，对角线上的则是这一个变量的分布直方图，当然，也可以改成别的。
同样的，
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
iris = sns.load_dataset("iris")
g = sns.PairGrid(iris)
g.map_diag(sns.kdeplot)
g.map_offdiag(sns.kdeplot, cmap="Blues_d", n_levels=6);
plt.show()
10.线性回归图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
tips = sns.load_dataset("tips")
# print tips
sns.regplot(x="total_bill", y="tip", data=tips)
plt.show()
tips的数据是这样的：
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 5 25.29 4.71 Male No Sun Dinner 4 6 8.77 2.00 Male No Sun Dinner 2 7 26.88 3.12 Male No Sun Dinner 4 8 15.04 1.96 Male No Sun Dinner 2 9 14.78 3.23 Male No Sun Dinner 2 10 10.27 1.71 Male No Sun Dinner 2 11 35.26 5.00 Female No Sun Dinner 4 12 15.42 1.57 Male No Sun Dinner 2 13 18.43 3.00 Male No Sun Dinner 4 14 14.83 3.02 Female No Sun Dinner 2
如果是下面这样的话，就是一个有条件的线性回归，或者说，分类别了。而类别就是smoker
sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips)
也可以再加入变量：
sns.lmplot(x="total_bill", y="tip", hue="smoker", col="time", data=tips)
还可以接着加。。
sns.lmplot(x="total_bill", y="tip", hue="smoker",
col="time", row="sex", data=tips)当然，还可以画成这样;
sns.jointplot(x="total_bill", y="tip", data=tips, kind="reg")
11.有点像箱体图
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
tips = sns.load_dataset("tips")
sns.stripplot(x="day", y="total_bill", data=tips, jitter=True)
plt.show()
12.比例变化累积图
import seaborn as sns
import numpy as np
np.random.seed(sum(map(ord, "aesthetics")))
ax = plt.subplot(1, 1, 1)
signal_weight_sz50.plot.area(figsize=(10, 5), ax=ax)
plt.show()这种图形不知道学名是什么，平时还是有很大的概率会用到。
