009.python科学计算库seaborn(中)

喜欢ヅ旅行 2021-10-06 15:20 368阅读 0赞

### Var ###

import matplotlib.pyplot as plt import numpy as np import seaborn as sns sns.set() x = np.random.normal(size=100) # distplot：灵活绘制观测的单变量分布 sns.distplot(x, kde=False) plt.show()

![20180926_16_39_56.png][]

--------------------

import matplotlib.pyplot as plt import numpy as np import seaborn as sns sns.set() x = np.random.normal(size=100) # distplot：灵活绘制观测的单变量分布 # bins:分成20份 sns.distplot(x, bins=20, kde=False) plt.show()

![20180926_16_41_35.png][]

--------------------

import matplotlib.pyplot as plt import numpy as np import seaborn as sns from scipy import stats, integrate sns.set() x = np.random.gamma(6, size=100) # 数据分布情况 sns.distplot(x, fit=stats.gamma, kde=False) plt.show()

![20180926_16_44_38.png][]

--------------------

import matplotlib.pyplot as plt import numpy as np import pandas as pd # 根据均值和协方差生成数据 mean, cov = [0, 1], [(1, .5), (.5, 1)] data = np.random.multivariate_normal(mean, cov, 200) df = pd.DataFrame(data, columns=["x", "y"]) plt.plot(df) plt.show()

![20180926_16_55_25.png][]

--------------------

import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns # 观测两个变量之间的分布关系最好用散点图 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()

![20180926_16_57_56.png][]

--------------------

import matplotlib.pyplot as plt import numpy as np import seaborn as sns # 观测两个变量之间的分布关系最好用散点图 mean, cov = [0, 1], [(1, .5), (.5, 1)] x, y = np.random.multivariate_normal(mean, cov, 1000).T with sns.axes_style("white"): sns.jointplot(x=x, y=y, kind="hex", color="k") plt.show()

![20180926_17_06_16.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns # 从联机存储库加载数据集(需要internet) iris = sns.load_dataset("iris") # 在数据集中绘制两两关系 sns.pairplot(iris) plt.show()

![20180926_17_08_46.png][]

--------------------

### REG ###

import matplotlib.pyplot as plt import seaborn as sns # regplot()和lmplot()都可以绘制回归关系,推荐regplot() tips = sns.load_dataset("tips") tips.head() sns.regplot(x="total_bill", y="tip", data=tips) plt.show()

![20180926_17_13_44.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns # regplot()和lmplot()都可以绘制回归关系,推荐regplot() tips = sns.load_dataset("tips") tips.head() sns.lmplot(x="total_bill", y="tip", data=tips) plt.show()

![20180926_17_14_40.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() # x_jitter x轴抖动范围 sns.regplot(x="total_bill", y="tip", data=tips, x_jitter=.05) plt.show()

![20180926_17_20_19.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() anscombe = sns.load_dataset("anscombe") print(anscombe) # scatter_kws:"s" 数据点为的大小,数值越大点位越大 sns.regplot(x="x", y="y", data=anscombe.query("dataset == 'I'"), ci=None, scatter_kws={ "s": 100}) plt.show()

![20180926_17_25_12.png][]  
![20180926_17_25_28.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() # lmplot 图数据和回归模型适用于FacetGrid. # 这个函数结合了:func: ' regplot '和:class: ' FacetGrid '。 # 它是一个方便的接口，用于跨数据集的条件子集拟合回归模型。 # hue:色调 # palette:调色板 # markers:标记 sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, markers=["o", "x"], palette="Set1") plt.show()

![20180926_17_34_29.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() print(tips) # lmplot 图数据和回归模型适用于FacetGrid. # 这个函数结合了:func: ' regplot '和:class: ' FacetGrid '。 # 它是一个方便的接口，用于跨数据集的条件子集拟合回归模型。 # hue:色调 # palette:调色板 # markers:标记 # col:以此列来进行分组渲染 sns.lmplot(x="total_bill", y="tip", hue="smoker", col="sex", data=tips) plt.show()

![20180926_17_40_58.png][]  
![20180926_17_41_13.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() print(tips) # lmplot 图数据和回归模型适用于FacetGrid. # 这个函数结合了:func: ' regplot '和:class: ' FacetGrid '。 # 它是一个方便的接口，用于跨数据集的条件子集拟合回归模型。 # hue:色调 # palette:调色板 # markers:标记 # col:以此列来进行分组渲染 # row:以此行来进行分组渲染 sns.lmplot(x="total_bill", y="tip", hue="smoker", col="sex",row="time", data=tips) plt.show()

![20180926_17_44_07.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() f, ax = plt.subplots(figsize=(5, 5)) # col_wrap：“Wrap” 列变量位于此宽度，以便列facet跨越多个行 # size ：每个面的高度(英寸) sns.lmplot(x="total_bill", y="tip", col="day", data=tips, col_wrap=2, size=4) plt.show()

![20180926_17_49_39.png][]

--------------------

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") tips.head() f, ax = plt.subplots(figsize=(5, 5)) # col_wrap：“Wrap” 列变量位于此宽度，以便列facet跨越多个行 # size ：每个面的高度(英寸) # aspect :横轴比上纵轴的比例 sns.lmplot(x="total_bill", y="tip", col="day", data=tips, col_wrap=2, size=4, aspect=.5) plt.show()

![20180926_17_53_45.png][]

--------------------

[20180926_16_39_56.png]: http://upyun.qubianzhong.cn/picture/20180926_16:39:56.png
[20180926_16_41_35.png]: http://upyun.qubianzhong.cn/picture/20180926_16:41:35.png
[20180926_16_44_38.png]: http://upyun.qubianzhong.cn/picture/20180926_16:44:38.png
[20180926_16_55_25.png]: http://upyun.qubianzhong.cn/picture/20180926_16:55:25.png
[20180926_16_57_56.png]: http://upyun.qubianzhong.cn/picture/20180926_16:57:56.png
[20180926_17_06_16.png]: http://upyun.qubianzhong.cn/picture/20180926_17:06:16.png
[20180926_17_08_46.png]: http://upyun.qubianzhong.cn/picture/20180926_17:08:46.png
[20180926_17_13_44.png]: http://upyun.qubianzhong.cn/picture/20180926_17:13:44.png
[20180926_17_14_40.png]: http://upyun.qubianzhong.cn/picture/20180926_17:14:40.png
[20180926_17_20_19.png]: http://upyun.qubianzhong.cn/picture/20180926_17:20:19.png
[20180926_17_25_12.png]: http://upyun.qubianzhong.cn/picture/20180926_17:25:12.png
[20180926_17_25_28.png]: http://upyun.qubianzhong.cn/picture/20180926_17:25:28.png
[20180926_17_34_29.png]: http://upyun.qubianzhong.cn/picture/20180926_17:34:29.png
[20180926_17_40_58.png]: http://upyun.qubianzhong.cn/picture/20180926_17:40:58.png
[20180926_17_41_13.png]: http://upyun.qubianzhong.cn/picture/20180926_17:41:13.png
[20180926_17_44_07.png]: http://upyun.qubianzhong.cn/picture/20180926_17:44:07.png
[20180926_17_49_39.png]: http://upyun.qubianzhong.cn/picture/20180926_17:49:39.png
[20180926_17_53_45.png]: http://upyun.qubianzhong.cn/picture/20180926_17:53:45.png