数据集准
将下列数据存储到一个 csv 格式的文件中。
96.3,1,70
96.7,1,71
96.9,1,74
97.0,1,80
97.1,1,73
97.1,1,75
97.1,1,82
97.2,1,64
97.3,1,69
97.4,1,70
97.4,1,68
97.4,1,72
97.4,1,78
97.5,1,70
97.5,1,75
97.6,1,74
97.6,1,69
97.6,1,73
97.7,1,77
97.8,1,58
97.8,1,73
97.8,1,65
97.8,1,74
97.9,1,76
97.9,1,72
98.0,1,78
98.0,1,71
98.0,1,74
98.0,1,67
98.0,1,64
98.0,1,78
98.1,1,73
98.1,1,67
98.2,1,66
98.2,1,64
98.2,1,71
98.2,1,72
98.3,1,86
98.3,1,72
98.4,1,68
98.4,1,70
98.4,1,82
98.4,1,84
98.5,1,68
98.5,1,71
98.6,1,77
98.6,1,78
98.6,1,83
98.6,1,66
98.6,1,70
98.6,1,82
98.7,1,73
98.7,1,78
98.8,1,78
98.8,1,81
98.8,1,78
98.9,1,80
99.0,1,75
99.0,1,79
99.0,1,81
99.1,1,71
99.2,1,83
99.3,1,63
99.4,1,70
99.5,1,75
96.4,2,69
96.7,2,62
96.8,2,75
97.2,2,66
97.2,2,68
97.4,2,57
97.6,2,61
97.7,2,84
97.7,2,61
97.8,2,77
97.8,2,62
97.8,2,71
97.9,2,68
97.9,2,69
97.9,2,79
98.0,2,76
98.0,2,87
98.0,2,78
98.0,2,73
98.0,2,89
98.1,2,81
98.2,2,73
98.2,2,64
98.2,2,65
98.2,2,73
98.2,2,69
98.2,2,57
98.3,2,79
98.3,2,78
98.3,2,80
98.4,2,79
98.4,2,81
98.4,2,73
98.4,2,74
98.4,2,84
98.5,2,83
98.6,2,82
98.6,2,85
98.6,2,86
98.6,2,77
98.7,2,72
98.7,2,79
98.7,2,59
98.7,2,64
98.7,2,65
98.7,2,82
98.8,2,64
98.8,2,70
98.8,2,83
98.8,2,89
98.8,2,69
98.8,2,73
98.8,2,84
98.9,2,76
99.0,2,79
99.0,2,81
99.1,2,80
99.1,2,74
99.2,2,77
99.2,2,66
99.3,2,68
99.4,2,77
99.9,2,79
100.0,2,78
100.8,2,77
代码实战
通过下面的代码进行判断。
>>> import pandas as pd
>>> data = pd.read_csv("/Users/wys/Desktop/height.csv",names=['temperature','sex',"heart_beat"])
>>> st.normaltest(data['temperature'], axis=None)
NormaltestResult(statistic=2.703801433319236, pvalue=0.2587479863488212) # # pvalue>0.05时，可以认为数据是呈正态分布的，小于为非正态性
还可以绘制图像来看数据的分布情况，从结果来看符合正态分布。
>>> import seaborn as sns
>>> sns.set_palette("hls")
>>> sns.distplot(data['temperature'],color="r",bins=130,kde=True)
>>> plt.show()
