结合pandas学习《极简统计学》。第一章《用频数分布表和直方图刻画数据的特征》练习。
根据原始数据什么也搞不明白,所以使用统计。
“统计”的手法,就是从原始数据,也就是“原始的现实”中,抽取出分布的特征和特点的方法。
统计学使用的方法叫“压缩”,是指“将作为数据列举的大量数字,以一定的基准进行整理,只抽取有意义的信息”。大致有以下两种手法:
做频数分布图,首先需要做频数分布表,步骤如下:
做直方图的步骤:
女大学生体重数据如下,请做频数分布表和直方图:
48, 54, 47, 50, 53, 43, 45, 43, 44, 47, 58, 46, 46, 63, 49, 50, 48, 43, 46, 45, 50, 53, 51, 58, 52, 53, 47, 49, 45, 42, 51, 49, 58, 54, 45, 53, 50, 69, 44, 50, 58, 64, 40, 57, 51, 69, 58, 47, 62, 47, 40, 60, 48, 47, 53, 47, 52, 61, 55, 55, 48, 48, 46, 52, 45, 38, 62, 47, 55, 50, 46, 47, 55, 48, 50, 50, 54, 55, 48, 50
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
weights = np.array([
48, 54, 47, 50, 53, 43, 45, 43, 44, 47,
58, 46, 46, 63, 49, 50, 48, 43, 46, 45,
50, 53, 51, 58, 52, 53, 47, 49, 45, 42,
51, 49, 58, 54, 45, 53, 50, 69, 44, 50,
58, 64, 40, 57, 51, 69, 58, 47, 62, 47,
40, 60, 48, 47, 53, 47, 52, 61, 55, 55,
48, 48, 46, 52, 45, 38, 62, 47, 55, 50,
46, 47, 55, 48, 50, 50, 54, 55, 48, 50])
sections = [35,40,45,50,55,60,65,70]
group_names = ['36~40','41~45','46~50','51~55','56~60','61~65','66~70']
cuts = pd.cut(weights,sections,labels=group_names)
计算频数:
counts = pd.value_counts(cuts)
dict(counts)
{'36~40': 3, '41~45': 11, '46~50': 33, '51~55': 19, '56~60': 7, '61~65': 5, '66~70': 2}
cuts.value_counts().plot(kind='bar')
直方图