python笔记：4.1.1.4统计量

# -*- coding: utf-8 -*- """ Created on Sun May 26 15:07:52 2019 @author: User """ import pandas as pd import numpy as np from scipy import stats stock=np.dtype([('id',np.str,5), ('time',np.str,10), ('code',np.str,10), ('open_p',np.float64), ('close_p',np.float64), ('low_p',np.float64), ('vol',np.int32), ('high_p',np.float64), ('col',np.int32)]) print('\n jd_stock:') jd_stock=np.loadtxt('data\stock.csv',delimiter=',',dtype=stock) print(jd_stock) print('\n jddf:') jddf=pd.read_csv('data\stock.csv',sep=',',header=None, names=['id','time','code','open_p','colse_p','low_p','vol','high_p','col']) print(jddf.head()) m=np.array([1,2,3,4,3,2,3,3,4,4,4,7,8]) print("\n stats.mode(m)") print(stats.mode(m)) print("\n stats.mode(jd_stock['open_p']):") print(stats.mode(jd_stock['open_p'])) print("\n stats.mode(jddf['open_p']):") print(stats.mode(jddf['open_p'])) print("\n pandas对象可以返回多个众数：") md=pd.DataFrame(m) print(md.mode())

运行：

 jd_stock: [('1', '20130902', '600028', 4.41, 4.43, 4.37,  17275, 4.41,  392662)  ('2', '20130903', '600028', 4.41, 4.46, 4.4 ,  19241, 4.45,  434177)  ('3', '20130904', '600028', 4.44, 4.49, 4.42,  20106, 4.47,  451470) ...  ('1356', '20190327', '600028', 5.71, 5.75, 5.69,  63601, 5.72, 1112544)  ('1357', '20190328', '600028', 5.69, 5.7 , 5.62,  65692, 5.64, 1162484)  ('1358', '20190329', '600028', 5.65, 5.75, 5.61, 112785, 5.74, 1981482)]

 jddf:    id      time    code  open_p  colse_p  low_p       vol  high_p     col 0   1  20130902  600028    4.41     4.43   4.37  17275.39    4.41  392662 1   2  20130903  600028    4.41     4.46   4.40  19241.84    4.45  434177 2   3  20130904  600028    4.44     4.49   4.42  20106.30    4.47  451470 3   4  20130905  600028    4.47     4.48   4.42  15582.48    4.47  349997 4   5  20130906  600028    4.46     4.52   4.45  19101.41    4.50  425777

 stats.mode(m) ModeResult(mode=array([3]), count=array([4]))

 stats.mode(jd_stock['open_p']): ModeResult(mode=array([5.96]), count=array([15]))

 stats.mode(jddf['open_p']): ModeResult(mode=array([5.96]), count=array([15]))    0 0  3 1  4