python笔记：3.2.1.1pandas数据结构_DataFrame2

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最新推荐文章于 2024-02-27 13:15:27 发布

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博客展示了将数组构造为DataFrame的过程。先是呈现了一组数值数组运行结果及相关DataFrame信息，后又给出股票数组，并将其构造为股票DataFrame，包含id、time、code等多列信息，还展示了该DataFrame的基本情况。

# -*- coding: utf-8 -*-
"""
Created on Wed May 22 17:07:10 2019

@author: User
"""

import pandas as pd
import numpy as np

numfram = np.random.randn(10, 5)
framnum = pd.DataFrame(numfram)
print(numfram)
print(framnum.info())
print(framnum.dtypes)

print('\n 前面股票数组构造为 DataFram:')
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)])
 
jd_stock=np.loadtxt('data\stock.csv', delimiter=',',dtype=stock)
print('\n 股票数组:')
print(jd_stock)
jd=pd.DataFrame(jd_stock)
print('\n 股票 DataFrame:')
print(jd.head())

print(jd.info())

运行：

[[-0.62009017 0.70829966 -0.48548659 1.26310006 -0.48235138]
[-1.67862693 -0.97096625 -1.65126175 0.35602323 0.78619157]
[ 0.46876188 1.45965403 1.69822388 1.35285213 0.86966089]
[ 0.77324385 2.17588443 -0.49302096 0.85118577 0.08857271]
[-0.84212732 -0.85268892 -0.49219341 -0.59472765 -0.4099793 ]
[ 1.22608899 1.67942467 -0.09757688 -0.68517965 0.12559482]
[-1.89127552 -0.02755593 -0.17825539 0.15061576 -0.01835327]
[ 0.38320852 1.26878589 -1.0170889 -1.58483841 -1.52350518]
[-0.79898396 0.68955353 -1.94068854 -1.87484369 -1.4181755 ]
[-0.59692944 -0.46468301 -0.45258183 -0.61153849 -0.97766694]]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 5 columns):
0 10 non-null float64
1 10 non-null float64
2 10 non-null float64
3 10 non-null float64
4 10 non-null float64
dtypes: float64(5)
memory usage: 480.0 bytes
None
0 float64
1 float64
2 float64
3 float64
4 float64
dtype: object

前面股票数组构造为 DataFram:

股票数组:
[('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) ...
('3980', '20190327', '600019', 7.14, 7.15, 7.08, 29373, 7.13, 412887)
('3981', '20190328', '600019', 7.1 , 7.12, 7.05, 25452, 7.08, 359576)
('3982', '20190329', '600019', 7.07, 7.25, 7.07, 54683, 7.23, 762021)]

股票 DataFrame:
id time code open_p close_p low_p vol high_p col
0 1 20130902 600028 4.41 4.43 4.37 17275 4.41 392662
1 2 20130903 600028 4.41 4.46 4.40 19241 4.45 434177
2 3 20130904 600028 4.44 4.49 4.42 20106 4.47 451470
3 4 20130905 600028 4.47 4.48 4.42 15582 4.47 349997
4 5 20130906 600028 4.46 4.52 4.45 19101 4.50 425777
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3982 entries, 0 to 3981
Data columns (total 9 columns):
id 3982 non-null object
time 3982 non-null object
code 3982 non-null object
open_p 3982 non-null float64
close_p 3982 non-null float64
low_p 3982 non-null float64
vol 3982 non-null int32
high_p 3982 non-null float64
col 3982 non-null int32
dtypes: float64(4), int32(2), object(3)
memory usage: 249.0+ KB
None