pandas基础-Python3
2024-06-13 13:15:20
未完
for examples:
example 1:
1 # Code based on Python 3.x
2 # _*_ coding: utf-8 _*_
3 # __Author: "LEMON"
4
5 import pandas as pd
6
7 d = pd.date_range('20170101', periods=7)
8 aList = list(range(1,8))
9
10 df = pd.DataFrame(aList, index=d, columns=[' '])
11 df.index.name = 'value'
12
13 print('----------df.index---------')
14 print(df.index)
15
16 print('---------df.columns---------')
17 print(df.columns)
18
19 print('----------df.values---------')
20 print(df.values)
21
22 print('----------df.describe--------')
23 print(df.describe)
24
25 print('----------information details--------')
26 print(df.head(2)) #获取开始的n条记录
27 print(df.tail(3)) #后去最后的n条记录
28 print(df[3:5]) # df[a:b]，获取第a+1至第b-1的记录运行结果如下：
1 ----------df.index--------- 2 DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04', 3 '2017-01-05', '2017-01-06', '2017-01-07'], 4 dtype='datetime64[ns]', name='value', freq='D') 5 ---------df.columns--------- 6 Index([' '], dtype='object') 7 ----------df.values--------- 8 [[1] 9 [2] 10 [3] 11 [4] 12 [5] 13 [6] 14 [7]] 15 ----------df.describe-------- 16 <bound method NDFrame.describe of 17 value 18 2017-01-01 1 19 2017-01-02 2 20 2017-01-03 3 21 2017-01-04 4 22 2017-01-05 5 23 2017-01-06 6 24 2017-01-07 7> 25 ----------information details-------- 26 27 value 28 2017-01-01 1 29 2017-01-02 2 30 31 value 32 2017-01-05 5 33 2017-01-06 6 34 2017-01-07 7 35 36 value 37 2017-01-04 4 38 2017-01-05 5
example 2:
1 # Code based on Python 3.x
2 # _*_ coding: utf-8 _*_
3 # __Author: "LEMON"
4
5 from pandas import Series, DataFrame
6 import pandas as pd
7
8 data = {'state': ['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada'],
9 'year': [2000, 2001, 2002, 2001, 2002],
10 'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}
11
12 df = DataFrame(data, index=list(range(1, 6)),
13 columns=['year', 'state', 'pop', 'name'])
14 print(df)
15
16 print('\n', '---------------')
17 print(list(df.ix[3]))
18
19 print('\n', '---------------')
20 print(list(df['year']))
21
22 aList = ['1', '2', '3', '4']
23 bList = ['aa', 'bb', 'cb', 'dd']
24 cList = ['lemon', 'apple', 'orange', 'banana']
25
26 d = {'num': aList, 'char': bList, 'fruit': cList}
27
28
29 df1 = DataFrame(d, index=['a', 'b', 'c', 'd'])
30 # df2 = DataFrame(bList)
31 print('\n', '---------------')
32 print(df1)
33 #print(df1.num)
34
35 print('\n', '---------------')
36 print(df1.ix['b']) # 获取索引号为 'b' 的行的数据
37
38
39 print('\n', '---------------')
40 print(df1.ix[:2, 1:3]) # 以切片形式获取部分数据运行结果如下：
1 year state pop name 2 1 2000 Ohino 1.5 NaN 3 2 2001 Ohino 1.7 NaN 4 3 2002 Ohino 3.6 NaN 5 4 2001 Nevada 2.4 NaN 6 5 2002 Nevada 2.9 NaN 7 8 --------------- 9 [2002, 'Ohino', 3.6000000000000001, nan] 10 11 --------------- 12 [2000, 2001, 2002, 2001, 2002] 13 14 --------------- 15 char fruit num 16 a aa lemon 1 17 b bb apple 2 18 c cb orange 3 19 d dd banana 4 20 21 --------------- 22 char bb 23 fruit apple 24 num 2 25 Name: b, dtype: object 26 27 --------------- 28 fruit num 29 a lemon 1 30 b apple 2
example 3 (数据选择-DateFrame.loc()和DateFrame.iloc()) :
1 # Code based on Python 3.x
2 # _*_ coding: utf-8 _*_
3 # __Author: "LEMON"
4
5 from matplotlib.finance import quotes_historical_yahoo_ochl
6 from datetime import date
7 import pandas as pd
8
9 today = date.today()
10
11 start =(today.year-4, today.month+11, today.day-1)
12 end = (today.year-4, today.month+11, today.day+3)
13 quotes = quotes_historical_yahoo_ochl('AMX', start, end)
14 # each items in quotes is type of "tuple"
15
16 fields = ['date', 'open', 'close', 'high', 'low', 'volume']
17
18 quotes1 = []
19 for t in quotes:
20 t1 = list(t)
21 quotes1.append(t1)
22 # each items in quotes1 is type of "list"
23
24 for i in range(0, len(quotes1)):
25 quotes1[i][0] = date.fromordinal(int(quotes1[i][0]))
26 # date format is changed
27
28 df = pd.DataFrame(quotes1, index=range(1, len(quotes1)+1), columns=fields)
29 # df = pd.DataFrame(quotes1, index=['a','b','c','d','e'], columns=fields)
30 # df = df.drop(['date'], axis=1)
31
32 print(df)
33
34 print(df['close'].mean()) #计算某列的mean值
35 # print(dict(df.mean())['close']) #计算某列的mean值
36
37 print(df.sort_values(['open'],ascending = True)) #进行排序，默认(True)是升序
38 print(df[df.open>=21].date)
39
40
41
42 # index是整数
43 print(df.loc[2:5, 'date':'close'])
44 print(df.loc[[2,5],['open','close']])
45 # loc方法在行和列的选择上是标签形式，可以是连续的选择，或者单个行或列的选择
46 print(df.iloc[1:6,0:4]) #iloc方法以切片形式选取数据
47
48
49 # index是标签形式
50 # print(df.loc['a':'d', 'date':'close'])
51 # print(df.loc[['b','e'],['open','close']])
52 # loc方法在行和列的选择上是标签形式，可以是连续的选择，或者单个行或列的选择
53
54 # 根据判断条件来选择数据
55 print(df[(df.index>=4) & (df.open>=21)])
56
57
58 # DateFrame 的均值
59 print(df.mean()) # 默认计算每列的均值
60 print(df.mean(axis=1)) # axis=1是计算每行的均值
61
62
63 '''
64 # 获取多只股票的信息
65 d1 = (today.year-1, today.month+11, today.day)
66
67 aList = ['BABA', 'KO', 'AMX'] # List of the stock code of companys
68
69
70 for i in aList:
71 q1 = quotes_historical_yahoo_ochl(i, d1, today)
72 df1 = pd.DataFrame(q1)
73 print(df1)
74 '''运行结果如下：
1 date open close high low volume
2 1 2013-12-03 20.999551 21.156955 21.184731 20.795851 5152600.0
3 2 2013-12-04 20.971773 20.934738 21.064364 20.703261 5174400.0
4 3 2013-12-05 20.518079 20.545857 21.231027 20.379193 7225600.0
5 4 2013-12-06 21.166215 20.601411 21.295841 20.536598 9989500.0
6 20.80974025
7 20.80974025
8 date open close high low volume
9 3 2013-12-05 20.518079 20.545857 21.231027 20.379193 7225600.0
10 2 2013-12-04 20.971773 20.934738 21.064364 20.703261 5174400.0
11 1 2013-12-03 20.999551 21.156955 21.184731 20.795851 5152600.0
12 4 2013-12-06 21.166215 20.601411 21.295841 20.536598 9989500.0
13 4 2013-12-06
14 Name: date, dtype: object
15
16 runfile('E:/Python/Anaco/test_yahoo.py', wdir='E:/Python/Anaco')
17 date open close high low volume
18 1 2013-12-03 20.999551 21.156955 21.184731 20.795851 5152600.0
19 2 2013-12-04 20.971773 20.934738 21.064364 20.703261 5174400.0
20 3 2013-12-05 20.518079 20.545857 21.231027 20.379193 7225600.0
21 4 2013-12-06 21.166215 20.601411 21.295841 20.536598 9989500.0
22 20.80974025
23 date open close high low volume
24 3 2013-12-05 20.518079 20.545857 21.231027 20.379193 7225600.0
25 2 2013-12-04 20.971773 20.934738 21.064364 20.703261 5174400.0
26 1 2013-12-03 20.999551 21.156955 21.184731 20.795851 5152600.0
27 4 2013-12-06 21.166215 20.601411 21.295841 20.536598 9989500.0
28 4 2013-12-06
29 Name: date, dtype: object
30 date open close
31 2 2013-12-04 20.971773 20.934738
32 3 2013-12-05 20.518079 20.545857
33 4 2013-12-06 21.166215 20.601411
34 open close
35 2 20.971773 20.934738
36 5 NaN NaN
37 date open close high
38 2 2013-12-04 20.971773 20.934738 21.064364
39 3 2013-12-05 20.518079 20.545857 21.231027
40 4 2013-12-06 21.166215 20.601411 21.295841
41 date open close high low volume
42 4 2013-12-06 21.166215 20.601411 21.295841 20.536598 9989500.0
43 open 2.091390e+01
44 close 2.080974e+01
45 high 2.119399e+01
46 low 2.060373e+01
47 volume 6.885525e+06
48 dtype: float64
49 1 1.030537e+06
50 2 1.034897e+06
51 3 1.445137e+06
52 4 1.997917e+06
53 dtype: float64View Code
examples 4: 求微软公司（MSFT）2015年每月股票收盘价的平均值。
1 # Code based on Python 3.x
2 # _*_ coding: utf-8 _*_
3 # __Author: "LEMON"
4
5 # 求微软公司（MSFT）2015年每月股票收盘价的平均值。
6
7
8 #Method 1 (update)
9
10 from matplotlib.finance import quotes_historical_yahoo_ochl
11 from datetime import date
12 import pandas as pd
13 from datetime import datetime
14
15 today = date.today()
16 fields = ['date', 'open', 'close', 'high', 'low', 'volume']
17
18 start = (today.year - 3, today.month, today.day)
19 end = today
20 quotes = quotes_historical_yahoo_ochl('MSFT', start, end)
21 # each items in quotes is type of "tuple"
22
23 df = pd.DataFrame(quotes, index=range(1, len(quotes) + 1), columns=fields)
24
25 list = df.date.tolist()
26 list1 = []
27 for x in list:
28 x = date.fromordinal(int(x))
29 y = date.strftime(x, '%Y/%m')
30 list1.append(y)
31
32 # print(list1)
33 df1 = df.set_index([list1]).drop('date',axis=1)
34 # 把日期设置成索引，并删除“date”列
35
36 df2 = df1['2015/01':'2015/12'] #选取2015年的数据
37 print(df2.groupby(df2.index).close.mean())
38 # 将数据按index进行聚类分析，并计算收盘价“close”的均值
39
40
41 # -----------------------------------------------------
42 # #Method 1 (old)
43 #
44 # from matplotlib.finance import quotes_historical_yahoo_ochl
45 # from datetime import date
46 # import pandas as pd
47 # from datetime import datetime
48 #
49 #
50 # today = date.today()
51 # fields = ['date', 'open', 'close', 'high', 'low', 'volume']
52 #
53 # start2 = (today.year - 3, today.month, today.day)
54 # end2 = today
55 # quotes2 = quotes_historical_yahoo_ochl('MSFT', start2, end2)
56 # # each items in quotes is type of "tuple"
57 #
58 # quotes3 = []
59 # for t in quotes2:
60 # t1 = list(t)
61 # quotes3.append(t1)
62 # # each items in quotes1 is type of "list"
63 #
64 # for i in range(0, len(quotes3)):
65 # quotes3[i][0] = date.fromordinal(int(quotes3[i][0]))
66 # # date format is changed
67 #
68 # df2 = pd.DataFrame(quotes3, index=range(1, len(quotes3) + 1), columns=fields)
69 #
70 # df2['date'] = pd.to_datetime(df2['date'], format='%Y-%m-%d') # 转化成pandas的日期格式
71 # # print(df2)
72 #
73 # start2015 = datetime(2015,1,1)
74 # end2015 = datetime(2015,12,31)
75 # # start2015 = datetime.strptime('2015-1-1', '%Y-%m-%d')
76 # # # 将'2015-1-1'字符串设置为时间格式
77 # # end2015 = datetime.strptime('2015-12-31', '%Y-%m-%d')
78 # # # 将'2015-12-31'字符串设置为时间格式
79 #
80 # df1 = df2[(start2015 <= df2.date) & (df2.date <= end2015)]
81 # # 通过时间条件来选择2015年的记录
82 #
83 # permonth1 = df1.date.dt.to_period('M') #data per month
84 # g_month1 = df1.groupby(permonth1)
85 # g_closequotes = g_month1['close']
86 #
87 # s_month = g_closequotes.mean() # s_month is Series class
88 # s_month.index.name = 'date_index'
89 #
90 # print(s_month)
91 # -----------------------------------------------------
92
93 # =================================================================
94 # Method 2
95
96 # from matplotlib.finance import quotes_historical_yahoo_ochl
97 # from datetime import date
98 #
99 # import pandas as pd
100 # today = date.today()
101 # start = (today.year-3, today.month, today.day)
102 # quotesMS = quotes_historical_yahoo_ochl('MSFT', start, today)
103 # attributes=['date','open','close','high','low','volume']
104 # quotesdfMS = pd.DataFrame(quotesMS, columns= attributes)
105 #
106 #
107 #
108 # list = []
109 # for i in range(0, len(quotesMS)):
110 # x = date.fromordinal(int(quotesMS[i][0]))
111 # y = date.strftime(x, '%y/%m/%d')
112 # list.append(y)
113 # quotesdfMS.index = list
114 # quotesdfMS = quotesdfMS.drop(['date'], axis = 1)
115 # list = []
116 # quotesdfMS15 = quotesdfMS['15/01/01':'15/12/31']
117 #
118 # print(quotesdfMS15)
119 #
120 # for i in range(0, len(quotesdfMS15)):
121 # list.append(int(quotesdfMS15.index[i][3:5])) #get month just like '02'
122 # quotesdfMS15['month'] = list
123 # print(quotesdfMS15.groupby('month').mean().close)
124 # =================================================================输出结果如下：
1 2015/01 43.124433 2 2015/02 40.956772 3 2015/03 40.203918 4 2015/04 41.477685 5 2015/05 45.472291 6 2015/06 44.145879 7 2015/07 43.807541 8 2015/08 43.838895 9 2015/09 42.114155 10 2015/10 47.082882 11 2015/11 52.252878 12 2015/12 53.916431 13 Name: close, dtype: float64
转载于:https://www.cnblogs.com/lemonbit/p/6243513.html
pandas基础-Python3相关推荐
- 数据分析：numpy和pandas基础操作详解
数据处理基础 numpy基础 1.numpy创建数组及基础属性 2.数组的特点 3.创建常见数组 4.数组数据类型 5.生成随机数 6.一维数组索引 7.布尔索引 8. 多维数组的索引 9.实际问题 ...
- 快乐学习Pandas入门篇：Pandas基础
Datawhale学习 作者:杨煜,Datawhale成员 寄语:本文对Pandas基础内容进行了梳理,从文件读取与写入.Series及DataFrame基本数据结构.常用基本函数及排序四个模块快速入 ...
- pandas object转float_数据分析篇 | Pandas基础用法6【完结篇】
这是最后一篇,至此Pandas系列终于连载完了,有需要的也可以看看前面6篇,尽请收藏. 数据分析篇 | Pandas 概览 数据分析篇 | Pandas基础用法1数据分析篇 | Pandas基础用法2 ...
- Numpy与Pandas基础
Numpy与Pandas基础 Cyczz 计算机小白 关注他 1 人赞同了该文章 本文包括: 一维数据分析 二维数据分析 一.一维数组 Numpy的一维数组(array) 创建一个一维数组,参数传入 ...
- 【Python】Pandas基础：结构化数据处理
python:Pandas基础:结构化数据处理 目录: 文章目录 @[toc] 一 pandas及其重要性 二 pandas的数据结构介绍 1 Series 2 DataFrame 3 索引对象 三 ...
- 利用Python进行数据分析(7) pandas基础: Series和DataFrame的简单介绍 一、pandas 是什么 pandas 是基于 NumPy 的一个 Python 数据分析包，主
利用Python进行数据分析(7) pandas基础: Series和DataFrame的简单介绍 一.pandas 是什么 pandas 是基于 NumPy 的一个 Python 数据分析包,主要目 ...
- Python科学计算之Pandas基础学习
Python科学计算之Pandas基础学习 导入Pandas 我们首先要导入我们的演出明星--Pandas. 这是导入Pandas的标准方式.显然,我们不希望每时每刻都在程序中写'pandas',但是 ...
- pandas pivot 计算占比_数据分析Pandas 基础（二）
推荐阅读:数据分析--Pandas 基础(一) 上一节课介绍了 Pandas 的基本用法,这一章节我们通过对 "泰坦尼克号" 幸存者进行数据分析,来进一步的学习 pandas. t ...
- 数据载入、Pandas基础和探索性数据分析
1. 载入数据及初步观察 1.1 载入数据 数据集下载 https://www.kaggle.com/c/titanic/overview 1.1.1 导入numpy和pandas import nu ...
最新文章
- 58前端内推笔试2017（含答案）
- 20155204 2016-2017-2 《Java程序设计》第3周学习总结
- android新浪微博sdk登录获取用户名_多账户的统一登录方案
- Flink三种运行模式安装部署
- 微软官方pe工具_很好却不够香！微软官方免费数据恢复工具体验
- 为什么说，每个人都应该多读些书？
- 1526B. I Hate 1111
- 用python画动态三维轨迹_python 画3维轨迹图并进行比较的实例
- mongo 让字段自增或自减
- linux和windows图形运算效率,linux和windows下UDP发送效率的有趣比较
- 一个函数解决【LeetCode 买卖股票的最佳时机】系列所有题目！
- 正则表达式语法和常用表达式列表
- java long精度问题_解决Long类型超过16位丢失精度问题的两种方法
- android照片同步到另一部手机,怎样可以把以前手机里的照片导入另一个手机？...
- container_of的用法
- 直接管理和维护计算机系统的程序称为,全国2008年4月高等教育自学考试计算机原理试题...
- 日记：2019新年计划，每日计划的思维导图
- 计算机视觉：视频分解图片和图片合成视频
- 二进制转化为十进制Java实现
- 【报告分享】2021小红书电商直播趋势报告-千瓜数据（附下载）
热门文章
- python如何创建一个列表_使用python中的format（）创建一个列表(make a list using format() in python)...
- css奇数、偶数样式
- java 批量替换字符串_# Java 一步一步实现高逼格的字符串替换工具（二）
- ViewData 和 echarts用法
- java打印正三角形_java实现打印正三角的方法
- linux expect sftp,expect实现sftp文件同步
- websocket php MySQL_PHP写了一个websocket服务,mysql连接实例丢失问题怎么解决？
- python和.net的区别_c#教程之.net和C#的区别
- php网上实训指导书,PHP网站开发技术-实训指导书2016吐血制作
- java 往excel中写数据库,poi将数据写入excel表格-怎么用java把数据库里的数据写入到excel表中...