未完

for examples:

example 1:

 # Code based on Python 3.x

 # _*_ coding: utf-8 _*_

 # __Author: "LEMON"

 import pandas as pd

 d = pd.date_range('', periods=7)

 aList = list(range(1,8))

 df = pd.DataFrame(aList, index=d, columns=[' '])

 df.index.name = 'value'

 print('----------df.index---------')

 print(df.index)

 print('---------df.columns---------')

 print(df.columns)

 print('----------df.values---------')

 print(df.values)

 print('----------df.describe--------')

 print(df.describe)

 print('----------information details--------')

 print(df.head(2)) #获取开始的n条记录

 print(df.tail(3)) #后去最后的n条记录

 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：

 ----------df.index---------

 DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',

                '2017-01-05', '2017-01-06', '2017-01-07'],

               dtype='datetime64[ns]', name='value', freq='D')

 ---------df.columns---------

 Index([' '], dtype='object')

 ----------df.values---------

 [[1]

  [2]

  [3]

  [4]

  [5]

  [6]

  [7]]

 ----------df.describe--------

 <bound method NDFrame.describe of

 value

 2017-01-01  1

 2017-01-02  2

 2017-01-03  3

 2017-01-04  4

 2017-01-05  5

 2017-01-06  6

 2017-01-07  7>

 ----------information details--------

 value

 2017-01-01  1

 2017-01-02  2

 value

 2017-01-05  5

 2017-01-06  6

 2017-01-07  7

 value

 2017-01-04  4

 2017-01-05  5

example 2:

 # Code based on Python 3.x

 # _*_ coding: utf-8 _*_

 # __Author: "LEMON"

 from pandas import Series, DataFrame

 import pandas as pd

 data = {'state': ['Ohino', 'Ohino', 'Ohino', 'Nevada', 'Nevada'],

         'year': [2000, 2001, 2002, 2001, 2002],

         'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}

 df = DataFrame(data, index=list(range(1, 6)),

                columns=['year', 'state', 'pop', 'name'])

 print(df)

 print('\n', '---------------')

 print(list(df.ix[3]))

 print('\n', '---------------')

 print(list(df['year']))

 aList = ['', '', '', '']

 bList = ['aa', 'bb', 'cb', 'dd']

 cList = ['lemon', 'apple', 'orange', 'banana']

 d = {'num': aList, 'char': bList, 'fruit': cList}

 df1 = DataFrame(d, index=['a', 'b', 'c', 'd'])

 # df2 = DataFrame(bList)

 print('\n', '---------------')

 print(df1)

 #print(df1.num)

 print('\n', '---------------')

 print(df1.ix['b'])  # 获取索引号为 'b' 的行的数据

 print('\n', '---------------')

 print(df1.ix[:2, 1:3]) # 以切片形式获取部分数据

运行结果如下：

  year   state  pop name

 1  2000   Ohino  1.5  NaN

 2  2001   Ohino  1.7  NaN

 3  2002   Ohino  3.6  NaN

 4  2001  Nevada  2.4  NaN

 5  2002  Nevada  2.9  NaN

  ---------------

 [2002, 'Ohino', 3.6000000000000001, nan]

  ---------------

 [2000, 2001, 2002, 2001, 2002]

  ---------------

   char   fruit num

 a   aa   lemon   1

 b   bb   apple   2

 c   cb  orange   3

 d   dd  banana   4

  ---------------

 char        bb

 fruit    apple

 num          2

 Name: b, dtype: object

  ---------------

    fruit num

 a  lemon   1

 b  apple   2

example 3 (数据选择-DateFrame.loc()和DateFrame.iloc()) :

 # Code based on Python 3.x

 # _*_ coding: utf-8 _*_

 # __Author: "LEMON"

 from matplotlib.finance import quotes_historical_yahoo_ochl

 from datetime import date

 import pandas as pd

 today = date.today()

 start =(today.year-4, today.month+11, today.day-1)

 end = (today.year-4, today.month+11, today.day+3)

 quotes = quotes_historical_yahoo_ochl('AMX', start, end)

 # each items in quotes is type of "tuple"

 fields = ['date', 'open', 'close', 'high', 'low', 'volume']

 quotes1 = []

 for t in quotes:

     t1 = list(t)

     quotes1.append(t1)

 # each items in quotes1 is type of "list"

 for i in range(0, len(quotes1)):

     quotes1[i][0] = date.fromordinal(int(quotes1[i][0]))

     # date format is changed

 df = pd.DataFrame(quotes1, index=range(1, len(quotes1)+1), columns=fields)

 # df = pd.DataFrame(quotes1, index=['a','b','c','d','e'], columns=fields)

 # df = df.drop(['date'], axis=1)

 print(df)

 print(df['close'].mean())  #计算某列的mean值

 # print(dict(df.mean())['close'])  #计算某列的mean值

 print(df.sort_values(['open'],ascending = True)) #进行排序，默认(True)是升序

 print(df[df.open>=21].date)

 # index是整数

 print(df.loc[2:5, 'date':'close'])

 print(df.loc[[2,5],['open','close']])

 # 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

 print(df.iloc[1:6,0:4])  #iloc方法以切片形式选取数据

 # index是标签形式

 # print(df.loc['a':'d', 'date':'close'])

 # print(df.loc[['b','e'],['open','close']])

 # 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

 # 根据判断条件来选择数据

 print(df[(df.index>=4) & (df.open>=21)])

 # DateFrame 的均值

 print(df.mean())  # 默认计算每列的均值

 print(df.mean(axis=1))  # axis=1是计算每行的均值

 '''

 # 获取多只股票的信息

 d1 = (today.year-1, today.month+11, today.day)

 aList = ['BABA', 'KO', 'AMX'] # List of the stock code of companys

 for i in aList:

     q1 = quotes_historical_yahoo_ochl(i, d1, today)

     df1 = pd.DataFrame(q1)

     print(df1)

 '''

运行结果如下：

          date       open      close       high        low     volume

 1  2013-12-03  20.999551  21.156955  21.184731  20.795851  5152600.0

 2  2013-12-04  20.971773  20.934738  21.064364  20.703261  5174400.0

 3  2013-12-05  20.518079  20.545857  21.231027  20.379193  7225600.0

 4  2013-12-06  21.166215  20.601411  21.295841  20.536598  9989500.0

 20.80974025

 20.80974025

          date       open      close       high        low     volume

 3  2013-12-05  20.518079  20.545857  21.231027  20.379193  7225600.0

 2  2013-12-04  20.971773  20.934738  21.064364  20.703261  5174400.0

 1  2013-12-03  20.999551  21.156955  21.184731  20.795851  5152600.0

 4  2013-12-06  21.166215  20.601411  21.295841  20.536598  9989500.0

 4    2013-12-06

 Name: date, dtype: object

 runfile('E:/Python/Anaco/test_yahoo.py', wdir='E:/Python/Anaco')

          date       open      close       high        low     volume

 1  2013-12-03  20.999551  21.156955  21.184731  20.795851  5152600.0

 2  2013-12-04  20.971773  20.934738  21.064364  20.703261  5174400.0

 3  2013-12-05  20.518079  20.545857  21.231027  20.379193  7225600.0

 4  2013-12-06  21.166215  20.601411  21.295841  20.536598  9989500.0

 20.80974025

          date       open      close       high        low     volume

 3  2013-12-05  20.518079  20.545857  21.231027  20.379193  7225600.0

 2  2013-12-04  20.971773  20.934738  21.064364  20.703261  5174400.0

 1  2013-12-03  20.999551  21.156955  21.184731  20.795851  5152600.0

 4  2013-12-06  21.166215  20.601411  21.295841  20.536598  9989500.0

 4    2013-12-06

 Name: date, dtype: object

          date       open      close

 2  2013-12-04  20.971773  20.934738

 3  2013-12-05  20.518079  20.545857

 4  2013-12-06  21.166215  20.601411

         open      close

 2  20.971773  20.934738

 5        NaN        NaN

          date       open      close       high

 2  2013-12-04  20.971773  20.934738  21.064364

 3  2013-12-05  20.518079  20.545857  21.231027

 4  2013-12-06  21.166215  20.601411  21.295841

          date       open      close       high        low     volume

 4  2013-12-06  21.166215  20.601411  21.295841  20.536598  9989500.0

 open      2.091390e+01

 close     2.080974e+01

 high      2.119399e+01

 low       2.060373e+01

 volume    6.885525e+06

 dtype: float64

 1    1.030537e+06

 2    1.034897e+06

 3    1.445137e+06

 4    1.997917e+06

 dtype: float64

examples 4: 求微软公司（MSFT）2015年每月股票收盘价的平均值。

 # Code based on Python 3.x

 # _*_ coding: utf-8 _*_

 # __Author: "LEMON"

 # 求微软公司（MSFT）2015年每月股票收盘价的平均值。

 #Method 1 (update)

 from matplotlib.finance import quotes_historical_yahoo_ochl

 from datetime import date

 import pandas as pd

 from datetime import datetime

 today = date.today()

 fields = ['date', 'open', 'close', 'high', 'low', 'volume']

 start = (today.year - 3, today.month, today.day)

 end = today

 quotes = quotes_historical_yahoo_ochl('MSFT', start, end)

 # each items in quotes is type of "tuple"

 df = pd.DataFrame(quotes, index=range(1, len(quotes) + 1), columns=fields)

 list = df.date.tolist()

 list1 = []

 for x in list:

     x = date.fromordinal(int(x))

     y = date.strftime(x, '%Y/%m')

     list1.append(y)

 # print(list1)

 df1 = df.set_index([list1]).drop('date',axis=1)

 # 把日期设置成索引，并删除“date”列

 df2 = df1['2015/01':'2015/12']  #选取2015年的数据

 print(df2.groupby(df2.index).close.mean())

 # 将数据按index进行聚类分析，并计算收盘价“close”的均值

 # -----------------------------------------------------

 # #Method 1 (old)

 #

 # from matplotlib.finance import quotes_historical_yahoo_ochl

 # from datetime import date

 # import pandas as pd

 # from datetime import datetime

 #

 #

 # today = date.today()

 # fields = ['date', 'open', 'close', 'high', 'low', 'volume']

 #

 # start2 = (today.year - 3, today.month, today.day)

 # end2 = today

 # quotes2 = quotes_historical_yahoo_ochl('MSFT', start2, end2)

 # # each items in quotes is type of "tuple"

 #

 # quotes3 = []

 # for t in quotes2:

 #     t1 = list(t)

 #     quotes3.append(t1)

 # # each items in quotes1 is type of "list"

 #

 # for i in range(0, len(quotes3)):

 #     quotes3[i][0] = date.fromordinal(int(quotes3[i][0]))

 #     # date format is changed

 #

 # df2 = pd.DataFrame(quotes3, index=range(1, len(quotes3) + 1), columns=fields)

 #

 # df2['date'] = pd.to_datetime(df2['date'], format='%Y-%m-%d')  # 转化成pandas的日期格式

 # # print(df2)

 #

 # start2015 = datetime(2015,1,1)

 # end2015 = datetime(2015,12,31)

 # # start2015 = datetime.strptime('2015-1-1', '%Y-%m-%d')

 # # # 将'2015-1-1'字符串设置为时间格式

 # # end2015 = datetime.strptime('2015-12-31', '%Y-%m-%d')

 # # # 将'2015-12-31'字符串设置为时间格式

 #

 # df1 = df2[(start2015 <= df2.date) & (df2.date <= end2015)]

 # # 通过时间条件来选择2015年的记录

 #

 # permonth1 = df1.date.dt.to_period('M')  #data per month

 # g_month1 = df1.groupby(permonth1)

 # g_closequotes = g_month1['close']

 #

 # s_month = g_closequotes.mean()  # s_month is Series class

 # s_month.index.name = 'date_index'

 #

 # print(s_month)

 # -----------------------------------------------------

 # =================================================================

 # Method 2

 # from matplotlib.finance import quotes_historical_yahoo_ochl

 # from datetime import date

 #

 # import pandas as pd

 # today = date.today()

 # start = (today.year-3, today.month, today.day)

 # quotesMS = quotes_historical_yahoo_ochl('MSFT', start, today)

 # attributes=['date','open','close','high','low','volume']

 # quotesdfMS = pd.DataFrame(quotesMS, columns= attributes)

 #

 #

 #

 # list = []

 # for i in range(0, len(quotesMS)):

 #     x = date.fromordinal(int(quotesMS[i][0]))

 #     y = date.strftime(x, '%y/%m/%d')

 #     list.append(y)

 # quotesdfMS.index = list

 # quotesdfMS = quotesdfMS.drop(['date'], axis = 1)

 # list = []

 # quotesdfMS15 = quotesdfMS['15/01/01':'15/12/31']

 #

 # print(quotesdfMS15)

 #

 # for i in range(0, len(quotesdfMS15)):

 #     list.append(int(quotesdfMS15.index[i][3:5])) #get month just like '02'

 # quotesdfMS15['month'] = list

 # print(quotesdfMS15.groupby('month').mean().close)

 # =================================================================

输出结果如下：

 2015/01    43.124433

 2015/02    40.956772

 2015/03    40.203918

 2015/04    41.477685

 2015/05    45.472291

 2015/06    44.145879

 2015/07    43.807541

 2015/08    43.838895

 2015/09    42.114155

 2015/10    47.082882

 2015/11    52.252878

 2015/12    53.916431

 Name: close, dtype: float64

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