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('', 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 = ['', '', '', '']
  23.  bList = ['aa', 'bb', 'cb', 'dd']
  24.  cList = ['lemon', 'apple', 'orange', 'banana']
  25.  
  26.  d = {'num': aList, 'char': bList, 'fruit': cList}
  27.  
  28.  df1 = DataFrame(d, index=['a', 'b', 'c', 'd'])
  29.  # df2 = DataFrame(bList)
  30.  print('\n', '---------------')
  31.  print(df1)
  32.  #print(df1.num)
  33.  
  34.  print('\n', '---------------')
  35.  print(df1.ix['b'])  # 获取索引号为 'b' 的行的数据
  36.  
  37.  print('\n', '---------------')
  38.  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.  # index是整数
  41.  print(df.loc[2:5, 'date':'close'])
  42.  print(df.loc[[2,5],['open','close']])
  43.  # 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
  44.  print(df.iloc[1:6,0:4])  #iloc方法以切片形式选取数据
  45.  
  46.  # index是标签形式
  47.  # print(df.loc['a':'d', 'date':'close'])
  48.  # print(df.loc[['b','e'],['open','close']])
  49.  # 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
  50.  
  51.  # 根据判断条件来选择数据
  52.  print(df[(df.index>=4) & (df.open>=21)])
  53.  
  54.  # DateFrame 的均值
  55.  print(df.mean())  # 默认计算每列的均值
  56.  print(df.mean(axis=1))  # axis=1是计算每行的均值
  57.  
  58.  '''
  59.  # 获取多只股票的信息
  60.  d1 = (today.year-1, today.month+11, today.day)
  61.  
  62.  aList = ['BABA', 'KO', 'AMX'] # List of the stock code of companys
  63.  
  64.  for i in aList:
  65.      q1 = quotes_historical_yahoo_ochl(i, d1, today)
  66.      df1 = pd.DataFrame(q1)
  67.      print(df1)
  68.  '''

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: float64

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.  #Method 1 (update)
  8.  
  9.  from matplotlib.finance import quotes_historical_yahoo_ochl
  10.  from datetime import date
  11.  import pandas as pd
  12.  from datetime import datetime
  13.  
  14.  today = date.today()
  15.  fields = ['date', 'open', 'close', 'high', 'low', 'volume']
  16.  
  17.  start = (today.year - 3, today.month, today.day)
  18.  end = today
  19.  quotes = quotes_historical_yahoo_ochl('MSFT', start, end)
  20.  # each items in quotes is type of "tuple"
  21.  
  22.  df = pd.DataFrame(quotes, index=range(1, len(quotes) + 1), columns=fields)
  23.  
  24.  list = df.date.tolist()
  25.  list1 = []
  26.  for x in list:
  27.      x = date.fromordinal(int(x))
  28.      y = date.strftime(x, '%Y/%m')
  29.      list1.append(y)
  30.  
  31.  # print(list1)
  32.  df1 = df.set_index([list1]).drop('date',axis=1)
  33.  # 把日期设置成索引，并删除“date”列
  34.  
  35.  df2 = df1['2015/01':'2015/12']  #选取2015年的数据
  36.  print(df2.groupby(df2.index).close.mean())
  37.  # 将数据按index进行聚类分析，并计算收盘价“close”的均值
  38.  
  39.  # -----------------------------------------------------
  40.  # #Method 1 (old)
  41.  #
  42.  # from matplotlib.finance import quotes_historical_yahoo_ochl
  43.  # from datetime import date
  44.  # import pandas as pd
  45.  # from datetime import datetime
  46.  #
  47.  #
  48.  # today = date.today()
  49.  # fields = ['date', 'open', 'close', 'high', 'low', 'volume']
  50.  #
  51.  # start2 = (today.year - 3, today.month, today.day)
  52.  # end2 = today
  53.  # quotes2 = quotes_historical_yahoo_ochl('MSFT', start2, end2)
  54.  # # each items in quotes is type of "tuple"
  55.  #
  56.  # quotes3 = []
  57.  # for t in quotes2:
  58.  #     t1 = list(t)
  59.  #     quotes3.append(t1)
  60.  # # each items in quotes1 is type of "list"
  61.  #
  62.  # for i in range(0, len(quotes3)):
  63.  #     quotes3[i][0] = date.fromordinal(int(quotes3[i][0]))
  64.  #     # date format is changed
  65.  #
  66.  # df2 = pd.DataFrame(quotes3, index=range(1, len(quotes3) + 1), columns=fields)
  67.  #
  68.  # df2['date'] = pd.to_datetime(df2['date'], format='%Y-%m-%d')  # 转化成pandas的日期格式
  69.  # # print(df2)
  70.  #
  71.  # start2015 = datetime(2015,1,1)
  72.  # end2015 = datetime(2015,12,31)
  73.  # # start2015 = datetime.strptime('2015-1-1', '%Y-%m-%d')
  74.  # # # 将'2015-1-1'字符串设置为时间格式
  75.  # # end2015 = datetime.strptime('2015-12-31', '%Y-%m-%d')
  76.  # # # 将'2015-12-31'字符串设置为时间格式
  77.  #
  78.  # df1 = df2[(start2015 <= df2.date) & (df2.date <= end2015)]
  79.  # # 通过时间条件来选择2015年的记录
  80.  #
  81.  # permonth1 = df1.date.dt.to_period('M')  #data per month
  82.  # g_month1 = df1.groupby(permonth1)
  83.  # g_closequotes = g_month1['close']
  84.  #
  85.  # s_month = g_closequotes.mean()  # s_month is Series class
  86.  # s_month.index.name = 'date_index'
  87.  #
  88.  # print(s_month)
  89.  # -----------------------------------------------------
  90.  
  91.  # =================================================================
  92.  # Method 2
  93.  
  94.  # from matplotlib.finance import quotes_historical_yahoo_ochl
  95.  # from datetime import date
  96.  #
  97.  # import pandas as pd
  98.  # today = date.today()
  99.  # start = (today.year-3, today.month, today.day)
  100.  # quotesMS = quotes_historical_yahoo_ochl('MSFT', start, today)
  101.  # attributes=['date','open','close','high','low','volume']
  102.  # quotesdfMS = pd.DataFrame(quotesMS, columns= attributes)
  103.  #
  104.  #
  105.  #
  106.  # list = []
  107.  # for i in range(0, len(quotesMS)):
  108.  #     x = date.fromordinal(int(quotesMS[i][0]))
  109.  #     y = date.strftime(x, '%y/%m/%d')
  110.  #     list.append(y)
  111.  # quotesdfMS.index = list
  112.  # quotesdfMS = quotesdfMS.drop(['date'], axis = 1)
  113.  # list = []
  114.  # quotesdfMS15 = quotesdfMS['15/01/01':'15/12/31']
  115.  #
  116.  # print(quotesdfMS15)
  117.  #
  118.  # for i in range(0, len(quotesdfMS15)):
  119.  #     list.append(int(quotesdfMS15.index[i][3:5])) #get month just like '02'
  120.  # quotesdfMS15['month'] = list
  121.  # print(quotesdfMS15.groupby('month').mean().close)
  122.  # =================================================================

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

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