第六节：pandas基础

　　pandas 是基于 Numpy 构建的，让以 Numpy 为中心的应用变得更加简单。pandas是公认的数据处理利器，本章内容主要介绍DataFrame数据结构，在此基础上进行数据处理。除了DataFrame格式，pandas 还包括series、Panel。

格式	数组	释义
Series	一维数组	与Numpy中的一维array类似。
DataFrame	二维的表格型数据结构	可以将DataFrame理解为Series的容器
Panel	三维的数组	可以理解为DataFrame的容器

　　开始之前，我们首先掌握导入pandas库，方式如下：

import pandas as pd #pd是pandas的别名

　　注意：以下内容必须在导入pandas库之后才能运行。

一、Series和DataFrame介绍¶

　　　1.Series
　　　由一组数据和与之相关的索引组成。可通过传递一个list对象来创建一个Series，pandas会默认创建整型索引。
　　　创建一个Series：

a=[1,3,5,7,6,8]
a[2]

5

s = pd.Series([1,3,5,7,6,8])
print(s)

0    1
1    3
2    5
3    7
4    6
5    8
dtype: int64

s.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb2e05c9b0>

s.plot.bar()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb2bd07ef0>

s.plot.pie()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb2bd444e0>

s.hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb2b5e76d8>

　　获取 Series 的索引:

s.index.values

array([0, 1, 2, 3, 4, 5])

s=pd.Series([1,3,5,7,6,8],index=['a','b','c','d','e','f'])
print(s)

a    1
b    3
c    5
d    7
e    6
f    8
dtype: int64

s['f']

8

t=pd.Series([10,11,12],index=['c','b','a'])
print(t)

c    10
b    11
a    12
dtype: int64

[1,2,3]+[4,5,6]

[1, 2, 3, 4, 5, 6]

t+s#Series同index的元素值相加

a    13.0
b    14.0
c    15.0
d     NaN
e     NaN
f     NaN
dtype: float64

s=pd.Series([1,3,5,7,6,8],index=['a','b','c','d','e','f'])
print(s)
t=pd.Series([10,11,12],index=['c','b','a'])
print(t)

a    1
b    3
c    5
d    7
e    6
f    8
dtype: int64
c    10
b    11
a    12
dtype: int64

　　2.DataFrame
　　DataFrame是一个表格型的数据结构，它含有一组有序的列，每一列的数据结构都是相同的，而不同的列之间则可以是不同的数据结构。DataFrame中的每一行是一个记录，名称为Index的一个元素，而每一列则为一个字段，是这个记录的一个属性，DataFrame既有行索引也有列索引。
　　创建DataFrame
　　首先来看如何从字典创建DataFrame。

d = {'one': [1, 2, 3], 'two': [1, 2, 3]}
df = pd.DataFrame(d)
print(df)

   one  two
0    1    1
1    2    2
2    3    3

d = {'one': [1, 2, 3], 'two': [1, 2, 3]}
df = pd.DataFrame(d,index=['a', 'b', 'c'])
print(df)

   one  two
a    1    1
b    2    2
c    3    3

      A B C
one   2 9 4
two   7 5 3
three 6 1 8

d = {'A':[2,7,6],'B':[9,5,1],'C':[4,3,8]}
df=pd.DataFrame(d,index=['one','two','three'])
print(df)

       A  B  C
one    2  9  4
two    7  5  3
three  6  1  8

　　可以使用dataframe.index和dataframe.columns来查看DataFrame的行和列，dataframe.values则以数组的形式返回DataFrame的元素：

print(df.index) #查看行
print(df.columns) #查看列
print(df.values) #查看元素

Index(['one', 'two', 'three'], dtype='object')
Index(['A', 'B', 'C'], dtype='object')
[[2 9 4]
 [7 5 3]
 [6 1 8]]

　　DataFrame从值是数组的字典创建时，其各个数组的长度需要相同，加强印象，可参考以下报错的例子。

d = {'one': [1, 2], 'two': [1, 2, 3]}
df = pd.DataFrame(d,index=['a', 'b', 'c'])
print(df)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py in create_block_manager_from_arrays(arrays, names, axes)
   1692     try:
-> 1693         blocks = form_blocks(arrays, names, axes)
   1694         mgr = BlockManager(blocks, axes)

/opt/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py in form_blocks(arrays, names, axes)
   1762     if len(items_dict["IntBlock"]):
-> 1763         int_blocks = _multi_blockify(items_dict["IntBlock"])
   1764         blocks.extend(int_blocks)

/opt/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py in _multi_blockify(tuples, dtype)
   1844 
-> 1845         values, placement = _stack_arrays(list(tup_block), dtype)
   1846 

/opt/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py in _stack_arrays(tuples, dtype)
   1874     for i, arr in enumerate(arrays):
-> 1875         stacked[i] = _asarray_compat(arr)
   1876 

ValueError: could not broadcast input array from shape (3) into shape (2)

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-26-6714bd1fff47> in <module>
      1 d = {'one': [1, 2], 'two': [1, 2, 3]}
----> 2 df = pd.DataFrame(d,index=['a', 'b', 'c'])
      3 print(df)

/opt/conda/lib/python3.6/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
    408             )
    409         elif isinstance(data, dict):
--> 410             mgr = init_dict(data, index, columns, dtype=dtype)
    411         elif isinstance(data, ma.MaskedArray):
    412             import numpy.ma.mrecords as mrecords

/opt/conda/lib/python3.6/site-packages/pandas/core/internals/construction.py in init_dict(data, index, columns, dtype)
    255             arr if not is_datetime64tz_dtype(arr) else arr.copy() for arr in arrays
    256         ]
--> 257     return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
    258 
    259 

/opt/conda/lib/python3.6/site-packages/pandas/core/internals/construction.py in arrays_to_mgr(arrays, arr_names, index, columns, dtype)
     85     axes = [ensure_index(columns), index]
     86 
---> 87     return create_block_manager_from_arrays(arrays, arr_names, axes)
     88 
     89 

/opt/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py in create_block_manager_from_arrays(arrays, names, axes)
   1696         return mgr
   1697     except ValueError as e:
-> 1698         construction_error(len(arrays), arrays[0].shape, axes, e)
   1699 
   1700 

/opt/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py in construction_error(tot_items, block_shape, axes, e)
   1716         raise ValueError("Empty data passed with indices specified.")
   1717     raise ValueError(
-> 1718         "Shape of passed values is {0}, indices imply {1}".format(passed, implied)
   1719     )
   1720 

ValueError: Shape of passed values is (2, 2), indices imply (3, 2)

　　如果DataFrame的值是非数组时，没有这一限制，且自动将缺失值补成NaN。如下示例

d= [{'a': 1.6, 'b': 2}, {'a': 3, 'b': 6, 'c': 9}]
df = pd.DataFrame(d)
print(df)

     a  b    c
0  1.6  2  NaN
1  3.0  6  9.0

d= [{'a': 1.6, 'b': 2}, {'a': 3, 'b': 6, 'c': 9}]
df = pd.DataFrame(d,index=['one','two'])
print(df)

       a  b    c
one  1.6  2  NaN
two  3.0  6  9.0

df['a'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb29cdd978>

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb29c6dc18>

　　在实际处理数据时，有时需要创建一个空的DataFrame，可以这么做：

df = pd.DataFrame()
print(df)

Empty DataFrame
Columns: []
Index: []

　　另一种创建DataFrame的方法十分有用，那就是使用concat函数创建DataFrame，其主要是通过两个行或列相同的DataFrame链接成一个。

a= [{'a': 1.6, 'b': 2}, {'a': 3, 'b': 6}]
df1 = pd.DataFrame(a)
b= [{'a': 4, 'b': 5}]
df2 = pd.DataFrame(b)
df = pd.concat([df1, df2], axis=0)
print(df1)
print(df2)
print(df)

     a  b
0  1.6  2
1  3.0  6
   a  b
0  4  5
     a  b
0  1.6  2
1  3.0  6
0  4.0  5

df = pd.concat([df1, df2], axis=1)
print(df)

     a  b    a    b
0  1.6  2  4.0  5.0
1  3.0  6  NaN  NaN

　　注意：concat函数内有axis参数，其中的axis=1表示按列进行合并，axis=0表示按行合并

二、数据查看¶

　先从简单的csv导入数据到DataFrame。

# 获取csv文件中的浦发银行的开盘价、最高价、最低价、收盘价，获取格式即为DataFrame
price=pd.read_csv('stock1-105.csv',encoding='gbk',usecols=['date','open','high','low','close'])
price=price.set_index('date')
price

df=pd.read_csv('/resources/data/TRD_Dalyr.csv')
df

df=df.set_index('Trddt')
df

df['Clsprc'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb2a162940>

取上证指数（000001.SH）从20160101到20201019的开盘价、最高价、最低价、收盘价

画出以上收盘价折线图

求所有天的日收益率，并画出直方图

df=pd.read_csv('/resources/data/IDX_Idxtrd.csv')
df.columns=['Code','Date','Open','High','Low','Close']
#df=df.rename(columns={'Idxtrd05':'Close'})
df=df.set_index('Date')
df

df['Close'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb29f5eef0>

import numpy as np
df['Return']=np.nan
df

for i in range(1,len(df)):
    df['Return'][i]=df['Close'][i]/df['Close'][i-1]-1
df

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

df['Return']=df['Close']/df['Close'].shift()-1
df['Return'].hist()

<matplotlib.axes._subplots.AxesSubplot at 0x7fcb29de95c0>

　　以下为数据查看常用的八项操作：

　　1.查看前几条数据：

price.head(10)

　　2.查看后几条数据：

price.tail(10)

　　3.查看 DataFrame 的索引

price.index

Index(['2017-01-03', '2017-01-04', '2017-01-05', '2017-01-06', '2017-01-09',
       '2017-01-10', '2017-01-11', '2017-01-12', '2017-01-13', '2017-01-16',
       ...
       '2017-09-04', '2017-09-05', '2017-09-06', '2017-09-07', '2017-09-08',
       '2017-09-11', '2017-09-12', '2017-09-13', '2017-09-14', '2017-09-15'],
      dtype='object', name='date', length=174)

　　4.查看 DataFrame 的列名

price.columns

Index(['open', 'high', 'low', 'close'], dtype='object')

　　5.查看 DataFrame 的值

price.values

array([[16.21, 16.44, 16.17, 16.3 ],
       [16.29, 16.35, 16.18, 16.33],
       [16.3 , 16.38, 16.24, 16.3 ],
       [16.3 , 16.3 , 16.13, 16.18],
       [16.24, 16.29, 16.13, 16.2 ],
       [16.18, 16.24, 16.14, 16.19],
       [16.24, 16.24, 16.15, 16.16],
       [16.18, 16.2 , 16.11, 16.12],
       [16.1 , 16.29, 16.1 , 16.27],
       [16.23, 16.6 , 16.1 , 16.56],
       [16.46, 16.54, 16.37, 16.4 ],
       [16.42, 16.55, 16.36, 16.48],
       [16.43, 16.64, 16.43, 16.54],
       [16.58, 16.66, 16.5 , 16.6 ],
       [16.66, 16.69, 16.51, 16.57],
       [16.58, 16.7 , 16.58, 16.69],
       [16.69, 16.74, 16.61, 16.69],
       [16.69, 16.84, 16.61, 16.74],
       [16.82, 16.85, 16.62, 16.63],
       [16.75, 16.78, 16.66, 16.66],
       [16.65, 16.74, 16.63, 16.67],
       [16.63, 16.69, 16.54, 16.67],
       [16.68, 16.77, 16.59, 16.72],
       [16.76, 16.84, 16.7 , 16.78],
       [16.88, 16.9 , 16.78, 16.85],
       [16.87, 16.88, 16.74, 16.75],
       [16.82, 16.93, 16.75, 16.84],
       [16.88, 16.91, 16.76, 16.78],
       [16.78, 16.82, 16.59, 16.64],
       [16.62, 16.98, 16.62, 16.91],
       [16.88, 17.08, 16.81, 16.88],
       [16.88, 16.9 , 16.73, 16.75],
       [16.78, 16.83, 16.64, 16.69],
       [16.67, 16.75, 16.66, 16.71],
       [16.69, 16.72, 16.53, 16.59],
       [16.58, 16.67, 16.53, 16.59],
       [16.58, 16.62, 16.52, 16.56],
       [16.62, 16.62, 16.39, 16.41],
       [16.42, 16.43, 16.32, 16.35],
       [16.37, 16.53, 16.35, 16.41],
       [16.38, 16.45, 16.37, 16.43],
       [16.4 , 16.44, 16.35, 16.39],
       [16.37, 16.4 , 16.22, 16.22],
       [16.23, 16.28, 16.17, 16.23],
       [16.23, 16.34, 16.16, 16.34],
       [16.34, 16.35, 16.24, 16.26],
       [16.24, 16.28, 16.17, 16.24],
       [16.27, 16.35, 16.25, 16.29],
       [16.3 , 16.32, 16.14, 16.2 ],
       [16.24, 16.25, 16.13, 16.14],
       [16.15, 16.16, 15.98, 16.  ],
       [15.98, 15.98, 15.7 , 15.78],
       [15.8 , 15.92, 15.79, 15.88],
       [15.85, 16.  , 15.83, 15.96],
       [15.97, 16.13, 15.9 , 16.04],
       [16.11, 16.13, 15.97, 16.01],
       [16.01, 16.11, 15.8 , 15.87],
       [15.88, 15.9 , 15.72, 15.81],
       [15.78, 16.05, 15.77, 16.01],
       [16.05, 16.2 , 15.89, 16.16],
       [16.09, 16.18, 16.03, 16.1 ],
       [16.06, 16.16, 16.02, 16.1 ],
       [16.12, 16.12, 15.94, 15.97],
       [15.97, 15.99, 15.81, 15.89],
       [15.88, 15.96, 15.75, 15.89],
       [15.88, 15.89, 15.73, 15.75],
       [15.76, 15.78, 15.42, 15.51],
       [15.53, 15.6 , 15.3 , 15.56],
       [15.5 , 15.52, 15.3 , 15.3 ],
       [15.25, 15.27, 15.11, 15.11],
       [15.12, 15.14, 14.9 , 14.91],
       [14.92, 15.07, 14.85, 15.05],
       [15.05, 15.11, 14.91, 15.  ],
       [15.02, 15.1 , 14.99, 15.05],
       [15.06, 15.11, 15.  , 15.05],
       [15.05, 15.25, 15.03, 15.21],
       [15.15, 15.22, 15.08, 15.21],
       [15.21, 15.22, 15.13, 15.16],
       [15.16, 15.16, 15.05, 15.08],
       [15.07, 15.07, 14.9 , 14.98],
       [14.95, 14.98, 14.52, 14.92],
       [14.78, 14.9 , 14.51, 14.86],
       [14.69, 14.84, 14.66, 14.76],
       [14.76, 14.85, 14.55, 14.61],
       [14.57, 14.79, 14.55, 14.75],
       [14.75, 15.23, 14.73, 15.21],
       [15.27, 15.43, 15.16, 15.27],
       [15.23, 15.29, 15.08, 15.27],
       [15.24, 15.26, 15.07, 15.21],
       [15.1 , 15.14, 14.95, 15.01],
       [15.04, 15.08, 14.91, 15.02],
       [15.02, 15.06, 14.93, 15.04],
       [15.  , 15.57, 14.98, 15.43],
       [15.38, 15.52, 15.21, 15.47],
       [11.75, 12.93, 11.72, 12.93],
       [12.81, 12.91, 12.54, 12.84],
       [12.75, 13.05, 12.66, 12.84],
       [12.78, 12.97, 12.69, 12.92],
       [12.93, 13.01, 12.74, 12.89],
       [12.79, 12.88, 12.51, 12.85],
       [12.8 , 12.84, 12.66, 12.8 ],
       [12.81, 12.99, 12.78, 12.84],
       [12.76, 12.86, 12.67, 12.84],
       [12.79, 13.2 , 12.77, 12.93],
       [12.9 , 13.14, 12.76, 12.78],
       [12.78, 12.78, 12.56, 12.59],
       [12.6 , 12.64, 12.31, 12.46],
       [12.41, 12.46, 12.28, 12.38],
       [12.38, 12.46, 12.27, 12.35],
       [12.36, 12.43, 12.3 , 12.4 ],
       [12.41, 12.44, 12.22, 12.29],
       [12.4 , 12.4 , 12.26, 12.34],
       [12.33, 12.84, 12.29, 12.57],
       [12.55, 12.65, 12.36, 12.64],
       [12.64, 12.82, 12.55, 12.64],
       [12.64, 12.73, 12.53, 12.7 ],
       [12.66, 12.91, 12.6 , 12.79],
       [12.81, 12.86, 12.7 , 12.76],
       [12.7 , 12.73, 12.59, 12.65],
       [12.64, 12.65, 12.47, 12.56],
       [12.55, 12.58, 12.41, 12.55],
       [12.5 , 12.65, 12.47, 12.62],
       [12.62, 12.72, 12.51, 12.66],
       [12.62, 12.69, 12.55, 12.6 ],
       [12.59, 12.64, 12.52, 12.53],
       [12.51, 12.94, 12.51, 12.8 ],
       [12.8 , 13.06, 12.74, 12.86],
       [12.82, 13.29, 12.77, 13.27],
       [13.34, 13.8 , 13.34, 13.59],
       [13.62, 14.02, 13.28, 13.76],
       [13.65, 13.72, 13.4 , 13.55],
       [13.56, 13.78, 13.48, 13.69],
       [13.67, 13.7 , 13.53, 13.62],
       [13.61, 13.63, 13.38, 13.45],
       [13.45, 13.74, 13.36, 13.67],
       [13.62, 13.75, 13.41, 13.51],
       [13.45, 13.7 , 13.36, 13.38],
       [13.38, 13.47, 13.28, 13.38],
       [13.37, 13.49, 13.3 , 13.47],
       [13.45, 13.52, 13.31, 13.36],
       [13.42, 13.49, 13.32, 13.43],
       [13.44, 13.6 , 13.39, 13.44],
       [13.42, 13.42, 13.04, 13.08],
       [13.09, 13.1 , 12.85, 12.87],
       [12.87, 12.95, 12.83, 12.93],
       [12.91, 12.93, 12.83, 12.87],
       [12.84, 12.88, 12.75, 12.76],
       [12.74, 12.88, 12.7 , 12.79],
       [12.72, 12.73, 12.46, 12.68],
       [12.62, 12.63, 12.52, 12.56],
       [12.58, 12.76, 12.56, 12.59],
       [12.56, 12.56, 12.47, 12.49],
       [12.49, 12.55, 12.47, 12.53],
       [12.5 , 12.57, 12.46, 12.51],
       [12.5 , 12.57, 12.46, 12.51],
       [12.51, 12.53, 12.41, 12.43],
       [12.44, 12.66, 12.41, 12.57],
       [12.58, 12.66, 12.45, 12.47],
       [12.47, 12.8 , 12.47, 12.78],
       [12.83, 12.96, 12.75, 12.89],
       [12.85, 12.98, 12.77, 12.96],
       [12.91, 12.98, 12.82, 12.87],
       [12.83, 12.84, 12.65, 12.71],
       [12.68, 12.87, 12.68, 12.77],
       [12.78, 12.84, 12.65, 12.78],
       [12.78, 13.09, 12.78, 13.03],
       [12.99, 13.05, 12.9 , 12.96],
       [12.94, 13.02, 12.82, 12.85],
       [12.85, 13.12, 12.83, 13.03],
       [13.15, 13.15, 12.95, 12.99],
       [13.  , 13.04, 12.91, 13.04],
       [13.01, 13.01, 12.86, 12.87],
       [12.88, 12.98, 12.81, 12.85],
       [12.82, 12.86, 12.75, 12.83]])

　　6.使用 describe() 函数对于数据的快速统计汇总：

price.describe()

　　7.对数据的转置：

price.T

　　8.按列对 DataFrame 进行排序

price.sort_values(by=['open','close'] , ascending=[False,True])

　　注意：sort_values函数内置参数有by和ascending，by参数是排序指定列，ascending是排序顺序，False是从大到小，True是从小到大。

三、选择数据¶

　　依旧采用上个小节案例，继续讲述选择数据的八项基本操作。
　　　1.选择一列数据，选取开盘价这列数据：

price['open']

date
2017-01-03    16.21
2017-01-04    16.29
2017-01-05    16.30
2017-01-06    16.30
2017-01-09    16.24
              ...  
2017-09-11    13.15
2017-09-12    13.00
2017-09-13    13.01
2017-09-14    12.88
2017-09-15    12.82
Name: open, Length: 174, dtype: float64

price.open

date
2017-01-03    16.21
2017-01-04    16.29
2017-01-05    16.30
2017-01-06    16.30
2017-01-09    16.24
              ...  
2017-09-11    13.15
2017-09-12    13.00
2017-09-13    13.01
2017-09-14    12.88
2017-09-15    12.82
Name: open, Length: 174, dtype: float64

　　同学们动手试试price.open~
　　它与price['open']是等效的！

　　2.选择多列数据：

price[['open','high','close']]

　　注意：price[['open','close']]中['open','close']是一个由两个字符串（列名）组成的列表，会自动对应到整个DataFrame表结构中，获取到相应的数据。
　　同学们试试price['open','close']，看看能不能获取到数据~

　　3.选择多行：

price[0:3]

　　4.按index选取多行:

price['2017-01-24':'2017-02-25']

　　5.使用标签选取数据：

　　　price.loc[行标签,列标签]
　　　price.loc['a':'b'] #选取 ab 两行数据
　　　price.loc[:,'open'] #选取 open 列的数据
　　　price.loc 的第一个参数是行标签，第二个参数为列标签，两个参数既可以是列表也可以是单个字符，如果两个参数都为列表则返回的是 DataFrame，否则，则为 Series。

price.loc['2017-01-24','open']

16.58

price.loc['2017-01-24':'2017-01-25']

price.loc[:, 'open']

date
2017-01-03    16.21
2017-01-04    16.29
2017-01-05    16.30
2017-01-06    16.30
2017-01-09    16.24
              ...  
2017-09-11    13.15
2017-09-12    13.00
2017-09-13    13.01
2017-09-14    12.88
2017-09-15    12.82
Name: open, Length: 174, dtype: float64

price.loc['2017-01-24':'2017-02-25',['open','close']]

　　6..使用位置选取数据：

　　　price.iloc[行位置,列位置]
　　　price.iloc[1,1] #选取第二行，第二列的值，返回的为单个值
　　　price.iloc[[0,2],:] #选取第一行及第三行的数据
　　　price.iloc[0:2,:] #选取第一行到第三行（不包含）的数据
　　　price.iloc[:,1] #选取所有记录的第二列的值，返回的为一个Series
　　　price.iloc[1,:] #选取第一行数据，返回的为一个Series

price.iloc[1,1] # 选取第二行，第二列的值，返回的为单个值

16.35

price.iloc[[0,2],:] # 选取第一行及第三行的数据

price.iloc[0:20,:] # 选取第一行到第三行（不包含）的数据

price.iloc[:,1:3] # 选取所有记录的第一列的值，返回的为一个Series

price.iloc[1,:] # 选取第一行数据，返回的为一个Series

open     16.29
high     16.35
low      16.18
close    16.33
Name: 2017-01-04, dtype: float64

　　7.更广义的切片方式是使用.ix，它自动根据给到的索引类型判断是使用位置还是标签进行切片

　　　price.ix[1,1]
　　　price.ix['a':'b']

price.ix[1,1]

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
  """Entry point for launching an IPython kernel.
/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:947: FutureWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
  return getattr(section, self.name)[new_key]

16.35

price.ix['2017-01-24':'2017-01-25']

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
  """Entry point for launching an IPython kernel.

price.ix['2017-01-24','open']

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
  """Entry point for launching an IPython kernel.

16.58

price.ix[1,'open']

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#ix-indexer-is-deprecated
  """Entry point for launching an IPython kernel.

16.29

price.ix['2018-01-24',0]

764.46000000000004

　　8.通过逻辑指针进行数据切片：

　　　price[逻辑条件]
　　　price[price.one >= 2] #单个逻辑条件
　　　price[(price.one >=1 ) & (df.one < 3) ] #多个逻辑条件组合

price.open>16.5

date
2017-01-03    False
2017-01-04    False
2017-01-05    False
2017-01-06    False
2017-01-09    False
              ...  
2017-09-11    False
2017-09-12    False
2017-09-13    False
2017-09-14    False
2017-09-15    False
Name: open, Length: 174, dtype: bool

#筛选出 open 大于 16.5的数据
price[price.open > 16.5][['open','close']]

print((price.open > 16.4))
print((price.close < 16.6))

date
2017-01-03    False
2017-01-04    False
2017-01-05    False
2017-01-06    False
2017-01-09    False
              ...  
2017-09-11    False
2017-09-12    False
2017-09-13    False
2017-09-14    False
2017-09-15    False
Name: open, Length: 174, dtype: bool
date
2017-01-03    True
2017-01-04    True
2017-01-05    True
2017-01-06    True
2017-01-09    True
              ... 
2017-09-11    True
2017-09-12    True
2017-09-13    True
2017-09-14    True
2017-09-15    True
Name: close, Length: 174, dtype: bool

#筛选出 open 大于 16.4 的数据，并且 close 小于 16.6 的数据
price[(price.open > 16.4) & (price.close < 16.6)]

#使用 条件过来更改数据。
price[price>16.5]

　　观察可以发现，price 中大于 16.5 的数都变为 NaN。

#我们还可以把大于 16.5 的数赋值为1.
price[price > 16.5] = 1
price

　　使用isin()方法来过滤在指定列中的数据,案例延续上面赋值后的price

# 选取 high 列中数为 1 和 774.00的数。
price[price['high'].isin([1,16.24])]

510500.OF 南方中证500ETF

510510.OF 广发中证500ETF

510520.OF 诺安中证500ETF

510560.OF 国寿安保中证500ETF

510580.OF 易方达中证500

000905.SH 中证500

etf=['510500.OF','510510.OF','510520.OF','510560.OF','510580.OF']

获取各自2017年1月1日以来的日收益率
获得各自的年跟踪误差TE

TE=std(ETF日收益率-中证500日收益率)*sqrt(250)

std()
哪个做的最好？

idx=pd.read_csv('/resources/data/IDX_Idxtrd_000905.csv')
idx.columns=['Code','Date','Open','High','Low','Close']
idx=idx.set_index('Date')
idxret=idx['Close']/idx['Close'].shift()-1
idxret=idxret[idxret.index>='2018-01-01']
idxret

Date
2018-01-02    0.013024
2018-01-03    0.008847
2018-01-04    0.004585
2018-01-05   -0.000045
2018-01-08    0.004508
                ...   
2020-10-12    0.028816
2020-10-13    0.001562
2020-10-14   -0.006568
2020-10-15   -0.007426
2020-10-16   -0.004854
Name: Close, Length: 676, dtype: float64

etf=pd.read_csv('/resources/data/Fund_NAV.csv')
etf=etf.set_index('TradingDate')
etf

etfsymbols=[510500,510510,510560,510580]
etflist=[]
for i in etfsymbols:
    e=pd.DataFrame(etf[etf['Symbol']==i]['AccumulativeNAV'])
    e.columns=[str(i)]
    etflist.append(e)
df=pd.concat(etflist,axis=1)
ret=df/df.shift()-1
ret.sub(idxret,axis=0).std()*sqrt(250)

/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.

To accept the future behavior, pass 'sort=False'.

To retain the current behavior and silence the warning, pass 'sort=True'.

  import sys

510500    0.004283
510510    0.005752
510560    0.005596
510580    0.015394
dtype: float64

	Stkcd	Trddt	Opnprc	Hiprc	Loprc	Clsprc	Dnshrtrd	Dnvaltrd	Dsmvosd	Dsmvtll	Adjprcwd
0	600600	2019-01-02	34.91	35.68	34.12	34.50	2866826	9.993306e+07	24009019.79	24009019.79	57.993438
1	600600	2019-01-03	34.80	34.89	32.93	33.30	3137838	1.058104e+08	23173923.45	23173923.45	55.976275
2	600600	2019-01-04	32.19	33.93	32.19	33.40	2816908	9.389348e+07	23243514.81	23243514.81	56.144372
3	600600	2019-01-07	33.49	35.18	33.10	34.86	4165242	1.435954e+08	24259548.69	24259548.69	58.598587
4	600600	2019-01-08	34.72	34.72	33.20	33.49	4465646	1.502569e+08	23306147.03	23306147.03	56.295659
5	600600	2019-01-09	33.51	34.68	33.28	33.75	4591794	1.564405e+08	23487084.57	23487084.57	56.732711
6	600600	2019-01-10	33.73	33.73	32.87	33.18	4342328	1.436151e+08	23090413.81	23090413.81	55.774559
7	600600	2019-01-11	33.20	33.60	32.90	33.21	3027414	1.004160e+08	23111291.22	23111291.22	55.824988
8	600600	2019-01-14	33.26	33.28	32.65	33.09	2502931	8.233961e+07	23027781.59	23027781.59	55.623272
9	600600	2019-01-15	32.98	36.40	32.83	36.40	10810126	3.784860e+08	25331255.66	25331255.66	61.187280
10	600600	2019-01-16	36.00	36.87	35.86	36.10	6596757	2.388105e+08	25122481.57	25122481.57	60.682989
11	600600	2019-01-17	36.50	36.50	35.80	35.80	4330457	1.564373e+08	24913707.49	24913707.49	60.178698
12	600600	2019-01-18	36.00	36.47	35.76	36.19	4685634	1.695381e+08	25185113.80	25185113.80	60.834276
13	600600	2019-01-21	36.17	38.76	36.09	36.78	7439315	2.771725e+08	25595702.83	25595702.83	61.826048
14	600600	2019-01-22	36.50	36.74	35.80	36.17	2824918	1.021891e+08	25171195.53	25171195.53	60.800657
15	600600	2019-01-23	36.00	36.68	35.81	36.30	2863203	1.040513e+08	25261664.30	25261664.30	61.019183
16	600600	2019-01-24	36.22	36.81	36.04	36.58	2333696	8.502156e+07	25456520.11	25456520.11	61.489854
17	600600	2019-01-25	36.57	37.56	36.16	36.83	3176591	1.168643e+08	25630498.51	25630498.51	61.910096
18	600600	2019-01-28	37.00	37.94	36.60	36.96	4363808	1.625587e+08	25720967.28	25720967.28	62.128622
19	600600	2019-01-29	36.80	38.38	36.10	38.13	4956990	1.859601e+08	26535186.22	26535186.22	64.095356
20	600600	2019-01-30	37.54	38.68	37.16	37.19	3985083	1.509411e+08	25881027.42	25881027.42	62.515245
21	600600	2019-01-31	37.05	38.12	37.05	37.73	2556359	9.631578e+07	26256820.77	26256820.77	63.422968
22	600600	2019-02-01	37.77	38.20	37.50	38.10	3193602	1.211643e+08	26514308.81	26514308.81	64.044927
23	600600	2019-02-11	38.10	39.00	37.50	38.70	4417674	1.690389e+08	26931856.98	26931856.98	65.053509
24	600600	2019-02-12	38.67	38.70	38.10	38.45	4249343	1.627973e+08	26757878.57	26757878.57	64.633267
25	600600	2019-02-13	38.40	38.70	38.25	38.55	3804185	1.463872e+08	26827469.94	26827469.94	64.801364
26	600600	2019-02-14	38.48	40.24	38.38	39.46	4301795	1.699792e+08	27460751.33	27460751.33	66.331046
27	600600	2019-02-15	39.39	39.39	38.20	38.33	3445361	1.336287e+08	26674368.94	26674368.94	64.431551
28	600600	2019-02-18	38.38	39.51	38.35	39.06	3449336	1.341243e+08	27182385.88	27182385.88	65.658659
29	600600	2019-02-19	39.14	39.37	37.55	38.20	5970855	2.285474e+08	26583900.17	26583900.17	64.213025
...	...	...	...	...	...	...	...	...	...	...	...
404	600600	2020-08-31	95.44	99.50	87.36	87.36	16336429	1.485624e+09	60795013.58	61948165.58	149.643013
405	600600	2020-09-01	87.36	87.66	82.50	86.30	12650742	1.073644e+09	60057345.15	61196505.15	147.827290
406	600600	2020-09-02	85.28	86.63	83.75	84.45	7235267	6.149912e+08	58769904.96	59884644.96	144.658339
407	600600	2020-09-03	84.45	86.86	83.55	85.34	6967213	5.962448e+08	59389268.07	60515756.07	146.182861
408	600600	2020-09-04	83.50	83.56	79.61	81.50	9595196	7.745201e+08	56716959.79	57792759.79	139.605146
409	600600	2020-09-07	80.57	81.30	77.40	78.40	7119585	5.663550e+08	54559627.57	55594507.57	134.295012
410	600600	2020-09-08	78.88	79.29	74.55	77.01	8062892	6.141779e+08	53592307.65	54608839.65	131.914016
411	600600	2020-09-09	75.88	76.19	73.70	74.98	4712071	3.520896e+08	52179603.00	53169339.00	128.436734
412	600600	2020-09-10	76.00	76.99	74.77	75.24	5188079	3.926766e+08	52360540.54	53353708.54	128.882100
413	600600	2020-09-11	74.68	77.11	73.82	76.77	4146573	3.145263e+08	53425288.38	54438652.38	131.502908
414	600600	2020-09-14	77.50	80.08	76.88	77.95	7192602	5.670469e+08	54246466.45	55275406.45	133.524185
415	600600	2020-09-15	77.77	79.50	76.23	78.85	4127403	3.231186e+08	54872788.70	55913608.70	135.065837
416	600600	2020-09-16	78.65	80.17	77.67	78.65	3399733	2.676203e+08	54733605.98	55771785.98	134.723248
417	600600	2020-09-17	78.00	78.00	75.49	75.90	5078531	3.865731e+08	52819843.53	53821723.53	130.012645
418	600600	2020-09-18	75.77	76.21	74.26	76.21	4966692	3.739078e+08	53035576.75	54041548.75	130.543658
419	600600	2020-09-21	76.20	76.20	74.28	74.92	4789182	3.579874e+08	52137848.19	53126792.19	128.333957
420	600600	2020-09-22	74.00	74.99	73.50	74.30	3666905	2.729507e+08	51706381.74	52687141.74	127.271930
421	600600	2020-09-23	74.30	74.64	73.36	74.15	3852423	2.854876e+08	51601994.70	52580774.70	127.014988
422	600600	2020-09-24	74.15	74.35	71.40	72.41	5948893	4.317549e+08	50391105.01	51346917.01	124.034461
423	600600	2020-09-25	72.41	73.67	70.00	73.51	6215862	4.502747e+08	51156609.99	52126941.99	125.918702
424	600600	2020-09-28	73.00	73.50	71.17	71.49	5088792	3.657537e+08	49750864.48	50694532.48	122.458550
425	600600	2020-09-29	71.98	72.50	71.17	72.08	4640643	3.343638e+08	50161453.51	51112909.51	123.469188
426	600600	2020-09-30	72.50	76.08	72.21	75.11	8449161	6.319768e+08	52270071.77	53261523.77	128.659416
427	600600	2020-10-09	75.83	76.81	74.37	74.62	6983298	5.230091e+08	51929074.10	52914058.10	127.820072
428	600600	2020-10-12	74.50	75.19	73.17	75.10	8951159	6.663092e+08	52263112.64	53254432.64	128.642286
429	600600	2020-10-13	74.85	76.70	74.08	76.49	7075436	5.376659e+08	53230432.56	54240100.56	131.023282
430	600600	2020-10-14	75.39	76.45	72.96	74.15	9407050	6.974287e+08	51601994.70	52580774.70	127.014987
431	600600	2020-10-15	74.15	76.60	73.37	76.00	6741385	5.082867e+08	52889434.89	53892634.89	130.183938
432	600600	2020-10-16	75.44	76.88	75.02	75.40	4823905	3.662679e+08	52471886.72	53467166.72	129.156170
433	600600	2020-10-19	76.16	76.16	74.00	74.53	3630250	2.712269e+08	51866441.88	52850237.88	127.665907

	Stkcd	Opnprc	Hiprc	Loprc	Clsprc	Dnshrtrd	Dnvaltrd	Dsmvosd	Dsmvtll	Adjprcwd
Trddt
2019-01-02	600600	34.91	35.68	34.12	34.50	2866826	9.993306e+07	24009019.79	24009019.79	57.993438
2019-01-03	600600	34.80	34.89	32.93	33.30	3137838	1.058104e+08	23173923.45	23173923.45	55.976275
2019-01-04	600600	32.19	33.93	32.19	33.40	2816908	9.389348e+07	23243514.81	23243514.81	56.144372
2019-01-07	600600	33.49	35.18	33.10	34.86	4165242	1.435954e+08	24259548.69	24259548.69	58.598587
2019-01-08	600600	34.72	34.72	33.20	33.49	4465646	1.502569e+08	23306147.03	23306147.03	56.295659
2019-01-09	600600	33.51	34.68	33.28	33.75	4591794	1.564405e+08	23487084.57	23487084.57	56.732711
2019-01-10	600600	33.73	33.73	32.87	33.18	4342328	1.436151e+08	23090413.81	23090413.81	55.774559
2019-01-11	600600	33.20	33.60	32.90	33.21	3027414	1.004160e+08	23111291.22	23111291.22	55.824988
2019-01-14	600600	33.26	33.28	32.65	33.09	2502931	8.233961e+07	23027781.59	23027781.59	55.623272
2019-01-15	600600	32.98	36.40	32.83	36.40	10810126	3.784860e+08	25331255.66	25331255.66	61.187280
2019-01-16	600600	36.00	36.87	35.86	36.10	6596757	2.388105e+08	25122481.57	25122481.57	60.682989
2019-01-17	600600	36.50	36.50	35.80	35.80	4330457	1.564373e+08	24913707.49	24913707.49	60.178698
2019-01-18	600600	36.00	36.47	35.76	36.19	4685634	1.695381e+08	25185113.80	25185113.80	60.834276
2019-01-21	600600	36.17	38.76	36.09	36.78	7439315	2.771725e+08	25595702.83	25595702.83	61.826048
2019-01-22	600600	36.50	36.74	35.80	36.17	2824918	1.021891e+08	25171195.53	25171195.53	60.800657
2019-01-23	600600	36.00	36.68	35.81	36.30	2863203	1.040513e+08	25261664.30	25261664.30	61.019183
2019-01-24	600600	36.22	36.81	36.04	36.58	2333696	8.502156e+07	25456520.11	25456520.11	61.489854
2019-01-25	600600	36.57	37.56	36.16	36.83	3176591	1.168643e+08	25630498.51	25630498.51	61.910096
2019-01-28	600600	37.00	37.94	36.60	36.96	4363808	1.625587e+08	25720967.28	25720967.28	62.128622
2019-01-29	600600	36.80	38.38	36.10	38.13	4956990	1.859601e+08	26535186.22	26535186.22	64.095356
2019-01-30	600600	37.54	38.68	37.16	37.19	3985083	1.509411e+08	25881027.42	25881027.42	62.515245
2019-01-31	600600	37.05	38.12	37.05	37.73	2556359	9.631578e+07	26256820.77	26256820.77	63.422968
2019-02-01	600600	37.77	38.20	37.50	38.10	3193602	1.211643e+08	26514308.81	26514308.81	64.044927
2019-02-11	600600	38.10	39.00	37.50	38.70	4417674	1.690389e+08	26931856.98	26931856.98	65.053509
2019-02-12	600600	38.67	38.70	38.10	38.45	4249343	1.627973e+08	26757878.57	26757878.57	64.633267
2019-02-13	600600	38.40	38.70	38.25	38.55	3804185	1.463872e+08	26827469.94	26827469.94	64.801364
2019-02-14	600600	38.48	40.24	38.38	39.46	4301795	1.699792e+08	27460751.33	27460751.33	66.331046
2019-02-15	600600	39.39	39.39	38.20	38.33	3445361	1.336287e+08	26674368.94	26674368.94	64.431551
2019-02-18	600600	38.38	39.51	38.35	39.06	3449336	1.341243e+08	27182385.88	27182385.88	65.658659
2019-02-19	600600	39.14	39.37	37.55	38.20	5970855	2.285474e+08	26583900.17	26583900.17	64.213025
...	...	...	...	...	...	...	...	...	...	...
2020-08-31	600600	95.44	99.50	87.36	87.36	16336429	1.485624e+09	60795013.58	61948165.58	149.643013
2020-09-01	600600	87.36	87.66	82.50	86.30	12650742	1.073644e+09	60057345.15	61196505.15	147.827290
2020-09-02	600600	85.28	86.63	83.75	84.45	7235267	6.149912e+08	58769904.96	59884644.96	144.658339
2020-09-03	600600	84.45	86.86	83.55	85.34	6967213	5.962448e+08	59389268.07	60515756.07	146.182861
2020-09-04	600600	83.50	83.56	79.61	81.50	9595196	7.745201e+08	56716959.79	57792759.79	139.605146
2020-09-07	600600	80.57	81.30	77.40	78.40	7119585	5.663550e+08	54559627.57	55594507.57	134.295012
2020-09-08	600600	78.88	79.29	74.55	77.01	8062892	6.141779e+08	53592307.65	54608839.65	131.914016
2020-09-09	600600	75.88	76.19	73.70	74.98	4712071	3.520896e+08	52179603.00	53169339.00	128.436734
2020-09-10	600600	76.00	76.99	74.77	75.24	5188079	3.926766e+08	52360540.54	53353708.54	128.882100
2020-09-11	600600	74.68	77.11	73.82	76.77	4146573	3.145263e+08	53425288.38	54438652.38	131.502908
2020-09-14	600600	77.50	80.08	76.88	77.95	7192602	5.670469e+08	54246466.45	55275406.45	133.524185
2020-09-15	600600	77.77	79.50	76.23	78.85	4127403	3.231186e+08	54872788.70	55913608.70	135.065837
2020-09-16	600600	78.65	80.17	77.67	78.65	3399733	2.676203e+08	54733605.98	55771785.98	134.723248
2020-09-17	600600	78.00	78.00	75.49	75.90	5078531	3.865731e+08	52819843.53	53821723.53	130.012645
2020-09-18	600600	75.77	76.21	74.26	76.21	4966692	3.739078e+08	53035576.75	54041548.75	130.543658
2020-09-21	600600	76.20	76.20	74.28	74.92	4789182	3.579874e+08	52137848.19	53126792.19	128.333957
2020-09-22	600600	74.00	74.99	73.50	74.30	3666905	2.729507e+08	51706381.74	52687141.74	127.271930
2020-09-23	600600	74.30	74.64	73.36	74.15	3852423	2.854876e+08	51601994.70	52580774.70	127.014988
2020-09-24	600600	74.15	74.35	71.40	72.41	5948893	4.317549e+08	50391105.01	51346917.01	124.034461
2020-09-25	600600	72.41	73.67	70.00	73.51	6215862	4.502747e+08	51156609.99	52126941.99	125.918702
2020-09-28	600600	73.00	73.50	71.17	71.49	5088792	3.657537e+08	49750864.48	50694532.48	122.458550
2020-09-29	600600	71.98	72.50	71.17	72.08	4640643	3.343638e+08	50161453.51	51112909.51	123.469188
2020-09-30	600600	72.50	76.08	72.21	75.11	8449161	6.319768e+08	52270071.77	53261523.77	128.659416
2020-10-09	600600	75.83	76.81	74.37	74.62	6983298	5.230091e+08	51929074.10	52914058.10	127.820072
2020-10-12	600600	74.50	75.19	73.17	75.10	8951159	6.663092e+08	52263112.64	53254432.64	128.642286
2020-10-13	600600	74.85	76.70	74.08	76.49	7075436	5.376659e+08	53230432.56	54240100.56	131.023282
2020-10-14	600600	75.39	76.45	72.96	74.15	9407050	6.974287e+08	51601994.70	52580774.70	127.014987
2020-10-15	600600	74.15	76.60	73.37	76.00	6741385	5.082867e+08	52889434.89	53892634.89	130.183938
2020-10-16	600600	75.44	76.88	75.02	75.40	4823905	3.662679e+08	52471886.72	53467166.72	129.156170
2020-10-19	600600	76.16	76.16	74.00	74.53	3630250	2.712269e+08	51866441.88	52850237.88	127.665907

	Code	Open	High	Low	Close
Date
2016-01-04	1	3536.59	3538.69	3295.74	3296.26
2016-01-05	1	3196.65	3328.14	3189.61	3287.71
2016-01-06	1	3291.20	3362.97	3288.93	3361.84
2016-01-07	1	3309.66	3309.66	3115.89	3125.00
2016-01-08	1	3194.63	3235.45	3056.88	3186.41
2016-01-11	1	3131.85	3166.22	3016.70	3016.70
2016-01-12	1	3026.16	3047.66	2978.46	3022.86
2016-01-13	1	3041.11	3059.02	2949.29	2949.60
2016-01-14	1	2874.05	3012.29	2867.55	3007.65
2016-01-15	1	2988.05	3001.71	2883.87	2900.97
2016-01-18	1	2847.54	2945.45	2844.70	2913.84
2016-01-19	1	2914.41	3012.07	2906.40	3007.74
2016-01-20	1	2993.01	3016.28	2951.92	2976.69
2016-01-21	1	2934.39	2998.79	2880.09	2880.48
2016-01-22	1	2911.11	2931.36	2851.73	2916.56
2016-01-25	1	2934.08	2955.78	2911.83	2938.52
2016-01-26	1	2907.72	2911.99	2743.84	2749.79
2016-01-27	1	2756.08	2768.77	2638.30	2735.56
2016-01-28	1	2711.16	2740.54	2647.49	2655.66
2016-01-29	1	2652.85	2755.37	2649.79	2737.60
2016-02-01	1	2730.98	2735.26	2655.62	2688.85
2016-02-02	1	2687.98	2755.16	2687.98	2749.57
2016-02-03	1	2719.57	2746.07	2696.88	2739.25
2016-02-04	1	2751.43	2793.30	2751.31	2781.02
2016-02-05	1	2783.08	2790.06	2762.16	2763.49
2016-02-15	1	2684.96	2760.36	2682.09	2746.20
2016-02-16	1	2758.58	2840.62	2758.58	2836.57
2016-02-17	1	2829.76	2868.70	2824.36	2867.34
2016-02-18	1	2881.78	2893.22	2857.70	2862.89
2016-02-19	1	2854.90	2872.72	2840.49	2860.02
...	...	...	...	...	...
2020-08-28	1	3346.29	3405.88	3339.65	3403.81
2020-08-31	1	3416.55	3442.74	3395.47	3395.68
2020-09-01	1	3389.74	3410.61	3381.71	3410.61
2020-09-02	1	3420.47	3421.40	3377.21	3404.80
2020-09-03	1	3404.03	3425.63	3374.26	3384.98
2020-09-04	1	3336.41	3360.11	3328.55	3355.37
2020-09-07	1	3349.92	3368.25	3285.63	3292.59
2020-09-08	1	3301.22	3324.39	3276.44	3316.42
2020-09-09	1	3281.00	3289.62	3238.56	3254.63
2020-09-10	1	3282.23	3285.47	3227.64	3234.82
2020-09-11	1	3225.78	3262.50	3220.54	3260.35
2020-09-14	1	3275.92	3283.53	3261.15	3278.81
2020-09-15	1	3277.13	3297.69	3263.75	3295.68
2020-09-16	1	3293.17	3302.46	3271.08	3283.92
2020-09-17	1	3277.32	3290.41	3248.50	3270.44
2020-09-18	1	3270.91	3338.32	3268.53	3338.09
2020-09-21	1	3348.90	3350.59	3313.07	3316.94
2020-09-22	1	3290.67	3320.23	3265.70	3274.30
2020-09-23	1	3278.84	3289.76	3264.89	3279.71
2020-09-24	1	3262.88	3265.35	3221.24	3223.18
2020-09-25	1	3234.37	3239.47	3208.04	3219.42
2020-09-28	1	3224.98	3238.18	3210.89	3217.54
2020-09-29	1	3231.86	3242.79	3219.79	3224.36
2020-09-30	1	3232.71	3244.91	3202.34	3218.05
2020-10-09	1	3262.61	3280.51	3260.19	3272.08
2020-10-12	1	3287.33	3359.15	3286.11	3358.47
2020-10-13	1	3353.12	3361.83	3334.50	3359.75
2020-10-14	1	3353.63	3353.63	3332.96	3340.78
2020-10-15	1	3342.92	3354.58	3330.00	3332.18
2020-10-16	1	3334.46	3348.95	3319.11	3336.36

	Code	Open	High	Low	Close	Return
Date
2016-01-04	1	3536.59	3538.69	3295.74	3296.26	NaN
2016-01-05	1	3196.65	3328.14	3189.61	3287.71	NaN
2016-01-06	1	3291.20	3362.97	3288.93	3361.84	NaN
2016-01-07	1	3309.66	3309.66	3115.89	3125.00	NaN
2016-01-08	1	3194.63	3235.45	3056.88	3186.41	NaN
2016-01-11	1	3131.85	3166.22	3016.70	3016.70	NaN
2016-01-12	1	3026.16	3047.66	2978.46	3022.86	NaN
2016-01-13	1	3041.11	3059.02	2949.29	2949.60	NaN
2016-01-14	1	2874.05	3012.29	2867.55	3007.65	NaN
2016-01-15	1	2988.05	3001.71	2883.87	2900.97	NaN
2016-01-18	1	2847.54	2945.45	2844.70	2913.84	NaN
2016-01-19	1	2914.41	3012.07	2906.40	3007.74	NaN
2016-01-20	1	2993.01	3016.28	2951.92	2976.69	NaN
2016-01-21	1	2934.39	2998.79	2880.09	2880.48	NaN
2016-01-22	1	2911.11	2931.36	2851.73	2916.56	NaN
2016-01-25	1	2934.08	2955.78	2911.83	2938.52	NaN
2016-01-26	1	2907.72	2911.99	2743.84	2749.79	NaN
2016-01-27	1	2756.08	2768.77	2638.30	2735.56	NaN
2016-01-28	1	2711.16	2740.54	2647.49	2655.66	NaN
2016-01-29	1	2652.85	2755.37	2649.79	2737.60	NaN
2016-02-01	1	2730.98	2735.26	2655.62	2688.85	NaN
2016-02-02	1	2687.98	2755.16	2687.98	2749.57	NaN
2016-02-03	1	2719.57	2746.07	2696.88	2739.25	NaN
2016-02-04	1	2751.43	2793.30	2751.31	2781.02	NaN
2016-02-05	1	2783.08	2790.06	2762.16	2763.49	NaN
2016-02-15	1	2684.96	2760.36	2682.09	2746.20	NaN
2016-02-16	1	2758.58	2840.62	2758.58	2836.57	NaN
2016-02-17	1	2829.76	2868.70	2824.36	2867.34	NaN
2016-02-18	1	2881.78	2893.22	2857.70	2862.89	NaN
2016-02-19	1	2854.90	2872.72	2840.49	2860.02	NaN
...	...	...	...	...	...	...
2020-08-28	1	3346.29	3405.88	3339.65	3403.81	NaN
2020-08-31	1	3416.55	3442.74	3395.47	3395.68	NaN
2020-09-01	1	3389.74	3410.61	3381.71	3410.61	NaN
2020-09-02	1	3420.47	3421.40	3377.21	3404.80	NaN
2020-09-03	1	3404.03	3425.63	3374.26	3384.98	NaN
2020-09-04	1	3336.41	3360.11	3328.55	3355.37	NaN
2020-09-07	1	3349.92	3368.25	3285.63	3292.59	NaN
2020-09-08	1	3301.22	3324.39	3276.44	3316.42	NaN
2020-09-09	1	3281.00	3289.62	3238.56	3254.63	NaN
2020-09-10	1	3282.23	3285.47	3227.64	3234.82	NaN
2020-09-11	1	3225.78	3262.50	3220.54	3260.35	NaN
2020-09-14	1	3275.92	3283.53	3261.15	3278.81	NaN
2020-09-15	1	3277.13	3297.69	3263.75	3295.68	NaN
2020-09-16	1	3293.17	3302.46	3271.08	3283.92	NaN
2020-09-17	1	3277.32	3290.41	3248.50	3270.44	NaN
2020-09-18	1	3270.91	3338.32	3268.53	3338.09	NaN
2020-09-21	1	3348.90	3350.59	3313.07	3316.94	NaN
2020-09-22	1	3290.67	3320.23	3265.70	3274.30	NaN
2020-09-23	1	3278.84	3289.76	3264.89	3279.71	NaN
2020-09-24	1	3262.88	3265.35	3221.24	3223.18	NaN
2020-09-25	1	3234.37	3239.47	3208.04	3219.42	NaN
2020-09-28	1	3224.98	3238.18	3210.89	3217.54	NaN
2020-09-29	1	3231.86	3242.79	3219.79	3224.36	NaN
2020-09-30	1	3232.71	3244.91	3202.34	3218.05	NaN
2020-10-09	1	3262.61	3280.51	3260.19	3272.08	NaN
2020-10-12	1	3287.33	3359.15	3286.11	3358.47	NaN
2020-10-13	1	3353.12	3361.83	3334.50	3359.75	NaN
2020-10-14	1	3353.63	3353.63	3332.96	3340.78	NaN
2020-10-15	1	3342.92	3354.58	3330.00	3332.18	NaN
2020-10-16	1	3334.46	3348.95	3319.11	3336.36	NaN

	Code	Open	High	Low	Close	Return
Date
2016-01-04	1	3536.59	3538.69	3295.74	3296.26	NaN
2016-01-05	1	3196.65	3328.14	3189.61	3287.71	-0.002594
2016-01-06	1	3291.20	3362.97	3288.93	3361.84	0.022548
2016-01-07	1	3309.66	3309.66	3115.89	3125.00	-0.070450
2016-01-08	1	3194.63	3235.45	3056.88	3186.41	0.019651
2016-01-11	1	3131.85	3166.22	3016.70	3016.70	-0.053261
2016-01-12	1	3026.16	3047.66	2978.46	3022.86	0.002042
2016-01-13	1	3041.11	3059.02	2949.29	2949.60	-0.024235
2016-01-14	1	2874.05	3012.29	2867.55	3007.65	0.019681
2016-01-15	1	2988.05	3001.71	2883.87	2900.97	-0.035470
2016-01-18	1	2847.54	2945.45	2844.70	2913.84	0.004436
2016-01-19	1	2914.41	3012.07	2906.40	3007.74	0.032226
2016-01-20	1	2993.01	3016.28	2951.92	2976.69	-0.010323
2016-01-21	1	2934.39	2998.79	2880.09	2880.48	-0.032321
2016-01-22	1	2911.11	2931.36	2851.73	2916.56	0.012526
2016-01-25	1	2934.08	2955.78	2911.83	2938.52	0.007529
2016-01-26	1	2907.72	2911.99	2743.84	2749.79	-0.064226
2016-01-27	1	2756.08	2768.77	2638.30	2735.56	-0.005175
2016-01-28	1	2711.16	2740.54	2647.49	2655.66	-0.029208
2016-01-29	1	2652.85	2755.37	2649.79	2737.60	0.030855
2016-02-01	1	2730.98	2735.26	2655.62	2688.85	-0.017808
2016-02-02	1	2687.98	2755.16	2687.98	2749.57	0.022582
2016-02-03	1	2719.57	2746.07	2696.88	2739.25	-0.003753
2016-02-04	1	2751.43	2793.30	2751.31	2781.02	0.015249
2016-02-05	1	2783.08	2790.06	2762.16	2763.49	-0.006303
2016-02-15	1	2684.96	2760.36	2682.09	2746.20	-0.006257
2016-02-16	1	2758.58	2840.62	2758.58	2836.57	0.032907
2016-02-17	1	2829.76	2868.70	2824.36	2867.34	0.010848
2016-02-18	1	2881.78	2893.22	2857.70	2862.89	-0.001552
2016-02-19	1	2854.90	2872.72	2840.49	2860.02	-0.001002
...	...	...	...	...	...	...
2020-08-28	1	3346.29	3405.88	3339.65	3403.81	0.016029
2020-08-31	1	3416.55	3442.74	3395.47	3395.68	-0.002388
2020-09-01	1	3389.74	3410.61	3381.71	3410.61	0.004397
2020-09-02	1	3420.47	3421.40	3377.21	3404.80	-0.001704
2020-09-03	1	3404.03	3425.63	3374.26	3384.98	-0.005821
2020-09-04	1	3336.41	3360.11	3328.55	3355.37	-0.008747
2020-09-07	1	3349.92	3368.25	3285.63	3292.59	-0.018710
2020-09-08	1	3301.22	3324.39	3276.44	3316.42	0.007237
2020-09-09	1	3281.00	3289.62	3238.56	3254.63	-0.018632
2020-09-10	1	3282.23	3285.47	3227.64	3234.82	-0.006087
2020-09-11	1	3225.78	3262.50	3220.54	3260.35	0.007892
2020-09-14	1	3275.92	3283.53	3261.15	3278.81	0.005662
2020-09-15	1	3277.13	3297.69	3263.75	3295.68	0.005145
2020-09-16	1	3293.17	3302.46	3271.08	3283.92	-0.003568
2020-09-17	1	3277.32	3290.41	3248.50	3270.44	-0.004105
2020-09-18	1	3270.91	3338.32	3268.53	3338.09	0.020685
2020-09-21	1	3348.90	3350.59	3313.07	3316.94	-0.006336
2020-09-22	1	3290.67	3320.23	3265.70	3274.30	-0.012855
2020-09-23	1	3278.84	3289.76	3264.89	3279.71	0.001652
2020-09-24	1	3262.88	3265.35	3221.24	3223.18	-0.017236
2020-09-25	1	3234.37	3239.47	3208.04	3219.42	-0.001167
2020-09-28	1	3224.98	3238.18	3210.89	3217.54	-0.000584
2020-09-29	1	3231.86	3242.79	3219.79	3224.36	0.002120
2020-09-30	1	3232.71	3244.91	3202.34	3218.05	-0.001957
2020-10-09	1	3262.61	3280.51	3260.19	3272.08	0.016790
2020-10-12	1	3287.33	3359.15	3286.11	3358.47	0.026402
2020-10-13	1	3353.12	3361.83	3334.50	3359.75	0.000381
2020-10-14	1	3353.63	3353.63	3332.96	3340.78	-0.005646
2020-10-15	1	3342.92	3354.58	3330.00	3332.18	-0.002574
2020-10-16	1	3334.46	3348.95	3319.11	3336.36	0.001254

	open	high	low	close
date
2017-01-03	16.21	16.44	16.17	16.30
2017-01-04	16.29	16.35	16.18	16.33
2017-01-05	16.30	16.38	16.24	16.30
2017-01-06	16.30	16.30	16.13	16.18
2017-01-09	16.24	16.29	16.13	16.20
2017-01-10	16.18	16.24	16.14	16.19
2017-01-11	16.24	16.24	16.15	16.16
2017-01-12	16.18	16.20	16.11	16.12
2017-01-13	16.10	16.29	16.10	16.27
2017-01-16	16.23	16.60	16.10	16.56
2017-01-17	16.46	16.54	16.37	16.40
2017-01-18	16.42	16.55	16.36	16.48
2017-01-19	16.43	16.64	16.43	16.54
2017-01-20	16.58	16.66	16.50	16.60
2017-01-23	16.66	16.69	16.51	16.57
2017-01-24	16.58	16.70	16.58	16.69
2017-01-25	16.69	16.74	16.61	16.69
2017-01-26	16.69	16.84	16.61	16.74
2017-02-03	16.82	16.85	16.62	16.63
2017-02-06	16.75	16.78	16.66	16.66
2017-02-07	16.65	16.74	16.63	16.67
2017-02-08	16.63	16.69	16.54	16.67
2017-02-09	16.68	16.77	16.59	16.72
2017-02-10	16.76	16.84	16.70	16.78
2017-02-13	16.88	16.90	16.78	16.85
2017-02-14	16.87	16.88	16.74	16.75
2017-02-15	16.82	16.93	16.75	16.84
2017-02-16	16.88	16.91	16.76	16.78
2017-02-17	16.78	16.82	16.59	16.64
2017-02-20	16.62	16.98	16.62	16.91
...	...	...	...	...
2017-08-07	12.87	12.95	12.83	12.93
2017-08-08	12.91	12.93	12.83	12.87
2017-08-09	12.84	12.88	12.75	12.76
2017-08-10	12.74	12.88	12.70	12.79
2017-08-11	12.72	12.73	12.46	12.68
2017-08-14	12.62	12.63	12.52	12.56
2017-08-15	12.58	12.76	12.56	12.59
2017-08-16	12.56	12.56	12.47	12.49
2017-08-17	12.49	12.55	12.47	12.53
2017-08-18	12.50	12.57	12.46	12.51
2017-08-21	12.50	12.57	12.46	12.51
2017-08-22	12.51	12.53	12.41	12.43
2017-08-23	12.44	12.66	12.41	12.57
2017-08-24	12.58	12.66	12.45	12.47
2017-08-25	12.47	12.80	12.47	12.78
2017-08-28	12.83	12.96	12.75	12.89
2017-08-29	12.85	12.98	12.77	12.96
2017-08-30	12.91	12.98	12.82	12.87
2017-08-31	12.83	12.84	12.65	12.71
2017-09-01	12.68	12.87	12.68	12.77
2017-09-04	12.78	12.84	12.65	12.78
2017-09-05	12.78	13.09	12.78	13.03
2017-09-06	12.99	13.05	12.90	12.96
2017-09-07	12.94	13.02	12.82	12.85
2017-09-08	12.85	13.12	12.83	13.03
2017-09-11	13.15	13.15	12.95	12.99
2017-09-12	13.00	13.04	12.91	13.04
2017-09-13	13.01	13.01	12.86	12.87
2017-09-14	12.88	12.98	12.81	12.85
2017-09-15	12.82	12.86	12.75	12.83

	open	high	low	close
count	174.000000	174.000000	174.000000	174.000000
mean	14.545747	14.657241	14.436609	14.556034
std	1.668836	1.642246	1.672180	1.648151
min	11.750000	12.400000	11.720000	12.290000
25%	12.810000	12.942500	12.740000	12.842500
50%	14.975000	15.070000	14.875000	14.990000
75%	16.230000	16.287500	16.137500	16.200000
max	16.880000	17.080000	16.810000	16.910000

	open	high	low	close
date
2017-02-22	16.88	16.90	16.73	16.75
2017-02-16	16.88	16.91	16.76	16.78
2017-02-13	16.88	16.90	16.78	16.85
2017-02-21	16.88	17.08	16.81	16.88
2017-02-14	16.87	16.88	16.74	16.75
2017-02-03	16.82	16.85	16.62	16.63
2017-02-15	16.82	16.93	16.75	16.84
2017-02-17	16.78	16.82	16.59	16.64
2017-02-23	16.78	16.83	16.64	16.69
2017-02-10	16.76	16.84	16.70	16.78
2017-02-06	16.75	16.78	16.66	16.66
2017-02-27	16.69	16.72	16.53	16.59
2017-01-25	16.69	16.74	16.61	16.69
2017-01-26	16.69	16.84	16.61	16.74
2017-02-09	16.68	16.77	16.59	16.72
2017-02-24	16.67	16.75	16.66	16.71
2017-01-23	16.66	16.69	16.51	16.57
2017-02-07	16.65	16.74	16.63	16.67
2017-02-08	16.63	16.69	16.54	16.67
2017-03-02	16.62	16.62	16.39	16.41
2017-02-20	16.62	16.98	16.62	16.91
2017-03-01	16.58	16.62	16.52	16.56
2017-02-28	16.58	16.67	16.53	16.59
2017-01-20	16.58	16.66	16.50	16.60
2017-01-24	16.58	16.70	16.58	16.69
2017-01-17	16.46	16.54	16.37	16.40
2017-01-19	16.43	16.64	16.43	16.54
2017-03-03	16.42	16.43	16.32	16.35
2017-01-18	16.42	16.55	16.36	16.48
2017-03-08	16.40	16.44	16.35	16.39
...	...	...	...	...
2017-09-01	12.68	12.87	12.68	12.77
2017-06-28	12.66	12.91	12.60	12.79
2017-07-03	12.64	12.65	12.47	12.56
2017-06-26	12.64	12.82	12.55	12.64
2017-06-27	12.64	12.73	12.53	12.70
2017-08-14	12.62	12.63	12.52	12.56
2017-07-07	12.62	12.69	12.55	12.60
2017-07-06	12.62	12.72	12.51	12.66
2017-06-14	12.60	12.64	12.31	12.46
2017-07-10	12.59	12.64	12.52	12.53
2017-08-24	12.58	12.66	12.45	12.47
2017-08-15	12.58	12.76	12.56	12.59
2017-08-16	12.56	12.56	12.47	12.49
2017-07-04	12.55	12.58	12.41	12.55
2017-06-23	12.55	12.65	12.36	12.64
2017-08-22	12.51	12.53	12.41	12.43
2017-07-11	12.51	12.94	12.51	12.80
2017-08-18	12.50	12.57	12.46	12.51
2017-08-21	12.50	12.57	12.46	12.51
2017-07-05	12.50	12.65	12.47	12.62
2017-08-17	12.49	12.55	12.47	12.53
2017-08-25	12.47	12.80	12.47	12.78
2017-08-23	12.44	12.66	12.41	12.57
2017-06-20	12.41	12.44	12.22	12.29
2017-06-15	12.41	12.46	12.28	12.38
2017-06-21	12.40	12.40	12.26	12.34
2017-06-16	12.38	12.46	12.27	12.35
2017-06-19	12.36	12.43	12.30	12.40
2017-06-22	12.33	12.84	12.29	12.57
2017-05-25	11.75	12.93	11.72	12.93

	Symbol	AccumulativeNAV
TradingDate
2018-01-02	510560	0.6205
2018-01-02	510520	1.6321
2018-01-02	510580	1.0146
2018-01-02	510500	1.8709
2018-01-02	510510	1.7612
2018-01-03	510560	0.6259
2018-01-03	510520	1.6457
2018-01-03	510580	1.0233
2018-01-03	510500	1.8874
2018-01-03	510510	1.7764
2018-01-04	510520	1.6529
2018-01-04	510510	1.7844
2018-01-04	510560	0.6287
2018-01-04	510580	1.0278
2018-01-04	510500	1.8958
2018-01-05	510520	1.6534
2018-01-05	510560	0.6287
2018-01-05	510580	1.0277
2018-01-05	510510	1.7843
2018-01-05	510500	1.8957
2018-01-08	510560	0.6316
2018-01-08	510500	1.9041
2018-01-08	510520	1.6610
2018-01-08	510580	1.0320
2018-01-08	510510	1.7920
2018-01-09	510510	1.7920
2018-01-09	510520	1.6606
2018-01-09	510560	0.6315
2018-01-09	510580	1.0319
2018-01-09	510500	1.9039
...	...	...
2020-09-29	510510	1.8165
2020-09-29	510560	0.6223
2020-09-30	510580	1.0389
2020-09-30	510510	1.8055
2020-09-30	510500	1.9114
2020-09-30	510560	0.6187
2020-10-09	510580	1.0667
2020-10-09	510510	1.8538
2020-10-09	510560	0.6350
2020-10-09	510500	1.9628
2020-10-12	510560	0.6532
2020-10-12	510580	1.0974
2020-10-12	510500	2.0197
2020-10-12	510510	1.9073
2020-10-13	510580	1.0991
2020-10-13	510500	2.0229
2020-10-13	510510	1.9102
2020-10-13	510560	0.6541
2020-10-14	510510	1.8980
2020-10-14	510580	1.0923
2020-10-14	510500	2.0099
2020-10-14	510560	0.6499
2020-10-15	510560	0.6451
2020-10-15	510580	1.0858
2020-10-15	510510	1.8841
2020-10-15	510500	1.9955
2020-10-16	510580	1.0806
2020-10-16	510510	1.8748
2020-10-16	510560	0.6420
2020-10-16	510500	1.9856

	open	high	low	close
date
2017-01-10	16.18	16.24	16.14	16.19
2017-01-11	16.24	16.24	16.15	16.16
2017-01-16	16.23	1.00	16.10	1.00
2017-01-17	16.46	1.00	16.37	16.40
2017-01-18	16.42	1.00	16.36	16.48
2017-01-19	16.43	1.00	16.43	1.00
2017-01-20	1.00	1.00	16.50	1.00
2017-01-23	1.00	1.00	1.00	1.00
2017-01-24	1.00	1.00	1.00	1.00
2017-01-25	1.00	1.00	1.00	1.00
2017-01-26	1.00	1.00	1.00	1.00
2017-02-03	1.00	1.00	1.00	1.00
2017-02-06	1.00	1.00	1.00	1.00
2017-02-07	1.00	1.00	1.00	1.00
2017-02-08	1.00	1.00	1.00	1.00
2017-02-09	1.00	1.00	1.00	1.00
2017-02-10	1.00	1.00	1.00	1.00
2017-02-13	1.00	1.00	1.00	1.00
2017-02-14	1.00	1.00	1.00	1.00
2017-02-15	1.00	1.00	1.00	1.00
2017-02-16	1.00	1.00	1.00	1.00
2017-02-17	1.00	1.00	1.00	1.00
2017-02-20	1.00	1.00	1.00	1.00
2017-02-21	1.00	1.00	1.00	1.00
2017-02-22	1.00	1.00	1.00	1.00
2017-02-23	1.00	1.00	1.00	1.00
2017-02-24	1.00	1.00	1.00	1.00
2017-02-27	1.00	1.00	1.00	1.00
2017-02-28	1.00	1.00	1.00	1.00
2017-03-01	1.00	1.00	1.00	1.00
2017-03-02	1.00	1.00	16.39	16.41
2017-03-06	16.37	1.00	16.35	16.41