利用Python进行数据分析的学习笔记——chap11

金融和经济数据应用

数据规整化方面的话题

时间序列以及截面对齐

import numpy as np
import pandas as pd
from pandas import DataFrame, Series

stock_px = pd.read_csv("E:\python_study_files\python\pydata-book-2nd-edition\examples\stock_px.csv",parse_dates=True,index_col=0)
prices = stock_px[['AAPL','JNJ','SPX','XOM']]['2011-09-06':'2011-09-14']
prices

	AAPL	JNJ	SPX	XOM
2011-09-06	379.74	64.64	1165.24	71.15
2011-09-07	383.93	65.43	1198.62	73.65
2011-09-08	384.14	64.95	1185.90	72.82
2011-09-09	377.48	63.64	1154.23	71.01
2011-09-12	379.94	63.59	1162.27	71.84
2011-09-13	384.62	63.61	1172.87	71.65
2011-09-14	389.30	63.73	1188.68	72.64

stock_pxx = pd.read_csv("E:\\python_study_files\\python\\pydata-book-2nd-edition\\examples\\volume.csv",parse_dates=True,index_col=0)
volume = stock_pxx[['AAPL','JNJ','XOM']]['2011-09-06':'2011-09-12']
volume

	AAPL	JNJ	XOM
2011-09-06	18173500.0	15848300.0	25416300.0
2011-09-07	12492000.0	10759700.0	23108400.0
2011-09-08	14839800.0	15551500.0	22434800.0
2011-09-09	20171900.0	17008200.0	27969100.0
2011-09-12	16697300.0	13448200.0	26205800.0

prices*volume

	AAPL	JNJ	SPX	XOM
2011-09-06	6.901205e+09	1.024434e+09	NaN	1.808370e+09
2011-09-07	4.796054e+09	7.040072e+08	NaN	1.701934e+09
2011-09-08	5.700561e+09	1.010070e+09	NaN	1.633702e+09
2011-09-09	7.614489e+09	1.082402e+09	NaN	1.986086e+09
2011-09-12	6.343972e+09	8.551710e+08	NaN	1.882625e+09
2011-09-13	NaN	NaN	NaN	NaN
2011-09-14	NaN	NaN	NaN	NaN

vwap = (prices*volume).sum()/volume.sum()
vwap

AAPL    380.655181
JNJ      64.394769
SPX            NaN
XOM      72.024288
dtype: float64

vwap.dropna()

AAPL    380.655181
JNJ      64.394769
XOM      72.024288
dtype: float64

prices.align(volume,join='inner')

(              AAPL    JNJ    XOM
 2011-09-06  379.74  64.64  71.15
 2011-09-07  383.93  65.43  73.65
 2011-09-08  384.14  64.95  72.82
 2011-09-09  377.48  63.64  71.01
 2011-09-12  379.94  63.59  71.84,
                   AAPL         JNJ         XOM
 2011-09-06  18173500.0  15848300.0  25416300.0
 2011-09-07  12492000.0  10759700.0  23108400.0
 2011-09-08  14839800.0  15551500.0  22434800.0
 2011-09-09  20171900.0  17008200.0  27969100.0
 2011-09-12  16697300.0  13448200.0  26205800.0)

s1 = Series(range(3),index=['a','b','c'])
s2 = Series(range(4),index=['d','b','c','e'])
s3 = Series(range(3),index=['f','a','c'])
DataFrame({
   
   'one':s1,'two':s2,'three':s3})

	one	two	three
a	0.0	NaN	1.0
b	1.0	1.0	NaN
c	2.0	2.0	2.0
d	NaN	0.0	NaN
e	NaN	3.0	NaN
f	NaN	NaN	0.0

DataFrame({
   
   'one':s1,'two':s2,'three':s3},index=list('face'))

	one	two	three
f	NaN	NaN	0.0
a	0.0	NaN	1.0
c	2.0	2.0	2.0
e	NaN	3.0	NaN

频率不同的时间序列的运算

ts1 = Series(np.random.randn(3),index=pd.date_range('2012-6-13',periods=3,freq='W-WED'))
ts1

2012-06-13    1.581292
2012-06-20    0.891047
2012-06-27   -0.209271
Freq: W-WED, dtype: float64

ts1.resample('B')

<pandas.core.resample.DatetimeIndexResampler object at 0x000001F9A5F93610>

ts1.resample('B').ffill()

2012-06-13    1.581292
2012-06-14    1.581292
2012-06-15    1.581292
2012-06-18    1.581292
2012-06-19    1.581292
2012-06-20    0.891047
2012-06-21    0.891047
2012-06-22    0.891047
2012-06-25    0.891047
2012-06-26    0.891047
2012-06-27   -0.209271
Freq: B, dtype: float64

dates = pd.DatetimeIndex(['2012-6-12','2012-6-17','2012-6-18','2012-6-21','2012-6-22','2012-6-29']