如何在PANDAS中对具有不同索引的数据帧或系列进行计算？

Question

I have two Series that are of the same length and datatype. Both are float64. The only difference are the indexes both are dates but one date is at the beginnning of the month and the other is at the end of the month. How can I do computations like correlation or covariance on Series or dataframes that have different indexes?

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

IPO=Quandl.get("RITTER/US_IPO_STATS", authtoken="api key")
ir=Quandl.get("FRBC/REALRT", authtoken="api key")

ipo_splice=IPO[264:662]
new_ipo=ipo_splice['Gross Number of IPOs'];
new_ipo=new_ipo.T


ir_splice=ir[0:398]
new_ir=ir_splice['RR 1 Month']
new_ir=new_ir.T

new_ipo.corr(new_ir)

Answer 1

reset_index(drop=True) for the things you want to correlate then concat.

s1 = pd.DataFrame(np.random.rand(10), list('abcdefghij'), columns=['s1'])
s2 = pd.DataFrame(np.random.rand(10), list('ABCDEFGHIJ'), columns=['s2'])

print pd.concat([s.reset_index(drop=True) for s in [s1, s2]], axis=1).corr()


          s1        s2
s1  1.000000 -0.437945
s2 -0.437945  1.000000

Answer 2

you can use resample() function in order to resample one of your indices (our goal is have either both indices BoM or EoM):

data:

In [63]: df_bom
Out[63]:
            val
2015-01-01   76
2015-02-01   27
2015-03-01   65
2015-04-01   71
2015-05-01    9
2015-06-01   23
2015-07-01   52
2015-08-01   10
2015-09-01   62
2015-10-01   25

In [64]: df_eom
Out[64]:
            val
2015-01-31   87
2015-02-28   16
2015-03-31   85
2015-04-30    4
2015-05-31   37
2015-06-30   63
2015-07-31    3
2015-08-31   73
2015-09-30   81
2015-10-31   69

Solution:

In [61]: df_eom.resample('MS') + df_bom
C:\envs\py35\Scripts\ipython:1: FutureWarning: .resample() is now a deferred operation
use .resample(...).mean() instead of .resample(...)
Out[61]:
            val
2015-01-01  163
2015-02-01   43
2015-03-01  150
2015-04-01   75
2015-05-01   46
2015-06-01   86
2015-07-01   55
2015-08-01   83
2015-09-01  143
2015-10-01   94

In [62]: df_eom.resample('MS').join(df_bom, lsuffix='_lft')
C:\envs\py35\Scripts\ipython:1: FutureWarning: .resample() is now a deferred operation
use .resample(...).mean() instead of .resample(...)
Out[62]:
            val_lft  val
2015-01-01       87   76
2015-02-01       16   27
2015-03-01       85   65
2015-04-01        4   71
2015-05-01       37    9
2015-06-01       63   23
2015-07-01        3   52
2015-08-01       73   10
2015-09-01       81   62
2015-10-01       69   25

alternative approach - merging DF's by year and month parts:

In [69]: %paste
(pd.merge(df_bom, df_eom,
          left_on=[df_bom.index.year, df_bom.index.month],
          right_on=[df_eom.index.year, df_eom.index.month],
          suffixes=('_bom','_eom')))
## -- End pasted text --
Out[69]:
   key_0  key_1  val_bom  val_eom
0   2015      1       76       87
1   2015      2       27       16
2   2015      3       65       85
3   2015      4       71        4
4   2015      5        9       37
5   2015      6       23       63
6   2015      7       52        3
7   2015      8       10       73
8   2015      9       62       81
9   2015     10       25       69

Setup:

In [59]: df_bom = pd.DataFrame({'val':np.random.randint(0,100, 10)}, index=pd.date_range('2015-01-01', periods=10, freq='MS'))

In [60]: df_eom = pd.DataFrame({'val':np.random.randint(0,100, 10)}, index=pd.date_range('2015-01-01', periods=10, freq='M'))