Python for Finance: Analyze Big Financial Data

In  [ 25 ]: df.index    =   dates

df

Out[25]:                                                                        No1                         No2                         No3                         No4

                                    2015-01-31  -0.737304       1.065173        0.073406        1.301174

                                    2015-02-28  -0.788818   -0.985819       0.403796    -1.753784

                                    2015-03-31  -0.155881   -1.752672       1.037444    -0.400793

                                    2015-04-30  -0.777546       1.730278        0.417114        0.184079

                                    2015-05-31  -1.763660   -0.375469       0.098678    -1.553824

                                    2015-06-30  -1.134258       1.401821        1.227124        0.979389

                                    2015-07-31      0.458838    -0.143187       1.565701    -2.085863

                                    2015-08-31  -0.103058   -0.366170   -0.478036   -0.032810

                                    2015-09-30      1.040318    -0.128799       0.786187        0.414084

When it comes to the generation of DatetimeIndex objects with the help of the

date_range function, there are a number of choices for the frequency parameter freq.

Table 6-3 lists all the options.

Table 6-3. Frequency parameter values for date_range function

Alias Description

B

Business day frequency

C

Custom business day frequency (experimental)

D

Calendar day frequency

W

Weekly frequency

M

Month end frequency

BM

Business month end frequency

MS

Month start frequency

BMS

Business month start frequency

Q

Quarter end frequency

BQ

Business quarter end frequency

QS

Quarter start frequency

BQS

Business quarter start frequency