Python Data analysis: Time series two

Convert Timestamp to Period

By using the to_period method, the Series and DataFrame objects indexed by the timestamp can be converted to a time -indexed

Rng=pd.date_range (' 1/1/2000 ', periods=3,freq= ' M ')

Ts=series (RANDN (3), index=rng)

Print (TS)

Pts2=ts.to_period (freq= ' M ')

Print (PTS2)

The results are as follows:TS is the date of the last day of each month,pts2 is the day of the month cycle

2000-01-31 on 0.990097

2000-02-29 on 0.439761

2000-03-31-3.395317

Freq:m, Dtype:float64

2000-01 0.990097

2000-02 0.439761

2000-03-3.395317

Freq:m, Dtype:float64

If you want to convert back to timestamp, you can use pts2.to_timestamp (how= ' end ') method

2000-01-31-0.489228

2000-02-29-1.583283

2000-03-31-2.414735

Freq:m, Dtype:float64

Resampling and Frequency conversion

Converting high-frequency data to low-frequency is called de-sampling, while low-frequency data is converted to high-frequency called L-sampling. Pandas in the resample method can be used for this frequency conversion

Rng=pd.date_range (' 1/1/2000 ', periods=50,freq= ' D ')

Ts=series (Randn (), index=rng)

Print (Ts.resample (' M '). Mean ())

The results of the operation are as follows, where TS is a day-level data, but is converted to monthly data by resample (' M ') , and averaged over data belonging to the same one-month period. The average of every month is what you get.

2000-01-31-0.276265

2000-02-29-0.052926

Freq:m, Dtype:float64

Drop Sampling:

There are two things to consider when you drop a sample:

1 which side of each zone is closed

2 How to mark each aggregation polygon, with the beginning or end of the interval

Consider the following code:

Rng=pd.date_range (' 1/1/2000 ', periods=12,freq= ' T ')

Ts=series (Np.arange (), index=rng)

Print (TS)

2000-01-01 00:00:00 0

2000-01-01 00:01:00 1

2000-01-01 00:02:00 2

2000-01-01 00:03:00 3

2000-01-01 00:04:00 4

2000-01-01 00:05:00 5

2000-01-01 00:06:00 6

2000-01-01 00:07:00 7

2000-01-01 00:08:00 8

2000-01-01 00:09:00 9

2000-01-01 00:10:00 10

2000-01-01 00:11:00 11

Print (Ts.resample (' 5min ', closed= ' left '). SUM ())

When left closed, the statistic is a 5 - minute cycle starting with 00:00:00 .

2000-01-01 00:00:00 10

2000-01-01 00:05:00 35

2000-01-01 00:10:00 21

Print (Ts.resample (' 5min ', closed= ' right '). SUM ())

When closing the right, The statistic is the 5 - minute cycle with 00:00:00 as the end, because the time is ahead to 1999-12-31 23:55:00 .

1999-12-31 23:55:00 0

2000-01-01 00:00:00 15

2000-01-01 00:05:00 40

2000-01-01 00:10:00 11

So left or right closing depends on the start and end of the time

In the financial world there is an omnipresent time-series aggregation, that is, the calculation of the 4 values of each polygon , the first value open: Open, the last value close: Close, Maximum high : Highest, minimum low : Lowest

Ts.resample (' 5min ', closed= ' left '). OHLC ()

Open High Low close

2000-01-01 00:00:00 0 4 0 4

2000-01-01 00:05:00 5 9 5 9

2000-01-01 00:10:00 10 11 10 11

Python Data analysis: Time series two