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Pandas Time Series Data

Time series data is a very important kind of data in data analysis. Things evolve over time, and data is generated at various points in time.

Time in Python-datetime module

Python's standard library datetime supports creation and manipulation of time, and Pandas 'time-series module builds on datetime.

1. import datetime = datetime.datetime.now ()#2022 -03-06 18:43:49.506048

The full picture of now is as follows, which is a datetime object:

now: datetime.datetime(2022, 3, 6, 18, 37, 10, 132078)

datetime.datetime object Common methods:

(1)strftime(): Transforms the format of datetime objects. Write the target format using placeholders containing %, such as:

newForm = now.strftime("%Y-%m-%d (%H:%M:%S)")# 2022-03-06 (18:52:31) is a string type

Each placeholder has the following meaning

% y Two-digit year representation (00 - 99) % Y Four-digit year representation (000 - 9999) % m Month (01 - 12) % d Day of Month (0 - 31) % H Hours of 24 Hours (0 - 23) % I Hours of 12 Hours (01 - 12) % M Minutes (00 = 59) % S Seconds (00 - 59) % a Local Simplified Week Name % A Local Full Week Name % b Local Simplified Month Name % B Local Full Month Name % c Local Corresponding Date and Time Representation % j Day of Year (001 - 366) % p Equivalent of local A.M. or P.M.% U Number of weeks of the year (00 - 53) Sunday is the beginning of the week % w Week (0 - 6), Sunday is the beginning of the week % W Number of weeks in the year (00 - 53) Monday is the beginning of the week % x Local corresponding date representation % X Local corresponding time representation % Z Name of current time zone % % % sign itself 2. Specify time

Pass the corresponding parameters into datetime.datetime(), and specify them in descending order by default.

birthday = datetime.datetime(2001, 1, 11) #print(birthday) specified by default

It can also be specified by parameters, where year, month and day must be specified.

yesterday = pd.Timestamp(2022, 3, 5)#2022 -03-05 00:00:00now = pd.Timestamp("now")#2022 -03-06 19:13:56.5076043. Operation

(1)datetime.datetime objects support subtraction, resulting in a datetime.timedelta object

delta = yearday- birthday # 7723 days, 0:00:00 II. Pandas processing timing sequence 1.pd.Timestamp()

pd.Timestamp() is the main function of Pandas to define event time, and supports richer construction methods to define time.

(1) Based on datetime.datetime object

now = pd.Timestamp(datetime.datetime.now())print(now) # 2022-03-06 19:07:07.253402

(2)based on string

today = pd.Timestamp("2022-03-06")print(today) # 2022-03-06 00:00:00

(3)More specified parameters

yesterday = pd.Timestamp(2022, 3, 5) # 2022-03-05 00:00:00now = pd.Timestamp("now") # 2022-03-06 19:13:56.507604

(4)from a timestamp, a

pd.Timestamp(1646565103.114923, unit="s") # unit Specifies the unit as seconds 2.pd.Timedelta()

pd.Timedelta() is used to create a time difference object and has a similar construction method as pd.Timestamp().

(1)Create from string

oneDay = pd.Timedelta("1 days") # 1 days 00:00:00duration = pd.Timedelta("2 days 2 hours") # 1 days 02:00:00

(2)Specify parameter creation

oneDay = pd.Timedelta(days=1) # 1 days 00:00:003. The operation is similar to the operation of the datetime module. Subtracting the Timestamp objects of the pandas results in the Timedelta object. 4. time index

Data tables often use time as an index, pandas supports the creation of long time series

(1) pd.to_datetime()

pd.to_datetime() supports converting time objects and time-like strings into datetimeIndex objects.

index = pd.to_datetime(["03/06/2022", datetime.datetime.now()])print(index)# DatetimeIndex(['2022-03-06 00:00:00', '2022-03-06 19:29:44.855267'], dtype='datetime64[ns]', freq=None)

(2) pd.date_range()

pd.date_range() can be given a start time or an end time, and specifies the cycle data, cycle frequency, will automatically generate time index data in this range:

index = pd.date_range(start="2022-01-01", periods=10)print(index)# DatetimeIndex(['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04', '2022-01-05', '2022-01-06', '2022-01-07', '2022-01-08', '2022-01-09', '2022-01-10'], dtype='datetime64[ns]', freq='D')

If you want to skip the rest day, you can use the pd.bdate_range() function

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