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Pandas Datetime

The Pandas can provide the features to work with time-series data for all domains. It also consolidates a large number of features from other Python libraries like scikits.timeseries by using the NumPy datetime64 and timedelta64 dtypes. It provides new functionalities for manipulating the time series data. The manipulations performed in Pandas Datetime can be listed as:

  1. DatetimeIndex
  2. Range Generation of Dates
  3. Localization and Conversions
  4. Time zone Handling
  5. Time Series Operations

The time series tools are most useful for data science applications and deals with other packages used in Python.

Example 1: DatetimeIndex

The below code generates a sequence of eight dates starting from '5/4/2013' with a frequency of one second.



DatetimeIndex(['2013-05-04 00:00:00', '2013-05-04 00:00:01',
               '2013-05-04 00:00:02', '2013-05-04 00:00:03',
               '2013-05-04 00:00:04', '2013-05-04 00:00:05',
               '2013-05-04 00:00:06', '2013-05-04 00:00:07'],
              dtype='datetime64[ns]', freq='S')

Example 2: Conversion

The below code converts a DataFrame with separate columns for year, month, and day into a single datetime format using the pd.to_datetime() function in Pandas.



0   2014-05-20
1   2012-07-17
dtype: datetime64[ns]

Example 3: Range Generation

This below code generates a sequence of five dates starting from '2017-06-04' with a frequency of one second.




DatetimeIndex(['2017-06-04 00:00:00', 
               '2017-06-04 00:00:01',
               '2017-06-04 00:00:02',
               '2017-06-04 00:00:03',
               '2017-06-04 00:00:04'],
               dtype='datetime64[ns]', freq='S')

Example 4: Localization

The below python datetime code localizes the timezone of a datetime sequence represented by the variable 'dmy' to UTC using the tz_localize() function in Pandas.



DatetimeIndex(['2017-06-04 00:00:00+00:00', '2017-06-04 00:00:01+00:00',
               '2017-06-04 00:00:02+00:00', 
               '2017-06-04 00:00:03+00:00',
               '2017-06-04 00:00:04+00:00'],
              dtype='datetime64[ns, UTC]', freq='S')

Example 5: Time Series Operations

The below code is written to perform time series operations like rolling mean.



2022-01-01     10
2022-01-02     15
2022-01-03      8
2022-01-04     12
2022-01-05      9

Rolling Mean:
2022-01-01          NaN
2022-01-02          NaN
2022-01-03     11.000000
2022-01-04     11.666667
2022-01-05      9.666667
Freq: D, Name: Value, dtype: float64


The Pandas library gives strong usefulness to working with datetime information in Python. It offers different elements to deal with, control, and break down dates and times productively.

Next TopicPandas Time Offset

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