Python | Pandas Working with Dates and Times
Pandas has proven very successful as a tool for working with time series data, especially in the financial data analysis space. Using the NumPy datetime64 and timedelta64 dtypes, we have consolidated a large number of features from other Python libraries like scikits.timeseries as well as created a tremendous amount of new functionality for manipulating time series data.
Example #1: Create a dates dataframe
import pandas as pd # Create dates dataframe with frequency data = pd.date_range('1/1/2011', periods = 10, freq ='H') data
Output:
Example #2: Create range of dates and show basic features
# Create date and time with dataframe data = pd.date_range('1/1/2011', periods = 10, freq ='H') x = datetime.now() x.month, x.year
Output:
(9, 2018)
Example #3: Break data and time into seperate features
# Create date and time with dataframe rng = pd.DataFrame() rng['date'] = pd.date_range('1/1/2011', periods = 72, freq ='H') # Print the dates in dd-mm-yy format rng[:5] # Create features for year, month, day, hour, and minute rng['year'] = rng['date'].dt.year rng['month'] = rng['date'].dt.month rng['day'] = rng['date'].dt.day rng['hour'] = rng['date'].dt.hour rng['minute'] = rng['date'].dt.minute # Print the dates divided into features rng.head(3)
Output:
Date Time Methods:
Function | Description |
---|---|
Timestamp() | This constructor method is used with a variety of inputs (strings, date objects, date objects) to create some Timestamp objects |
to_datetime() | Method is used to convert various input arguements to datetime |
date_range() | Method is used to generate a DatetimeIndex of Timestamp objects. This constructor method includes 3 critical parameters (start, end, and periods) |
date_range(start, periods) | This approach creates a set number of dates beginning from a specific point |
date_range(end, periods) | This approach creates a set number of dates, proceeding backwards from a specified date point |
DateOffset | This object is used to add hours, days, weeks, months, and years to a DatetimeIndex |
dt accessor | It allows us to access specific datetime properties on a Series of Timestamp object |
loc[], .iloc[], and .ix[] | Methods are used for extracting rows from a DataFrame with a DatetimeIndex |
truncate() | Method is used for slicing operations on objects with a DatetimeIndex. It includes two parameters — before and after — to specify the start and end of our date range |
More on Pandas