Python | Pandas Working with Dates and Times
Last Updated :
25 Jan, 2024
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
- Python | Pandas Series
- Python | Pandas DataFrame
- Python | Pandas Working With Text Data
- Python | Pandas Merging, Joining, and Concatenating
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