DateTime in Pandas: An Uncomplicated Guide (2023)

uncomplicated guide to datetime in Pandas for 2023:

• Timestamps

A Timestamp is a Pandas object that represents a single point in time. It can be created from a variety of data types, including strings, numbers, and dates.

For example, the following code creates a Timestamp from a string:

import pandas as pd

ts = pd.Timestamp('2023-06-04 10:44:43')

print(ts)

This will print the following output:

2023-06-04 10:44:43

• Periods

A Period is a Pandas object that represents a time period. It can be created from a variety of data types, including strings, numbers, and dates.

For example, the following code creates a Period from a string:

import pandas as pd

p = pd.Period('2023-06-04', freq='D')

print(p)

This will print the following output:

2023-06-04

• TimeSeries DataFrames

A TimeSeries DataFrame is a Pandas DataFrame that has a datetime index. This makes it easy to work with time-series data.

For example, the following code creates a TimeSeries DataFrame from a list of timestamps:

import pandas as pd

ts_list = [pd.Timestamp('2023-06-04 10:44:43'), pd.Timestamp('2023-06-05 10:44:43'), pd.Timestamp('2023-06-06 10:44:43')]

df = pd.DataFrame({'value': [1, 2, 3]}, index=ts_list)

print(df)

This will print the following output:

   value
2023-06-04  1
2023-06-05  2
2023-06-06  3

• Slicing Time-Series

You can slice a TimeSeries DataFrame using the same syntax as you would use to slice a regular DataFrame. For example, the following code slices the DataFrame created above to only include the values for the first two days:

df = df.loc['2023-06-04':'2023-06-05']

print(df)

This will print the following output:

   value
2023-06-04  1
2023-06-05  2

• DateTimeIndex Object and its Methods

The DateTimeIndex object is the underlying object that represents the index of a TimeSeries DataFrame. It has a number of methods that can be used to work with the index, such as:

• year(): Returns the year of the index.

• month(): Returns the month of the index.

• day(): Returns the day of the month of the index.

• hour(): Returns the hour of the day of the index.

• minute(): Returns the minute of the hour of the index.

• second(): Returns the second of the minute of the index.

• Resampling Time-Series Data

Resampling is the process of aggregating or summarizing time-series data at a different frequency. For example, you could resample a daily time-series to weekly or monthly frequencies.

Pandas provides a number of methods for resampling time-series data, such as:

• resample(): This method allows you to specify the frequency of the resampling.
• mean(): This method will calculate the mean of the values at the specified frequency.
• sum(): This method will calculate the sum of the values at the specified frequency.
• max(): This method will calculate the maximum value of the values at the specified frequency.
• min(): This method will calculate the minimum value of the values at the specified frequency.

I hope this guide has been helpful. For more information on datetime in Pandas, please see