Python Pandas Timestamp.ceil() Function

Python Pandas Timestamp.ceil() Function

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What is Timestamp?

A timestamp is a sequence of characters or encoded information that identifies when a particular event occurred, typically providing the date and time of day, and can be accurate to a fraction of a second.

The timestamp method is used for a variety of synchronization purposes, including assigning a sequence order to a multievent transaction so that the transaction can be canceled if a fault occurs. A timestamp can also be used to record time in reference to a specific starting point in time.

Uses of Timestamp:

Timestamps are used to maintain track of information stored online or on a computer. A timestamp indicates when data was generated, shared, modified, or removed.

Here are some examples of how timestamps can be used:

  • A timestamp in a computer file indicates when the file was last modified.
  • Photographs with digital cameras have timestamps that show the date and time of day they were taken.
  • The date and time of the post are included in social media posts.
  • Timestamps are used in online chat and instant messages to record the date and time that a message was delivered, received, or viewed.
  • Timestamps are used in blockchain blocks to confirm the validity of transactions, such as those involving cryptocurrencies.
  • To secure the integrity and quality of data, data management relies on timestamps.
  • Timestamps are used in digital contracts and digital signatures to signify when a document was signed.

Pandas Timestamp.ceil() Function:

The Timestamp.ceil() function of the Pandas module gives a new Timestamp ceiled to this resolution. As an input, the function accepts the specified time series frequency.

Syntax:

Timestamp.ceil(freq)

Parameters: 

freq: It is a frequency string that represents the ceiling resolution.

Return Value:

A new Timestamp is returned by the Timestamp.ceil() function of the Pandas module

Pandas Timestamp.ceil() Function in Python

Example1

Here, The Timestamp.ceil() function has ceiled the time series frequency of the given Timestamp object to the input frequency.

Approach:

  • Import pandas module using the import keyword.
  • Pass some random year, month, day, hour, minute, tz =’Asia/Kolkata’ (Timezone) as the arguments to the Timestamp() function of the pandas module to get the Timestamp object
  • Print the above-obtained Timestamp object
  • Pass freq = ‘D’ as an argument the floor() function and apply it on the above Timestamp object to get floored values of the above Timestamp object to daily frequency.
  • The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
  
# Pass some random year, month, day, hour, minute, tz ='Asia/Kolkata'
# (Timezone) as the arguments to the Timestamp() function of the
# pandas module to get the Timestamp object
time_stamp_obj = pd.Timestamp(year = 2017,  month = 5, day = 16, hour = 12, 
                            minute = 28, tz = 'Asia/Kolkata' ) 
# Print the above obtained Timestamp object
print("The above obtained Timestamp object:", time_stamp_obj)
print()
# Pass freq = 'D' as an argument the ceil() function and apply it on the above 
# Timestamp object to get ceiled values of the above Timestamp object to 
# daily frequency
print("The ceiled values of the above Timestamp object to daily frequency:")
time_stamp_obj.ceil(freq ='D')

Output:

The above obtained Timestamp object: 2017-05-16 12:28:00+05:30

The ceiled values of the above Timestamp object to daily frequency:
Timestamp('2017-05-17 00:00:00+0530', tz='Asia/Kolkata')

Example2

Approach:

  • Import pandas module using the import keyword.
  • Pass some random year, month, day, hour, minute, tz =’US/Central'(Timezone) as the arguments to the Timestamp() function of the pandas module to get the Timestamp object
  • Print the above-obtained Timestamp object
  • Pass freq = ‘T’ as an argument to the ceil() function and apply it on the above Timestamp object to get ceiled values of the above Timestamp object to minutely frequency.
  • The Exit of the Program.

Below is the implementation:

# Import pandas module using the import keyword.
import pandas as pd
  
# Pass some random year, month, day, hour, minute, tz ='US/Central'
# (Timezone) as the arguments to the Timestamp() function of the
# pandas module to get the Timestamp object
time_stamp_obj = pd.Timestamp(year = 2020,  month = 8, day = 5, hour = 4, 
                            minute = 30, tz = 'US/Central' ) 
# Print the above obtained Timestamp object
print("The above obtained Timestamp object:", time_stamp_obj)
print()
# Pass freq = 'T' as an argument to the ceil() function and apply it on the above 
# Timestamp object to get ceiled values of the above Timestamp object to 
# minutely frequency
print("The ceiled values of the above Timestamp object to minutely frequency:")
time_stamp_obj.ceil(freq ='T')

Output:

The above obtained Timestamp object: 2020-08-05 04:30:00-05:00

The ceiled values of the above Timestamp object to minutely frequency:
Timestamp('2020-08-05 04:30:00-0500', tz='US/Central')