Python | Pandas Timestamp.now
Python is a great language for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages that makes importing and analyzing data much easier.
Pandas Timestamp.now()
function returns the current time in the local timezone. It is Equivalent to datetime. now([tz]).
Pandas Timestamp.now Syntax
Syntax :Timestamp.now()
Parameters : None
Return : A
Timestamp
object representing the current time in the specified timezone.
Timestamp.now in Pandas Examples
The Timestamp.now()
method in Pandas is used to create a Timestamp
object representing the current time. A Timestamp
object is a data type in Pandas that represents a specific point in time. It includes the date, time, and timezone information. Here we will see different examples on how to use this method:
Get Current Timestamp with Pandas
The code shows how to use Pandas Timestamp.now() to get the current date and time.
Python3
import pandas as pd # Capture the current timestamp current_time = pd.Timestamp.now() print ( "Current Timestamp:" , current_time) |
Output:
Current Timestamp: 2023-10-12 07:24:35.042577
Get Current Time with Timestamp Objects in Pandas
Use Timestamp.now()
the function to return the current time in the local timezone.
Python3
# importing pandas as pd import pandas as pd # Create the Timestamp object ts = pd.Timestamp(year = 2011 , month = 11 , day = 21 , hour = 10 , second = 49 , tz = 'US/Central' ) # Print the Timestamp object print (ts) # return the current time ts.now() |
Output :
2011-11-21 10:00:49-06:00
Timestamp('2023-10-12 07:25:32.822525')
As we can see in the output, the Timestamp.now()
function has returned the current time in the local timezone. It auto-detects the local timezone.
Generating Timestamps and Creating Time-Series Data using Pandas
Timestamped data associates each record with a specific timestamp. This is common when recording temperature, stock prices, or any other measurement over time. The pd.Timestamp.now() function creates timestamped data.
Python3
import pandas as pd # Generate timestamps for the last 5 days timestamps = pd.date_range(end = pd.Timestamp.now(), periods = 5 , freq = "D" ) # Create a DataFrame with timestamped data data = { "timestamp" : timestamps, "temperature" : [ 21.2 , 23.2 , 27.2 , 29.2 , 31.2 ] } df = pd.DataFrame(data) print (df) |
Output:
timestamp temperature
0 2023-08-20 04:40:14.707909 21.2
1 2023-08-21 04:40:14.707909 23.2
2 2023-08-22 04:40:14.707909 27.2
3 2023-08-23 04:40:14.707909 29.2
4 2023-08-24 04:40:14.707909 31.2
Creating a Time-Series DataFrame with Timestamped Indices using Pandas
When working with time-series data, it’s crucial to use timestamped indices for easy time-based operations and calculations.
Python3
import pandas as pd # Generate timestamps for the last 7 days timestamps = pd.date_range(end = pd.Timestamp.now(), periods = 7 , freq = "D" ) # Create a DataFrame with timestamped indices data = { "temperature" : [ 23.3 , 34.5 , 22.1 , 22 , 31.3 , 33.4 , 43.2 ], "humidity" : [ 43 , 58 , 54 , 34 , 47 , 56 , 40 ] } df = pd.DataFrame(data, index = timestamps) print (df) |
Output:
temperature humidity
2023-08-18 04:44:10.054030 23.3 43
2023-08-19 04:44:10.054030 34.5 58
2023-08-20 04:44:10.054030 22.1 54
2023-08-21 04:44:10.054030 22.0 34
2023-08-22 04:44:10.054030 31.3 47
2023-08-23 04:44:10.054030 33.4 56
2023-08-24 04:44:10.054030 43.2 40