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]).
Syntax :Timestamp.now()
Parameters : None
Return : Timestamp
Example
Use Timestamp.now() the function to return the current time in the local timezone.
Python3
# importing pandas as pdimport pandas as pd# Create the Timestamp objectts = pd.Timestamp(year = 2011, month = 11, day = 21, hour = 10, second = 49, tz = 'US/Central')# Print the Timestamp objectprint(ts) |
Output :
2011-11-21 10:00:49-06:00
Now we will use the Timestamp.now() function to find the current time in the local timezone.
Python3
# return the current timets.now() |
Output :
Timestamp('2023-07-21 10:07:23.622458')
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.
Create Tempstamp Data
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 daystimestamps = pd.date_range(end=pd.Timestamp.now(), periods=5, freq="D")# Create a DataFrame with timestamped datadata = { "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
Create Timestamp Indices
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 daystimestamps = pd.date_range(end=pd.Timestamp.now(), periods=7, freq="D")# Create a DataFrame with timestamped indicesdata = { "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
