Operations on timestamp data

Convert data from a string to a timestamp:

The date range is converted into a dataframe with the help of pd.DataFrame() method. The column is converted to DateTime using to_datetime() method. info() method gives information about the dataframe if there are any null values and the datatype of the columns.

Python3

# importing pandas 
import pandas as pd 
  
# creating a date range 
Date_range = pd.date_range(start='1/12/2020', end='20/5/2021', freq='M') 
  
# creating a Dataframe 
Data = pd.DataFrame(Date_range, columns=['Date']) 
  
# converting the column to datetime 
Data['Date'] = pd.to_datetime(Data['Date']) 
print(Data.info()) 

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16 entries, 0 to 15
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   Date    16 non-null     datetime64[ns]
dtypes: datetime64[ns](1)
memory usage: 256.0 bytes

Manipulating Time Series Data in Python

A collection of observations (activity) for a single subject (entity) at various time intervals is known as time-series data. In the case of metrics, time series are equally spaced and in the case of events, time series are unequally spaced. We may add the date and time for each record in this Pandas module, as well as fetch dataframe records and discover data inside a specific date and time range.

Operations on timestamp data

Python3

Manipulating Time Series Data in Python

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