Datetime - ( Pandas Time Series )

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Syntax

pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix', cache=True)

Example

import pandas as pd

date_str = '2021-09-22'
date = pd.to_datetime(date_str)

print(date)

Output

2021-09-22 00:00:00

Explanation

Pandas is a powerful library for data manipulation and analysis in Python. It provides various functions for working with date and time data, including the to_datetime() function, which can be used to convert a string or integer to a pandas datetime object.

In the above example, we use pd.to_datetime() to convert a string representation of a date to a pandas datetime object. The to_datetime() function automatically recognizes the date format and converts it to a standardized format.

Use

pd.to_datetime() is useful for converting date and time data to a standardized format that can be easily manipulated and analyzed using Pandas. It can be used in various applications such as financial analysis, time series analysis, and machine learning.

Important Points

• Pandas provides the to_datetime() function for converting date and time data to a standardized format
• The to_datetime() function can be used to convert a string or integer to a pandas datetime object
• The function automatically recognizes the date and time format and converts it to a standardized format
• The converted datetime object can be easily manipulated and analyzed using Pandas

Summary

In conclusion, the pd.to_datetime() function in Pandas is a powerful tool for working with date and time data in Python. It can be used to convert date and time data to a standardized format that can be easily manipulated and analyzed using Pandas. The function is useful in various applications such as financial analysis, time series analysis, and machine learning.