Datasets : Vectorized String Operations | DateTime in Pandas (

Timestamp Object

A Timestamp object in Pandas represents a specific moment in time, such as “October 24th, 2022 at 7:00 PM.”

Creating Timestamp Objects

Timestamp objects can be created using various formats and variations:

# Creating a timestamp

# Variations
pd.Timestamp('2023, 1, 5')
pd.Timestamp('2023')  # Only year
pd.Timestamp('5th January 2023')
pd.Timestamp('5th January 2023 9:21AM')

Using datetime.datetime Object

You can also create Timestamp objects using datetime.datetime objects:

import datetime as dt

x = pd.Timestamp(dt.datetime(2023, 1, 5, 9, 21, 56))

Fetching Attributes

You can fetch various attributes of a Timestamp object:


Why Separate Objects for Handling Date and Time?

While Python’s datetime functionality is convenient, it can be inefficient for handling large datasets. The datetime64 dtype in NumPy provides a more efficient way to work with dates, especially in large arrays.

DatetimeIndex Object

A DatetimeIndex in Pandas is a collection of Timestamp objects.

Creating DatetimeIndex Objects

DatetimeIndex objects can be created from strings, Python datetime objects, or existing Timestamp objects:

type(pd.DatetimeIndex(['2023/1/1', '2022/1/1', '2021/1/1']))

pd.DatetimeIndex([dt.datetime(2023, 1, 1), dt.datetime(2022, 1, 1), dt.datetime(2021, 1, 1)])

dt_index = pd.DatetimeIndex([pd.Timestamp(2023, 1, 1), pd.Timestamp(2022, 1, 1), pd.Timestamp(2021, 1, 1)])

Using DatetimeIndex as Series Index

You can use DatetimeIndex as the index for a Series:

pd.Series([1, 2, 3], index=dt_index)

date_range Function

The date_range function generates a range of dates based on the specified parameters.

Examples of date_range Function

pd.date_range(start='2023/1/5', end='2023/2/28', freq='3D')  # Daily dates with a 3-day frequency

pd.date_range(start='2023/1/5', end='2023/2/28', freq='B')  # Business days

pd.date_range(start='2023/1/5', end='2023/2/28', freq='M')  # Month end

pd.date_range(start='2023/1/5', end='2023/2/28', freq='A')  # Year end

to_datetime Function

The to_datetime function converts existing objects to Pandas Timestamp or DatetimeIndex objects.

Examples of to_datetime Function

s = pd.Series(['2023/1/1', '2022/1/1', '2021/1/1'])

s = pd.Series(['2023/1/1', '2022/1/1', '2021/130/1'])
pd.to_datetime(s, errors='coerce').dt.month_name()

dt Accessor

The dt accessor provides access to datetimelike properties of Series values.

Example of dt Accessor


Plotting Graphs Using dt Accessor

import matplotlib.pyplot as plt

plt.plot(df['Date'], df['INR'])

Grouping and Plotting Based on Datetime Properties

df['day_name'] = df['Date'].dt.day_name()

df['month_name'] = df['Date'].dt.month_name()


These functionalities provided by Pandas make working with dates and times more efficient and convenient, allowing for easy manipulation and analysis of time series data.

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