Datasets : Vectorized String Operations | DateTime in Pandas (kaggle.com)

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
type(pd.Timestamp('2023/1/5'))

# Variations
pd.Timestamp('2023-1-5')
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:

x.year
x.month
x.day
x.hour
x.minute
x.second

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'])
pd.to_datetime(s).dt.day_name()

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

df['Date'].dt.is_quarter_start

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.groupby('day_name')['INR'].mean().plot(kind='bar')

df['month_name'] = df['Date'].dt.month_name()
df.groupby('month_name')['INR'].sum().plot(kind='bar')

df[df['Date'].dt.is_month_end]

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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