Python Why Python is best for Data Sciences Python Efficient Code Data Types For Data Science Working With CSV Counter built-in class most_common() - collections module OrderedDict power feature - subclass namedtuple is a powerful tool From String to datetime DateTime Components TimeZone in Action TimeDelta - Time Travel with timedelta Parsing time with pendulum Data Manipulation with Pandas Creating DataFrames with Pandas Creating DataFrames with Dictionaries in Pandas DataFrame With CSV File Summary statistics Summarizing numerical data Summarizing dates The .agg() method Summaries on multiple columns Multiple summaries Cumulative sum Cumulative statistics Dropping duplicate names Dropping duplicate pairs Summaries by group Multiple grouped summaries Grouping by multiple variables Many groups, many summaries Pivot tables Group by to pivot table Different statistics in a pivot table Multiple statistics in pivot table Pivot on two variables Filling missing values in pivot tables Summing with pivot tables Explicit indexes Slicing lists Sort the index before slice Slicing the outer index level Slicing the inner index levels badly Slicing the inner index levels correctly Slicing columns Slice twice Slicing by dates Slicing by partial dates Subsetting by row/column number Slicing - .loc[] + slicing is a power combo The axis argument Calculating summary stats across columns Visualizing data Histograms Bar plots Line plots Rotating axis labels Scatter plots Layering plots Plot with Legend Plot with Transparency Avocados Missing values Detecting missing values Detecting any missing values with .isna().any() Detecting any missing values Counting missing values Plotting missing values Removing missing values Replacing missing values List of dictionaries - by row Dictionary of lists - by column DataFrame manipulation Built-in functions Defining a function Function parameters Return values from functions Docstrings Multiple Parameters and Return Values Basic ingredients of a function Global vs. local scope Nested functions Returning functions Using nonlocal Default and flexible arguments Lambda functions Anonymous functions Introduction to error handling The float() function Passing an incorrect argument Passing valid arguments Passing invalid arguments Errors and exceptions Errors and exceptions - 2 What is iterate Iterating with a for loop Iterators vs. iterables Iterating over iterables: next() Iterating at once with * Iterating with dictionaries Iterating with file connections Using enumerate() enumerate() and unpack Using zip() zip() and unpack Print zip with * Using iterators to load large files into memory Loading data in chunks Iterating over data Populate a list with a for loop A list comprehension For loop And List Comprehension List comprehension with range() Nested loops Conditionals in comprehensions Dict comprehensions Generator expressions List comprehensions vs. generators Conditionals in generator expressions Build generator function Using generator function Generators for the large data limit Build a generator function Using pandas read_csv iterator for streaming data Building with builtins Built-in function: range() with Efficient Code Built-in function: enumerate() with Efficient Code Built-in function: map() with Efficient Code The power of NumPy arrays with Efficient Code NumPy array broadcasting NumPy array boolean indexing Why should we time our code? 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Dates in Python

Working with dates in Python can be done using the built-in datetime module. This module provides several classes for working with dates and times, including date, time, datetime, timedelta, and tzinfo.

The date class represents a date (year, month, day) and provides methods for working with dates, such as formatting and arithmetic. The datetime class represents a date and time (year, month, day, hour, minute, second, microsecond) and provides similar methods for working with dates and times.

Here are some examples of working with dates using the datetime module:

import datetime
 
# create a date object for today's date
today = datetime.date.today()
print(today)  # 2023-03-14
 
# create a datetime object for a specific date and time
dt = datetime.datetime(2023, 3, 14, 12, 30, 0)
print(dt)  # 2023-03-14 12:30:00
 
# format a date as a string
date_str = today.strftime('%Y-%m-%d')
print(date_str)  # 2023-03-14
 
# parse a string as a date
date_str = '2023-03-14'
date = datetime.datetime.strptime(date_str, '%Y-%m-%d').date()
print(date)  # 2023-03-14
 
# perform arithmetic with dates
tomorrow = today + datetime.timedelta(days=1)
print(tomorrow)  # 2023-03-15
 
# compare dates
if tomorrow > today:
    print('Tomorrow is later than today')

There are many more methods available for working with dates and times in Python, and the datetime module provides a powerful and flexible way to work with dates and times in your code.

Working with dates in Python Exploring the properties of a date Determining the day of the week for a given date Performing calculations with dates in Python Augmenting variables with the += operator Converting dates to string format in Python ISO 8601 date format: YYYY-MM-DD (e.g. Formatting dates using strftime in Python Incorporating time values into date calculations Substituting elements in a datetime object Displaying datetime values in Python Extracting datetime values using strptime in Python Managing time durations in Python Generating time differences with timedeltas in Python Representing backward time intervals using negative timedeltas Understanding and managing UTC offsets in Python Modifying time zone: Adjusting timezone information vs changing tzinfo Utilizing the time zone database for accurate time calculations Pythons implementation of Daylight Saving Time starts Transitioning out of Daylight Saving Time Importing and parsing date/time data in Pandas Parsing date/time values using parse_dates in Pandas Performing arithmetic operations with timezone-aware datetime objects Aggregating and summarizing datetime information in pandas Exploring advanced datetime functions in Pandas Managing timezones in Pandas for accurate datetime handling Comprehensive guide to datetime operations in Pandas Comprehensive guide to all aspects of Pandas library Exploring advanced datetime functions in Pandas