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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Replacing parts of a datetime

In Python, you can replace parts of a datetime using the replace() method. This method returns a new datetime object with the specified parts replaced. Here's an example:

from datetime import datetime
 
dt = datetime(2022, 3, 14, 13, 23, 45)
new_dt = dt.replace(year=2023, month=4, day=15)
 
print(dt)  # 2022-03-14 13:23:45
print(new_dt)  # 2023-04-15 13:23:45

In this example, we first create a datetime object representing March 14, 2022 at 1:23:45 PM. We then call the replace() method on this datetime object to create a new datetime object with the year set to 2023, the month set to April, and the day set to 15. The replace() method returns this new datetime object, which we assign to the new_dt variable. Finally, we print both datetime objects to verify that the replacement worked.

Note that when you use the replace() method to replace parts of a datetime, any parts that are not specified are left unchanged. In the example above, we only specified the year, month, and day, so the time components (hours, minutes, and seconds) of the datetime remained the same.

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