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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The double_args decorator Time a function Using timer() When to use decorators with timer() Decorators and metadata The timer decorator Access to the original function Decorators that take arguments run_n_times() A decorator factory Timeout(): a real world example Querying Python interpreter's memory usage Allocating memory for an array Allocating memory for a computation Querying array memory Usage Querying DataFrame memory usage Using pd.read_csv() with chunksize Examining a chunk Filtering a chunk Chunking & filtering together Using pd.concat() Plotting the filtered results Managing Data with Generators Filtering in a list comprehension Filtering & summing with generators Examining consumed generators Reading many files Examining a sample DataFrame Aggregating with Generators Computing the fraction of long trips Delaying Computation with Dask Composing functions Deferring computation with `delayed` Visualizing a task graph Renaming decorated functions Using decorator @-notation Deferring Computation with Loops Aggregating with delayed Functions Computing fraction of long trips with `delayed` functions Chunking Arrays in Dask Working with Numpy arrays Working with Dask arrays Aggregating in chunks Aggregating with Dask arrays Dask array methods/attributes Timing array computations Computing with Multidimensional Arrays A Numpy array of time series data Reshaping time series data Reshaping: Getting the order correct! 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Building Dask Bags & Globbing Sequences to bags Reading text files Glob expressions Using Python's glob module Functional Approaches using Dask Bags Functional programming Functional programming - Using map Functional programming - Using Filter Functional Approaches - Using dask.bag.map Functional Approaches - Using dask.bag.filter Functional Approaches - Using .str & string methods JSON data files Using json module JSON Files into Dask Bags Plucking values Merging DataFrames Dask DataFrame pipelines Repeated reads & performance Using persistence Python, data science, & software engineering Software engineering concepts Django Introduction Datatypes Lists Combining Lists Finding and Removing Elements in a List Iterating and Sorting Tuples Zipping and Unpacking More Unpacking in Loops Enumerating positions Sets for Unordered and Unique Data with Tuples in Python Set Creating Sets in Python: Harnessing the Power of Unique Collections Modifying Sets in Python: Adding and Removing Elements with Ease Removing Data from Sets in Python: Streamlining Set Operations Exploring Set Operations in Python: Uncovering Similarities among Sets Set Operations in Python: Unveiling Differences among Sets Exploring Dictionaries in Python: A Key-Value Data Structure Creating and Looping Through Dictionaries in Python: A Comprehensive Guide Safely Finding Values in Python Dictionaries: A Guide to Avoiding Key Errors Safely Finding Values in Python Dictionaries: Advanced Techniques for Key Lookup Dictionaries-Working with Nested Data in Python: Exploring Hierarchical Structures Adding and Extending Python Dictionaries: Flexible Data Manipulation Popping and Deleting from Python Dictionaries: Managing Key-Value Removal Working with Dictionaries More Pythonically: Efficient Data Manipulation Checking Dictionaries for Data: Effective Data Validation in Python Working with CSV Files in Python: Simplify Data Processing and Analysis Creating a Dictionary from a File in Python: Simplify Data Mapping and Access Counting Made Easy in Python: Harness the Power of Counting Techniques Exploring the Collections Module in Python: Enhance Data Structures and Operations Understanding the Counter Class in Python: Simplify Counting and Frequency Analysis Working with Dictionaries of Unknown Structure using defaultdict in Python Advanced Usage of defaultdict in Python for Flexible Data Handling Maintaining Dictionary Order with OrderedDict in Python Harnessing the Power of OrderedDict's Advanced Features in Python Unleashing the Power of namedtuple in Python Leveraging the Power of namedtuples in Python Working with Datetime Components and Current Time in Python Exploring Datetime Components in Python Understanding "now" in Python's Datetime Module Exploring Timezones in Python's Datetime Module Time Travel in Python: Adding and Subtracting Time HELP! Libraries to Make Python Development Easier Parsing Time with Pendulum: Simplify Your Date and Time Operations Timezone Hopping with Pendulum: Seamlessly Manage Time across Different Timezones Humanizing Differences: Making Time Intervals More Readable with Pendulum

Humanizing Differences: Making Time Intervals More Readable with Pendulum

When working with dates and times, dealing with time intervals in a human-readable format can greatly enhance the user experience. Pendulum, a powerful Python library, provides convenient methods to humanize time differences, making them easier to understand for both developers and end-users. In this article, we will explore how to leverage Pendulum to humanize time intervals and showcase practical examples of its usage.

  1. Installing Pendulum: Ensure that you have Pendulum installed in your Python environment. Open your terminal or command prompt and run the following command:

    pip install pendulum

  2. Importing Pendulum and Creating Time Objects: To start, import the Pendulum library into your Python script or interactive session:

    import pendulum

Create two Pendulum DateTime objects representing different points in time:

start = pendulum.datetime(2022, 1, 1, 12, 0, 0)

end = pendulum.datetime(2022, 1, 1, 14, 30, 0)

  1. Humanizing Time Differences: Pendulum provides the in_words() method to humanize time differences, making them more readable:

    difference = end - start

    humanized_diff = difference.in_words()

    print(humanized_diff) # Output: 2 hours and 30 minutes

  2. Customizing Humanized Output: Pendulum allows you to customize the humanized output by specifying the granularity and other formatting options. For example:

    humanized_diff = difference.in_words(locale='en', only_distance=True)

    print(humanized_diff) # Output: 2.5 hours

  3. Handling Future and Past Time Differences: Pendulum's in_words() method automatically adjusts the humanized output based on whether the time difference is in the future or the past:

    future_diff = pendulum.duration(hours=3, minutes=15)

    past_diff = pendulum.duration(hours=-2, minutes=-45)

    print(future_diff.in_words()) # Output: 3 hours and 15 minutes from now

    print(past_diff.in_words()) # Output: 2 hours and 45 minutes ago

  4. Internationalization Support: Pendulum supports internationalization (i18n) by providing translations for various languages. You can specify the locale when humanizing time differences:

    humanized_diff_fr = difference.in_words(locale='fr')

    print(humanized_diff_fr) # Output: 2 heures et 30 minutes

Conclusion: Humanizing time differences is crucial for improving the user experience when dealing with dates and times. With the help of Pendulum, we can effortlessly convert time intervals into human-readable formats. In this article, we explored how to leverage Pendulum to humanize time differences, including customizing the output, handling future and past time differences, and supporting internationalization. By incorporating Pendulum's humanizing capabilities into your Python projects, you can provide users with more intuitive and user-friendly representations of time intervals. Embrace the power of Pendulum and make time differences easily understandable for everyone. Happy coding!

Essential data types for data science Introduction to Datatypes in Python: Understanding the Basics of Data Types. Exploring the Power of Lists. Merging Lists in Python: Combining Data. Exploring List Operations: Finding and Removing Elements. Iterating and Sorting: Exploring List Operations. Exploring the Versatility of Tuples. Zipping and Unpacking in Python: Exploring Data Combination. Unpacking in Loops: Exploring Efficient Data Extraction. Enumerating Positions: Efficient Index Tracking in Python. Exploring the Power of Sets for Unordered and Unique Data with Tuples in Python. Exploring the Power of Sets in Python. Harnessing the Power of Unique Collections: Creating Sets in Python. Adding and Removing Elements with Ease: Modifying Sets in Python. Streamlining Set Operations: Removing Data from Sets in Python. Uncovering Similarities among Sets: Exploring Set Operations in Python. Unveiling Differences among Sets: Set Operations in Python. A Key-Value Data Structure: Exploring Dictionaries in Python. A Comprehensive Guide: Creating and Looping Through Dictionaries in Python. A Guide to Avoiding Key Errors: Safely Finding Values in Python Dictionaries. Advanced Techniques for Key Lookup: Safely Finding Values in Python Dictionaries. Exploring Hierarchical Structures: Working with Nested Data in Python. Flexible Data Manipulation: Adding and Extending Python Dictionaries. Managing Key-Value Removal: Popping and Deleting from Python Dictionaries. Efficient Data Manipulation: Working with Dictionaries More Pythonically. Effective Data Validation in Python: Checking Dictionaries for Data. Simplify Data Processing and Analysis: Working with CSV Files in Python. Simplify Data Mapping and Access: Creating a Dictionary from a File in Python. Harness the Power of Counting Techniques: Counting Made Easy in Python. Enhance Data Structures and Operations: Exploring the Collections Module in Python. Simplify Counting and Frequency Analysis: Understanding the Counter Class in Python. Working with defaultdict in Python: Handling Dictionaries of Unknown Structure. Flexible Data Handling with defaultdict in Python: Advanced Usage for Versatility. Maintaining Dictionary Order in Python: Exploring OrderedDict for Ordered Data. Navigating the advanced features of OrderedDict in Python for efficient data manipulation and organization Unleashing the Power of namedtuple in Python: Empowering Data Structure Handling. Leveraging the Power of namedtuples in Python: Harnessing Data Structure Efficiency. Working with Datetime Components and Current Time in Python: Managing Time-based Data. Exploring Datetime Components in Python: Understanding Time-based Data. Navigating the concept of "now" in Python"s Datetime module for current date and time retrieval Navigating the concept of timezones in Python"s Datetime module for handling localized date and time information Time Travel in Python: Adding and Subtracting Time for Temporal Manipulation. NumPy: A powerful library for numerical computations Simplifying Date and Time Operations with Pendulum: Harnessing the Power of Time Parsing. Timezone Hopping with Pendulum: Effortlessly Navigate Time across Different Timezones. Humanizing Time Intervals with Pendulum: Enhancing Readability in Time Differences.