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? Using %timeit %timeit output Specifying number loops Using %timeit in line magic mode Using %timeit in cell magic mode Saving output Comparing times Code profiling for runtime %lprun output Code profilling for memory usage %mprun output Efficiently Combining, Counting, and iterating Combining objects Combining objects with zip Counting with loop collections.Counter() The itertools module Combinations with loop itertools.combinations() Comparing objects with loops Set method difference Set method symmetric difference Set method union Uniques with sets Beneifits of eleiminating loops Eliminate loops with NumPy Moving calculations above a loop Using holistic conversions Introduction to pandas DataFrame iteration Calculating win percentage Adding win percentage to DataFrame Iterating with .iloc Iterating with .iterrows() .itertuples() Iterating with .itertuples() pandas .apply() method Dates in Python Attributes of a date Finding the weekday of a date Math with Dates Incrementing variables += Turning dates into strings ISO 8601 format with Exmples Format strftime Adding time to the mix Replacing parts of a datetime Printing datetimes Parsing datetimes with strptime Working with durations Creating timedeltas Negative timedeltas UTC offsets Adjusting timezone vs changing tzinfo Time zone database Starting Daylight Saving Time Ending Daylight Saving Time Reading date and time data in Pandas Loading datetimes with parse_dates Timezone-aware arithmetic Summarizing datetime data in pandas Additional datetime methods in Pandas Timezones in Pandas All datetime operations in Pandas All parts of Pandas Additional datetime methods in Pandas Introduction to string manipulation Concatenation Indexing Slicing Stride String operations Adjusting cases Splitting Joining Stripping characters Finding and replacing Finding substrings Index function Counting occurrences Replacing substrings Positional formatting string formatting Methods for formatting Positional formatting Reordering values Named placeholders Format specifier Formatting datetime Formatted string literal - f-strings Type conversion Index lookups Escape sequences Inline operations Calling functions Template method Substitution The re module Supported metacharacters Repeated characters Quantifiers in re module Regex metacharacters Special characters OR operator in re Module OR operand in re module Greedy vs. nongreedy matching Grouping and capturing re module Pipe | re module Non-capturing groups Backreferences Numbered groups Named groups Lookaround Look-ahead Positive look-ahead Negative look-ahead Look-behind Positive look-behind Negative look-behind Web Scraping With Python Slashes and Brackets in web scrapping Introduction to the scrapy Selector Setting up a Selector Selecting Selectors Extracting Data from a SelectorList CSS Locators Attributes in CSS Selectors with CSS Text Extraction Crawl A Classy Spider Docstrings Docstring formats Don't repeat yourself (DRY) Pass by assignment Immutable or Mutable? 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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! 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Generator expressions

In Python, generator expressions are similar to list comprehensions, but instead of creating a new list, they create a generator object. Generator expressions are useful when you need to iterate over a large dataset and don't want to store all of the results in memory at once.

Here's an example of a generator expression:

# A list of numbers
numbers = [1, 2, 3, 4, 5]
 
# A generator expression that yields the square of each number
squares_generator = (num**2 for num in numbers)
 
# Iterate over the generator and print each result
for square in squares_generator:
    print(square)

In this example, we define a list of numbers called numbers, and use a generator expression to create a generator object called squares_generator. The generator expression uses the syntax (expression for item in iterable) to yield the square of each number in the iterable. We then use a for loop to iterate over the generator and print each square.

The main difference between a list comprehension and a generator expression is that a list comprehension returns a list object that contains all of the computed values at once, while a generator expression returns a generator object that generates the values one at a time as you iterate over it. This means that generator expressions can be more memory-efficient, especially when working with large datasets.

Another advantage of generator expressions is that they can be used with functions that expect iterable objects, such as sum() and max(), without the need to create a separate list. For example:

# A generator expression that yields the square of each number
squares_generator = (num**2 for num in numbers)
 
# Calculate the sum of the squares using the built-in sum() function
sum_of_squares = sum(squares_generator)
 
# Print the result
print(sum_of_squares)

In this example, we use the generator expression to calculate the sum of the squares of the numbers in the numbers list, and pass the generator object directly to the built-in sum() function. The function iterates over the generator and computes the sum of the squared values without the need to create a separate list.

Essential built-in functions in Python Pythonic way of defining functions Pythonic way of using function parameters Pythonic way of returning values from functions Pythonic way of writing docstrings Pythonic way of using multiple parameters and return values Pythonic way of defining basic function ingredients Understanding the difference between global and local scope in Python Pythonic way of defining nested functions Pythonic way of returning functions Pythonic way of using nonlocal variables Python offers default and flexible arguments. Python supports lambda functions. Python supports anonymous functions. Python provides an introduction to error handling. Python includes the float() function. In Python Valid arguments must be passed for Python functions to work correctly. Passing invalid arguments to Python functions can result in errors. Pythons errors and exceptions system helps with debugging code. Pythons errors and exceptions system helps with debugging code. Iterating in Python: What it means and how it works Python for loops: A beginners guide to iteration Understanding the difference between iterators and iterables in Python Python iteration with next(): A beginners guide Python iteration made easy with the * operator Python dictionaries: A beginners guide to iteration Python file connections: A beginners guide to iteration Python iteration made easy with enumerate() Python iteration: Using enumerate() and unpack for efficient code Python iteration made easy with zip() Pythons zip() function can be used to combine multiple lists into a single iterable. Print the zip code with * using Python In Python In Python In Python In Python In Python In Python In Python Pythons nested loops allow for complex iterations. Using conditionals in comprehensions is a powerful feature of Python. Pythons dict comprehensions allow for concise and efficient creation of dictionaries. Pythons generator expressions offer a memory-efficient way to process large data sets. List comprehensions and generators are both powerful features of Python for data processing Using conditionals in generator expressions is a powerful way to create data sequences in Python. Function to generate sequence using a generator Utilizing a generator function for data generation Handling large data with efficient generators In python Employing pandas read_csv iterator to handle streaming data