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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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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type()


Python is a dynamic, high-level programming language that offers a range of built-in functions to perform various operations. One of the most fundamental built-in functions in Python is "type()". This function is used to determine the type of an object in Python. In this article, we will take a closer look at the type() function, what it does, and how it can be used in Python programming.

What is the type() function in Python?

In Python, the type() function is used to determine the type of an object. It is a built-in function that takes an object as its argument and returns the type of that object. The type() function can be used to check the type of any Python object, such as variables, functions, lists, dictionaries, classes, and modules.

How does the type() function work?

The type() function works by taking an object as its argument and returning its type. The syntax for using the type() function is as follows:

 

type(object)

Here, "object" is the argument that is passed to the function, and type() returns the type of the object.

Here are some examples to illustrate how the type() function works:

  1. Checking the type of a string variable:
my_string = "Hello, World!"
print(type(my_string))

In this example, we define a string variable called "my_string" and then use the type() function to check its type. The output of the above code will be:

<class 'str'>

  1. Checking the type of a list:
my_list = [1, 2, 3, 4, 5]
print(type(my_list))

In this example, we define a list called "my_list" and then use the type() function to check its type. The output of the above code will be:

<class 'list'>

  1. Checking the type of a function:
def my_function():
    pass
 
print(type(my_function))

In this example, we define a function called "my_function" and then use the type() function to check its type. The output of the above code will be:

<class 'function'>

  1. Checking the type of a class:
class MyClass:
    pass
my_instance = MyClass()
print(type(my_instance))
 
In this example, we define a class called "MyClass" and then create an instance of the class called "my_instance". We then use the type() function to check the type of the instance. The output of the above code will be:

<class '__main__.MyClass'>

Conclusion

In conclusion, the type() function is a fundamental built-in function in Python that is used to determine the type of an object. It takes an object as its argument and returns the type of that object. The type() function can be used to check the type of any Python object, such as variables, functions, lists, dictionaries, classes, and modules. Understanding the type() function is essential for Python programmers as it helps in writing efficient, error-free code.

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