Pandas is a powerful and easy-to-use data analysis and data manipulation library for Python. It's built on top of NumPy and is widely used in data science, machine learning, and data analysis tasks.

Pandas is a powerful and easy-to-use data analysis and data manipulation library for Python. It's built on top of NumPy and is widely used in data science, machine learning, and data analysis tasks.

Pandas is a powerful and easy-to-use data analysis and data manipulation library for Python. It's built on top of NumPy and is widely used in data science, machine learning, and data analysis tasks.

You can visit the official NumPy website by clicking the link below: 🔗 https://numpy.org The site includes: Documentation Tutorials Installation instructions Release notes Community links

Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free of charge access to computing resources, including GPUs and TPUs. Colab is especially well suited to machine learning, data science, and education. Yes. Colab is free of charge to use. https://colab.research.google.com

Built-in Data Types In programming, data type is an important concept. Variables can store data of different types, and different types can do different things.

Python allows for user input. That means we are able to ask the user for input. The method is a bit different in Python 3.6 than Python 2

Print Statement and How to produce output

A list is an ordered, mutable collection of elements. A tuple is an ordered, immutable collection of elements. A set is an unordered collection of unique elements. A dictionary is an unordered collection of key-value pairs.

Following are the common string operations that can be performed in Python: Concatenation of two or more strings. Extracting or slicing partial strings from string values. Adding or removing spaces. Converting to lower or upper case

Arithmetic operator Comparison operator Assignment operator Logical operator Bitwise operator Membership operator Identity operator

append() clear() copy() count() extend() index() insert() pop() remove() reverse() sort()

tuple() Function. len() Function. count() Function. index() Function. sorted() Function. min() Function. max() Function. sum() Function.

In Python, a set is an unordered, mutable collection of unique elements. Mathematical set operations like union, intersection, and difference can be performed on them. Two different sets can be compared with the subset, superset, and disjoint relations. The members of a set must be hashable.

The process of converting the value of one data type (integer, string, float, etc.) to another data type is called Type Conversion. Python has two types of type conversion. Implicit Type Conversion. Explicit Type Conversion.

Indentation refers to the spaces at the beginning of a code line. Where in other programming languages the indentation in code is for readability only, the indentation in Python is very important. Python uses indentation to indicate a block of code.

The randrange() function is a built-in Python function used to generate random integers between a given start and stop value.

Type of Loops For Loop. A for loop in Python is used to iterate over a sequence (list, tuple, set, dictionary, and string). Flowchart: ... While Loop. The while loop is used to execute a set of statements as long as a condition is true. ... Nested Loop. If a loop exists inside the body of another loop, it is called a nested loop. Type of Conditional Statements There are 4 types of if statements: if, If else, Nested if else and If else Ladder. What is the if-else conditional structure? if else is a conditional statement, and they define conditions for their execution.

Python has a built-in module that you can use for mathematical tasks. The math module has a set of methods and constants.

Python date is an object that's used to represent the calendar date with no time of date, while a Python datetime object includes the date and time of day.

open() function is used to open the file in write mode. It is similar to the built-in open() function but provides additional options, such as specifying the encoding. The file is created with the specified text, and the success message is printed. import io # Using io.

Exception handling in Python is a process of resolving errors that occur in a program. This involves catching exceptions, understanding what caused them, and then responding accordingly. Exceptions are errors that occur at runtime when the program is being executed.

The collection Module in Python provides different types of containers. A Container is an object that is used to store different objects and provide a way to access the contained objects and iterate over them. Some of the built-in containers are Tuple, List, Dictionary, etc. In this article, we will discuss the different containers provided by the collections module.

A RegEx, or Regular Expression, is a sequence of characters that forms a search pattern. RegEx can be used to check if a string contains the specified search pattern.

* List comprehension offers a shorter syntax when you want to create a new list based on the values of an existing list. * Frozen set is just an immutable version of a Python set object. While elements of a set can be modified at any time, elements of the frozen set remain the same after creation. * The assert keyword lets you test if a condition in your code returns True, if not, the program will raise an AssertionError.

Reading from a CSV file is done using the reader object. The CSV file is opened as a text file with Python's built-in open() function, which returns a file object. This is then passed to the reader , which does the heavy lifting.

If you define the function yourself, it is a user-defined function. On the other hand, the Python function that come along with Python are known as in-built functions. All the functions apart from in-built functions and library functions come under the category of user-defined functions.

An iterator is an object that contains a countable number of values. An iterator is an object that can be iterated upon, meaning that you can traverse through all the values. Technically, in Python, an iterator is an object which implements the iterator protocol, which consist of the methods __iter__() and __next__().

Decorators and generators are powerful concepts in Python that enhance code reusability, readability, and performance. Decorators let you add functionality before and/or after function execution dynamically, while generators enable the generation of sequence values on the fly instead of loading them all at once.

Local variables exist only during the function's executions, while global variables remain in memory for the duration of the program. Local variables can be accessed only within the function or block where it is defined, whereas global variables are accessed throughout the entire program.

Python logging is a module that allows you to track events that occur while your program is running. You can use logging to record information about errors, warnings, and other events that occur during program execution. And logging is a useful tool for debugging, troubleshooting, and monitoring your program.

To work with map(), the lambda should have one parameter in, representing one element from the source list. Choose a suitable name for the parameter, like n for a list of numbers, s for a list of strings. The result of map() is an "iterable" map object which mostly works like a list, but it does not print.

It is an in-built module for implementing a queue in Python. You can use different functions available in the module to perform operations on a queue. Below is an example of implementing a queue with the help of a queue, along with the use of different functions.

1. isinstance() function returns True if the specified object is of the specified type, otherwise False. If the type parameter is a tuple, this function will return True if the object is one of the types in the tuple. 2. format() method formats the specified value(s) and insert them inside the string's placeholder. The placeholder is defined using curly brackets: {}. Read more about the placeholders in the Placeholder section below. format() method returns the formatted string. 3. timeit() method measure execution time of small code snippets. This module provides a simple way to time small bits of Python code. 4. round() function returns a floating point number that is a rounded version of the specified number, with the specified number of decimals. The default number of decimals is 0, meaning that the function will return the nearest integer. 5. slice() function returns a slice object. A slice object is used to specify how to slice a sequence. You can specify where to start the slicing, and where to end. You can also specify the step, which allows you to e.g. slice only every other item. 6. abs() function returns the absolute value of the specified number.

The zip() function returns a zip object, which is an iterator of tuples where the first item in each passed iterator is paired together, and then the second item in each passed iterator are paired together etc. If the passed iterables have different lengths, the iterable with the least items decides the length of the new iterator.

* The eval() function evaluates the specified expression, if the expression is a legal Python statement, it will be executed. * The exec() function executes the specified Python code. The exec() function accepts large blocks of code, unlike the eval() function which only accepts a single expression. * The repr() function returns a string containing a printable representation of an object.

The switch-case statement is not a built-in feature in Python, but it can be implemented using a combination of if-elif-else statements, classes, or using a dictionary that maps the cases to its corresponding behavior.

Python Object-Oriented Programming (OOP) is a programming paradigm that organizes code into objects, which are instances of classes. It allows for better code reuse, modularity, and organization.

Python is a high-level, interpreted programming language known for its simplicity, readability, and versatility. It supports multiple paradigms including procedural, object-oriented, and functional programming. Python is widely used in: 1. Web development 2. Data science and machine learning 3. Automation and scripting 4. Software development 5. Game and app development

Python NumPy (Numerical Python) is a powerful open-source library used for numerical computing. It provides support for: 1. Multidimensional arrays (ndarray) 2. Mathematical functions for operations on arrays 3. Linear algebra, Fourier transforms, and random number generation 4. Efficient performance through optimized C backend It is a core library for scientific computing and forms the foundation for many other libraries like Pandas, SciPy, TensorFlow, and scikit-learn. In one line: NumPy is a fundamental Python library for fast, efficient numerical and array-based computing.

Python NumPy (Numerical Python) is a powerful open-source library used for numerical computing. It provides support for: 1. Multidimensional arrays (ndarray) 2. Mathematical functions for operations on arrays 3. Linear algebra, Fourier transforms, and random number generation 4. Efficient performance through optimized C backend It is a core library for scientific computing and forms the foundation for many other libraries like Pandas, SciPy, TensorFlow, and scikit-learn. In one line: NumPy is a fundamental Python library for fast, efficient numerical and array-based computing.

Python NumPy (Numerical Python) is a powerful open-source library used for numerical computing. It provides support for: 1. Multidimensional arrays (ndarray) 2. Mathematical functions for operations on arrays 3. Linear algebra, Fourier transforms, and random number generation 4. Efficient performance through optimized C backend It is a core library for scientific computing and forms the foundation for many other libraries like Pandas, SciPy, TensorFlow, and scikit-learn. In one line: NumPy is a fundamental Python library for fast, efficient numerical and array-based computing.

Python NumPy (Numerical Python) is a powerful open-source library used for numerical computing. It provides support for: 1. Multidimensional arrays (ndarray) 2. Mathematical functions for operations on arrays 3. Linear algebra, Fourier transforms, and random number generation 4. Efficient performance through optimized C backend It is a core library for scientific computing and forms the foundation for many other libraries like Pandas, SciPy, TensorFlow, and scikit-learn. In one line: NumPy is a fundamental Python library for fast, efficient numerical and array-based computing.

Python NumPy (Numerical Python) is a powerful open-source library used for numerical computing. It provides support for: 1. Multidimensional arrays (ndarray) 2. Mathematical functions for operations on arrays 3. Linear algebra, Fourier transforms, and random number generation 4. Efficient performance through optimized C backend It is a core library for scientific computing and forms the foundation for many other libraries like Pandas, SciPy, TensorFlow, and scikit-learn. In one line: NumPy is a fundamental Python library for fast, efficient numerical and array-based computing.

Python NumPy (Numerical Python) is a powerful open-source library used for numerical computing. It provides support for: 1. Multidimensional arrays (ndarray) 2. Mathematical functions for operations on arrays 3. Linear algebra, Fourier transforms, and random number generation 4. Efficient performance through optimized C backend It is a core library for scientific computing and forms the foundation for many other libraries like Pandas, SciPy, TensorFlow, and scikit-learn. In one line: NumPy is a fundamental Python library for fast, efficient numerical and array-based computing.

Python NumPy (Numerical Python) is a powerful open-source library used for numerical computing. It provides support for: 1. Multidimensional arrays (ndarray) 2. Mathematical functions for operations on arrays 3. Linear algebra, Fourier transforms, and random number generation 4. Efficient performance through optimized C backend It is a core library for scientific computing and forms the foundation for many other libraries like Pandas, SciPy, TensorFlow, and scikit-learn. In one line: NumPy is a fundamental Python library for fast, efficient numerical and array-based computing.