Python for Data Analysts

🔰 Learning Python for Data Analysis and Visualization ⏱ 21 Hours 📦 110 Lessons Learn python and how to use it to analyze,visualize and present data. Includes tons of sample code and hours of video! Taught By: Jose Portilla Download Full Course: https://t.me/pythonanalyst/26 Download All Courses: https://t.me/DataAnalystInterview

Python Generators In Python, a generator is a function that returns an iterator that produces a sequence of values when iterated over. Generators are useful when we want to produce a large sequence of values, but we don't want to store all of them in memory at once. There is a lot of complexity in creating iteration in Python; we need to implement iter() and next() method to keep track of internal states. It is a lengthy process to create iterators. That's why the generator plays an essential role in simplifying this process. If there is no value found in iteration, it raises StopIteration exception.

Example: Add 10 to argument a, and return the result: x = lambda a : a + 10 print(x(5)) Output will be 15

Python Lambda: A lambda function is a small anonymous function. A lambda function can take any number of arguments, but can only have one expression. lambda arguments : expression The expression is executed and the result is returned. Use lambda functions when an anonymous function is required for a short period of time.

Syntax: newlist = [expression for item in iterable if condition == True] The return value is a new list, leaving the old list unchanged. The condition is like a filter that only accepts the items that valuate to True. The condition is optional and can be omitted. The iterable can be any iterable object, like a list, tuple, set etc. The expression is the current item in the iteration, but it is also the outcome, which you can manipulate before it ends up like a list item in the new list.

👉 List comprehensions: List comprehension offers a shorter syntax when you want to create a new list based on the values of an existing list. Example: Based on a list of fruits, you want a new list, containing only the fruits with the letter "a" in the name. Without list comprehension you will have to write a for statement with a conditional test inside: fruits = ["apple", "banana", "cherry", "kiwi", "mango"] newlist = [] for x in fruits:   if "a" in x:     newlist.append(x) print(newlist) With list comprehension you can do all that with only one line of code: fruits = ["apple", "banana", "cherry", "kiwi", "mango"] newlist = [x for x in fruits if "a" in x] print(newlist)

What is Python Loop? When you want some statements to execute a hundred times, you don’t repeat them 100 times. Think of when you want to print numbers 1 to 99. Or that you want to say Hello to 99 friends. In such a case, you can use loops in python. Here, we will discuss 4 types of Python Loop: Python For Loop Python While Loop Python Loop Control Statements Nested For Loop in Python Python While Loop A while loop in python iterates till its condition becomes False. In other words, it executes the statements under itself while the condition it takes is True. Python For Loop Python for loop can iterate over a sequence of items. The structure of a for loop in Python is different than that in C++ or Java. That is, for(int i=0;i<n;i++) won’t work here. In Python, we use the ‘in’ keyword. Nested for Loops in Python You can also nest a loop inside another. You can put a for loop inside a while, or a while inside a for, or a for inside a for, or a while inside a while. Or you can put a loop inside a loop inside a loop. You can go as far as you want. Loop Control Statements in Python Sometimes, you may want to break out of normal execution in a loop. For this, we have three keywords in Python- break, continue, and Python

👉 What is Python Data Structures? You can think of a data structure as a way of organizing and storing data such that we can access and modify it efficiently. We have primitive data types like integers, floats, Booleans, and strings. 👉 What is Python List? A list in Python is a heterogeneous container for items. This would remind you of an array in C++, but since Python does not support arrays, we have Python Lists. 👉 Python Tuple This Python Data Structure is like a, like a list in Python, is a heterogeneous container for items. But the major difference between the two (tuple and list) is that a list is mutable, but a tuple is immutable. This means that while you can reassign or delete an entire tuple, you cannot do the same to a single item or a slice. 👉 Python Dictionaries Finally, we will take a look at Python dictionaries. Think of a real-life dictionary. What is it used for? It holds word-meaning pairs. Likewise, a Python dictionary holds key-value pairs. However, you may not use an unhashable item as a key. To declare a Python dictionary, we use curly braces. But since it has key-value pairs instead of single values, this differentiates a dictionary from a set.

Python Constructs 1. Functions in Python A function in Python is a collection of statements grouped under a name. You can use it whenever you want to execute all those statements at a time. You can call it wherever you want and as many times as you want in a program. A function may return a value. 2. Classes in Python Python is an object-oriented language. It supports classes and objects. A class is an abstract data type. In other words, it is a blueprint for an object of a certain kind. It holds no values. An object is a real-world entity and an instance of a class. 3. Modules in Python Python module is a collection of related classes and functions. We have modules for mathematical calculations, string manipulations, web programming, and many more. 4. Packages in Python Python package is a collection of related modules. You can either import a package or create your own. Python has a lot of other constructs. These include control structures, functions, exceptions, etc

Data analysis with Python Important Topics 😄❤️