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Python for Data Analysts

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9. Example Projects - Part 02

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9. Example Projects - Part 01

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8. Data Visualization

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7. Working with Data Part 3

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6. Working with Data Part 2

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5. Working with Data Part 1

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4. Intro to Pandas

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3. Learning Numpy

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2. Setup

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1. Intro to Course and Python

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🔰 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

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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.

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Output of this code? x = lambda a, b, c : a + b + c print(x(5, 6, 2))

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What will be the output of this code in python? x = lambda a, b : a * b print(x(5, 6))

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Example: Add 10 to argument a, and return the result:

x = lambda a : a + 10
print(x(5))

Output will be 15

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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.

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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.

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👉 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)

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Data Visualization in Python with Dash.zip137.29 MB

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