List Comprehension III - If Else If

Using if-else Statements in Python List Comprehensions

Using if-else Statements in Python List Comprehensions


If-else statements are a fundamental control structure in Python, allowing the code to execute different actions based on whether a certain condition is met. Combining this feature with list comprehensions can create highly dynamic and efficient code. In this blog post, we'll explore some complex examples of using if-else statements within list comprehensions.

Assigning Values Based on a Condition

One common use case is assigning different values based on a condition. Let's create a new list where each element is determined by the parity of the corresponding element in the original list:


old_list = [1, 2, 3, 4, 5]
new_list = ['Even' if i % 2 == 0 else 'Odd' for i in old_list]
print(new_list)  # Outputs: ['Odd', 'Even', 'Odd', 'Even', 'Odd']

In this example, we assign 'Even' if the number is even, and 'Odd' if the number is odd.

Performing Different Operations Based on a Condition

Another use case is to perform different operations on the list elements based on a condition. For example, let's square the number if it's odd, and halve it if it's even:


old_list = [1, 2, 3, 4, 5]
new_list = [i**2 if i % 2 != 0 else i / 2 for i in old_list]
print(new_list)  # Outputs: [1, 1.0, 9, 2.0, 25]

Multiple Conditions

If-else statements within list comprehensions can handle multiple conditions as well. Let's assign 'Positive', 'Negative', or 'Zero' to a new list based on the values of the old list:


old_list = [-2, -1, 0, 1, 2]
new_list = ['Positive' if i > 0 else 'Negative' if i < 0 else 'Zero' for i in old_list]
print(new_list)  # Outputs: ['Negative', 'Negative', 'Zero', 'Positive', 'Positive']

In this example, we use a nested if-else statement to handle the three possible cases.

If-else statements can undoubtedly add significant flexibility to your list comprehensions. But remember, while they make your code more powerful, they can also make it more complex and harder to read if overused. Always balance between power and readability when writing code.

Comments

Post a Comment

Popular posts from this blog

Drawing Tables with ReportLab: A Comprehensive Example

Image
Tables play an integral part in visualizing and summarizing complex data in a clear, readable format. ReportLab offers a powerful object called `Table` for rendering data in table form within a PDF. By pairing this with `TableStyle`, users can customize tables to fit any styling requirements. Let's break down an example to understand the power and flexibility of ReportLab's table creation capabilities. The first step is to know your table and cast it to a list of list.  I didn't know how to do this off the top of my head, so some quick searching gave me the results! It is a common task for ReportLab users so there are many example out there.  Here is my example:  #Start working with the table budgetinlist = [budget.columns.tolist()] + budget.values.tolist() # Format numbers with commas for i in range(1, len(budgetinlist)):     for j in range(1, len(budgetinlist[i])):         try:             budgetinlist[i][j] = ...
Read more

Blog Topics

Image
This platform aims to systematically curate and preserve my favored Python techniques for future reference. This digital journal, accessible globally wherever an internet connection is available, is a convenient and effective learning tool.  Going down a wooded path at night, alone, with your trusted companion by your side, can be scary and daunting.  However, with support from your friends, documenting your path (curating a code base like a blog), and continuously working on the code base can make the process appear as a walk in the park!  In a world dominated by sophisticated language models like OpenAI's GPT, blogging may seem like a relic of the past. But here's a nuanced perspective: this blog serves as an innovative instrument for both teaching and mastering new programming languages. It's about immersing in syntax, applying it, and then translating the information into layman's terms for broader understanding. Each topic presented here is elaborated in a separate...
Read more

fillna

Image
Handling Missing Data with Pandas: A Comprehensive Guide Handling Missing Data with Pandas: A Comprehensive Guide Dealing with missing data is an essential part of the data cleaning process in Python programming. The Pandas library provides various methods to fill or drop missing values, depending on the nature of the data and the desired outcome. In this guide, we'll explore different techniques to handle missing data, including hard-coded values, filling specific columns, forward fill, and using rolling averages. 1. Filling All Missing Values with a Fixed Number You can simply use the fillna() function to replace all NaNs with a specific value: import pandas as pd df = pd.DataFrame({'A': [1, 2, None], 'B': [None, 5, 6]}) df.fillna(0) 2. Filling Specific Columns with Different Values df.fillna({1: 0.5, 2: 0}) 3. Forward Fill Method df.fillna(method='ff...
Read more