I’ve been working with Alteryx on moderately large datasets, and performance starts to slow down as workflows get more complex.

Looking for practical approaches others are using to:

• Reduce processing time
• Handle memory limitations
• Optimize joins and transformations

Would be helpful to understand what’s working in real-world scenarios.

I’m working with a Python dictionary and need to replace all None values with an empty string "".

For example:

mydict = {
    "name": "Alice",
    "age": None,
    "city": "New York",
    "email": None
}

I started with:

for k, v in mydict.items():
    if v is None:
        # update value here?

What’s the correct and cleanest way to modify the dictionary in place while iterating?

Is it safe to update values directly inside the loop, or is there a more Pythonic approach (e.g., dictionary comprehension)?

Would appreciate best-practice suggestions.