Chapter 1: Transforming Your Pandas Practices

Greetings, fellow Python lovers! I'm Gabe A, and I'm thrilled to share some pivotal lessons I've gathered over my ten-year journey in Python and data analytics. My goal is to unveil "10 Essential Practices I Stopped Following in Pandas After Learning from the Experts."

1. Avoiding Iteration for DataFrame Modifications

Gone are the days of iterating through rows for calculations! I've learned that vectorized operations are the key to faster and cleaner code. Here's how my approach has evolved:

# Old way

for index, row in df.iterrows():

df.at[index, 'total'] = row['quantity'] * row['price']

# New way

df['total'] = df['quantity'] * df['price']

2. Moving Beyond Lambdas for Clarity

Previously, I relied heavily on lambda functions, leading to cluttered code. Now, I prefer using the apply method with clearly defined functions for improved readability:

# Old way

df['total_discount'] = df.apply(lambda row: row['t