4 Pandas Tricks That Most People Don’t Know

They’re Using Pandas Wrong — Are You?

4 Pandas Tricks That Most People Don’t Know

If you’ve worked with Python for data analysis, chances are you’ve used Pandas. It’s powerful, flexible, and… sometimes just plain frustrating.

Most people use Pandas the “brute force” way — loading data, running loops, and writing verbose filtering statements that make your notebook look like a spaghetti mess. But Pandas has hidden gems — elegant, often-overlooked features that can save you hours of debugging, cleaning, and performance bottlenecks.

In this article, I’ll share 4 powerful Pandas tricks that most people don’t know — and that you’ll wish you’d learned sooner. These aren’t the usual groupby or merge tutorials. We’re diving deeper.

Let’s level up your Pandas game.

1. Use .at[] and .iat[] Instead of .loc[] and .iloc[] (When You Can)

We all love .loc[] and .iloc[] for accessing rows and columns. But did you know that .at[] and .iat[] are faster alternatives?

• .at[]: Label-based scalar accessor — much faster for accessing or setting a single value.
• .iat[]: Integer-position based scalar accessor — also much faster.

Why does this matter? Performance. Especially in tight loops or large datasets.

Example:

# Slow (uses .loc) 
df.loc[1000, 'price'] = 299 
 
# Faster (uses .at) 
df.at[1000, 'price'] = 299 
 
# Same for integer positions 
df.iat[1000, 2] = 299

Use .at[] or .iat[] only when working with a single cell. For multiple values or rows, stick with .loc[] or .iloc[].

2. Use query() for Cleaner Filtering

Pandas’ traditional filtering can quickly get unreadable, especially with multiple conditions.

Enter query() — a method that lets you filter rows using a string expression. It reads like plain English and keeps your code clean.

Traditional vs. Query Style

# Traditional 
df[(df['price'] > 100) & (df['category'] == 'books')] 
 
# Cleaner with query() 
df.query("price > 100 and category == 'books'")

The best part? It supports variables too.

threshold = 100 
df.query("price > @threshold")

query() shines in notebooks and quick analysis. Just be cautious if your column names contain spaces or special characters — wrap them in backticks ( `column name` ).

3. Map Multiple Columns at Once with assign()

Most people use df['new_col'] = ... to add one column at a time. But what if you want to chain transformations or add multiple columns elegantly?

That’s where assign() shines.

df = df.assign( 
    revenue = df['price'] * df['quantity'], 
    profit = lambda x: x['revenue'] - x['cost'] 
)

• You can add multiple columns at once.
• You can use lambdas to reference the updated DataFrame in-line.
• You can chain it after other operations like groupby() or query().

Real-World Example:

df.query("category == 'electronics'") \ 
  .assign( 
      revenue = lambda x: x['price'] * x['units_sold'], 
      margin = lambda x: (x['revenue'] - x['cost']) / x['revenue'] 
  )

Use assign() when doing transformation pipelines. It keeps your logic tidy and functional — great for readability and reproducibility.

4. Use explode() to Normalize List-Like Data

Have a column with list-like values? Maybe from JSON, API data, or groupby().agg(list)?

Instead of manually iterating, just use explode() to flatten it.

Example:

data = { 
    'order_id': [1, 2], 
    'items': [['apple', 'banana'], ['orange']] 
} 
df = pd.DataFrame(data) 
 
df.explode('items')

Output:

| order\_id | items  | 
| --------- | ------ | 
| 1         | apple  | 
| 1         | banana | 
| 2         | orange |

This is incredibly useful when dealing with nested or denormalized data, especially from NoSQL databases or REST APIs.

Combine explode() with json_normalize() or .apply(pd.Series) to clean up deeply nested data structures.

Bonus: .pipe() for Cleaner Pipelines

Want to chain custom functions like you do with groupby() or assign()?

Use .pipe() to create a functional pipeline.

def add_profit(df): 
    df['profit'] = df['revenue'] - df['cost'] 
    return df 
 
def high_margin(df, min_margin=0.2): 
    return df[df['margin'] > min_margin] 
 
df.pipe(add_profit).pipe(high_margin, min_margin=0.3)

This makes your transformations modular, testable, and elegant.

Final Thoughts

Pandas is deceptively deep. On the surface, it’s intuitive — but behind the scenes, it has powerful tools that are criminally underused.

Here’s a quick recap of what you just learned:

• Use .at[] and .iat[] for faster scalar access.
• Filter like a human using query() — it’s clean and readable.
• Chain column creation elegantly with assign().
• Flatten list-like data with explode() in a single line.
• Use .pipe() to make your transformations composable.

Mastering these techniques won’t just make you write better Pandas code — it’ll make you think more like a data engineer and less like a spreadsheet user.

Want to Go Deeper?

• Refactor one of your messy Pandas scripts using at least two tricks from this article.
• Bookmark this guide and come back the next time your DataFrame starts misbehaving.
• Share this article with that one teammate who really needs to stop writing nested loops in Pandas.

You’re not just writing code — you’re shaping data workflows. Let’s do it better.

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