Python vs SQL: Which is Best for Querying and Cleaning Data?

When working with data, we are familiar with two tools: SQL and Python. Both are important for data professionals, but they serve different purposes. So let me break down which one should you use for querying and cleaning data.

Querying Data: SQL is the best. reasons is that:
Optimized for Databases – SQL is built specifically for querying structured data in relational databases (PostgreSQL, MySQL, BigQuery, etc.).
Faster Queries – Databases are optimized for SQL, making it much faster than Python for filtering, aggregating, and joining tables.
Simple Syntax – for example when one needs sales data from last month? SQL is direct: SELECT customer_id, SUM(amount) FROM sales WHERE date >= '2024-01-01' GROUP BY customer_id;
Works with big Datasets – SQL databases handle billions of rows efficiently, unlike Python, which struggles with memory.

When to use Python for querying

Unstructured Data – If your data is in JSON, APIs, or web scraped, Python (with requests + pandas) is more flexible.
Advanced Calculations – SQL can do math, but Python (NumPy, SciPy) is better for complex statistics or machine learning prep.

Cleaning Data: Python is More Powerful Why Python is the best for Data Cleaning
More Flexible Transformations – SQL can filter and aggregate, but Python (pandas) excels at:

Handling missing values (df.fillna())

Regex-based text cleaning (df.str.replace())

Complex reshaping (pivot_table, melt)

Custom functions (apply lambda logic easily)

Better for Messy Data – CSV files, Excel sheets, and semi-structured data are easier to clean in Python.
Automation & Reproducibility – Python scripts can clean data the same way every time.

When to use SQL for Cleaning

Basic Filtering & Deduplication – SQL can remove duplicates (DISTINCT), filter rows (WHERE), and simple transformations (CASE WHEN).
Database-Level Cleaning – If your data lives in a database, cleaning it there avoids extra steps.

lets see examples for cleaning in Python vs SQL
Python(pandas):
df['email'] = df['email'].str.lower().str.strip() # Clean emails
df.drop_duplicates(inplace=True) # Remove duplicates
SQL:
UPDATE customers
SET email = LOWER(TRIM(email)); -- Clean emails

DELETE FROM customers
WHERE **row_id **NOT IN (
SELECT MIN(row_id)
FROM customers
GROUP BY email); -- Remove duplicates

3. Performance & Scalability

SQL is faster for querying large datasets (thanks to database optimizations like indexing).
Python (Pandas) can slow down with >1M rows unless you use optimized libraries like Dask or Polars.

Best Practice: Do heavy filtering/aggregation in SQL first, then refine in Python.

In my opinion for the best analyst you must learn to use both
step 1: pull data efficiently with SQL.
Step 2. clean and analyze further with Python.
an example in a workflow:
SQL- **
-- Fast filtering & aggregation
**SELECT user_id, COUNT() as purchases
**FROM* transactions
GROUP BY user_id;

python -
-- Python: Advanced cleaning & visualization
df = pd.read_sql_query("SELECT * FROM clean_data", engine)
df['purchase_category'] = df.apply(lambda x: categorize(x), axis=1)
df.plot(kind='bar') # Visualize

In Conclusion