Pandas vs SQL: A Rosetta Stone

It's **Day 102**. Today we bridge the gap between SQL and the most important library in Data Science: **Pandas**.

The Translation Table

| SQL Command | Pandas Equivalent |

|-------------|-------------------|

| `SELECT *` | `df` |

| `SELECT col1, col2` | `df[['col1', 'col2']]` |

| `WHERE col1 = 'val'` | `df[df['col1'] == 'val']` |

| `GROUP BY col1` | `df.groupby('col1')...` |

| `ORDER BY col1` | `df.sort_values('col1')` |

| `LIMIT 5` | `df.head(5)` |

A Real Example

In SQL:

SELECT name FROM users WHERE age > 25 LIMIT 5;

In Python (Pandas):

import pandas as pd

df = pd.read_csv('users.csv')

# The translation:

result = df[df['age'] > 25][['name']].head(5)

print(result)

Why Pandas?

Pandas allows you to treat data as a "Table" (Dataframe) in memory. It's incredibly fast for mathematical operations and much more flexible than writing raw Python loops.

Your Task for Today

Rewrite a basic SQL `SELECT` and `WHERE` query into a Pandas line of code.

*Day 103: The Pandas DataFrame Explained.*