Build on your SQL & BI skills / CORE PATH
DataFrames through a SQL lens
Use labeled tabular operations with explicit data types.
Map WHERE → filtering, GROUP BY → groupby and SELECT → column projection.
Understand the idea
Inspect a sample, shape, dtypes and missingness before choosing a transformation. Keep a raw copy and a small typed contract so coercion failures remain visible.
When comparing pandas with SQL or Power Query, use identical row grain, null rules and grouping keys. Reconciliation is more useful than a screenshot of a successful notebook cell.
What to learn
- Inspect schema and null patterns before cleaning.
- Use vectorized transformations and groupby.
- Retain raw values when coercing dates or numbers.
Practice on your own data
Reproduce ledger category totals with pandas and compare them with your pure-Python result.
Practice this lesson in the Python lab →
Both outputs agree; explain how missing groups and numeric conversion are handled.
Prerequisites
Video and playlist companions
Choose one explanation for the concept, then attempt the practice. These are topic mappings, not a claim that every linked lesson covers every outcome.
تحليل البيانات في بايثون (الدورة المصغرة) | Python for Data Analysis (Crash Course)
Loads YouTube on request. Open on YouTube ↗ if the owner restricts embedding.
Python for Data Analysis
Loads YouTube on request. Open on YouTube ↗ if the owner restricts embedding.
Data Exploration and Preparation
Loads YouTube on request. Open on YouTube ↗ if the owner restricts embedding.
Sources and further reading
- pandas · getting started tutorials ↗Official documentation
- تحليل البيانات في بايثون (الدورة المصغرة) | Python for Data Analysis (Crash Course) ↗User-selected YouTube playlist
- Python for Data Analysis ↗User-selected YouTube playlist
- Data Exploration and Preparation ↗User-selected YouTube playlist
Track progress on this topic → · Markdown source ↗