Data Science Comparison Cards: 42 Side-by-Side Guides
About these cards
42 comparison cards: 13 Python basics (py01–py13) plus 28 chapter cards (ch01–ch28) and ch65 time-series. Each card puts related tools side by side with a Blinkit Maharashtra teaching example. Scan the QR for the full lesson.
या cards बद्दल
४२ comparison cards: Python basics (py01–py13) + chapter cards (ch01–ch28) + ch65. संबंधित साधने बाजूने-बाजूने, Blinkit Maharashtra उदाहरणासह. पूर्ण lesson साठी QR स्कॅन करा.
इन cards के बारे में
42 comparison cards: 13 Python basics (py01–py13) plus 28 chapter cards (ch01–ch28) and ch65 time-series. Each card puts related tools side by side with a Blinkit Maharashtra teaching example. Scan the QR for the full lesson.
Contents
Python basics
- Card P1: Jupyter Notebook vs a .py script for first Python practice
- Card P2: Python int vs float vs str for money and labels
- Card P3: Python string: indexing vs slicing
- Card P4: Python list vs tuple: which one for Blinkit store prices?
- Card P5: Python: chained if/elif vs nested if
- Card P6: Python for-loop vs list comprehension
- Card P7: Python: return vs print inside a function
- Card P8: Python: try/except vs if-check before you convert
- Card P9: Python: csv.DictReader vs json.load
- Card P10: Python: pip install vs import
- Card P11: Python: plain function vs method on a class
- Card P12: NumPy: boolean mask filter vs Python if inside a loop
- Card P13: Mini project: notebook vs .py script before pandas
Data Science chapters
- Card 1: Excel vs Power BI vs Python for Blinkit GMV
- Card 2: Which packages for this Data Science course?
- Card 3: if vs for vs function in a Blinkit notebook
- Card 4: Python for-loop vs NumPy vectorised math
- Card 5: head vs info vs dtypes on a Blinkit extract
- Card 6: loc vs iloc vs plain column select
- Card 7: value_counts vs groupby sum for Blinkit
- Card 8: merge how= left vs inner vs outer
- Card 9: dropna vs fillna vs drop_duplicates
- Card 10: Naive datetime vs IST localize vs UTC
- Card 11: melt vs pivot_table for Blinkit summaries
- Card 12: describe vs crosstab vs corr (with caution)
- Card 13: Line vs hist for Blinkit order charts
- Card 14: countplot vs barplot vs boxplot
- Card 15: Sorted bar vs pie vs chart junk
- Card 16: Raw extract vs cleaned table vs summary export
- Card 17: Practice types: filter vs groupby vs merge
- Card 18: Interview classic: concat vs merge vs join ideas
- Card 19: Cheat sheet blocks: cleaning vs pandas vs viz
- Card 20: Glossary trio: Series vs DataFrame vs grain
- Card 21: Mean vs median vs mode for Blinkit ₹ amounts
- Card 22: Sharing analysis: to_csv vs to_excel vs savefig PNG
- Card 23: Chained assign vs .loc vs .copy()
- Card 24: Filter early: SQL WHERE vs pandas .loc on a Blinkit pull
- Card 25: pandas rank ties: method="dense" vs method="min"
- Card 26: JSON to pandas: pd.DataFrame vs pd.json_normalize
- Card 27: Jupyter + Git: commit with outputs vs clear outputs first
- Card 28: pandas merge: how="inner" vs how="left" on Blinkit orders + stores
- Card 65: Sales forecast: naive last-value vs lag + LinearRegression
Python basics
Data Science chapters
