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20/09/2025

⚡ PYTHON HACK – Use itertools.filterfalse to Invert Conditions

from itertools import filterfalse

nums = [10, 15, 20, 25, 30]
odds = list(filterfalse(lambda x: x % 2 == 0, nums))
print(odds) # [15, 25]

🔎 WHAT IT DOES
✅ Keeps items where the condition is False
✅ Acts like the opposite of filter()
✅ Works with any iterable

🛠 USAGE
— Extracting invalid records
— Separating data into matches vs non-matches
— Quick negative filtering in pipelines

💡 WHY IT MATTERS
⟶ Avoids clunky not conditions in filters
⟶ Cleaner and more expressive than list comprehensions with negatives
⟶ Ideal for validation and preprocessing steps

📚 TRIVIA
In Python 2.x, this was called itertools.ifilterfalse. In Python 3, renamed to filterfalse().

⚙️ COMPATIBILITY
✅ Python 3.x
⛔ Not directly available in Python 2.x without ifilterfalse

19/09/2025

⚡ EXCEL HACK – Return the Minimum Numeric Value That Meets Multiple Conditions

Need the lowest number for a region-product pair?

Use this formula:

=MIN(FILTER(C2:C100,(A2:A100="North")*(B2:B100="Product A")*ISNUMBER(C2:C100)))

🔎 WHAT IT DOES
✅ Filters column C where Region = "North" and Product = "Product A"
✅ Excludes blanks and text entries
✅ Returns the smallest numeric value from that subset

🛠 USAGE
— Find the lowest sales figure for a region
— Track minimum KPI scores by category
— Automate “worst-case” dashboards

💡 WHY IT MATTERS
⟶ Simpler than old MIN(IF(...)) array formulas
⟶ Ensures only numeric values are considered
⟶ Perfect for identifying underperformers

📚 TRIVIA
Pair this with MAX(FILTER(...)) to create a high-low range check.
Great for spotting performance spreads in a dataset.

⚙️ COMPATIBILITY
✅ Excel 365 / Excel 2021 (FILTER required)
⛔ Not supported in older versions without Ctrl+Shift+Enter arrays

📊 Olympics Sports Analytics — Post #6: Tracking Streaks & Performance GapsAthletes rarely compete just once — coaches w...
19/09/2025

📊 Olympics Sports Analytics — Post #6: Tracking Streaks & Performance Gaps

Athletes rarely compete just once — coaches want to know whether they’re improving, declining, or holding steady over time.

In this post, we use LAG() and LEAD() window functions to track trends in results:

✅ LAG() compares the current performance to the previous one
✅ LEAD() looks ahead for projections
✅ CASE logic interprets results as Improved, Declined, Same, or First appearance
✅ Partitioning ensures times (seconds) aren’t mixed with weights (kg)

Example insights:

First race → “First appearance”

Faster than last race → “Improved”

Slower than last race → “Declined”

Takeaway: LAG() and LEAD() turn raw numbers into trends — perfect for progress dashboards, scouting reports, or injury recovery tracking.

🔜 Next in Post #7: Per-segment splits analysis with UNPIVOT and JSON parsing to measure pace consistency.

19/09/2025

⚡ GOOGLE SHEETS HACK – Display Current Date in Custom Format

Want today’s date to show in a custom style (like 11-Sep-2025)? Use:

=TEXT(TODAY(),"dd-mmm-yyyy")

🔎 WHAT IT DOES
✅ Converts today’s date into a custom format
✅ Works with any valid date
✅ Updates automatically each day

🛠 USAGE
— Show dates neatly in reports
— Format logs for readability
— Match regional date styles

💡 WHY IT MATTERS
⟶ Gives full control over how dates appear
⟶ No need to adjust cell formatting manually
⟶ Makes reports professional and clear

📚 TRIVIA
You can use "mmmm yyyy" to show just the month and year (e.g., September 2025).

⚙️ COMPATIBILITY
✅ Works in Google Sheets and Excel

📊 Olympics Sports Analytics — Post #5: Qualification Rules — Top-N + Fastest LosersOlympic qualifiers don’t always work...
19/09/2025

📊 Olympics Sports Analytics — Post #5: Qualification Rules — Top-N + Fastest Losers

Olympic qualifiers don’t always work on simple “Top N per heat.” Often, athletes advance if they:
1️⃣ Finish in the top spots of their heat, and
2️⃣ Are among the fastest losers across all heats.

In this post, we built a query that combines both rules:

✅ ROW_NUMBER() to rank athletes within each heat
✅ Selected the top 2 per heat
✅ Added the next 2 fastest overall (fastest losers)
✅ Used indexes for fast lookups on performance

Example outcomes from our seeded 100m data:

Heat 1 → Noah Williams (USA), Liam Dupont (FRA)

Heat 2 → Sora Tanaka (JPN), Amina Kiptoo (KEN)

Fastest losers → Marco Bianchi (ITA), Elena Rossi (ITA)

Takeaway: With ROW_NUMBER() + TOP, you can reproduce the real Olympic qualification process — fair, flexible, and production-ready.

🔜 Next in Post #6: Detecting streaks and performance gaps using LAG() and LEAD().

19/09/2025

⚡ SQL HACK – Get the Nth Value in a Group with NTH_VALUE()

Need to fetch the 1st, 2nd, or Nth record within each group—like the 2nd highest sale per region?
Use NTH_VALUE() with window functions for precise control:

SELECT
region,
NTH_VALUE(sales, 2) OVER (
PARTITION BY region
ORDER BY sales DESC
ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
) AS second_highest_sale
FROM sales_data;

🔎 WHAT IT DOES
✅ Retrieves the Nth row within each ordered group
✅ Works for any column (numeric, date, text)
✅ Flexible for ranking-style analytics

🛠 USAGE
— 2nd best-selling product per region
— 3rd most recent login per user
— Nth milestone in performance tracking

💡 WHY IT MATTERS
⟶ Cleaner than subqueries for Nth values
⟶ Works seamlessly with partitions and ordering
⟶ Essential for advanced analytics and BI

📚 TRIVIA

NTH_VALUE(col, 1) = FIRST_VALUE()

Requires window frame specification for correct results

Alternative: ROW_NUMBER() + filtering for more control

⚙️ COMPATIBILITY
✅ SQL Server, PostgreSQL, Oracle, MySQL 8.0+
⛔ Not supported in older MySQL/SQLite

📊 Olympics Sports Analytics — Post #4: From Heats to FinalsNot every Olympic event goes straight to the final — most ha...
19/09/2025

📊 Olympics Sports Analytics — Post #4: From Heats to Finals

Not every Olympic event goes straight to the final — most have heats or qualifiers. In this post, we show how to model heats and advance athletes fairly.

✅ New event_entries table to store heat results
✅ Used ROW_NUMBER() to rank athletes within each heat
✅ Advanced the top 2 per heat to the next round
✅ Guaranteed data quality with constraints (no duplicate athlete per heat)

Example outcomes from our seeded data:

Heat 1 → Noah Williams (USA), Liam Dupont (FRA) advance

Heat 2 → Sora Tanaka (JPN), Amina Kiptoo (KEN) advance

Takeaway: ROW_NUMBER() is the clean, production-ready way to rank competitors and handle progression logic in tournaments.

🔜 Next in Post #5: Combine position-based qualifiers with time-based fastest losers to mimic real Olympic rules.

19/09/2025

⚡ PYTHON HACK – Use itertools.starmap for Multi-Argument Functions

from itertools import starmap
import operator

pairs = [(2, 5), (3, 7), (4, 9)]
results = list(starmap(operator.mul, pairs))
print(results) # [10, 21, 36]

🔎 WHAT IT DOES
✅ Applies a function to unpacked tuple arguments
✅ Cleaner than using map() with a lambda
✅ Works with any multi-argument function

🛠 USAGE
— Applying math operations to value pairs
— Transforming datasets with multiple fields
— Simplifying pipelines with tuple data

💡 WHY IT MATTERS
⟶ Eliminates the need for explicit unpacking
⟶ More concise and expressive than alternatives
⟶ Perfect for analytics and functional workflows

📚 TRIVIA
starmap() is part of Python’s itertools module. Its name comes from the “*” (star) unpacking operator it mimics.

⚙️ COMPATIBILITY
✅ Python 2.3+ and all Python 3.x versions
⛔ Input must be an iterable of tuples or sequences

🔒 Python Snippet: Password Generator (Production-Ready)Need strong, secure passwords for your apps or accounts?This snip...
19/09/2025

🔒 Python Snippet: Password Generator (Production-Ready)

Need strong, secure passwords for your apps or accounts?
This snippet uses Python’s secrets module to generate cryptographically safe passwords.

✅ Secure against brute-force attacks
✅ Configurable length & symbols
✅ Includes type hints & error handling
✅ Perfect for account setups, API keys, and authentication

💡 Example Output:
✅ Strong Password: v9@NqL7*Za1!rX2Q

A must-have utility for real-world projects where security matters most 🚀

19/09/2025

⚡ EXCEL HACK – Return the Maximum Numeric Value That Meets Multiple Conditions

Need the highest number for a region-product pair?

Use this formula:

=MAX(FILTER(C2:C100,(A2:A100="North")*(B2:B100="Product A")*ISNUMBER(C2:C100)))

🔎 WHAT IT DOES
✅ Filters column C where Region = "North" and Product = "Product A"
✅ Ignores blanks and text entries
✅ Returns the largest numeric value from that subset

🛠 USAGE
— Find peak sales for a product in a region
— Track maximum KPI under conditions
— Build dashboards showing best performance

💡 WHY IT MATTERS
⟶ Cleaner than legacy MAX(IF(...)) array formulas
⟶ Ensures only numeric values are considered
⟶ Perfect for high-performance analytics

📚 TRIVIA
Pair with MIN(FILTER(...)) to get the lowest numeric value under the same conditions.
Great for quick high-low comparisons in reports.

⚙️ COMPATIBILITY
✅ Excel 365 / Excel 2021 (FILTER required)
⛔ Not supported in older Excel without Ctrl+Shift+Enter arrays

19/09/2025

⚡ GOOGLE SHEETS HACK – Get Today’s Date and Time Together

Want to display the current date and exact time in one cell? Use:

=NOW()

🔎 WHAT IT DOES
✅ Returns both today’s date and the current time
✅ Updates automatically whenever the sheet recalculates
✅ Works in any cell without manual typing

🛠 USAGE
— Timestamp dashboards
— Track log entries in real-time
— Monitor deadlines or events

💡 WHY IT MATTERS
⟶ Keeps sheets always time-aware
⟶ Eliminates manual updates
⟶ Perfect for reports and trackers

📚 TRIVIA
Use =TODAY() if you only need the date without the time.

⚙️ COMPATIBILITY
✅ Works in Google Sheets and Excel

🚀 JavaScript Snippet — Find Difference of Two ArraysWhen working with data, you often need to compare two lists and figu...
19/09/2025

🚀 JavaScript Snippet — Find Difference of Two Arrays

When working with data, you often need to compare two lists and figure out what’s missing or unique to one of them.
Here’s a clean modern vanilla JS approach using Set for efficient lookups:

const difference = (a, b) => {
const setB = new Set(b);
return a.filter(x => !setB.has(x));
};

✨ Why this is useful:

🔍 Find items present in one list but not in another

📊 Compare datasets (e.g., users, products, IDs)

⚡ O(n) performance with Set instead of nested loops

Example Output:

difference([1,2,3,4], [3,4,5,6]) → [1,2]

difference([3,4,5,6], [1,2,3,4]) → [5,6]

difference(["apple","banana","cherry"], ["banana","date"]) → ["apple","cherry"]

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