Category: 100 Days of Code

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Charting Chapters and Chasing Genres With A Book Tracker App

Day 42 of 100 Days Coding Challenge: Python

Instead of starting a shiny new project file (and the inevitable spiral of new virtual environments), I decided to stick with yesterday’s book tracker and give it a little more love. Reading is one of my enduring passions—and also a data problem waiting to happen. Sure, I’ve used Excel for book logs, and I’ve even flirted with BI tools. But I want something more personal. More tailored. I want a program that doesn’t just track what I’ve read, but shows me how I’m reading.

The truth is, my reading speed shifts wildly. I can fly through The Hunger Games in a weekend, but with Ulysses, it feels like I’ve aged ten years by chapter three. Hemingway, despite his famously minimal style, still slows me down—not because of the vocabulary, but because of the silences between the lines. That kind of nuance doesn’t show up on a bar graph. But I want it to. I want to see the shape of my reading: genre shifts, reading slumps, maybe even gaps that whisper, “You never finished that biography, did you?” So today’s work is about evolving this project—bit by bit—into something that could actually help me change the way I read. I am going to make a Python book tracker with genre analysis.

Today’s Motivation / Challenge

This project matters because books shape who we are—and being able to map that influence with code feels like giving your brain a mirror. Today’s challenge is part introspection, part data design. How do you build a program that not only records what you read, but reveals patterns about you as a reader? That’s the kind of rabbit hole worth crawling into. Also, I finally fixed the bug that’s haunted me for days. Turns out it wasn’t a deep Python issue—it was just me calling .env instead of .venv. Classic user error.

Purpose of the Code (Object)

The script is designed to help you track your reading habits in a CSV-based log, with details like title, author, page count, genre, and the date you finished each book. It also lets you visualize your progress with bar charts and filter your reading by genre. And yes, now it can import data directly from a Notion-exported CSV file, so you’re not stuck entering everything manually like it’s 1994.

AI Prompt:

Improve a Python program that logs and visualizes books read, adds genre filtering, and supports importing from CSV. Ensure proper datetime handling for visualizations.

Functions & Features

  • Log books with title, author, pages, genre, and date
  • Import book data from a Notion-exported or custom CSV
  • Display reading progress by month
  • Filter and visualize reading stats by genre
  • Prevent duplicate entries using pandas

Requirements / Setup

  • Python 3.7+
  • Install these packages:

pip install pandas matplotlib

Minimal Code Sample

df = pd.read_csv(FILE_NAME)

df[‘Date Finished’] = pd.to_datetime(df[‘Date Finished’], errors=’coerce’)

df[‘Month’] = df[‘Date Finished’].dt.to_period(‘M’)

monthly_pages = df.groupby(‘Month’)[‘Pages’].sum()

monthly_pages.plot(kind=’bar’)

This reads your log, groups it by month, and shows how many pages you read each month.

Book Tracker App

Notes / Lessons Learned

A project like this isn’t just about writing code—it’s about designing your data. Today, I realized that if I want to categorize my books properly, I need to structure the genre field more carefully. “Science Fiction” could mean a dystopian classic or a wild space opera. So I’m thinking of building a two-level genre system: a broad Category for reporting (like Fiction or Memoir) and a Detailed Genre for personal insights (like Time Travel Romance or Existential Cyberpunk). This kind of structure is what we use in BI dashboards at work—it keeps the data tidy while allowing for nuance.

I also made peace with PowerShell today. The mysterious failure to activate my virtual environment? Yeah… I typed .env instead of .venv. It took three days, multiple Google searches, and a small existential crisis to realize I was chasing a typo. But hey, I’ll never forget it again.

Optional Ideas for Expansion

  • Create a two-tier genre system (Category + Subgenre)
  • Add a reading speed calculator (pages per day) based on start/end dates
  • Build a tag cloud of favorite authors or keywords

Day 41
Day 43

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Graphing My Way Through the Library With A Book Tracker App

Day 41 of 100 Days Coding Challenge: Python

Book Tracker Program (Day 41 – Day54)

I moved on from absolute-beginner apps to something a little more demanding. Over the past two weeks, most of what I built wasn’t flashy—it was practical. These small apps were about learning the exact functions I actually need.

The file organizer, for example, solves a very real problem: keeping files consistently formatted instead of turning my folders into abstract art. The PDF merger came from pure necessity. My scanner insists on scanning one page at a time, and finding a decent free merger online—one that doesn’t ask me to upload tax documents to the internet gods—turned out to be surprisingly difficult.

That’s when it clicked. With Python—and a bit of help from AI—I can build these functions myself. No subscriptions, no privacy risks, no compromises. So I decided to aim slightly higher and work on an app connected to something I genuinely care about. For my first “real” project, I chose a Book Tracker Program—because if I’m going to code for hours, it might as well involve books.

Introduction

I love reading. My idea of a good time is curling up with a 1,000-page fantasy epic and thinking, Yes, this is how I’ll spend the next two weeks. Naturally, the data nerd in me can’t resist tracking my progress. I’ve used everything from Excel spreadsheets to a system I call “Analog Notion” (also known as a notebook) to log my reads. But now I want more—stats, graphs, the whole dashboard treatment. I’ve seen those beautiful book journals online with charts showing how many pages were devoured each month, and I thought, “Why not me?” Today’s project is a humble beginning: a simple book tracker App in Python. It’s nowhere near as pretty as those Instagram-worthy bullet journals, but it’s a start. And hey, if Brandon Sanderson can write 400,000 words in a single book, I can write a few lines of code to track it.

Today’s Motivation / Challenge

Every January, I wonder how many books I actually read last year, and how many times I reread Mistborn instead of trying something new. This little tool is my attempt to turn that curiosity into data—and maybe guilt myself into broadening my literary horizons. Also, it’s a good excuse to code something that feels both nerdy and delightfully personal.

Purpose of the Code (Object)

This script lets you log the books you’ve finished, track how many pages you’ve read, and generate a basic bar chart showing your monthly reading habits. It’s not fancy yet, but it turns your pile of finished books into pretty bars on a graph—which is basically visual applause.

AI Prompt:

Write a Python script that logs books read (title, author, pages, and date finished), stores them in a CSV file, and displays a monthly bar graph of pages read using pandas and matplotlib.

Functions & Features

  • Add a finished book to your reading log
  • View your full list of logged books
  • Display a graph of how many pages you read per month

Requirements / Setup

pip install pandas matplotlib

Python 3.7+ is recommended (though any recent version should work).

Minimal Code Sample

df[‘Month’] = df[‘Date Finished’].dt.to_period(‘M’)

monthly_stats = df.groupby(‘Month’)[‘Pages’].sum()

monthly_stats.plot(kind=’bar’)

This groups books by month and creates a bar chart of pages read.

Book Tracker

Notes / Lessons Learned

Let me tell you the saga of activating a virtual environment in PowerShell. It started, as all good tales do, with an error message claiming the activation script didn’t exist—even though I was staring right at it. After the usual ritual of Set-ExecutionPolicy RemoteSigned -Scope CurrentUser, and trying the exact same command three times, it suddenly worked. I have no explanation. It was like summoning a reluctant ghost. Once I got the environment running, I entered four books from my holiday reading binge (December 23 – January 1). Manually. One by one. It’s not glamorous. The graph? Let’s call it “functional minimalism.” This is a bare-bones tool for now, but it has potential. Someday, I’d love to feed it directly from my Notion log and automate the whole thing. Until then, I type.

Optional Ideas for Expansion

  • Import book data automatically from a Notion export or CSV
  • Add genre filters to visualize trends (e.g., “Too much fantasy?”)
  • Track time spent reading, not just pages

Day 40
Day 42

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Pie Charts and Poor Spending Habits: A Colorful Confession

Day 40 of 100 Days Coding Challenge: Python

I’ve been tracking my personal expenses in Excel for years—part frugality, part paranoia, and part curiosity about where my coffee budget keeps disappearing. While rows and columns get the job done, they aren’t exactly thrilling.

So, I decided to give my budget a little makeover. I created a Python Expense Tracker App. Over the past couple of days, I’ve been fiddling with matplotlib, playing with lines and bars like a spreadsheet Picasso. Today, I thought, “Why not turn this into a pie chart?”—because nothing says responsible adulting like slicing your expenses into pizza-shaped regret. For this mini project, I spared myself the embarrassment of my actual spending and used a clean, innocent example instead. Baby steps.

Today’s Motivation / Challenge


We all love to believe we’re financially savvy… until we see it in a chart and realize we’ve spent more on takeout than utilities. Visualizing data is like turning on a light in a messy room—you suddenly see everything you were ignoring. Pie charts are a beginner-friendly way to practice working with visualizations, and let’s face it: they’re much easier to interpret than bar charts if you’re half-awake with your morning tea.

Purpose of the Code (Object)


This code creates a pie chart that shows how your expenses are split among categories like rent, groceries, and “miscellaneous,” which is often just code for impulse purchases. It helps turn numbers into visuals so you can actually understand where your money goes—without scrolling through endless rows in a spreadsheet.

AI Prompt: 


 Write a Python script that generates a pie chart of personal expenses using matplotlib.

Functions & Features

  • Breaks down expenses into categories.
  • Assigns colors to each slice for easier viewing.
  • Automatically calculates percentages and displays them on the chart.

Requirements / Setup

You’ll need:

pip install matplotlib

Minimal Code Sample

plt.pie(expenses, labels=categories, autopct=’%1.1f%%’)

plt.axis(‘equal’)  # Makes the pie chart look like a circle

This generates the core of your pie chart and ensures it’s not shaped like an egg.

Expense Tracker Pie Chart

Notes / Lessons Learned


The code itself wasn’t hard to wrangle—it’s the file names that got me. I confidently ran my script, only to watch Python stare at me blankly. Turns out I’d named the file incorrectly and had the right environment with the wrong target. It’s like dialing the right number but calling the wrong person. Also, adding more categories is easy, but unless you want all your slices to look the same shade of gray, you’ll need to manage your colors better. Next time, pulling data from a CSV file, like a bank statement, would make the tool far more practical—and possibly more shocking.

Optional Ideas for Expansion

  • Import expense data directly from a CSV file.
  • Add a legend to clarify which color means what (before purple becomes both rent and groceries).
  • Turn it into a simple GUI with category input for non-coders.

Day 39
Day 41

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Flattening the Curve… with Code

Day 39 of 100 Days Coding Challenges: Python

Ah, 2020. The year when “going viral” took on a whole new (and not-so-fun) meaning. I remember first hearing about COVID-19 on the news—some distant reports from China that felt like a world away. A blink later, and boom: the whole globe was upside down, hand sanitizer became currency, and toilet paper was basically gold.

Fast forward to today, and here I am, playing with data from that very same pandemic—except now, instead of panic-buying canned beans, I’m pulling country-specific COVID data from the internet and turning it into lovely little graphs using matplotlib to create my own COVID data visualization program. Over the past few days, I’ve been on a visual journey with this powerful library, and let me tell you: nothing makes data feel more satisfying than watching it turn into a line chart especially when that line isn’t going straight up.

The data source of the day is disease.sh, a surprisingly friendly API for something that deals with viruses. My plan? Fetch real-time data, choose a country, and see what the case numbers look like. And who knows? Maybe one day, this skill will sneak its way into my work life when nobody’s looking.

Today’s Motivation / Challenge

Today’s project is about turning chaos into clarity—something all programmers secretly dream of. There’s something almost poetic about visualizing a pandemic: it’s the difference between abstract panic and concrete trends. This project connects data science to real life, while giving your Python skills some real-world relevance. Bonus points if you feel like a data sorcerer by the end.

Purpose of the Code (Object)

This little script fetches historical COVID-19 case data for any country you choose and plots a neat line graph of daily new cases. It’s like Google Trends, but you made it yourself—with actual code and zero ads. The result? A quick, clear visual of what’s going on behind the numbers.

AI Prompt:

“Create a simple Python program that fetches COVID-19 case data from disease.sh and visualizes daily new cases using matplotlib.”

Functions & Features

  • Fetches COVID-19 historical case data for the last 30 days
  • Converts cumulative case data into daily new case counts
  • Plots the results using a simple, clean line chart

Requirements / Setup

pip install requests matplotlib

Minimal Code Sample

response = requests.get(“https://disease.sh/v3/covid-19/historical/USA?lastdays=30”)

data = response.json()

cases = list(data[‘timeline’][‘cases’].values())

new_cases = [cases[i] – cases[i-1] for i in range(1, len(cases))]

plt.plot(new_cases)

plt.show()

This snippet fetches the last 30 days of COVID cases for the U.S. and plots daily new cases.

COVID Tracker

Notes / Lessons Learned

So, here’s the thing—I’ve been dancing with matplotlib for a couple of days now, and while I’m getting the hang of it, I still feel like it’s a slow waltz with occasional toe-stomping. The library itself is amazing if you’re pulling data off the internet, but setting up the project environment still feels like building IKEA furniture without the instructions.

I made the rookie mistake of renaming my project folder halfway through and then spent way too long trying to activate the wrong virtual environment. Hint: Python doesn’t respond to psychic guesses—you’ve gotta get the name right. Lesson learned. Anyhow, I managed to create a COVID data visualization in a Python program.

The structure of the plotting code feels eerily familiar now—almost like déjà vu with braces and colons. Maybe that’s a good sign: I’m starting to notice patterns, which means I’m leveling up.

Also, when I ran the U.S. data? There wasn’t much new info (thankfully), but the graph still did its thing like a loyal little data dog. Even in a post-peak-pandemic world, the code works, and that’s a small but nerdy joy.

Optional Ideas for Expansion

  • Add an input field to let users type any country name
  • Include a bar chart version for comparison
  • Export the graph as an image to share with friends who still think Python is a snake

Day 38
Day 40

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How to Track Stock Prices Using Python and yFinance

Day 38 of 100 Days Coding Challenge: Python

Investing has always been a guilty pleasure of mine—right up there with overpriced coffee and late-night book binges. Back when I barely had enough money to buy lunch, I was already trying to buy tiny slices of companies like I was collecting Pokémon cards. Fast forward to today, and every bank app practically throws stock charts at you like confetti. Still, I wanted something that was mine—a tiny, personal stock tracker. One that didn’t nag me about “low balances” or “risky decisions.” So, today’s project? A DIY stock price tracker built with Python. Just me, a graph, and the ever-fluctuating mood swings of the market.

Today’s Motivation / Challenge

There’s something oddly thrilling about watching a stock price graph crawl across your screen like a timid squirrel in traffic. Whether you’re new to investing or just want to see if your gut instinct about Tesla was right last week, tracking prices can be both educational and addictive. Plus, building your own tracker means you’re not relying on whatever clunky UI your bank thinks is “innovative.”

Purpose of the Code (Object)

This simple Python program fetches historical stock prices and plots them in a neat, interactive graph. You enter the stock symbol and date range, and it shows you the closing prices over time—no logins, no ads, no “upgrade to premium” pop-ups.

AI Prompt: 

 “Write a Python script that tracks a stock’s historical closing price over a specified date range and plots it using matplotlib.”

Functions & Features

  • Accepts a stock ticker symbol and a date range
  • Fetches stock data using Yahoo Finance
  • Plots closing prices in an interactive graph
  • Lets you zoom in, pan, and even save the chart as an image

Requirements / Setup

bash

CopyEdit

pip install yfinance matplotlib pandas

Minimal Code Sample

python

CopyEdit

import yfinance as yf

import matplotlib.pyplot as plt

data = yf.download(“AAPL”, start=”2024-01-01″, end=”2024-06-30″)

plt.plot(data.index, data[“Close”])

plt.title(“AAPL Closing Price”)

plt.show()

Fetches Apple’s closing prices and plots them—simple and sweet.

Stock Price Tracker

Notes / Lessons Learned


 Today’s hiccup was classic: wrong environment, right idea. I had dutifully created a virtual environment and installed everything I needed—yfinance, matplotlib, and pandas. But when I launched Visual Studio Code, my eager fingers opened the wrong project folder. I ran the code. Boom. Error. No yfinance to be found. After a brief existential crisis and a quick switch to the correct environment, the code ran beautifully. A delightful little pop-up window showed me a clean graph of stock price movements. I could zoom in, pan around, even save it as a PNG. It felt like giving my inner stock nerd a new toy to play with. And yes—I fully plan to turn this into a functional tracker for my own portfolio. No offense to my bank’s app, but mine’s cooler.

Optional Ideas for Expansion

  • Add a moving average overlay to smooth out wild price swings
  • Include volume bars below the price graph for more context
  • Let the user compare two or more stocks on the same chart

Day 37
Day 39

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Breaking News, Built by Me

Day 37 of 100 Days Coding Challenge: Python

I have so many morning routines now that I need a morning routine just to organize them. Somewhere between meditating, hydrating, and pretending I’ll stretch for ten minutes, I give myself about five sacred minutes to scroll headlines on Google Pixie—my beloved phone, not a whimsical AI assistant (though honestly, close). It’s funny to think how much easier it is now than in the ‘90s, when “catching up on the news” meant flipping through an actual newspaper or—if you were tech-savvy—tapping into Yahoo News. I used to wonder whether those top headlines were handpicked by real people or magically summoned by a robot in a cubicle. Well, today I decided to find out what it feels like to be that robot. Except mine runs on Python and coffee.

Today’s Motivation / Challenge

There’s something delightful about making your own mini news reader. It’s like building a personal butler who only tells you the important stuff—no ads, no clickbait, no Kardashian updates (unless you really want them). Today’s challenge wasn’t just about coding; it was about reclaiming five minutes of my day from mindless scrolling and putting a bit of personality into my news.

Purpose of the Code (Object)

This little script fetches the top headlines from the internet and displays them in your terminal—clean, fast, and focused. You don’t need to open a browser or wade through a swamp of autoplay videos. It’s just a straight shot to what’s happening in the world, courtesy of your own code.

AI Prompt:

Write a Python script that uses NewsAPI to display the top 10 headlines in the terminal. Use environment variables for the API key. Keep the script simple and readable.

Functions & Features

  • Connects to NewsAPI and fetches top news headlines
  • Displays each headline and a brief description (if available)
  • Includes clickable links for more information
  • Clean, readable terminal output

Requirements / Setup

  • Python 3.x

Install these packages:

pip install requests python-dotenv

Minimal Code Sample

from dotenv import load_dotenv

import os, requests

load_dotenv()

api_key = os.getenv(“NEWS_API_KEY”)

response = requests.get(“https://newsapi.org/v2/top-headlines”, params={

    “country”: “us”, “apiKey”: api_key, “pageSize”: 10

})

for article in response.json()[“articles”]:

    print(article[“title”])

This snippet grabs the top 10 headlines and prints their titles. Simple and effective.

News Headline Reader

Notes / Lessons Learned

The coding went suspiciously smoothly today—almost unsettling. I finally did what every tutorial whispers about but no one actually does: I created a dedicated virtual environment. Then I installed the required libraries like a responsible adult. Getting an API key from newsapi.org was straightforward, and I even tucked it safely into a .env file (no more hardcoded sins). .gitignore? Created. requirements.txt? Generated with pride. After a few past failures, I’m beginning to feel like I almost know what I’m doing. It’s like learning to ride a bike, except the bike is made of code and occasionally throws a syntax error.

Optional Ideas for Expansion

  • Add voice support using pyttsx3 to read headlines aloud (hello, audio butler)
  • Let the user filter headlines by category (e.g., sports, tech, science)
  • Include headlines from multiple countries using user input

Day 36
Day 38
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I Built a Python Translator App I Needed in 1996

Day 36 of 100 days coding challenge: Python

I was born and raised in Japan, where nearly everyone speaks Japanese—surprise! But my household was a bit unusual. We had one of those rare satellite dishes that could pull in BBC and CNN, which meant English voices echoed through our living room long before they echoed through my brain. Add to that a small mountain of English children’s books, and you’d think I’d grow up effortlessly bilingual. Think again.

Later, I landed at an English-speaking university in Montreal, where people didn’t just speak English and French—they often casually tossed in a third or fourth language, like it was a party trick. Some of my classmates had parents who spoke Spanish at home, studied in French, and flirted in English. Meanwhile, I was sweating over conjugating irregular French verbs.

Despite my early exposure, mastering English (let alone French) was no walk in the linguistic park. Reading was one thing, but writing or speaking? That’s where the wheels came off. And back then—mid-90s Internet era—Google Translate didn’t exist. I had Netscape and patience. What I didn’t have was a little button that magically told me what “mettre en valeur” meant in context.

So today’s project? Python translator app. It’s my time-travel gift to my 1996 self: a translator app that could’ve saved me hours of flipping through dusty dictionaries.

Today’s Motivation / Challenge

Language learners know the pain: you read a sentence, understand each word, and still have no idea what it actually means. That’s the magic of context—and a good translator app can bridge the gap. Today, we build just that: a compact, no-nonsense translator you can summon from your command line like a 90s wizard with a keyboard instead of a wand. The goal isn’t to impress Google—it’s to outdo what the 90s Internet couldn’t give us.

Purpose of the Code (Object)

This simple translator app takes any sentence you feed it and converts it into your language of choice. It automatically detects the source language and spits out the translation, so you don’t even have to guess what language you’re struggling with. Think of it as your pocket linguist—minus the academic snobbery.

AI Prompt

That’s all it took. And with a few nudges, the app started behaving like the polite little translator it was always meant to be.

Functions & Features

  • Detects the input language automatically
  • Translates text to your chosen language
  • Supports dozens of language codes (like en, fr, ja, es)
  • Simple input-output interface via terminal

Requirements / Setup

pip install deep-translator

Tested on Python 3.13, but it works on any modern Python 3.x version.

Minimal Code Sample

from deep_translator import GoogleTranslator

translated = GoogleTranslator(source=’auto’, target=’fr’).translate(“I love books”)

print(translated)  # Prints: J’aime les livres

This line auto-detects the language and translates the sentence into French.

Python translator app

Notes / Lessons Learned

I started with an error today—like a true developer. I tried installing googletrans==4.0.0-rc1, which decided to throw a tantrum because it needed httpx 0.13.3, but my existing openai package was having none of it. The moment I hit run, Python practically screamed: “Pick a side!”

This was the moment I remembered virtual environments exist for a reason. If you’re juggling multiple packages like a circus clown with too many flaming clubs, isolate your apps before things catch fire. I’ll revisit virtual environments properly another day—but for now, I needed a workaround that worked.

Enter deep-translator. Easy install, no dependency drama, and—bonus—it respects your choice to lowercase your language codes. But be warned: this library is case-sensitive. Type FR instead of fr, and it gives you nothing but disappointment. You’ve been warned.

Optional Ideas for Expansion

  • Add a GUI interface with Tkinter or Streamlit to make it look more like a real app
  • Build a “translation history” feature to track past phrases
  • Integrate with a text-to-speech tool for pronunciation help

Day 35
Day 37

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When Banks Get Boring, Build Your Own Converter

Day 35 of 100 Days Coding Challenge: Python

As an accountant, I sometimes find myself wrangling with currencies like the Euro or the Yen—especially when it’s time to pay international vendors. Sure, I could just hop over to a bank’s website to check the latest rate and be done with it. But where’s the fun in that? There’s something satisfying about making a mini tool that does the fetching for you. It’s like teaching your computer to do the boring part while you sip coffee and pretend you’re working very hard. Besides, who wouldn’t want their own personal exchange rate butler?

Today’s Motivation / Challenge

Today’s project was all about merging curiosity with practicality. Currency converters might not sound glamorous, but they’re surprisingly useful—and they offer the perfect excuse to explore how APIs work in the wild. Plus, if you’ve ever had to double-check if you’re about to overpay a vendor in yen, you’ll understand the thrill of watching your own Python script spit out the answer faster than your bank’s clunky interface ever could.

Purpose of the Code (Object)

This script fetches real-time exchange rates and converts any amount between two currencies. Instead of trusting your memory or the whims of Google, you can just run this quick script and let it do the math. It’s lightweight, no fuss, and runs right in your terminal.

AI Prompt:

Convert currency between any two valid codes using live data. Keep it light. No API keys, no hassle.

Functions & Features

  • Converts between any two standard currency codes (e.g., USD to JPY)
  • Fetches live exchange rates from a free public API
  • Displays the converted amount in real time
  • Includes error handling for invalid codes or bad input

Requirements / Setup

You’ll need:

pip install requests

Works with: Python 3.x

Minimal Code Sample

url = f”https://open.er-api.com/v6/latest/{from_currency.upper()}”

response = requests.get(url)

rate = response.json()[“rates”].get(to_currency.upper())

converted = amount * rate

This fetches the latest rates and calculates the converted amount.

Currency Converter

Notes / Lessons Learned

This program did not go down without a fight. There are two main ways to fetch real-time exchange rates:
Option 1 is using https://api.exchangerate.host/convert, which is free and doesn’t need an API key. It sounded perfect. I plugged in the URL, hit run, and got… nothing. After far too much debugging, I realized the problem wasn’t the code—it was that something on my system was redirecting that link to exchangeratesapi.io, a completely different API that demands an API key. DNS goblins? Old environment settings? Ghosts of APIs past? Who knows.

So, I switched to a new free option: https://open.er-api.com. No signup, no key, and it worked like a charm. The moral of the story? Sometimes the easiest-looking door is secretly locked, and the best path forward is just to find a different door. Preferably one that doesn’t ask for your email address.

Optional Ideas for Expansion

  • Add a list of common currency codes for reference
  • Let users select currencies from a simple dropdown in a GUI version
  • Track historical exchange rates for past transactions
Day 34
Day 36

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BMI: Bridging the Gap Between Kilograms and Inches

Day 34 of 100 Days Coding Challenge: Python

I built my first Flask BMI calculator back on Day 7—just me, Python, and a simple formula. It worked well for metric units, crunchiDay 34 – BMI: Bridging the Gap Between Kilograms and Inchesng the numbers in centimeters and kilograms without complaint. But today, I decided to revisit it with a friendlier approach: one that welcomes both metric and imperial users. After all, some people think in kilos, others think in pounds—and both deserve an app that meets them where they are. So, I gave it a new home in Flask and added support for both systems. A small upgrade, but a meaningful one.

Today’s Motivation / Challenge

BMI calculators are a classic beginner’s project. They’re easy to understand, quick to build, and surprisingly useful. But for me, this wasn’t just about calculating numbers—it was about building a more thoughtful, inclusive tool. Rewriting it in Flask gave me a chance to sharpen my web development skills while creating something a little more flexible and user-friendly.

Purpose of the Code (Object)


 This code creates a simple web app that calculates your Body Mass Index (BMI) based on your height and weight. You can choose between metric or imperial units, and the app will give you your BMI along with a general category, like “Normal” or “Overweight.” No fuss, just quick feedback.

AI Prompt:

“Create a Flask web app that calculates BMI from user input. Include support for both metric and imperial units, and display the result with a category like ‘Normal’ or ‘Overweight’.”

Functions & Features

  • Lets users select metric (kg/cm) or imperial (lb/ft+in)
  • Accepts user input for height and weight
  • Calculates BMI using the appropriate formula
  • Categorizes results (e.g., Underweight, Normal, Overweight, Obese)

Requirements / Setup

  • pip install flask

Minimal Code Sample

if unit == “imperial”:

    total_inches = float(ft) * 12 + float(inch)

    bmi = round((float(weight_lb) / (total_inches ** 2)) * 703, 2)

else:

    height_m = float(height_cm) / 100

    bmi = round(float(weight_kg) / (height_m ** 2), 2)

// Calculates BMI based on the selected unit system.

MBI Calculator Using Flask

*** I am very sorry, I realized I forgot to add to GitHub. I uploaded later. ***

Notes / Lessons Learned


When I first started working with Flask, things got messy fast. Each new app ended up in its own makeshift folder, and before long, I discovered that Flask can be downright fussy about file locations—especially when it comes to templates. That lesson pushed me to get organized. Now, every project gets its own environment, a dedicated folder, and a clearly named app file (I’ve officially retired the one-size-fits-all “app.py”). I also learned to run multiple apps on different ports, which made testing far less chaotic. A bit of structure early on, it turns out, saves a world of confusion later.

Optional Ideas for Expansion

  • Add JavaScript to display BMI results instantly without reloading
  • Store BMI history so users can track changes over time
  • Include wellness tips based on BMI results

Day 33
Day 35

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Flask Forward: Building a Blog From Scratch (and Mild Panic)

Day 33 of 100 days coding challenge: Python

Personal Experience

I’ve been blogging for over six years, but not like this. My usual workflow involves logging into a WordPress site, clicking “New Post,” and typing away like a caffeinated raccoon. No programming. No servers. Definitely no manually structured file trees.

Then yesterday, I discovered Flask—a micro web framework in Python—and I couldn’t resist the “Hello, World” charm offensive. Today, I took it a step further: I built a miniature blog app. Not styled. Not database-backed. But it works. I can write a post, hit submit, and watch it appear like magic (or slightly awkward sorcery). It’s not pretty—yet—but it’s mine. And honestly, playing with this new toy has me feeling like the proud owner of a digital Easy-Bake Oven. You throw in some code, and out pops a web page. Delicious.

Today’s Motivation / Challenge

I’ve used blogs for years, but never thought of building one from the ground up. Today’s challenge wasn’t just about learning Flask—it was about flipping the curtain on the software I use every day. It’s like going from “driving a car” to “building one with duct tape and curiosity.” And you know what? Turns out the engine runs just fine.

Purpose of the Code (Object)

This mini app creates a simple blogging website. You can add a new post, view all existing ones, and pretend—for just a moment—that you’re running your own minimalist content empire. It doesn’t use a database yet—just keeps your posts in memory—but it’s a great stepping stone.

AI Prompt:

Write a simple Flask app that displays blog posts and lets users add a new one through a form. Keep it minimal, with in-memory storage and two HTML templates: one for the homepage and one for the post form.

Functions & Features

  • Displays a list of all blog posts
  • Lets users create a new blog post via a form
  • Stores posts temporarily (in memory)

Requirements / Setup

pip install flask

You’ll need Python 3.6 or later. Then just run app.py and open your browser.

Minimal Code Sample

@app.route(‘/new’, methods=[‘GET’, ‘POST’])

def new_post():

    if request.method == ‘POST’:

        title = request.form[‘title’]

        content = request.form[‘content’]

        blog_posts.append({‘title’: title, ‘content’: content})

        return redirect(url_for(‘home’))

    return render_template(‘new_post.html’)

This route displays a form or saves a post, depending on the request.

Mini Blog App

Notes / Lessons Learned

When working with Flask, file structure isn’t just a “nice to have”—it’s the entire vibe. The templates folder must be in the exact same level as app.py, or Flask will act like it doesn’t know you. I spent a good hour wondering why my beautiful new form page was blank. A quick “View Page Source” revealed… nothing. That’s when I realized: I hadn’t saved the file. Turns out I’d disabled auto-save in VS Code, and my new_post.html was basically a digital tumbleweed. Once I retyped it, saved everything properly, and restarted the app, the blog sprang to life—albeit looking like a Geocities relic. But I made it. And that’s what counts.

This experience reminded me: fancy tools are fun, but building something with your own code—flaws and all—is a different kind of thrill. I’m starting to see the potential here. Flask is no longer just “that framework with a cool name”—it might be the engine behind my next real web project.

Optional Ideas for Expansion

  • Add a timestamp to each post
  • Style it with Bootstrap for instant visual credibility
  • Save posts to a text file or SQLite database so they don’t disappear on refresh

Day 32
Day 34