Data Loading, Wrangling & Visualisation

Data Loading, Wrangling & Visualisation#

⏳ Loading Pyodide…

Welcome to the most honest part of Machine Learning — data wrangling, also known as “90% of the job no one posts about on LinkedIn.” 😅

If math was theory, this chapter is practice with mud. You’ll roll up your sleeves, clean messy data, and make it look like something a CEO would actually want to see in a dashboard.

🧠 Why This Matters#

Machine Learning models are like gourmet chefs — they can only make good predictions if you give them clean ingredients.

Unfortunately, business data often looks like this:

Customer

Age

Revenue

Gender

Notes

A-102

27

$2,000

F

missing

NaN

$500

?

typo

C-554

45

-$200

Male

refund

D-999

300

$1,000

cat

who let this happen

So before we even think about algorithms, we’ll:

  1. Load data from messy sources.

  2. Clean it like digital laundry.

  3. Transform it into model-ready features.

  4. Visualize it like a storytelling pro.

💾 1. The Data Wrangling Trifecta#

Step

Name

Business Goal

Data Loading

Get data into Python

“Where’s my Excel file again?”

Data Cleaning

Fix mistakes & missing values

“Why is revenue negative?”

Feature Engineering

Add useful variables

“Let’s create a loyalty score!”

By the end of this chapter, you’ll make data look so clean it could get a job at McKinsey.

📚 Prerequisite: Python Refresher#

If you’re new to Python or Pandas, don’t panic — it’s easier than assembling IKEA furniture. 👉 Check out my other book: 📘 Programming for Business It covers everything from reading files to basic Python data manipulation.

💡 Tip: You’ll be using libraries like pandas, numpy, and matplotlib. If these look like Pokémon names right now, that book is your Pokédex.

🧩 Practice Corner: “Guess the Data Disaster”#

Match each messy situation with the tool that saves the day:

Situation

Tool

File is 200MB Excel sheet with multiple tabs

pandas.read_excel()

Missing values everywhere

df.fillna() or df.dropna()

Categorical columns like “Yes/No”

pd.get_dummies()

Data stored in a SQL database

pandas.read_sql()

REST API providing JSON data

requests.get()

✅ Pro tip: Pandas is your Swiss Army Knife for data chaos.

🔍 2. Why Businesses Love Clean Data#

Messy data → Confused analysts → Wrong dashboards → Angry executives. Clean data → Confident models → Actionable insights → Happy bonuses. 🎉

You’ll soon realize:

Data cleaning is not boring — it’s debugging reality.

For example:

  • Missing age? → Estimate with median.

  • Wrong gender field? → Normalize text values.

  • Negative revenue? → Check for refunds.

  • Timestamp errors? → Convert to datetime.

You’re not just fixing numbers — you’re restoring business logic.

🎨 3. Visualisation: Turning Data into Business Art#

Once you’ve tamed the chaos, it’s time to make your data pretty and persuasive.

This section covers:

  • Histograms that show sales trends 📊

  • Scatter plots revealing marketing ROI 💸

  • Correlation heatmaps for KPIs 🔥

  • Dashboards that make execs say “wow” ✨

Remember: “If it’s not visualized, it didn’t happen.” — Every Data Scientist, ever.

💬 4. Business Analogy: The Data Spa#

Think of your dataset like a customer entering a spa:

Step

Data Action

Spa Equivalent

Loading

Getting checked in

“Welcome, Mr. CSV!”

Cleaning

Removing noise & junk

Exfoliation time 🧼

Transformation

Standardizing features

Facial mask & makeover 💅

Visualization

Presenting results

Walking the runway 🕺

When your data leaves this spa, it’s ready for the runway — or your next board meeting.

🧩 Practice Corner: “Wrangle This!”#

Here’s a messy dataset in Python. Try cleaning it up using what you’ll learn in this chapter:

🧽 Challenge:

  1. Replace None and ? with proper values

  2. Fix negative revenue

  3. Correct impossible ages

  4. Print the clean version

🧭 5. What’s Coming Up#

File

Topic

Funny Summary

data_loading

Loading data from CSV, Excel, SQL & APIs

“The Great Data Buffet” 🍽️

data_cleaning

Cleaning & preprocessing

“Digital Laundry Day” 🧺

handling_missing_outliers

Fixing missing data & outliers

“CSI: Data Edition” 🕵️

feature_encoding

Encoding categories & scaling features

“Teaching Machines English” 🗣️

eda

Exploratory Data Analysis

“Detective Work with Graphs” 🧠

visualisation

Making plots & charts

“Turning KPIs into art” 🎨

business_dashboards

Interactive dashboards

“Your Data’s TED Talk” 🧑‍💼

🚀 Summary#

✅ Data wrangling = preparing the battlefield for ML ✅ Visualization = storytelling for business impact ✅ Clean data = clean insights ✅ Dirty data = bad decisions (and maybe a career pivot)

Remember: “Garbage in → Garbage out” — but in business, garbage often comes with formatting errors.

🔜 Next Stop#

👉 Head to Data Loading (CSV, Excel, SQL, APIs) to learn how to bring all your data under one roof — without crying over file formats.

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