This wasn’t just another week.

This was the week.

The week I stopped copying code from notebooks… and started asking my own questions.

The week I stayed up until 3:17 AM — not because I had to — but because I couldn’t stop.

This is the full, unfiltered story of Week 8.

Weeks 1–7: The Struggle Was Real

Then came Week 8.

And the instructor said:

“No more examples. No more files given. Download any two World Bank indicators you want. Merge them. Find a correlation. Explain what it means. Now do it yourself.”

I stared at my laptop for 15 minutes.

Then I opened data.worldbank.org at 1:47 AM.

My Project: Does Electricity Access = More CO₂ Pollution?

I’m Nigerian. I grew up with NEPA taking light every night.

So I asked the question that keeps me up:

“When Nigeria finally gets 24/7 electricity… are we destroying the planet?”

I chose two indicators that hurt to think about:

1. EG.ELC.ACCS.ZS → Access to electricity (% of population)

2. EN.ATM.CO2E.PC → CO₂ emissions per capita (metric tons)

The Full Pipeline (Everything I Did — Step by Step)

# 1. Download ZIP files → extract → rename
#    API_EG.ELC.ACCS.ZS_...csv → WB_Electricity_2013.csv
#    API_EN.ATM.CO2E.PC_...csv → WB_CO2_2013.csv

# 2. Load with World Bank magic
elec = pd.read_csv('WB_Electricity_2013.csv', skiprows=4)  # Genius move
co2  = pd.read_csv('WB_CO2_2013.csv', skiprows=4)

# 3. Clean country names (the eternal enemy)
def clean(name):
    return str(name).replace(' ', '').replace('-', '').replace("'", '').replace(',', '').isalpha()

# 4. Merge → convert → drop NaN → 216 countries
# 5. Plot + correlation

The Result That Broke My Heart (Then Healed It)

Electricity vs CO2 2013

Correlation = 0.465

I wanted 0.9. I wanted to be able to say: “See? Electricity = climate doom.”

But the data said: “No. I**t’s more complicated th**an that.”

What the Graph Actually Taught Me

The Real Insight (The One That Changed Me)

Electricity access and CO₂ emissions are linked — but not locked together.

The correlation is only moderate because:

• France, Sweden, Norway, Switzerland → 100% electricity, low CO₂ (nuclear + hydro)

• Qatar, UAE, Kuwait → 100% electricity, sky-high CO₂ (oil money)

It’s not electricity that pollutes.

It’s the fuel we use to make it.

My Final Conclusion (The Truth I Found at 3:17 AM)

“Nigeria doesn’t have to choose between light and climate. We don’t have to follow Qatar’s path. We can follow France’s path — or better, Morocco’s path (90% renewable).

The data doesn’t say ‘electricity = pollution’. It says: ‘Your energy source decides your future’.”

The Skills I Mastered This Week

Final Thought

Week 8 wasn’t about getting a high correlation.

It was about:

• Staying up until 3 AM because I cared

• Learning that moderate results can be more honest than strong ones

• Realizing that data doesn’t give answers — it gives choices

And I made mine.

I’m not just learning data science anymore.

I’m using it to understand the world.

From Nigeria, with love and hope Raphael Ajiboye Olabanji

P.S. Yes, I really stayed up until 3:17 AM for this. And I’d do it again in a heartbeat.

Drop a if Week 8 made you stay up late for data too. Drop a if you believe Nigeria can have light and clean air.

#DataScience #Pandas #Python #ClimateChange #EnergyAccess #WorldBank #NigeriaTech #LearnInPublic #100DaysOfCode #BlackExcellence