Overview.qmd

---
title: "Building Stock Analysis"
format: html
execute:
  echo: False
---

## Load Libraries

```{python}
import pandas as pd
import matplotlib.pyplot as plt


df = pd.read_csv('AggData.csv')
print('Total number of buildings recognised: ', 1961)
print("Number of buildings with footprints:", len(df))

df["Atemp"] = pd.to_numeric(df["Atemp"], errors="coerce")
df["BldYear"] = pd.to_numeric(df["BldYear"], errors="coerce")
df["PrimEnergy"] = pd.to_numeric(df["PrimEnergy"], errors="coerce")
df["nbFloor"] = pd.to_numeric(df["nbFloor"], errors="coerce")
print('Number of missing construction year: ', df["BldYear"].isna().sum()+(df["BldYear"] == 0).sum())
print('Number of missing Primary Energy: ', df["PrimEnergy"].isna().sum())
print('Number of missing number of floor: ', df["nbFloor"].isna().sum())
print('Number of missing heated area: ', df["Atemp"].isna().sum())

# remove invalid construction years
df = df[(df["BldYear"].notna()) & (df["BldYear"] != 0)]

# drop rows with other missing values
df = df.dropna(subset=["Atemp","PrimEnergy","nbFloor"])
df = df.dropna(subset=["Atemp","PrimEnergy","nbFloor"])

print("Number of buildings after filtering out nan values:", len(df))
print("Oldest building:", df["BldYear"].min())
print("Newest building:", df["BldYear"].max())
```
## Construction Year Distribution

```{python}
plt.figure()
plt.hist(df["BldYear"], bins=20)

plt.xlabel("Construction Year")
plt.ylabel("Number of Buildings")
plt.title("Distribution of Building Construction Years")

plt.show()
```

## Energy Performance vs Construction Year
```{python}

plt.figure()

plt.scatter(df["BldYear"], df["PrimEnergy"])

plt.xlabel("Construction Year")
plt.ylabel("Primary Energy ((kWh/m².y))")
plt.title("Energy Performance vs Building Age")

plt.show()
```

## Heated Area Distribution
```{python}

plt.figure()

plt.hist(df["Atemp"], bins=20)

plt.xlabel("Heated Area (m²)")
plt.ylabel("Number of Buildings")
plt.title("Distribution of Heated Area")

plt.show()
```

## Building Floors Distribution
```{python}
floor_counts = df["nbFloor"].value_counts().sort_index()

plt.figure()

plt.bar(floor_counts.index, floor_counts.values)

plt.xlabel("Number of Floors")
plt.ylabel("Number of Buildings")
plt.title("Building Height Distribution")

plt.show()
```

## Construction Period Analysis
```{python}
bins = [1900,1960,1975,1990,2005,2025]
labels = ["<1960","1960–75","1976–90","1991–2005","2006+"]

df["period"] = pd.cut(df["BldYear"], bins=bins, labels=labels)

period_counts = df["period"].value_counts().sort_index()

plt.figure()

plt.bar(period_counts.index, period_counts.values)

plt.xlabel("Construction Period")
plt.ylabel("Number of Buildings")
plt.title("Building Stock by Construction Period")

plt.show()
```

## Energy declaration by Building Age
```{python}
plt.figure()

for ec in sorted(df["EnergyClass"].unique()):
    subset = df[df["EnergyClass"] == ec]
    plt.scatter(subset["BldYear"], subset["PrimEnergy"], label=ec)

plt.legend(title="Energy Class")
plt.xlabel("Construction Year")
plt.ylabel("Primary Energy (kWh/m².y)")
plt.title("Energy Performance by Building Age")
plt.show()
```