| import pandas as pd
|
| import matplotlib.pyplot as plt
|
| import seaborn as sns
|
|
|
| OUT = "eda_outputs"
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| import os
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| os.makedirs(OUT, exist_ok=True)
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|
|
| df = pd.read_csv("PaavoDataset.csv", encoding="latin1", sep=";")
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| df.columns = df.columns.str.strip()
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| df = df[~df["Area"].str.strip().str.startswith("MK")].copy()
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|
|
|
|
| print("--- Info ---")
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| print(df.info())
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| print("\n--- Descriptive statistics ---")
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| desc = df.describe().T
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| print(desc)
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| desc.to_csv(f"{OUT}/summary_statistics.csv")
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|
|
|
|
| key_vars = [
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| "Inhabitants",
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| "Average age of inhabitants",
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| "Average income of inhabitants",
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| "Median income of inhabitants",
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| "Average size of households",
|
| ]
|
|
|
| fig, axes = plt.subplots(len(key_vars), 1, figsize=(8, 4 * len(key_vars)))
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| for ax, col in zip(axes, key_vars):
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| sns.histplot(df[col], kde=True, ax=ax, color="#6fa8dc")
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| ax.set_title(f"Distribution of {col}")
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| plt.tight_layout()
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| fig.savefig(f"{OUT}/01_distributions.png", dpi=150)
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| plt.close(fig)
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|
|
|
|
| numeric_df = df.select_dtypes(include="number")
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| corr = numeric_df.corr()
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|
|
| fig, ax = plt.subplots(figsize=(14, 12))
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| sns.heatmap(corr, cmap="coolwarm", center=0, annot=False, ax=ax)
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| ax.set_title("Correlation heatmap")
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| fig.savefig(f"{OUT}/02_correlation_heatmap.png", dpi=150, bbox_inches="tight")
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| plt.close(fig)
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|
|
|
|
| top_income = df.nlargest(15, "Average income of inhabitants")[["Area", "Average income of inhabitants"]]
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| top_population = df.nlargest(15, "Inhabitants")[["Area", "Inhabitants"]]
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| print("\n--- Top 15 areas by average income ---")
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| print(top_income)
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| print("\n--- Top 15 areas by population ---")
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| print(top_population)
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|
|
|
|
| income_cats = [
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| "Inhabitants belonging to the lowest income category",
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| "Inhabitants belonging to the middle income category",
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| "Inhabitants belonging to the highest income category",
|
| ]
|
|
|
| top20_income = df.nlargest(20, "Inhabitants").set_index("Area")[income_cats].iloc[::-1]
|
| top20_income.columns = ["Lowest income cat.", "Middle income cat.", "Highest income cat."]
|
|
|
| fig, ax = plt.subplots(figsize=(10, 8))
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| top20_income.plot(kind="barh", stacked=True, color=["#6fa8dc", "#cc6659", "#e69138"], ax=ax)
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| ax.set_title("Income category composition - top 20 areas by population")
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| ax.set_xlabel("Number of inhabitants")
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| fig.savefig(f"{OUT}/03_income_categories_top20.png", dpi=150, bbox_inches="tight")
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| plt.close(fig)
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|
|
|
|
| household_cats = [
|
| "Households belonging to the lowest income category",
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| "Households belonging to the middle income category",
|
| "Households belonging to the highest income category",
|
| ]
|
|
|
| top20_households = (df.nlargest(20, "Households").set_index("Area")[household_cats].iloc[::-1])
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| top20_households.columns = ["Lowest income cat.", "Middle income cat.", "Highest income cat."]
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|
|
| fig, ax = plt.subplots(figsize=(10, 8))
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| top20_households.plot(kind="barh", stacked=True, color=["#6fa8dc", "#cc6659", "#e69138"], ax=ax)
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| ax.set_title("Household income category composition - top 20 areas by number of households")
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| ax.set_xlabel("Number of households")
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| fig.savefig(f"{OUT}/04_household_income_categories_top20.png", dpi=150, bbox_inches="tight")
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| plt.close(fig)
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|
|
|
|
| df["Inhabitants per residential building"] = (
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| df["Inhabitants"] / df["Residential buildings"].replace(0, pd.NA)
|
| )
|
|
|
| density_top20 = df.nlargest(20, "Inhabitants per residential building")[
|
| ["Area", "Inhabitants per residential building"]
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| ].set_index("Area")
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|
|
| fig, ax = plt.subplots(figsize=(9, 8))
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| density_top20.sort_values("Inhabitants per residential building").plot(
|
| kind="barh", legend=False, color="#6fa8dc", ax=ax
|
| )
|
| ax.set_title("Top 20 areas by inhabitants per residential building)")
|
| ax.set_xlabel("Inhabitants per residential building")
|
| fig.savefig(f"{OUT}/05_inhabitants_per_building_top20.png", dpi=150, bbox_inches="tight")
|
| plt.close(fig)
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|
|
|
|
| purchasing_top20 = df.nlargest(20, "Accumulated purchasing power of inhabitants")[
|
| ["Area", "Accumulated purchasing power of inhabitants"]
|
| ].set_index("Area")
|
|
|
| fig, ax = plt.subplots(figsize=(9, 8))
|
| purchasing_top20.sort_values("Accumulated purchasing power of inhabitants").plot(
|
| kind="barh", legend=False, color="#e69138", ax=ax
|
| )
|
| ax.set_title("Top 20 areas by accumulated purchasing power of inhabitants")
|
| ax.set_xlabel("Accumulated purchasing power (EUR)")
|
| fig.savefig(f"{OUT}/06_purchasing_power_top20.png", dpi=150, bbox_inches="tight")
|
| plt.close(fig)
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|
|
| print(f"\nAll plots and summary CSV saved to ./{OUT}/") |
