Personal Finance Analyzer, Revisited With pandas

What We're Building

The same expense data py4-8 tracked — amount, category, description — now with one addition a real finance tool needs: a date per expense. Loaded into a DataFrame, the exact same two summaries py4-8 built by hand become one-line pandas operations, and two genuinely new analyses (monthly trends, a real chart) become possible that weren't practical to hand-roll before.

# expenses.json — py4-8's own schema, plus one new field: date
[
  {"date": "2026-01-03", "amount": 12.50, "category": "Food", "description": "Lunch"},
  {"date": "2026-01-05", "amount": 45.00, "category": "Transport", "description": "Fuel"},
  {"date": "2026-02-01", "amount": 89.99, "category": "Food", "description": "Groceries"}
]

Step 1: Loading Into a DataFrame

import pandas as pd

df = pd.read_json("expenses.json")
df["date"] = pd.to_datetime(df["date"])
print(df.head())

One line replaces py4-8's own json.loads(FILE.read_text()) plus manually keeping the result as a list of dicts — pd.read_json() parses the file directly into a DataFrame.

Step 2: Totals — The Direct Payoff

totals = {}
for e in expenses:
    totals[e["category"]] = (
        totals.get(e["category"], 0)
        + e["amount"]
    )

totals = df.groupby("category")["amount"].sum()

sum(e["amount"] for e in expenses)

df["amount"].sum()

py4-8's own totals.get(category, 0) + amount pattern was itself a direct reuse of the site's own word-frequency-counter idiom — real, working code. .groupby("category")["amount"].sum() does the identical job in one call, built on the same vectorized, C-level operations ds1-2 introduced as NumPy's own real advantage over a plain Python loop.

Step 3: What py4-8 Genuinely Couldn't Do Easily — Monthly Trends

monthly = df.set_index("date").resample("ME")["amount"].sum()
print(monthly)
# 2026-01-31    57.50
# 2026-02-28    89.99

Reproducing this in plain Python would mean manually parsing every date string, bucketing entries by year-and-month into a dict, and keeping that bucketing logic in sync everywhere it's needed — not impossible, but real, fiddly work py4-8's own scope never asked for. .resample("ME") does the entire "group by calendar month" operation in one call, because the DataFrame's own datetime index already understands calendar structure.

Step 4: A Real Chart

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.5))

totals.plot(kind="pie", ax=ax1, autopct="%1.0f%%", ylabel="",
            colors=["#15803D", "#EAB308", "#4ade80", "#facc15"])
ax1.set_title("Spending by Category")

monthly.plot(kind="line", marker="o", ax=ax2, color="#15803D")
ax2.set_title("Spending Over Time")
ax2.set_ylabel("$")

plt.tight_layout()
plt.savefig("finance_dashboard.png")

Both charts plot directly off the groupby/resample results from Steps 2 and 3 — a genuine payoff of keeping the data in a DataFrame throughout, rather than converting back to plain lists and dicts just to hand them to a chart.

The Complete Analysis Script

import pandas as pd, matplotlib.pyplot as plt

df = pd.read_json("expenses.json")
df["date"] = pd.to_datetime(df["date"])

totals = df.groupby("category")["amount"].sum()
monthly = df.set_index("date").resample("ME")["amount"].sum()

print("Totals by category:\n", totals)
print("\nMonthly spending:\n", monthly)
print(f"\nOverall total: ${df['amount'].sum():.2f}")

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.5))
totals.plot(kind="pie", ax=ax1, autopct="%1.0f%%", ylabel="")
ax1.set_title("Spending by Category")
monthly.plot(kind="line", marker="o", ax=ax2)
ax2.set_title("Spending Over Time")
plt.tight_layout()
plt.savefig("finance_dashboard.png")

What's Next

Chapter 5: Movie Ratings Explorer — the course's own central EDA chapter, applying ds1-9's/ds1-10's own seven-step methodology to a fresh public dataset without a checklist to lean on.