tsb — Weather Trends

1 · Aggregate daily readings into monthly means

Synthetic but realistic year of daily highs in °C.

TypeScript

import { DataFrame, Series } from "tsb";

// 365 daily readings simulating a temperate climate (cold winter, warm summer)
const days: Date[] = [];
const temps: number[] = [];
const start = Date.UTC(2024, 0, 1);
for (let d = 0; d < 365; d++) {
  days.push(new Date(start + d * 86400000));
  // Sinusoidal seasonal pattern + small deterministic "noise"
  const seasonal = 14 + 12 * Math.sin(((d - 110) / 365) * 2 * Math.PI);
  const noise = ((d * 9301 + 49297) % 233280) / 233280 * 4 - 2;
  temps.push(Number((seasonal + noise).toFixed(1)));
}

// Use a Series so we can reach for the dt accessor
const dateSeries = new Series({ data: days, name: "date" });
const months = dateSeries.dt.month();

const df = DataFrame.fromColumns({ temp_c: temps }).assign({ month: months });

const monthly = df.groupby("month").agg({ temp_c: "mean" }, false);
console.log(monthly.toString());

console.log("\nMin month avg:", Number(monthly.col("temp_c").min()).toFixed(2), "°C");
console.log("Max month avg:", Number(monthly.col("temp_c").max()).toFixed(2), "°C");

import pandas as pd
days = ['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05', '2024-01-06', '2024-01-07', '2024-01-08', '2024-01-09', '2024-01-10', '2024-01-11', '2024-01-12', '2024-01-13', '2024-01-14', '2024-01-15', '2024-01-16', '2024-01-17', '2024-01-18', '2024-01-19', '2024-01-20', '2024-01-21', '2024-01-22', '2024-01-23', '2024-01-24', '2024-01-25', '2024-01-26', '2024-01-27', '2024-01-28', '2024-01-29', '2024-01-30', '2024-01-31', '2024-02-01', '2024-02-02', '2024-02-03', '2024-02-04', '2024-02-05', '2024-02-06', '2024-02-07', '2024-02-08', '2024-02-09', '2024-02-10', '2024-02-11', '2024-02-12', '2024-02-13', '2024-02-14', '2024-02-15', '2024-02-16', '2024-02-17', '2024-02-18', '2024-02-19', '2024-02-20', '2024-02-21', '2024-02-22', '2024-02-23', '2024-02-24', '2024-02-25', '2024-02-26', '2024-02-27', '2024-02-28', '2024-02-29', '2024-03-01', '2024-03-02', '2024-03-03', '2024-03-04', '2024-03-05', '2024-03-06', '2024-03-07', '2024-03-08', '2024-03-09', '2024-03-10', '2024-03-11', '2024-03-12', '2024-03-13', '2024-03-14', '2024-03-15', '2024-03-16', '2024-03-17', '2024-03-18', '2024-03-19', '2024-03-20', '2024-03-21', '2024-03-22', '2024-03-23', '2024-03-24', '2024-03-25', '2024-03-26', '2024-03-27', '2024-03-28', '2024-03-29', '2024-03-30', '2024-03-31', '2024-04-01', '2024-04-02', '2024-04-03', '2024-04-04', '2024-04-05', '2024-04-06', '2024-04-07', '2024-04-08', '2024-04-09', '2024-04-10', '2024-04-11', '2024-04-12', '2024-04-13', '2024-04-14', '2024-04-15', '2024-04-16', '2024-04-17', '2024-04-18', '2024-04-19', '2024-04-20', '2024-04-21', '2024-04-22', '2024-04-23', '2024-04-24', '2024-04-25', '2024-04-26', '2024-04-27', '2024-04-28', '2024-04-29', '2024-04-30', '2024-05-01', '2024-05-02', '2024-05-03', '2024-05-04', '2024-05-05', '2024-05-06', '2024-05-07', '2024-05-08', '2024-05-09', '2024-05-10', '2024-05-11', '2024-05-12', '2024-05-13', '2024-05-14', '2024-05-15', '2024-05-16', '2024-05-17', '2024-05-18', '2024-05-19', '2024-05-20', '2024-05-21', '2024-05-22', '2024-05-23', '2024-05-24', '2024-05-25', '2024-05-26', '2024-05-27', '2024-05-28', '2024-05-29', '2024-05-30', '2024-05-31', '2024-06-01', '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17', '2024-06-18', '2024-06-19', '2024-06-20', '2024-06-21', '2024-06-22', '2024-06-23', '2024-06-24', '2024-06-25', '2024-06-26', '2024-06-27', '2024-06-28', '2024-06-29', '2024-06-30', '2024-07-01', '2024-07-02', '2024-07-03', '2024-07-04', '2024-07-05', '2024-07-06', '2024-07-07', '2024-07-08', '2024-07-09', '2024-07-10', '2024-07-11', '2024-07-12', '2024-07-13', '2024-07-14', '2024-07-15', '2024-07-16', '2024-07-17', '2024-07-18', '2024-07-19', '2024-07-20', '2024-07-21', '2024-07-22', '2024-07-23', '2024-07-24', '2024-07-25', '2024-07-26', '2024-07-27', '2024-07-28', '2024-07-29', '2024-07-30', '2024-07-31', '2024-08-01', '2024-08-02', '2024-08-03', '2024-08-04', '2024-08-05', '2024-08-06', '2024-08-07', '2024-08-08', '2024-08-09', '2024-08-10', '2024-08-11', '2024-08-12', '2024-08-13', '2024-08-14', '2024-08-15', '2024-08-16', '2024-08-17', '2024-08-18', '2024-08-19', '2024-08-20', '2024-08-21', '2024-08-22', '2024-08-23', '2024-08-24', '2024-08-25', '2024-08-26', '2024-08-27', '2024-08-28', '2024-08-29', '2024-08-30', '2024-08-31', '2024-09-01', '2024-09-02', '2024-09-03', '2024-09-04', '2024-09-05', '2024-09-06', '2024-09-07', '2024-09-08', '2024-09-09', '2024-09-10', '2024-09-11', '2024-09-12', '2024-09-13', '2024-09-14', '2024-09-15', '2024-09-16', '2024-09-17', '2024-09-18', '2024-09-19', '2024-09-20', '2024-09-21', '2024-09-22', '2024-09-23', '2024-09-24', '2024-09-25', '2024-09-26', '2024-09-27', '2024-09-28', '2024-09-29', '2024-09-30', '2024-10-01', '2024-10-02', '2024-10-03', '2024-10-04', '2024-10-05', '2024-10-06', '2024-10-07', '2024-10-08', '2024-10-09', '2024-10-10', '2024-10-11', '2024-10-12', '2024-10-13', '2024-10-14', '2024-10-15', '2024-10-16', '2024-10-17', '2024-10-18', '2024-10-19', '2024-10-20', '2024-10-21', '2024-10-22', '2024-10-23', '2024-10-24', '2024-10-25', '2024-10-26', '2024-10-27', '2024-10-28', '2024-10-29', '2024-10-30', '2024-10-31', '2024-11-01', '2024-11-02', '2024-11-03', '2024-11-04', '2024-11-05', '2024-11-06', '2024-11-07', '2024-11-08', '2024-11-09', '2024-11-10', '2024-11-11', '2024-11-12', '2024-11-13', '2024-11-14', '2024-11-15', '2024-11-16', '2024-11-17', '2024-11-18', '2024-11-19', '2024-11-20', '2024-11-21', '2024-11-22', '2024-11-23', '2024-11-24', '2024-11-25', '2024-11-26', '2024-11-27', '2024-11-28', '2024-11-29', '2024-11-30', '2024-12-01', '2024-12-02', '2024-12-03', '2024-12-04', '2024-12-05', '2024-12-06', '2024-12-07', '2024-12-08', '2024-12-09', '2024-12-10', '2024-12-11', '2024-12-12', '2024-12-13', '2024-12-14', '2024-12-15', '2024-12-16', '2024-12-17', '2024-12-18', '2024-12-19', '2024-12-20', '2024-12-21', '2024-12-22', '2024-12-23', '2024-12-24', '2024-12-25', '2024-12-26', '2024-12-27', '2024-12-28', '2024-12-29', '2024-12-30']
temps = [1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.6, 2.7, 2.8, 3.0, 3.1, 3.3, 3.4, 3.6, 3.7, 3.9, 0.0, 0.2, 0.4, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2.1, 2.3, 2.5, 2.7, 2.9, 3.1, 3.3, 3.6, 3.8, 4.0, 4.3, 4.5, 4.7, 5.0, 1.2, 1.5, 1.7, 2.0, 2.3, 2.5, 2.8, 3.1, 3.3, 3.6, 3.9, 4.2, 4.4, 4.7, 5.0, 5.3, 5.6, 5.9, 6.2, 6.5, 6.8, 7.1, 7.4, 7.8, 8.1, 4.4, 4.7, 5.0, 5.4, 5.7, 6.0, 6.3, 6.7, 7.0, 7.3, 7.7, 8.0, 8.4, 8.7, 9.0, 9.4, 9.7, 10.1, 10.4, 10.8, 11.1, 11.5, 11.9, 12.2, 12.6, 12.9, 9.3, 9.7, 10.0, 10.4, 10.7, 11.1, 11.5, 11.8, 12.2, 12.6, 12.9, 13.3, 13.7, 14.0, 14.4, 14.8, 15.1, 15.5, 15.9, 16.2, 16.6, 16.9, 17.3, 17.7, 18.0, 14.4, 14.8, 15.1, 15.5, 15.8, 16.2, 16.6, 16.9, 17.3, 17.6, 18.0, 18.3, 18.7, 19.0, 19.4, 19.7, 20.1, 20.4, 20.8, 21.1, 21.4, 21.8, 22.1, 22.4, 22.8, 19.1, 19.4, 19.8, 20.1, 20.4, 20.7, 21.0, 21.3, 21.7, 22.0, 22.3, 22.6, 22.9, 23.2, 23.5, 23.8, 24.0, 24.3, 24.6, 24.9, 25.2, 25.5, 25.7, 26.0, 26.3, 22.5, 22.8, 23.0, 23.3, 23.6, 23.8, 24.0, 24.3, 24.5, 24.8, 25.0, 25.2, 25.4, 25.7, 25.9, 26.1, 26.3, 26.5, 26.7, 26.9, 27.1, 27.3, 27.5, 27.7, 27.9, 24.1, 24.2, 24.4, 24.6, 24.7, 24.9, 25.1, 25.2, 25.4, 25.5, 25.7, 25.8, 25.9, 26.1, 26.2, 26.3, 26.5, 26.6, 26.7, 26.8, 26.9, 27.0, 27.1, 27.2, 27.3, 23.4, 23.5, 23.6, 23.7, 23.7, 23.8, 23.9, 24.0, 24.0, 24.1, 24.1, 24.2, 24.3, 24.3, 24.4, 24.4, 24.4, 24.5, 24.5, 24.5, 24.6, 24.6, 24.6, 24.7, 24.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.7, 20.6, 20.6, 20.6, 20.6, 20.6, 20.5, 20.5, 20.5, 20.4, 16.4, 16.4, 16.3, 16.3, 16.3, 16.2, 16.2, 16.1, 16.1, 16.1, 16.0, 16.0, 15.9, 15.9, 15.8, 15.8, 15.8, 15.7, 15.7, 15.6, 15.6, 15.5, 15.5, 15.4, 15.4, 11.3, 11.3, 11.2, 11.2, 11.1, 11.1, 11.1, 11.0, 11.0, 10.9, 10.9, 10.8, 10.8, 10.8, 10.7, 10.7, 10.7, 10.6, 10.6, 10.5, 10.5, 10.5, 10.5, 10.4, 10.4, 6.4, 6.4, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.2, 6.2, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.3, 6.4, 6.4, 6.4, 6.4, 2.5, 2.5, 2.5, 2.6, 2.6, 2.7, 2.7, 2.8, 2.8, 2.9, 3.0, 3.0, 3.1, 3.2, 3.2, 3.3, 3.4, 3.5, 3.6]
df = pd.DataFrame({"date": days, "temp_c": temps})
df["date"] = pd.to_datetime(df["date"])
df["month"] = df["date"].dt.month
monthly = df.groupby("month", as_index=False)["temp_c"].mean()
print(monthly)

Click ▶ Run to execute

Ctrl+Enter to run · Tab to indent

🌦️ Monthly Weather Trends

1 · Aggregate daily readings into monthly means

2 · Visualise the seasonal curve