README.md

---
title: harpertoken/stat
library_name: pandas
tags:
- system-monitoring
- time-series
- anomaly-detection
- predictive-maintenance
- macOS
license: mit
language:
- en
---

# harpertoken/stat

## Summary

A single-session capture of live macOS system telemetry: 6,190 samples of CPU,
memory, disk, network, and battery readings taken at one-second intervals over
roughly 1 hour 44 minutes on 2025-03-02.

This is one machine, one continuous session, one workload. It is small enough to
load in a second and structured for time-series experiments, not a benchmark.

## Contents

| Field | Type | Range | Notes |
|---|---|---|---|
| `timestamp` | timestamp | 2025-03-02 10:24:07 → 12:07:52 | 1 s median interval, 2 s max |
| `cpu_usage` | list[8] of float | 0.0 → 100.0 | One value per logical core, 8 cores |
| `memory_used_mb` | float | 2762.0 → 3585.7 | Used RAM, not total |
| `disk_read_mb` | float | 4,141,332 → 4,306,966 | **Cumulative** counter, unit unverified |
| `disk_write_mb` | float | 1,462,612 → 1,611,402 | **Cumulative** counter, unit unverified |
| `net_sent_mb` | float | 5,563.3 → 6,063.3 | **Cumulative** counter, unit unverified |
| `net_recv_mb` | float | 2,379.4 → 2,937.2 | **Cumulative** counter, unit unverified |
| `battery_status` | float | 15.0 → 35.0 | Battery percentage; falls to 15 then recovers to 35 |
| `cpu_temp` | null | none | Always null; see below |

Three things are worth knowing before you use this.

**The disk and network columns are since-boot counters, not per-interval
activity.** They only ever increase, and they start well above zero because the
machine was already running when capture began. Across the session they advance
by 165,634 (`disk_read_mb`), 148,790 (`disk_write_mb`), 500 (`net_sent_mb`) and
558 (`net_recv_mb`).

Treat those advances as **raw counts, not confirmed volumes.** The `_mb` suffix is
unverifiable from the data alone: read as MB they imply ~95 GB/h of reads and
~85 GB/h of writes, which is high but possible; read as KB they imply ~95 MB/h,
which also fits. Only a byte reading (162 KB total) is inconsistent with this
machine's CPU profile. Confirm the unit with whatever produced the capture before
reporting throughput in any physical unit.

Feed the raw values to a model and the dominant signal is "how long has this host
been up," which correlates with the target and will inflate your results.
Difference them first:

```python
df["disk_read_delta"] = df["disk_read_mb"].diff()
```

**`cpu_temp` is empty.** All 6,190 rows are `N/A`; the temperature probe was not
available on this machine. The column is retained so the schema matches the
collector, but it carries no information and should be dropped rather than
imputed.

## Loading

`load_dataset("harpertoken/stat")` works and returns all 6,190 rows in a single
`train` split, with `cpu_usage` already decoded into a list of eight floats and
`cpu_temp` as `None`. The full CSV and a Parquet rebuilt from it are published
alongside it; read one explicitly when you want to control the parsing:

```python
import pandas as pd

df = pd.read_csv(
    "https://huggingface.co/datasets/harpertoken/stat/resolve/main/system_monitoring_dataset.csv"
)
df["timestamp"] = pd.to_datetime(df["timestamp"])
df["cpu_usage"] = df["cpu_usage"].apply(
    lambda v: [float(x) for x in v.strip("[]").split(",")]
)
df = df.drop(columns=["cpu_temp"])

# per-core CPU -> mean, and cumulative counters -> per-interval deltas
df["cpu_mean"] = df["cpu_usage"].apply(sum) / df["cpu_usage"].apply(len)
for col in ["disk_read_mb", "disk_write_mb", "net_sent_mb", "net_recv_mb"]:
    df[col.replace("_mb", "_delta")] = df[col].diff()
```

Prefer a parquet round-trip, which keeps the list column typed and skips the
string parsing above. `pyarrow.read_table` will not open an HTTPS URL, so fetch the
file first:

```python
from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq

path = hf_hub_download(
    "harpertoken/stat", "system_monitoring_dataset.parquet", repo_type="dataset"
)
df = pq.read_table(path).to_pandas()

# cpu_usage arrives as an array of 8 floats, and timestamp is already datetime
```

## Intended uses

Short-horizon forecasting of memory and CPU on a single host, and unsupervised
anomaly detection where an alert is raised on a burst of load or memory growth.
Because the session is one hour on one machine, treat it as a demonstration of
the collection format rather than evidence of generalization; a model tuned on
this capture will need refitting on data from the deployment host.

## Out of scope

Multimachine or fleet monitoring, multi-day trends, thermal analysis
(`cpu_temp` is null), and battery-cycle modeling: the session spans a single
discharge from 33% to 15% followed by a recharge to 35%, which is one partial
charge cycle, not enough to model cycling behaviour.

## Provenance

Collected locally on a Mac via a system-metric sampler. No network payloads,
process names, or user data are present; the telemetry is host-level only.
Licensed MIT.

Contact: coccinella.labs@icloud.com