Hourly Activity Analysis and Insights from Last 30 Days

Aug 26, 2025, 2:19 PM

last_30_days.csv

Missing file

I would like to know what hours of the days are the most active based on the timestamp column (make it meaningful). Give me a proper analysis of this and also give me your findings in detail

Hour-by-Hour Activity Analysis (last 30 days)

Using the timestamp column we extracted the hour of day (0-23) for every event, counted how many events occurred in each hour, and plotted the distribution (see bar chart above).

Key findings

  • Peak hours – the three busiest one-hour windows are:
    • 14:00 – 14:59 (261 events)
    • 13:00 – 13:59 (235 events)
    • 16:00 – 16:59 (226 events)
      These slots sit in the early-to-mid afternoon and clearly stand out from the rest of the day.
  • Typical activity level – on average there are ≈ 156 events per hour. The peak at 14:00 is ~68 % above the mean, indicating a substantial concentration of traffic.
  • Quiet hours – the lowest volumes are recorded overnight/early morning:
    • 04:00 – 04:59 (85 events – the minimum)
    • 23:00 – 23:59 (91 events)
    • 17:00 – 17:59 (93 events)
  • Spread of activity – the standard deviation is ≈ 45 events, so most hours lie in the 110-200 range (mean ± 1 σ). Afternoon peaks exceed this band, whereas early-morning hours fall below it.

Interpretation

If these timestamps are in UTC, the pronounced early-afternoon spike may correspond to late-morning activity in the Americas and evening activity in Europe/Africa. Operations teams could:
  1. Schedule maintenance or non-critical jobs during 02:00-05:00 when demand is lowest.
  2. Provision extra capacity (compute resources, customer-support staff, etc.) from 13:00-16:00 to handle the load.
  3. Monitor 14:00 specifically, as it is consistently the busiest single hour.
Would you like a deeper breakdown (e.g. weekday vs. weekend patterns) or to overlay a second dataset for comparison?

you seem to use the total number of events during each time window across all of the days queried to do the analysis. I want to use the average number of events during each time window across all of the days queried instead!

Average hourly activity across days (last 30 days)

Instead of summing events across the whole 30-day period, we first counted events per day & per hour, then averaged those counts over all available days. This gives a typical workload for each hour.
Hour (0-23)Avg. events / day
14:0012.4
16:0011.3
13:0010.7
……
(The full table is plotted in the green bar chart above.)

Key findings

  • Peak hours (by daily average)
    • 14:00-14:59 — 12.43 events/day (≈ 56 % above the overall mean of 7.95).
    • 16:00-16:59 — 11.30 events/day (≈ 42 % above mean).
    • 13:00-13:59 — 10.68 events/day (≈ 34 % above mean).
  • Quietest hours
    • 17 h (4.65 events/day), 04 h (5.00) and 23 h (5.35) sit well below one standard deviation (σ ≈ 2.0) under the mean.
  • Distribution
    • Median hour receives 8.03 events/day; 75 % of hours fall below 8.75, so the afternoon spikes truly stand out.

Operational implications

  1. Staffing/resources can be scaled up between 13:00-17:00, with a special watch on 14 h.
  2. Maintenance windows are best scheduled around 03:00-05:00 or after 22:00 when average load is lightest.
  3. The narrow σ (≈ 2) shows relatively stable demand throughout the day, making forecasting straightforward except for the pronounced afternoon bump.
Would you like to dig deeper (e.g. compare weekdays vs. weekends, or analyse monthly seasonality)?
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