StephanieNelima/Wildlife-At-The-Photohide

Wildlife activity at the photohide analyses the appearances of different wild life at the photographic hide. This aims to understand the most common animals at the watering hole as well as their most common appearance times in terms of hours weeks and months in an effort to further wildlife conservation efforts in Kenya. # Photohide Activity Analysis — Documentation **Overview** I analyzed wildlife activity data recorded at a photographic hide situated at a water source in Lumo, Kenya. The objective was to characterize which species visit the hide, how frequently, at what times, and for how long, from raw sighting records through to summarized, exportable outputs. This aimed to further wildlife conservation efforts in Kenya. **Data preparation** I loaded the source workbook and renamed its unlabelled columns to Date, Type of animal, Quantity, Time in, Time out, and Mins. Spent, then dropped three extraneous unnamed columns that carried no data. I removed the first three rows, which held header remnants rather than observations, and converted Quantity to a numeric type. I subsequently parsed Date, Time in, and Time out into proper datetime objects and Mins. Spent into a timedelta, discarding unparseable values, to make the records suitable for time-based aggregation. **Species frequency analysis** I grouped the cleaned records by animal type and counted total appearances, sorting to identify the twenty most frequently observed species at the hide. **Temporal aggregation** I aggregated total and average animal counts by calendar month and separately by week, in each case discarding periods with zero recorded activity, to characterize seasonal and short-term patterns in hide usage. **Duration analysis** I computed the average time spent at the hide for each animal type by grouping on species and averaging the Mins. Spent field. **Peak-hour analysis** I grouped sightings by animal type and by hour of Time in to find, for each species, the maximum number of animals recorded in any given hour, exporting this as an intermediate file. I then identified, per species, the hour associated with that maximum count, and joined the two results using a concatenated animal-and-count key to isolate the specific peak hour for each species. The result was exported as a consolidated peak-hou …