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12  How many people attend our classes and performances?

Total attendance across classes, performances/events, and community gatherings, by fiscal year, with what those attendees paid in tickets and class fees. Attendance counts people — each registration’s ticket records — rather than registration counts (about 1.7 people per registration); the fiscal year runs July–June and is labeled by its ending year. See Methodology for the event categories, the headcount definition, and the cross-cutting fundraiser / youth / film flags. Class-taking households specifically are profiled in Who takes classes at Hubbard Hall?.

Code 
import pandas as pd
import plotly.express as px
from hh.io import read_parquet
from hh.viz import style, BLUE

regs = read_parquet("processed", "registrations_enriched.parquet")

from hh.analytics.attendance import attendance_by_fy
from hh.analytics.mailing import fiscal_year

tab = attendance_by_fy(regs)
current_fy = int(fiscal_year(pd.Series(pd.Timestamp.today()))[0])
complete = tab.loc[tab.index < current_fy]  # the current FY is partial

CATS = ["classes", "performances_events", "community"]
LABEL = {"classes": "Classes", "performances_events": "Performances & events",
         "community": "Community"}
Code 
attendees = complete[[f"{c}_att" for c in CATS]].astype(int)
attendees["Total"] = attendees.sum(axis=1)
attendees.columns = [*map(LABEL.get, CATS), "Total"]
attendees.index = [f"FY{fy - 2000}" for fy in attendees.index]
attendees.index.name = "Fiscal year"
ytd = int(tab.loc[current_fy, "total_att"])
attendees
Table 12.1
Classes Performances & events Community Total
Fiscal year
FY14 596 2459 100 3155
FY15 1209 2818 335 4362
FY16 1364 2832 314 4510
FY17 1766 2678 308 4752
FY18 2473 2948 221 5642
FY19 2705 1603 276 4584
FY20 1388 1283 192 2863
FY21 390 645 41 1076
FY22 1032 763 58 1853
FY23 2207 1661 0 3868
FY24 2212 1214 67 3493
FY25 1246 2221 134 3601
FY26 1071 2589 150 3810

FY27 attendance is year-to-date as of the latest data pull and will rise as the year fills out (1,021 attendees so far).

Code 
long = (complete[[f"{c}_att" for c in CATS]]
        .rename(columns={f"{c}_att": LABEL[c] for c in CATS})
        .reset_index()
        .melt(id_vars="fy", var_name="Category", value_name="Attendees"))
long["FY"] = long["fy"].map(lambda y: f"FY{y - 2000}")
fig = px.bar(long, x="FY", y="Attendees", color="Category",
             category_orders={"Category": [LABEL[c] for c in CATS]},
             color_discrete_sequence=[BLUE, "#b3cde3", "#f0e9d8"])
style(fig).show()
Figure 12.1
Code 
revenue = complete[[f"{c}_rev" for c in CATS]].round(0)
revenue["Total"] = complete["total_rev"].round(0)
revenue.columns = [*map(LABEL.get, CATS), "Total"]
revenue.index = [f"FY{fy - 2000}" for fy in revenue.index]
revenue.index.name = "Fiscal year"
revenue.style.format("${:,.0f}")
Table 12.2
  Classes Performances & events Community Total
Fiscal year        
FY14 $65,871 $42,229 $3,755 $111,855
FY15 $97,202 $71,070 $8,255 $176,526
FY16 $92,445 $69,972 $8,072 $170,489
FY17 $96,018 $70,520 $5,730 $172,269
FY18 $115,593 $89,730 $6,589 $211,911
FY19 $109,800 $54,277 $3,863 $167,940
FY20 $84,025 $37,548 $2,660 $124,234
FY21 $43,043 $17,335 $410 $60,788
FY22 $46,940 $16,985 $0 $63,925
FY23 $79,094 $36,195 $0 $115,289
FY24 $91,515 $26,406 $780 $118,701
FY25 $97,375 $66,141 $1,937 $165,453
FY26 $97,215 $81,782 $2,241 $181,238

Notes on these figures: