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Cinema admission

As explained in The Well-Grounded Data Analyst

A cinema admission is a recorded instance or count of a person attending a film screening.

Cinema admission measures attendance at film screenings, either as a total or as a count recorded for a month, country, region, or year. It supports comparisons of audience patterns before, during, and after COVID-19 lockdowns. section 5.2.1 section 5.4.1

Useful admission views include:

  • Total admissions by country. section 5.4.1
  • Monthly admissions in the United Kingdom for identifying seasonal patterns. section 5.4.1
  • Annual admission figures extending back to 1935. section 5.4.1
  • Admission distributions across pre-COVID, during-COVID, and post-lockdown periods. section 5.4.3

Comparing admission periods

Load monthly admissions, combine month and year into a date, assign ordered COVID-period categories, check for missing assignments, and compare monthly means, medians, distributions, and time-series plots. Separating PDF extraction from analysis avoids rerunning extraction whenever the analysis changes. section 5.4.3

import pandas as pd
import matplotlib.pyplot as plt
import datetime
admissions = pd.read_csv("admissions.csv")
print(admissions.shape)
admissions.head()

Shows the extracted admissions dataset used as the starting point for inspection and analysis. section 5.4.3

How The Well-Grounded Data Analyst covers it

How deep it goes
Analyzes monthly values through date conversion, ordered period labels, quality checks, plots, summary statistics, and histograms.
Examples
Uses country totals, UK monthly values, historical annual figures, COVID-period plots, averages, medians, and histograms.
What it assumes
Assumes extracted admissions data while demonstrating date handling, categorical ordering, missing-data checks, grouping, and plotting.
What you can do after
Evaluate admission trends by constructing periods, validating extracted data, and comparing attendance levels and distributions.
How it connects
Links cinema admissions to COVID-19 lockdowns, seasonal patterns, genres, distributors, and market share.
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