The income of the film industry offers a window into how the world of cinema and media is changing. Financial metrics help reveal the shift from movie theaters to digital platforms and streaming services.
This project combines data analysis with visual research into contemporary media.
Introduction
What is the essence of the work? What data is analyzed: Data on the revenues of the film industry are analyzed. A teaching dataset, created in Python, was used.
Why was this specific topic chosen: Cinema is a major industry where economic changes and the impact of technology can be traced.
Types of charts used in the work: — Line charts—to show year-over-year dynamics — Bar charts—for budget comparisons — Scatter plots—to illustrate streaming growth
Different charts were created for greater visual clarity.
Stages of Work
Data Processing: A dataset was created, a Pandas table was made, and charts were generated using Matplotlib.
Neural Network Usage: Standard ChatGPT was utilized for assistance with coding and analysis.
Styling: The charts were designed in a unified minimalist style.
Visualization Format: Concise explanations were added to each chart.
Statistical Methods: Descriptive statistics, comparison, and trend analysis.
Import Libraries
import pandas as pd import matplotlib.pyplot as plt
Dataset Creation
data = { «Year»: [2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023],
«Films_Released»: [450, 470, 500, 520, 540, 560, 580, 600, 630, 650,
680, 700, 720, 740, 760, 500, 520, 680, 750],
«Average_Budget_Million»: [40, 42, 45, 47, 50, 52, 55, 58, 60, 63,
65, 68, 70, 72, 75, 60, 62, 74, 78],
«Total_BoxOffice_Billion»: [25, 27, 30, 32, 35, 38, 41, 45, 48, 52,
55, 58, 62, 66, 70, 42, 46, 68, 75],
«Streaming_Revenue_Billion»: [0, 0, 0, 0, 1, 2, 3, 5, 7, 9,
12, 15, 18, 22, 26, 35, 40, 48, 55]
}
Converting Data to a Pandas DataFrame
df = pd.DataFrame (data) print (df)
Schedule 1: Number of films produced by year
plt.figure () plt.plot (df[«Year»], df[«Films_Released»]) plt.title («Number of Films Released by Year») plt.xlabel («Year») plt.ylabel («Number of Films») plt.show ()
Film production was growing, but it experienced a sharp decline in 2020.
Conclusion: The industry is dependent on external events, but it is capable of recovery.
Schedule 2: Average Film Budget
plt.figure () plt.bar (df[«Year»], df[«Average_Budget_Million»]) plt.title («Average Film Budget (Million $)») plt.xlabel («Year») plt.ylabel («Budget (Million $)») plt.show ()
Budgets are steadily increasing over time.
Conclusion: Filmmaking is becoming more expensive due to technology and production scale.
Schedule 3: General Box Office Receipts
plt.figure () plt.plot (df[«Year»], df[«Total_BoxOffice_Billion»]) plt.title («Total Box Office Revenue (Billions USD)») plt.xlabel («Year») plt.ylabel («Revenue (Billions USD)») plt.show ()
Cinema revenues grew alongside the market, then temporarily declined.
Conclusion: the profitability of the cinema industry is tied to the availability of in-person screenings.
Schedule 4: Revenue of Streaming Services
plt.figure () plt.scatter (df[«Year»], df[«Streaming_Revenue_Billion»]) plt.title («Streaming Service Revenue (Billion $)») plt.xlabel («Year») plt.ylabel («Revenue (Billion $)») plt.show ()
There is rapid and sustained growth in online platforms.
Conclusion: Streaming services are becoming a key revenue source and are changing viewing models.
Overall Conclusion