SpaceX Launch Trends & Operational Analysis

The Challenge of Official Statistics

While SpaceX provides summarized success rates, I wanted to build a system that verifies these numbers independently. The challenge was to pull multi-dimensional data from disparate API endpoints (Launches, Launchpads, and Rockets) and consolidate them into a single source of truth for auditing purposes.

Objective & Scope

My objective was to automate the data extraction process and perform exploratory data analysis (EDA) to answer two key questions:

• How has recovery technology improved mission success rates over time?
• Does a specific launchpad correlate with mission failure rates?

The "Audit" Mindset in Data Joining

Applying my Chartered Accountancy background, Applying my Chartered Accountancy background, I approached the data joining process like a financial reconciliation. Instead of using the pre-calculated success columns in the Launchpad table, I performed an Inner Join between the raw mission logs and the infrastructure metadata.

Acutal Data From SPACEX

"I compared the actual SPACEX data using the launchpads table."

Fig 2: Plotting calculated success rates provided by SPACEX API.

Verification Logic

"I converted boolean success flags into binary (1/0) integers and calculated the mean across joined datasets to verify official reports."

Fig 1: Manually calculated success rates verified via raw launch logs.

I utilized Seaborn for high-fidelity visualizations, focusing on 'failure gaps'—the visual distance between total attempts and successful outcomes. This allowed for an immediate visual audit of each launch site's operational efficiency.

Fairings Recovery Attempts

"Analyzing the attempts of fairing recovery and successes."

Fig 3: Fairings recovery attempts.

Fairings Recovery Success trend

"Analyzing the Success Rate of Recovery Over the Years."

Fig 4: Fairings recovery trend over the years.