A data-driven exploration of the Space Race — from Sputnik in 1957 to the present day.
Astrolytics is a Python-based data analysis project that digs into more than six decades of space mission history. Using a dataset of 4,300+ launches scraped from nextspaceflight.com, this project explores launch trends, mission costs, success/failure rates, and the historic USA vs. USSR rivalry that kicked off the era of spaceflight.
Astrolytics answers questions like:
- Which organisations have launched the most rockets — and who dominates today?
- How many rockets are still active vs. retired?
- How expensive is a launch, and how has that cost changed over time?
- Which countries lead in total launches and mission failures? (visualized on choropleth maps)
- How did the Cold War-era Space Race between the USA and USSR play out year by year?
- Which months are the most popular for launches?
- Who led the "launch race" globally, year by year, from 1957 to 2020?
The analysis is presented as a fully worked Jupyter notebook, combining data cleaning, descriptive statistics, and rich interactive visualizations.
- Python 3
- pandas & numpy — data cleaning, wrangling, and analysis
- matplotlib & seaborn — statistical plotting
- plotly (
plotly.express) — interactive charts, choropleth maps, sunburst charts - iso3166 — mapping country names to ISO codes for geographic visualizations
- Jupyter Notebook
astrolytics/
├── mission_launches.csv # Raw dataset (4,324 launch records)
├── Space_Missions_Analysis__start_.ipynb # Starter notebook (unsolved / template)
├── Space_Missions_Analysis__solved_.ipynb # Full analysis with visualizations
└── README.md
The dataset (mission_launches.csv) contains launch records with the following fields:
| Column | Description |
|---|---|
Organisation |
Agency or company that conducted the launch |
Location |
Launch site |
Date |
Date and time of launch (UTC) |
Detail |
Rocket and payload/mission details |
Rocket_Status |
Whether the rocket is currently active or retired |
Price |
Estimated cost of the launch (USD, millions) |
Mission_Status |
Outcome of the mission (Success / Failure, etc.) |
- Preliminary Data Exploration — cleaning missing values, duplicates, and descriptive stats
- Launches per Company — who's launching the most rockets?
- Active vs. Retired Rockets
- Mission Status Distribution
- Launch Cost Analysis — how expensive is spaceflight, and by whom?
- Choropleth Maps — launches and failures by country
- Sunburst Chart — countries → organisations → mission outcomes
- Spending Analysis — total and per-launch spend by organisation
- Launch Trends Over Time — yearly and month-on-month patterns
- Cold War Space Race — USA vs. USSR launches, failures, and failure rates over time
- Year-by-Year Leaders — which country/organisation led the launch count each year
Make sure you have Python 3 and Jupyter installed, then install the required packages:
pip install numpy pandas matplotlib seaborn plotly iso3166
git clone https://github.com/rhitamcoder/astrolytics.git
cd astrolytics
jupyter notebook Space_Missions_Analysis__solved_.ipynb
If you'd like to work through the analysis yourself, start with Space_Missions_Analysis__start_.ipynb instead — it contains the setup and questions without the completed solutions.
- The Space Race saw dramatic shifts in dominance — from the USSR's early lead through the Cold War era, to the rise of organisations like CASC and SpaceX in more recent decades.
- Launch costs vary enormously between organisations, reflecting differences in rocket technology and reusability.
- Certain months show consistently higher launch activity, hinting at scheduling and weather-related patterns.
The code in this repository (notebooks and analysis) is licensed under the MIT License — see the LICENSE file for details.
Note: The MIT License applies to the code only. The dataset (
mission_launches.csv) was scraped from a third-party source and is included here for educational/analysis purposes — it is not covered by this repository's license. Please check nextspaceflight.com for their own terms if you intend to reuse the data itself.
Dataset sourced from nextspaceflight.com.