Space launches 🚀

Overview of space launches up to at least earth orbit.

Source: GCAT: General Catalog of Artifical Space Objects by J. McDowell

Last 10 launches

• 10. Aug 2026 H3-22S from Tanegashima Space Center, Tanegashima, Nippon (J)
• 10. Aug 2026 Chang Zheng 7A from Wenchang Space Center, Hainan (CN)
• 8. Aug 2026 Falcon 9 from South Base, Vandenberg Space Force Base, California (US)
• 6. Aug 2026 Electron from Rocket Lab Launch Complex 1, Onenui Station, Mahia Peninsula (NZ)
• 5. Aug 2026 Falcon 9 from Cape Canaveral Air Station, Florida (US)
• 5. Aug 2026 Jielong-3 from Huang Hai CZ-11 launch zone (CN)
• 4. Aug 2026 Falcon 9 from South Base, Vandenberg Space Force Base, California (US)
• 4. Aug 2026 Chang Zheng 8A from Hainan Shangye Hangtian Fashe Chang (Hainan Commercial Space Launch Site) (CN)
• 1. Aug 2026 Falcon 9 from South Base, Vandenberg Space Force Base, California (US)
• 30. Jul 2026 Falcon 9 from Cape Canaveral Air Station, Florida (US)

Source code

import inspect
import sys
import pandas as pd 
from novem import Plot

# Retrieve data for Launches and Launch Sites 
URL_LAUNCHES     = 'https://planet4589.org/space/gcat/tsv/launch/launch.tsv'
URL_LAUNCH_SITES = 'https://planet4589.org/space/gcat/tsv/tables/sites.tsv'


COUNTRY_MAP = {
    "US": "US",
    "TTPI": "US", # Trust Territory of the Pacific Islands
    "UM67": "US", # United States Minor Outlying Islands
    "UM79": "US", # United States Minor Outlying Islands
    "PCZ": "US", # Pacific Trust Territory
    "RU": "RU",
    "SU": "RU", # USSR
    "CN": "CN",
}

COUNTRY_ORDER = ["US", "CN", "RU",  "Others"]


def load_data():
    launches = pd.read_csv(URL_LAUNCHES, sep="\t")
    sites    = pd.read_csv(URL_LAUNCH_SITES, sep="\t")
    return launches.merge(sites[["#Site", "StateCode","Name"]],
                          left_on="Launch_Site", right_on="#Site", how="left")

def clean_data(df):
    # Extract Year from messy String
    df['Date'] = pd.to_datetime(df['Launch_Date'].str.rstrip("?").str.split().str[:3].str.join(" "), errors='coerce')
    df['Year'] = df['Date'].dt.year
    # Apply mapping, send everything else to "Others"
    df["Country"] = df["StateCode"].map(COUNTRY_MAP).fillna("Others")
    df["Country"] = pd.Categorical(
        df["Country"], categories=COUNTRY_ORDER, ordered=True
    )
    df_space = df[df["LaunchCode"].astype(str).str.startswith("O")] # O for Orbital
    
    return df_space



df = load_data()
df_space = clean_data(df)



novem_data_table = df_space.pivot_table(index='Year', columns='Country', aggfunc='size', observed=False).fillna(0)

plt = Plot('ex-space-launch')
plt.data = novem_data_table
plt.type = 'sbar'
plt.name = 'Dawn of a new space age 🚀'
plt.summary = 'Number of space launches from GCAT that reached Earth orbit, by country and year.'
plt.shared = 'public'

# Description includes source, markup table and this python code itself 
plt.description = '''# Space launches 🚀
Overview of space launches up to at least earth orbit. 

Source:  *GCAT: General Catalog of Artifical Space Objects* by J. McDowell\n

## Last 10 launches

'''

df_last_10 = df_space.sort_values(by='Launch_JD', ascending=False).head(10)
df_last_10_table = '\n'.join(
    f"- {row['Date'].strftime('%d. %b %Y')} {row['LV_Type']} from {row['Name']} ({row['StateCode']}) "
    for _, row in df_last_10.iterrows()
)

plt.description += df_last_10_table

plt.description += '\n\n## Source code\n\n'
plt.description += '\n\n```python' + ' ' + inspect.getsource(sys.modules[__name__]) + '```'