Space launches 🚀<\/h1>
Overview of space launches up to at least earth orbit.<\/p>
Source: GCAT: General Catalog of Artifical Space Objects<\/em> by J. McDowell<\/p>

Last 10 launches<\/h2>

Jun 2026 Falcon 9 from South Base, Vandenberg Space Force Base, California (US)<\/li> <\/ol> <\/li>

Jun 2026 Lijian-1 from Jiuquan Space Center, Nei Monggol Zizhiqu, China (CN)<\/li> <\/ol> <\/li>

Jun 2026 Falcon 9 from Cape Canaveral Air Station, Florida (US)<\/li> <\/ol> <\/li>

Jun 2026 H3-30S from Tanegashima Space Center, Tanegashima, Nippon (J)<\/li> <\/ol> <\/li>

Jun 2026 Falcon 9 from South Base, Vandenberg Space Force Base, California (US)<\/li> <\/ol> <\/li>

Jun 2026 HASTE from Mid-Atlantic Regional Spaceport, Wallops Island, Virginia (US)<\/li> <\/ol> <\/li>

Jun 2026 Chang Zheng 5 from Wenchang Space Center, Hainan (CN)<\/li> <\/ol> <\/li>

Jun 2026 Zhuque-2E from Jiuquan Space Center, Nei Monggol Zizhiqu, China (CN)<\/li> <\/ol> <\/li>

Jun 2026 Falcon 9 from Cape Canaveral Air Station, Florida (US)<\/li> <\/ol> <\/li>

Jun 2026 Falcon 9 from South Base, Vandenberg Space Force Base, California (US)<\/li> <\/ol> <\/li> <\/ul>
Source code<\/h2>
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__]) + '```' <\/code><\/pre>

Space launches 🚀<\/h1> Overview of space launches up to at least earth orbit.<\/p> Source: GCAT: General Catalog of Artifical Space Objects<\/em> by J. McDowell<\/p>

Space launches 🚀<\/h1>
Overview of space launches up to at least earth orbit.<\/p>
Source: GCAT: General Catalog of Artifical Space Objects<\/em> by J. McDowell<\/p>

Dawn of a new space age 🚀

Variables