Datasets:
File size: 6,876 Bytes
444825d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 | {
"nbformat": 4,
"nbformat_minor": 5,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": ["# Witnessed Meteorite Falls - NASA Catalog\n\nExplore **1,097 meteorites** observed falling to Earth from 1688 to 2013.\n\n**Dataset Highlights:**\n- Witnessed falls only (not discovered finds)\n- 325 years of documented observations\n- Pristine samples for scientific study\n- ~3% of all known meteorites"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["import json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport warnings\nwarnings.filterwarnings('ignore')\n\nplt.style.use('seaborn-v0_8-whitegrid')\nprint('Libraries loaded')"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## 1. Load Dataset"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["with open('witnessed_meteorite_falls.json') as f:\n data = json.load(f)\n\ndf = pd.DataFrame(data)\nprint(f'Total witnessed falls: {len(df):,}')\ndf.head()"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## 2. Why Witnessed Falls Matter"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["print('Witnessed Falls vs Discovered Finds:')\nprint('=' * 50)\nprint(f'Witnessed Falls in this dataset: {len(df):,}')\nprint(f'Total NASA catalog: ~38,400')\nprint(f'Percentage witnessed: {len(df)/38400*100:.1f}%')\nprint()\nprint('Benefits of witnessed falls:')\nprint('- Exact fall date known')\nprint('- Often trajectory documented')\nprint('- Minimal terrestrial contamination')\nprint('- Highest scientific value')"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## 3. Classification Distribution"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["# Extract main classification\ndf['main_class'] = df['meteorite_class'].str.split('[,\\s]').str[0]\nclass_counts = df['main_class'].value_counts().head(15)\n\nprint('Top Meteorite Classifications:')\nprint('=' * 40)\nfor cls, count in class_counts.items():\n pct = count / len(df) * 100\n print(f'{cls:15s} {count:5,} ({pct:5.1f}%)')"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["fig, ax = plt.subplots(figsize=(10, 6))\nclass_counts.plot(kind='barh', ax=ax, color='darkblue')\nax.set_xlabel('Number of Falls')\nax.set_title('Witnessed Falls by Classification', fontweight='bold')\nplt.tight_layout()\nplt.show()"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## 4. Temporal Distribution"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["df['year'] = pd.to_datetime(df['date'], errors='coerce').dt.year\ndf['century'] = (df['year'] // 100) * 100\n\ncentury_counts = df.groupby('century').size()\nprint('Falls by Century:')\nprint('=' * 40)\nfor century, count in century_counts.items():\n if century >= 1600:\n print(f'{int(century)}s: {count:5,}')"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["recent = df[df['year'] >= 1800]\nyearly = recent.groupby('year').size()\n\nfig, ax = plt.subplots(figsize=(14, 5))\nyearly.plot(ax=ax, color='darkblue')\nax.set_xlabel('Year')\nax.set_ylabel('Number of Witnessed Falls')\nax.set_title('Witnessed Meteorite Falls by Year (1800+)', fontweight='bold')\nplt.tight_layout()\nplt.show()"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## 5. Mass Distribution"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["with_mass = df[df['mass_g'].notna() & (df['mass_g'] > 0)]\nprint(f'Falls with mass data: {len(with_mass):,}')\nprint(f'\\nMass Statistics (grams):')\nprint(f'Minimum: {with_mass[\"mass_g\"].min():,.0f} g')\nprint(f'Median: {with_mass[\"mass_g\"].median():,.0f} g')\nprint(f'Maximum: {with_mass[\"mass_g\"].max():,.0f} g')"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["heaviest = with_mass.nlargest(10, 'mass_g')[['name', 'date', 'mass_g', 'meteorite_class']]\nprint('\\nLargest Witnessed Falls:')\nprint('=' * 70)\nfor i, row in heaviest.iterrows():\n mass_kg = row['mass_g'] / 1000\n print(f\"{row['name']:25s} {mass_kg:10,.0f} kg {row['meteorite_class']}\")"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## 6. Geographic Distribution"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["valid_coords = df[(df['latitude'].notna()) & (df['longitude'].notna())]\nprint(f'Falls with coordinates: {len(valid_coords):,}')\n\nfig, ax = plt.subplots(figsize=(14, 7))\nax.scatter(valid_coords['longitude'], valid_coords['latitude'], \n alpha=0.6, s=20, c='darkblue')\nax.set_xlabel('Longitude')\nax.set_ylabel('Latitude')\nax.set_title('Witnessed Meteorite Fall Locations', fontweight='bold')\nax.set_xlim(-180, 180)\nax.set_ylim(-90, 90)\nax.grid(alpha=0.3)\nplt.tight_layout()\nplt.show()"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## 7. Famous Witnessed Falls"]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": ["famous_names = ['Murchison', 'Allende', 'Peekskill', 'Chelyabinsk', 'Sikhote-Alin', 'Tunguska']\n\nprint('Famous Witnessed Falls in Dataset:')\nprint('=' * 60)\nfor name in famous_names:\n matches = df[df['name'].str.contains(name, case=False, na=False)]\n if len(matches) > 0:\n for i, row in matches.iterrows():\n print(f\"{row['name']:25s} {row['date'][:10]} {row['meteorite_class']}\")"]
},
{
"cell_type": "markdown",
"metadata": {},
"source": ["## Conclusion\n\nThis notebook demonstrated:\n- Loading 1,097 witnessed meteorite falls\n- Scientific value of witnessed vs found meteorites\n- Classification distribution (L chondrites most common)\n- 325 years of documented observations\n- Geographic patterns of fall locations\n\n**Source**: NASA Meteoritical Bulletin\n\n**Author**: Luke Steuber | @lukesteuber.com (Bluesky)"]
}
]
}
|