Impact craters on Earth

Oldest crater: Suavjärvi (63.1, 33.4), 16.0 km, 2400.0 Ma
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Largest impact crater in North America:
Chicxulub (21.3, -88.5), 150.0 km, 64.98 Ma
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Largest impact crater in South America:
Araguainha (-15.2, -51.0), 40.0 km, 254.7 Ma
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Largest impact crater in Europe:
Siljan (61.0, 14.9), 52.0 km, 376.8 Ma
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Largest impact crater in Asia:
Popigai (71.7, 111.2), 90.0 km, 35.7 Ma
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Largest impact crater in Africa:
Vredefort (-27.0, 27.5), 160.0 km, 2023.0 Ma
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Largest impact crater in Australia:
Acraman (-32.0, 135.4), 90.0 km, 590.0 Ma

import os
import urllib.request
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import geopandas

# The directory we're going to save local copies of the HTML files into.
HTML_DIR = 'html'
if not os.path.exists(HTML_DIR):
    os.mkdir(HTML_DIR)

# The URLs for the HTML files at the online Earth Impact Database (EID).
BASE_URL = 'http://www.passc.net/EarthImpactDatabase/New%20website_05-2018/'
urls = {'North America': BASE_URL + 'NorthAmerica.html',
        'South America': BASE_URL + 'SouthAmerica.html',
        'Europe': BASE_URL + 'Europe.html',
        'Asia': BASE_URL + 'AsiaRussia.html',
        'Africa': BASE_URL + 'Africa.html',
        'Australia': BASE_URL + 'Australia.html'
       }
# Make a dictionary of local filenames (without path). Use the same filename as
# on the EID server.
filenames = dict((continent,
                  url.rsplit('/', 1)[-1]) for continent ,url in urls.items())

def get_htmlpath(filename):
    """Get qualified path to local HTML file filename."""
    return os.path.join(HTML_DIR, filename)

def fetch_html(continent, filename):
    """Fetch HTML file for continent from internet and save as filename."""

    print('Fetching HTML file', filename)
    url = urls[continent]
    req = urllib.request.urlopen(url)
    html = req.read().decode()
    with open(get_htmlpath(filename), 'w') as fo:
        fo.write(html)

# Fetch the HTML into local files if we don't have them already.
for continent, filename in filenames.items():
    if not os.path.exists(get_htmlpath(filename)):
        fetch_html(continent, filename)

def parse_latlong(s):
    """Parse an angle in degrees and minutes into a decimal float."""

    s = s.replace('N','').replace('S','-').replace('W','-').replace('E','')
    s = s.replace(' ','').replace("'",'')
    d, m = s.split('°')
    return float(d) + float(m)/60

def parse_numeric(s):
    """Parse s into a float if possible, ignoring asterisks."""

    s = s.replace('*', '')
    return pd.to_numeric(s, errors='coerce')

def parse_age(s):
    """Parse s into a crater age if possible, handling uncertainties."""

    note = ''
    s = s.replace('*', '')

    if '<' in s:
        note = 'upper bound'
    elif '>' in s:
        note = 'lower bound'
    elif '~' in s:
        note = 'approximate age'
    s = s.replace('<', '').replace('>', '').replace('~', '')
    if '±' in s:
        age, unc = s.split('±')
        return float(age), float(unc), note
    elif '-' in s:
        lower_age, upper_age = s.split('-')
        try:
            lower_age, upper_age = float(lower_age), float(upper_age)
        except ValueError:
            return np.nan, np.nan, 'parsing age failed'
        mean_age = (lower_age + upper_age) / 2
        unc = mean_age - lower_age
        return mean_age, unc, note

    try:
        return float(s), np.nan, note
    except ValueError:
        return np.nan, np.nan, 'parsing age failed'

def read_html_to_pd(filename):
    filepath = get_htmlpath(filename)
    # It's the second table we want.
    df = pd.read_html(filepath)[1]

    # The column names have been put in the first row: fix this.
    df.columns = df.iloc[0]
    df = df.reindex(df.index.drop(0))

    # Convert longitude and latitude strings to floats
    df['Longitude'] = df['Longitude'].apply(parse_latlong)
    df['Latitude'] = df['Latitude'].apply(parse_latlong)
    df['Diameter (km)'] = df['Diameter (km)'].apply(parse_numeric)
    df['Age (Ma)*'], df['Age unc. (Ma)'], df['Note'] = zip(
                                *df['Age (Ma)*'].apply(parse_age))
    df['Exposed'] = df['Exposed'] == 'Y'
    df['Drilled'] = df['Drilled'] == 'Y'
    return df

dd = {}
# Parse the HTML tables for the data, for each continent
for continent, filename in filenames.items():
    dd[continent] = read_html_to_pd(filename)
df = pd.concat(dd)

def crater_summary(crater):
    """Return a string summarizing the important features of crater."""
    return (f"{crater['Crater Name']} "
            f"({crater['Latitude']:.1f}, {crater['Longitude']:.1f})"
            f", {crater['Diameter (km)']} km, {crater['Age (Ma)*']} Ma")

i_oldest = df['Age (Ma)*'].idxmax()
print('Oldest crater:', crater_summary(df.loc[i_oldest]))

for continent in filenames.keys():
    i_largest = df.loc[continent].loc[:,'Diameter (km)'].idxmax()
    print('--')
    print('Largest impact crater in {}:'.format(continent))
    print(crater_summary(df.loc[continent].loc[i_largest]))

fig, ax = plt.subplots()
ax.set_aspect('equal')

world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.plot(ax=ax)
sizes = np.clip((np.pi*df['Diameter (km)']/2)**2 / 80, 20, None)
ax.scatter(df['Longitude'], df['Latitude'], c='r', alpha=0.4, s=sizes,
           edgecolor='none')
ax.axis('off')
plt.show()