Reading a published book¶
This guide covers the consumer side of the SDK: addressing a book, choosing an edition, and pulling data out of it.
Reading published data needs no credentials, so everything below runs unauthenticated.
Connecting¶
Bookshelf provides a number of high-level functions on top of the bookshelf API. The deployment it talks to resolves from the base_url argument, then $BOOKSHELF_URL, then the production URL. These guides pin the staging deployment so they are reproducible.
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import os
os.environ.setdefault("BOOKSHELF_URL", "https://bookshelf-staging.ovh.climateresource.com.au")
from bookshelf import Bookshelf
bs = Bookshelf()
import os os.environ.setdefault("BOOKSHELF_URL", "https://bookshelf-staging.ovh.climateresource.com.au") from bookshelf import Bookshelf bs = Bookshelf()
The client is long lived by design. Token state lives in the credential provider and each surface pools connections, so build one client and keep it. A notebook can construct it plainly and never close it. A script or service should use it as a context manager, or call bs.close() at shutdown.
Addressing a book¶
A book is addressed by volume and version. Omitting edition= resolves the latest published edition.
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book = bs.book("rcmip-emissions", "v5.1.0")
book
book = bs.book("rcmip-emissions", "v5.1.0") book
Out[2]:
Bookshelf Book
| volume | rcmip-emissions |
|---|---|
| version | v5.1.0 |
| edition | 1 |
| entries | complete, magicc |
The metadata carries the coordinates that were actually resolved. Record the edition whenever a result needs to be reproducible later, because the latest edition moves as data is reprocessed.
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book.metadata.volume_name, book.metadata.version, book.metadata.edition
book.metadata.volume_name, book.metadata.version, book.metadata.edition
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('rcmip-emissions', 'v5.1.0', 1) Pass edition= to pin one. This is the form to use in analysis that has to give the same answer next year.
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pinned = bs.book("primap-hist", "v2.6", edition=5)
pinned.metadata.version, pinned.metadata.edition
pinned = bs.book("primap-hist", "v2.6", edition=5) pinned.metadata.version, pinned.metadata.edition
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('v2.6', 5) Book entries¶
A book holds one or more named entries, and indexing it returns a BookEntry.
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entry = book["magicc"]
entry
entry = book["magicc"] entry
Out[5]:
Bookshelf Book Entry
| name | magicc |
|---|---|
| tracking_id | f075ea3a-fb12-5805-8fe9-83a8024d3db1 |
| type | timeseries |
| book_id | 5237d4b9-74f0-4ee6-b351-c35fd8a848dd |
Asking for an entry that does not exist reports what is available, so a typo does not turn into a lookup through the API docs.
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try:
book["does-not-exist"]
except KeyError as exc:
print(exc)
try: book["does-not-exist"] except KeyError as exc: print(exc)
"book v5.1.0_e001 has no entry 'does-not-exist', available: complete, magicc"
Exploring before pulling data¶
magicc is a large entry, so explore its shape first rather than downloading it to find out. Three helpers describe an entry without pulling any of it: facets() returns each index column and its distinct values, schema() describes the timeseries structure, and preview() returns a small tabular sample.
Start with facets(), where total_unique counts across the whole entry.
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facets = entry.facets()
[(facet.column, facet.total_unique) for facet in facets.facets]
facets = entry.facets() [(facet.column, facet.total_unique) for facet in facets.facets]
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[('activity_id', 2),
('mip_era', 2),
('model', 11),
('region', 6),
('scenario', 22),
('unit', 51),
('variable', 53)] The values themselves hang off each facet, carrying the number of series that use them, which is how to work out what is worth filtering on.
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scenarios = next(facet for facet in facets.facets if facet.column == "scenario")
sorted((value.value, value.count) for value in scenarios.values)[:10]
scenarios = next(facet for facet in facets.facets if facet.column == "scenario") sorted((value.value, value.count) for value in scenarios.values)[:10]
Out[8]:
[('esm-bell-1000PgC', 40),
('esm-bell-2000PgC', 40),
('esm-bell-750PgC', 40),
('esm-pi-CO2pulse', 40),
('esm-pi-cdr-pulse', 40),
('esm-piControl', 40),
('historical', 113),
('historical-cmip5', 40),
('rcp26', 40),
('rcp45', 40)] schema() reports the same structure without any values at all, so total_rows is the number of series waiting behind it.
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schema = entry.schema()
schema.columns, schema.total_rows
schema = entry.schema() schema.columns, schema.total_rows
Out[9]:
(['activity_id', 'mip_era', 'model', 'region', 'scenario', 'unit', 'variable'], 1683)
When none of that substitutes for looking at the data, preview() returns a sample.
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entry.preview(limit=5)
entry.preview(limit=5)
Out[10]:
PreviewResponse(columns=[ColumnMetadata(key='activity_id', label='Activity Id', type=<Type1.string: 'string'>), ColumnMetadata(key='mip_era', label='Mip Era', type=<Type1.string: 'string'>), ColumnMetadata(key='model', label='Model', type=<Type1.string: 'string'>), ColumnMetadata(key='region', label='Region', type=<Type1.string: 'string'>), ColumnMetadata(key='scenario', label='Scenario', type=<Type1.string: 'string'>), ColumnMetadata(key='unit', label='Unit', type=<Type1.string: 'string'>), ColumnMetadata(key='variable', label='Variable', type=<Type1.string: 'string'>), ColumnMetadata(key='1750-01-01 00:00:00', label='1750-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1751-01-01 00:00:00', label='1751-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1752-01-01 00:00:00', label='1752-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1753-01-01 00:00:00', label='1753-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1754-01-01 00:00:00', label='1754-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1755-01-01 00:00:00', label='1755-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1756-01-01 00:00:00', label='1756-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1757-01-01 00:00:00', label='1757-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1758-01-01 00:00:00', label='1758-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1759-01-01 00:00:00', label='1759-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1760-01-01 00:00:00', label='1760-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1761-01-01 00:00:00', label='1761-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1762-01-01 00:00:00', label='1762-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1763-01-01 00:00:00', label='1763-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1764-01-01 00:00:00', label='1764-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1765-01-01 00:00:00', label='1765-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1766-01-01 00:00:00', label='1766-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1767-01-01 00:00:00', label='1767-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1768-01-01 00:00:00', label='1768-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1769-01-01 00:00:00', label='1769-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1770-01-01 00:00:00', label='1770-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1771-01-01 00:00:00', label='1771-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1772-01-01 00:00:00', label='1772-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1773-01-01 00:00:00', label='1773-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1774-01-01 00:00:00', label='1774-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1775-01-01 00:00:00', label='1775-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1776-01-01 00:00:00', label='1776-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1777-01-01 00:00:00', label='1777-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1778-01-01 00:00:00', label='1778-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1779-01-01 00:00:00', label='1779-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1780-01-01 00:00:00', label='1780-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1781-01-01 00:00:00', label='1781-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1782-01-01 00:00:00', label='1782-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1783-01-01 00:00:00', label='1783-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1784-01-01 00:00:00', label='1784-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1785-01-01 00:00:00', label='1785-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1786-01-01 00:00:00', label='1786-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1787-01-01 00:00:00', label='1787-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1788-01-01 00:00:00', label='1788-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1789-01-01 00:00:00', label='1789-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1790-01-01 00:00:00', label='1790-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1791-01-01 00:00:00', label='1791-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1792-01-01 00:00:00', label='1792-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1793-01-01 00:00:00', label='1793-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1794-01-01 00:00:00', label='1794-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1795-01-01 00:00:00', label='1795-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1796-01-01 00:00:00', label='1796-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1797-01-01 00:00:00', label='1797-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1798-01-01 00:00:00', label='1798-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1799-01-01 00:00:00', label='1799-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1800-01-01 00:00:00', label='1800-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1801-01-01 00:00:00', label='1801-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1802-01-01 00:00:00', label='1802-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1803-01-01 00:00:00', label='1803-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1804-01-01 00:00:00', label='1804-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1805-01-01 00:00:00', label='1805-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1806-01-01 00:00:00', label='1806-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1807-01-01 00:00:00', label='1807-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1808-01-01 00:00:00', label='1808-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1809-01-01 00:00:00', label='1809-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1810-01-01 00:00:00', label='1810-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1811-01-01 00:00:00', label='1811-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1812-01-01 00:00:00', label='1812-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1813-01-01 00:00:00', label='1813-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1814-01-01 00:00:00', label='1814-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1815-01-01 00:00:00', label='1815-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1816-01-01 00:00:00', label='1816-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1817-01-01 00:00:00', label='1817-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1818-01-01 00:00:00', label='1818-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1819-01-01 00:00:00', label='1819-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1820-01-01 00:00:00', label='1820-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1821-01-01 00:00:00', label='1821-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1822-01-01 00:00:00', label='1822-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1823-01-01 00:00:00', label='1823-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1824-01-01 00:00:00', label='1824-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1825-01-01 00:00:00', label='1825-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1826-01-01 00:00:00', label='1826-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1827-01-01 00:00:00', label='1827-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1828-01-01 00:00:00', label='1828-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1829-01-01 00:00:00', label='1829-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1830-01-01 00:00:00', label='1830-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1831-01-01 00:00:00', label='1831-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1832-01-01 00:00:00', label='1832-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1833-01-01 00:00:00', label='1833-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1834-01-01 00:00:00', label='1834-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1835-01-01 00:00:00', label='1835-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1836-01-01 00:00:00', label='1836-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1837-01-01 00:00:00', label='1837-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1838-01-01 00:00:00', label='1838-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1839-01-01 00:00:00', label='1839-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1840-01-01 00:00:00', label='1840-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1841-01-01 00:00:00', label='1841-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1842-01-01 00:00:00', label='1842-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1843-01-01 00:00:00', label='1843-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1844-01-01 00:00:00', label='1844-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1845-01-01 00:00:00', label='1845-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1846-01-01 00:00:00', label='1846-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1847-01-01 00:00:00', label='1847-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1848-01-01 00:00:00', label='1848-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1849-01-01 00:00:00', label='1849-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1850-01-01 00:00:00', label='1850-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1851-01-01 00:00:00', label='1851-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1852-01-01 00:00:00', label='1852-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1853-01-01 00:00:00', label='1853-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1854-01-01 00:00:00', label='1854-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1855-01-01 00:00:00', label='1855-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1856-01-01 00:00:00', label='1856-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1857-01-01 00:00:00', label='1857-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1858-01-01 00:00:00', label='1858-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1859-01-01 00:00:00', label='1859-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1860-01-01 00:00:00', label='1860-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1861-01-01 00:00:00', label='1861-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1862-01-01 00:00:00', label='1862-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1863-01-01 00:00:00', label='1863-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1864-01-01 00:00:00', label='1864-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1865-01-01 00:00:00', label='1865-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1866-01-01 00:00:00', label='1866-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1867-01-01 00:00:00', label='1867-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1868-01-01 00:00:00', label='1868-01-01 00:00:00', type=<Type1.number: 'number'>), ColumnMetadata(key='1869-01-01 00:00:00', label='1869-01-01 00:00:00', type=<Type1.number: 'number'>), 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'2413-01-01 00:00:00': None, '2414-01-01 00:00:00': None, '2415-01-01 00:00:00': None, '2416-01-01 00:00:00': None, '2417-01-01 00:00:00': None, '2418-01-01 00:00:00': None, '2419-01-01 00:00:00': None, '2420-01-01 00:00:00': None, '2421-01-01 00:00:00': None, '2422-01-01 00:00:00': None, '2423-01-01 00:00:00': None, '2424-01-01 00:00:00': None, '2425-01-01 00:00:00': None, '2426-01-01 00:00:00': None, '2427-01-01 00:00:00': None, '2428-01-01 00:00:00': None, '2429-01-01 00:00:00': None, '2430-01-01 00:00:00': None, '2431-01-01 00:00:00': None, '2432-01-01 00:00:00': None, '2433-01-01 00:00:00': None, '2434-01-01 00:00:00': None, '2435-01-01 00:00:00': None, '2436-01-01 00:00:00': None, '2437-01-01 00:00:00': None, '2438-01-01 00:00:00': None, '2439-01-01 00:00:00': None, '2440-01-01 00:00:00': None, '2441-01-01 00:00:00': None, '2442-01-01 00:00:00': None, '2443-01-01 00:00:00': None, '2444-01-01 00:00:00': None, '2445-01-01 00:00:00': None, '2446-01-01 00:00:00': None, '2447-01-01 00:00:00': None, '2448-01-01 00:00:00': None, '2449-01-01 00:00:00': None, '2450-01-01 00:00:00': None, '2451-01-01 00:00:00': None, '2452-01-01 00:00:00': None, '2453-01-01 00:00:00': None, '2454-01-01 00:00:00': None, '2455-01-01 00:00:00': None, '2456-01-01 00:00:00': None, '2457-01-01 00:00:00': None, '2458-01-01 00:00:00': None, '2459-01-01 00:00:00': None, '2460-01-01 00:00:00': None, '2461-01-01 00:00:00': None, '2462-01-01 00:00:00': None, '2463-01-01 00:00:00': None, '2464-01-01 00:00:00': None, '2465-01-01 00:00:00': None, '2466-01-01 00:00:00': None, '2467-01-01 00:00:00': None, '2468-01-01 00:00:00': None, '2469-01-01 00:00:00': None, '2470-01-01 00:00:00': None, '2471-01-01 00:00:00': None, '2472-01-01 00:00:00': None, '2473-01-01 00:00:00': None, '2474-01-01 00:00:00': None, '2475-01-01 00:00:00': None, '2476-01-01 00:00:00': None, '2477-01-01 00:00:00': None, '2478-01-01 00:00:00': None, '2479-01-01 00:00:00': None, '2480-01-01 00:00:00': None, '2481-01-01 00:00:00': None, '2482-01-01 00:00:00': None, '2483-01-01 00:00:00': None, '2484-01-01 00:00:00': None, '2485-01-01 00:00:00': None, '2486-01-01 00:00:00': None, '2487-01-01 00:00:00': None, '2488-01-01 00:00:00': None, '2489-01-01 00:00:00': None, '2490-01-01 00:00:00': None, '2491-01-01 00:00:00': None, '2492-01-01 00:00:00': None, '2493-01-01 00:00:00': None, '2494-01-01 00:00:00': None, '2495-01-01 00:00:00': None, '2496-01-01 00:00:00': None, '2497-01-01 00:00:00': None, '2498-01-01 00:00:00': None, '2499-01-01 00:00:00': None, '2500-01-01 00:00:00': None}], total_rows=1683, limit=5, offset=0, has_more=True) Pulling data¶
as_df() returns pandas. For a timeseries entry it is wide indexed: the index carries the metadata dimensions and the columns are years.
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frame = entry.as_df()
frame.shape
frame = entry.as_df() frame.shape
Out[11]:
(1683, 751)
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frame.index.names
frame.index.names
Out[12]:
FrozenList(['activity_id', 'mip_era', 'model', 'region', 'scenario', 'unit', 'variable'])
Trimming on the server¶
The full entry above is 1683 series across 751 years. Trim it in the request rather than after it arrives.
year_min and year_max bound the year window. This is the safest control, because it touches the columns and leaves every index dimension intact.
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window = entry.as_df(year_min=2020, year_max=2100)
window.shape
window = entry.as_df(year_min=2020, year_max=2100) window.shape
Out[13]:
(1683, 81)
Filters select rows. On a book entry a filter is a plain column=value keyword, and repeating a column ORs its values together.
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world = entry.as_df(region="World", year_min=2020, year_max=2100)
world.shape
world = entry.as_df(region="World", year_min=2020, year_max=2100) world.shape
Out[14]:
(1023, 81)
Warning: Filter syntax differs by path
A book entry accepts bare
column=valuefilters only. The richercol.opgrammar (region.in,variable.neqand friends) is silently ignored here rather than rejected, so a mistyped filter returns the full unfiltered result. Check the row count when a filter is meant to narrow something.
top_n keeps only the largest series, ranked by their latest non-null value over the whole filtered result. It is a presentation control, useful for a chart rather than an analysis.
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top = entry.as_df(top_n=5, year_min=2020, year_max=2100)
top.shape
top = entry.as_df(top_n=5, year_min=2020, year_max=2100) top.shape
Out[15]:
(5, 81)
Warning: Trimming can drop index dimensions
When
top_norlimitnarrows the result to rows sharing a value, the server drops that column from the index rather than repeating it. The frame above has fewer index levels than the untrimmed one. That is fine for a chart, and it breaksas_scmrun(), which requiresregion,unit,variable,modelandscenarioto be present. Use a year window and filters when the index has to stay whole.
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top.index.names
top.index.names
Out[16]:
FrozenList(['mip_era', 'model', 'scenario', 'variable'])
drop_constant=True does the same thing deliberately, removing dimensions that carry a single value across the filtered data.
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entry.as_df(region="World", year_min=2020, year_max=2100, drop_constant=True).index.names
entry.as_df(region="World", year_min=2020, year_max=2100, drop_constant=True).index.names
Out[17]:
FrozenList(['activity_id', 'mip_era', 'model', 'scenario', 'unit', 'variable'])
Long format¶
as_long_df() returns the tidy form, one row per series and year, with year and value columns.
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entry.as_long_df(region="World", variable="Emissions|CO2", year_min=2020, year_max=2030).head()
entry.as_long_df(region="World", variable="Emissions|CO2", year_min=2020, year_max=2030).head()
Out[18]:
| activity_id | mip_era | model | scenario | year | value | |
|---|---|---|---|---|---|---|
| 0 | ZECMIP | CMIP6 | idealised | esm-bell-1000PgC | 2020 | 0.000000 |
| 1 | ZECMIP | CMIP6 | idealised | esm-bell-2000PgC | 2020 | 0.000000 |
| 2 | ZECMIP | CMIP6 | idealised | esm-bell-750PgC | 2020 | 0.000000 |
| 3 | not_applicable | CMIP5 | AIM | rcp60 | 2020 | 34307.900000 |
| 4 | not_applicable | CMIP5 | IMAGE | rcp26 | 2020 | 37621.833333 |
Where to next¶
- Converting and plotting covers the other converters, the content cache, and hash verification.
- Reading asynchronously covers the awaited surface.