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There are a couple of ways of getting a list of the available snapshots for a particular url. In this notebook, we'll compare the Internet Archive's CDX index API, with their Memento Timemap API. Do they give us the same data?
See Exploring the Internet Archive's CDX API for more information about the CDX API.
import pandas as pd
import requests
def query_timemap(url):
"""
Get a Timemap in JSON format for the specified url.
"""
response = requests.get(
f"https://web.archive.org/web/timemap/json/{url}", headers={"User-Agent": ""}
)
response.raise_for_status()
return response.json()
def query_cdx(url, **kwargs):
"""
Query the IA CDX API for the supplied url.
You can optionally provide any of the parameters accepted by the API.
"""
params = kwargs
params["url"] = url
params["output"] = "json"
# User-Agent value is necessary or else IA gives an error
response = requests.get(
"http://web.archive.org/cdx/search/cdx",
params=params,
headers={"User-Agent": ""},
)
response.raise_for_status()
return response.json()
url = "http://nla.gov.au"
tm_data = query_timemap(url)
tm_df = pd.DataFrame(tm_data[1:], columns=tm_data[0])
cdx_data = query_cdx(url)
cdx_df = pd.DataFrame(cdx_data[1:], columns=cdx_data[0])
list(cdx_df.columns)
['urlkey', 'timestamp', 'original', 'mimetype', 'statuscode', 'digest', 'length']
list(tm_df.columns)
['urlkey', 'timestamp', 'original', 'mimetype', 'statuscode', 'digest', 'redirect', 'robotflags', 'length', 'offset', 'filename']
The Timemap data includes three extra columns: robotflags
, offset
, and filename
. The offset
and filename
columns tell you where to find the snapshot, but I'm not sure what robotflags
is for (it's not in the specification). Let's gave a look at what sort of values it has.
tm_df["robotflags"].value_counts()
- 4404 Name: robotflags, dtype: int64
There's nothing in it – at least for this particular url.
For my purposes, it doesn't look like the Timemap adds anything useful.
tm_df.shape
(4404, 11)
cdx_df.shape
(4405, 7)
So there are more snapshots in the CDX results than the Timemap. Can we find out what they are?
# Combine the two dataframes, then only keep rows that aren't duplicated based on timestamp, original, digest, and statuscode
pd.concat([cdx_df, tm_df]).drop_duplicates(
subset=["timestamp", "original", "digest", "statuscode"], keep=False
)
urlkey | timestamp | original | mimetype | statuscode | digest | length | redirect | robotflags | offset | filename |
---|
Hmm, if there were rows in the cdx_df
that weren't in the tm_df
I'd expect them to show up, but there are no rows that aren't duplicated based on the timestamp
, original
, digest
, and statuscode
columns...
Let's try this another way, by finding the number of unique shapshots in each df.
# Remove duplicate rows
cdx_df.drop_duplicates(
subset=["timestamp", "digest", "statuscode", "original"], keep="first"
).shape
(4304, 7)
# Remove duplicate rows
tm_df.drop_duplicates(
subset=["timestamp", "digest", "statuscode", "original"], keep="first"
).shape
(4304, 11)
Ah, so both sets of data contain duplicates, and there are really only 4,304 unique shapshots. Let's look at some of the duplicates in the CDX data.
dupes = cdx_df.loc[
cdx_df.duplicated(subset=["timestamp", "digest"], keep=False)
].sort_values(by="timestamp")
dupes.head(10)
urlkey | timestamp | original | mimetype | statuscode | digest | length | |
---|---|---|---|---|---|---|---|
878 | au,gov,nla)/ | 20090327043759 | http://www.nla.gov.au/ | text/html | 200 | 537C3S5FANRHGLW3A6WPE6A57LULWNOF | 6306 |
879 | au,gov,nla)/ | 20090327043759 | http://www.nla.gov.au/ | text/html | 200 | 537C3S5FANRHGLW3A6WPE6A57LULWNOF | 6473 |
880 | au,gov,nla)/ | 20090515004007 | http://www.nla.gov.au/ | text/html | 200 | CC747V3CYGCYQZELL37KNOW5DRPEMFEW | 6614 |
881 | au,gov,nla)/ | 20090515004007 | http://www.nla.gov.au/ | text/html | 200 | CC747V3CYGCYQZELL37KNOW5DRPEMFEW | 6614 |
883 | au,gov,nla)/ | 20090521102300 | http://www.nla.gov.au/ | text/html | 200 | 25VWCDZDMMC57PLHGKIJ6XUBG566EW33 | 6619 |
884 | au,gov,nla)/ | 20090521102300 | http://www.nla.gov.au/ | text/html | 200 | 25VWCDZDMMC57PLHGKIJ6XUBG566EW33 | 6619 |
885 | au,gov,nla)/ | 20090521230410 | http://nla.gov.au/ | warc/revisit | - | BDOBBSVBWA4WL3PLC7TSVIA5PE2RZKRD | 469 |
886 | au,gov,nla)/ | 20090521230410 | http://nla.gov.au/ | warc/revisit | - | BDOBBSVBWA4WL3PLC7TSVIA5PE2RZKRD | 469 |
887 | au,gov,nla)/ | 20090528133919 | http://www.nla.gov.au/ | text/html | 200 | IBVDKIMFCMXC3HU6RFHJOXEOGRKASMTM | 6755 |
888 | au,gov,nla)/ | 20090528133919 | http://www.nla.gov.au/ | text/html | 200 | IBVDKIMFCMXC3HU6RFHJOXEOGRKASMTM | 6755 |
print(
f'Date range of duplicates: {dupes["timestamp"].min()} to {dupes["timestamp"].max()}'
)
Date range of duplicates: 20090327043759 to 20210302172503
So it seems they provide the same number of unique snapshots, but the CDX index adds a few more duplicates.
%%timeit
tm_data = query_timemap(url)
4.99 s ± 1.39 s per loop (mean ± std. dev. of 7 runs, 1 loop each)
%%timeit
cdx_data = query_cdx(url)
1.34 s ± 261 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Both methods provide much the same data, so it just comes down to convenience and performance.
Created by Tim Sherratt for the GLAM Workbench. Support me by becoming a GitHub sponsor!
Work on this notebook was supported by the IIPC Discretionary Funding Programme 2019-2020.
The Web Archives section of the GLAM Workbench is sponsored by the British Library.