This Python scripts creates four graphs for the evolution of author counts in astronomy papers from 1980 to 2022. It requires an [ADS Config Token][1] in order to work. [1]: https://ui.adsabs.harvard.edu/user/settings/token

ADS_citation_count_evolution.py

import ads
import pandas as pd

ads.config.token='putYourOwnAdsConfigTokenHere'

bibgroups = [
    "ALMA", 
    "CfA",
    "CFHT",
    "Chandra",
    "ESO/Telescopes",
    "Gemini",
    "HST",
    "NOAO",
    "NOIRlab",
    "SETI",
]

years = range(1980,2022+1)

# Please note that the query below is not necessary if you are recovering from a pickled file
results = {}
for bibgroup in bibgroups:
    results[bibgroup] = {}
    print(f"Bibgroup: {bibgroup}")
    for year in years:
        print(f"Year: {year}")
        query_string = f'year:{year} bibgroup:"{bibgroup}"'
        paper_query = ads.SearchQuery(
            q=query_string, 
            fl=['id', 'bibcode', 'title', 'author', 'bibgroup', 'author_count'],
            rows=2000,
            sort="author_count"
        )
        results[bibgroup][year]=list(paper_query)

# Pickle data for saving and retrieval
# Save Pickled results
import pickle
with open('ads_pubs_results.pkl', 'wb') as rd:
    pickle.dump(results, rd)
# Import Pickled results
with open('ads_pubs_results.pkl', 'rb') as rd:
    results = pickle.load(rd)

# Create the Data Frame
data_rows = []
for telescope in results.keys():
    for year in results[telescope].keys():
        for article in results[telescope][year]:
            row = {
               'id': article.id,
               'year': year,
               'telescope': telescope,
               'bibcode': article.bibcode,
               'title': article.title,
               'author': article.author,
               'author_count': article.author_count,
            }
            data_rows.append(row)

articles = pd.DataFrame(data_rows)

# Create the graphics
median_all_observatories = articles.pivot_table(
    index='year',
    columns='telescope',
    values='author_count',
    aggfunc='median').plot.bar()

median_all_observatories.figure.savefig('All Observatories - Median.png')

mean_all_observatories = articles.pivot_table(
    index='year',
    columns='telescope',
    values='author_count',
    aggfunc='mean').plot.bar()

mean_all_observatories.figure.savefig('All Observatories - Mean.png')

mean_alma = articles[articles['telescope']=='ALMA'].pivot_table(
    index='year',
    columns='telescope',
    values='author_count',
    aggfunc='mean').plot.bar()

mean_alma.figure.savefig('ALMA - Mean.png')

median_alma = articles[articles['telescope']=='ALMA'].pivot_table(
    index='year',
    columns='telescope',
    values='author_count',
    aggfunc='median').plot.bar()

median_alma.figure.savefig('ALMA - Median.png')