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• CV is much bigger than DS, by now. Considering the fact that CV is 6 years older than DS (source and source), I guess this is natural. It would be interesting to get some data for stack exchange sites to try to predict the growth :-)
• DS seems to attract more people from computer science, where CV seems to attract more people from mathematics.

• CV is much bigger than DS, by now. Considering the fact that CV is 6 years older than DS (source and source), I guess this is natural. It would be interesting to get some data for stack exchange sites to try to predict the growth :-)
• DS seems to attract more people from computer science, where CV seems to attract more people from mathematics.

1. machine-learning: 5881 (vs machine-learning: 12818 on CV)
2. python: 3219 (vs python: 2531 on CV)
3. neural-network: 2511 (vs neural-networks: 4709 on CV)
4. deep-learning: 2337 (vs deep-learning: 2077 on CV)
5. classification: 1623 (vs classification: 4859 on CV)
6. keras: 1385 (doesn't exist?)
7. scikit-learn: 1084 (vs scikit-learn: 1133 on CV)
8. r: 1012 (vs r: 20392 on CV)
9. tensorflow: 997 (doesn't exist?)
10. nlp: 954 (doesn't exist?)

1. r: 20392 (vs r: 1012 on CV)
2. regression: 18726 (vs regression: 726 on CV)
3. machine-learning: 12818 (vs machine-learning: 5881 on CV)
4. time-series: 9621 (vs time-series: 835 on CV)
5. probability: 7606 (vs probability: 159 on CV)
6. hypothesis-testing: 6468 (doesn't exist?)
7. distributions: 6210 (doesn't exist?)
8. self-study: 6171 (doesn't exist?)
9. logistic: 5170 (doesn't exist?)
10. bayesian: 5047 (doesn't exist?)

import requests


def get_api_result(uri):
    resp = requests.get(uri)
    return resp.json()


def get_toplist(ds, ds_tags, cv_tag_dict):
    ds2cv_tagname = {"neural-network": "neural-networks"}
    print(f"Top tags of {ds}")
    for i, tag in enumerate(ds_tags["items"], start=1):
        new_tagname = ds2cv_tagname.get(tag['name'], tag['name'])
        tag_cv = cv_tag_dict.get(new_tagname, None)
        if tag_cv is None:
            vs_string = "doesn't exist?"
        else:
            vs_string = f"vs {new_tagname}: {tag_cv} on CV"
        print(f"{i}. {tag['name']}: {tag['count']} ({vs_string})")
        if i == 10:
            break

base = "https://api.stackexchange.com/2.2"
fstring = "{base}/tags?page=1&pagesize=100&order=desc&sort=popular&site={site}"
ds_tags = get_api_result(fstring.format(base=base, site="datascience"))
cv_tags = get_api_result(fstring.format(base=base, site="stats"))

ds_tag_dict = {tag['name']: tag['count'] for tag in ds_tags['items']}
cv_tag_dict = {tag['name']: tag['count'] for tag in cv_tags['items']}
get_toplist(ds="DS", ds_tags=ds_tags, cv_tag_dict=cv_tag_dict)
get_toplist(ds="CV", ds_tags=cv_tags, cv_tag_dict=ds_tag_dict)

1. machine-learning: 5881 (vs machine-learning: 12819 on CV)
2. python: 3219 (vs python: 2529 on CV)
3. neural-network: 2511 (vs neural-networks: 4711 on CV)
4. deep-learning: 2338 (vs deep-learning: 2077 on CV)
5. classification: 1623 (vs classification: 4859 on CV)
6. keras: 1385 (doesn't exist?)
7. scikit-learn: 1084 (vs scikit-learn: 1133 on CV)
8. r: 1011 (vs r: 20394 on CV)
9. tensorflow: 997 (doesn't exist?)
10. nlp: 954 (doesn't exist?)

1. r: 20394 (vs r: 1011 on DS)
2. regression: 18727 (vs regression: 726 on DS)
3. machine-learning: 12819 (vs machine-learning: 5881 on DS)
4. time-series: 9621 (vs time-series: 835 on DS)
5. probability: 7606 (vs probability: 159 on DS)
6. hypothesis-testing: 6468 (doesn't exist?)
7. distributions: 6212 (doesn't exist?)
8. self-study: 6171 (doesn't exist?)
9. logistic: 5170 (doesn't exist?)
10. bayesian: 5048 (doesn't exist?)

import requests


def get_api_result(uri):
    resp = requests.get(uri)
    return resp.json()


def get_toplist(ds, other, ds_tags, cv_tag_dict):
    ds2cv_tagname = {"neural-network": "neural-networks"}
    print(f"Top tags of {ds}")
    for i, tag in enumerate(ds_tags["items"], start=1):
        new_tagname = ds2cv_tagname.get(tag['name'], tag['name'])
        tag_cv = cv_tag_dict.get(new_tagname, None)
        if tag_cv is None:
            vs_string = "doesn't exist?"
        else:
            vs_string = f"vs {new_tagname}: {tag_cv} on {other}"
        print(f"{i}. {tag['name']}: {tag['count']} ({vs_string})")
        if i == 10:
            break

base = "https://api.stackexchange.com/2.2"
fstring = "{base}/tags?page=1&pagesize=100&order=desc&sort=popular&site={site}"
ds_tags = get_api_result(fstring.format(base=base, site="datascience"))
cv_tags = get_api_result(fstring.format(base=base, site="stats"))

ds_tag_dict = {tag['name']: tag['count'] for tag in ds_tags['items']}
cv_tag_dict = {tag['name']: tag['count'] for tag in cv_tags['items']}
get_toplist(ds="DS", other="CV", ds_tags=ds_tags, cv_tag_dict=cv_tag_dict)
get_toplist(ds="CV", other="DS", ds_tags=cv_tags, cv_tag_dict=ds_tag_dict)

1. machine-learning: 5881 (vs machine-learning: 12818 on CV)
2. python: 3219 (vs python: 2531 on CV)
3. neural-network: 2511 (vs neural-networks: 4709 on CV)
4. deep-learning: 2337 (vs deep-learning: 2077 on CV)
5. classification: 1623 (vs classification: 4859 on CV)
6. keras: 1385 (doesn't exist?)
7. scikit-learn: 1084 (vs scikit-learn: 1133 on CV)
8. r: 1012 (vs r: 20392 on CV)
9. tensorflow: 997 (doesn't exist?)
10. nlp: 954 (doesn't exist?)

1. r: 20392 (vs r: 1012 on CV)
2. regression: 18726 (vs regression: 726 on CV)
3. machine-learning: 12818 (vs machine-learning: 5881 on CV)
4. time-series: 9621 (vs time-series: 835 on CV)
5. probability: 7606 (vs probability: 159 on CV)
6. hypothesis-testing: 6468 (doesn't exist?)
7. distributions: 6210 (doesn't exist?)
8. self-study: 6171 (doesn't exist?)
9. logistic: 5170 (doesn't exist?)
10. bayesian: 5047 (doesn't exist?)

• CV is much bigger than DS, by now. Considering the fact that DS is only 623 days in beta (source), but CV is 5 years and 6 months old (source), I guess this is natural. It would be interesting to get some data for stack exchange sites to try to predict the growth :-)
• DS seems to attract more people from computer science, where CV seems to attract more people from mathematics.

1. machine-learning: 5881 (vs machine-learning: 12818 on CV)
2. python: 3219 (vs python: 2531 on CV)
3. neural-network: 2511 (vs neural-networks: 4709 on CV)
4. deep-learning: 2337 (vs deep-learning: 2077 on CV)
5. classification: 1623 (vs classification: 4859 on CV)
6. keras: 1385 (doesn't exist?)
7. scikit-learn: 1084 (vs scikit-learn: 1133 on CV)
8. r: 1012 (vs r: 20392 on CV)
9. tensorflow: 997 (doesn't exist?)
10. nlp: 954 (doesn't exist?)

1. r: 20392 (vs r: 1012 on CV)
2. regression: 18726 (vs regression: 726 on CV)
3. machine-learning: 12818 (vs machine-learning: 5881 on CV)
4. time-series: 9621 (vs time-series: 835 on CV)
5. probability: 7606 (vs probability: 159 on CV)
6. hypothesis-testing: 6468 (doesn't exist?)
7. distributions: 6210 (doesn't exist?)
8. self-study: 6171 (doesn't exist?)
9. logistic: 5170 (doesn't exist?)
10. bayesian: 5047 (doesn't exist?)

• CV is much bigger than DS, by now. Considering the fact that CV is 6 years older than DS (source and source), I guess this is natural. It would be interesting to get some data for stack exchange sites to try to predict the growth :-)
• DS seems to attract more people from computer science, where CV seems to attract more people from mathematics.