analysis.py

import re
import json
import pandas as pd

# Drop in the JSON of `allRows`

data = pd.DataFrame(json.loads("""

<DATA>

"""), columns=['from', 'to', 'plate', 'duration'])

exp = re.compile(
  '((?P<uur>[0-9]+) (?:uren|uur))?'
  ' '
  '?((?P<min>[0-9]+) (?:minuten|min))?'
)
data['year'] = data['from'].str[6:10]
duration = data['duration'].apply(lambda x: pd.Series(exp.match(x).groupdict())).astype(float).fillna(0).astype(int)
data['duration'] = duration['uur'] * 60 + duration['min']

data.groupby(['year', 'plate'])['duration'].sum() / 60

collect.js

// Drop at the history page of the parking admin of the municipality Groningen

var allRows=[];
document.querySelectorAll('ol.pagination > li:nth-child(1) > a')[0].click();

var nPages = parseInt(
    document.querySelectorAll('ol.pagination > li:nth-child(3) > a')[0].textContent.split(' van ')[1]
)

for(var i=0; i<nPages; ++i) {
    setTimeout(() => {
        var rows = Array.from(document.querySelectorAll('table tr.ng-scope')).filter(
            row => row.children.length == 3
        ).map(
            row => Array.from(
                row.children[0].children
            ).concat(
                Array.from(row.children).slice(1)
            )
        ).map(row => row.map(cell => cell.textContent))
        allRows = allRows.concat(rows)
        document.querySelectorAll('ol.pagination > li:nth-child(4) > a')[0].click();
    }, 1000 * (i+1) )

}
setTimeout(() => console.log(JSON.stringify(allRows)), nPages+2)