Perhaps you can try to use their Ajax API to load the table data. Example:
import requests
import pandas as pd
url = 'https://bff.capitoltrades.com/trades'
params = {
            "txType": "buy",
            "tradeSize": [
                "4",
                "5",
                "6",
                "7",
                "8",
                "9",
                "10"
            ],
            "page": "1",
            "pageSize": "12"
        }
all_data = []
for params['page'] in range(1, 4):  # <-- increase number of pages here
    all_data.extend(requests.get(url, params=params).json()['data'])
df = pd.DataFrame(all_data)
df = pd.concat([df, df.pop('asset').apply(pd.Series).add_prefix('asset_')], axis=1)
df = pd.concat([df, df.pop('issuer').apply(pd.Series).add_prefix('issuer_')], axis=1)
df = pd.concat([df, df.pop('politician').apply(pd.Series).add_prefix('politician_')], axis=1)
print(df)
Prints:
| _txId | _politicianId | _assetId | _issuerId | pubDate | filingDate | txDate | txType | txTypeExtended | hasCapitalGains | owner | chamber | price | size | sizeRangeHigh | sizeRangeLow | value | filingId | filingURL | reportingGap | comment | committees | labels | asset_assetType | asset_assetTicker | asset_instrument | issuer__stateId | issuer_c2iq | issuer_country | issuer_issuerName | issuer_issuerTicker | issuer_sector | issuer_lastEOD | politician__stateId | politician_chamber | politician_dob | politician_firstName | politician_gender | politician_lastName | politician_nickname | politician_party | 
| 20003761503 | P000608 | 100012044 | 435544 | 2023-03-29T13:05:01Z | 2023-03-28 | 2023-02-07 | buy |  | False | spouse | house | nan | nan | nan | nan | 175000 | 204584522 | https://disclosures-clerk.house.gov/public_disc/ptr-pdfs/2023/20022633.pdf | 49 |  | [] | [] | municipal-security |  |  |  |  |  | US TREASURY BILLS |  |  | nan | ca | house | 1958-06-17 | Scott | male | Peters |  | democrat | 
| 10000060750 | S001217 | 100005792 | 430468 | 2023-03-23T17:15:11Z | 2023-03-23 | 2023-02-24 | buy |  | False | spouse | senate | nan | nan | nan | nan | 750000 | 100114523 | https://efdsearch.senate.gov/search/view/ptr/83de647b-ddf0-49c3-bd56-8b32f23c0e78/ | 27 | Rate/Coupon: 5.0% Matures: 01/01/2040 | [] | [] | municipal-security |  |  |  |  |  | CENTRAL TEXAS REGIONAL MOBILITY AUTHORITY |  |  | nan | fl | senate | 1952-12-01 | Richard | male | Scott | Rick | republican | 
| 20003761347 | M001157 | 100006340 | 430955 | 2023-03-23T13:05:01Z | 2023-03-20 | 2023-02-13 | buy |  | False | spouse | house | 112.31 | 1559 | 2226 | 891 | 175000 | 204572563 | https://disclosures-clerk.house.gov/public_disc/ptr-pdfs/2023/8219432.pdf | 35 |  | [] | [] | stock | COP:US |  | tx | A2QVU1B8 | us | Conocophillips | COP:US | energy | ['2022-04-01', 100.58] | tx | house | 1962-01-14 | Michael | male | McCaul |  | republican | 
| 20003761348 | M001157 | 100010442 | 434294 | 2023-03-23T13:05:01Z | 2023-03-20 | 2023-02-09 | buy |  | False | spouse | house | nan | nan | nan | nan | 175000 | 204572563 | https://disclosures-clerk.house.gov/public_disc/ptr-pdfs/2023/8219432.pdf | 39 |  | [] | [] | municipal-security |  |  |  |  |  | RACINE UNIFIED SCHOOL DISTRICT |  |  | nan | tx | house | 1962-01-14 | Michael | male | McCaul |  | republican | 
| 20003761349 | M001157 | 100006594 | 431178 | 2023-03-23T13:05:01Z | 2023-03-20 | 2023-02-02 | buy |  | False | spouse | house | nan | nan | nan | nan | 175000 | 204572563 | https://disclosures-clerk.house.gov/public_disc/ptr-pdfs/2023/8219432.pdf | 46 |  | [] | [] | municipal-security |  |  |  |  |  | CITIES OF DALLAS AND FORT WORTH TEXAS |  |  | nan | tx | house | 1962-01-14 | Michael | male | McCaul |  | republican |