First download the json: https://www.kegg.jp/kegg-bin/get_htext?ko00001.keg

or do some fancy urllib and StringIO to pull directly from web:

A bit messy but here's my Python code. Forgive the lack of documentation but it's pretty straightforward. I coded it up on a plane ride:

import pandas as pd
from collections import * 

database = list()
for _, v in pd.read_json("/Users/jespinoz/Downloads/ko00001.json").iterrows():
    d = v["children"]
    cat_1 = d["name"]
    for child_1 in d["children"]:
        cat_2 = child_1["name"] # Module?
        for child_2 in child_1["children"]:
            cat_3 = child_2["name"]
            if "children" in child_2:
                for child_3 in child_2["children"]:
                    cat_4 = child_3["name"]
                    fields = [cat_1, cat_2, cat_3, cat_4]
                    database.append(fields)
df_kegg = pd.DataFrame(database, columns=["Level_A", "Level_B", "Level_C", "Level_D"])


def parse_ko_identifiers(x):
    x = x.upper()
    kos = list()
    elements = x.split(" ")
    for word in elements:
        if word:

            conditions = [
                word[0] == "K",
                word[1:].isnumeric(),
                len(word) == 6,
            ]
            if all(conditions):
                kos.append(word)
    return set(kos)
df_kegg["Level_D-KOs"] = df_kegg["Level_D"].map(parse_ko_identifiers)

database_expanded = dict()
for i, row in df_kegg.iterrows():
    for id_ko in row["Level_D-KOs"]:
        database_expanded[id_ko] = row
df_kegg_expanded = pd.DataFrame(database_expanded).T
df_kegg_expanded.index.name = "KO"
df_kegg_expanded.columns = df_kegg_expanded.columns.map(lambda x: (x.split("-")[0], x))

for id_cat in ["Level_A", "Level_B", "Level_C"]:
    df_kegg_expanded[(id_cat, "ID")] = df_kegg_expanded[(id_cat, id_cat)].map(lambda x: x.split(" ")[0])
    df_kegg_expanded[(id_cat, "Name")] = df_kegg_expanded[(id_cat, id_cat)].map(lambda x: " ".join(x.split(" ")[1:]))

def f(x):
    if "; " in x:
        return x.split("; ")[1]
    else:
        return x
df_kegg_expanded[("Level_D", "Name")] = df_kegg_expanded[("Level_D", "Level_D")].map(f)
df_kegg_expanded.columns = df_kegg_expanded.columns.map(lambda x: (x[0], "Full") if x[0] == x[1] else x)
df_kegg_expanded = df_kegg_expanded.sort_index(axis=1)

Looks like this: