<jupyter_start><jupyter_text>MetaCyc* Adapt Unit Tests for pulling data from KEGG to be compatible with MetaCyc* Create unit tests for<jupyter_code>import csv
import pandas as pd



with open('enzrxns.dat') as f:
    reader = csv.reader(f, delimiter="\t")
    for line in reader:
        print(line)
with open('enzrxns.dat','r') as f:
    next(f) # skip first row
    df = pd.DataFrame(l.rstrip().split() for l in f)

print(df)
import numpy as np
get_col = lambda col: (line.split('\t')[col-1] for line in open('enzrxns.dat'))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12949640287769784, 'fraction_numerical': 0.0017985611510791368, 'mean_word_length': 4.304761904761905, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '173845', 'license': 'MIT', 'n_tokens_mistral': 206, 'n_tokens_neox': 198, 'n_words': 62, 'path': 'notebooks/MetaCyc Development.ipynb', 'repo_name': 'metamoles/metamoles', 'size': '556'}