<jupyter_start><jupyter_text>Ref lnc HGNC count done below<jupyter_code>df_ref_lnc_genes = pd.read_csv('/dataset/refseq_numeric/all_type=lncRNA_data/all_lncrna_sorted_chr/all_lncrna_gene_data_sorted_chr.csv', index_col=0)
df_ref_lnc_genes.head(2)
df_ref_lnc_index = df_ref_lnc_genes.set_index(['RefSeq Gene ID'])
ref_lnc_gene_ids = df_feature_file['Ref lncRNA gene ID'].tolist()
df_ref_lnc_index.loc['gene-MIR1302-2HG']['Name'][0:4]
ref_lnc_hgnc = []
%%time
def is_ref_lnc_hgnc():
    for i in tqdm(range(len(ref_lnc_gene_ids))):
        if df_ref_lnc_index.loc[ref_lnc_gene_ids[i]]['Name'][0:4] == 'LINC':
            ref_lnc_hgnc.append(1)
        else:
            ref_lnc_hgnc.append(0)
is_ref_lnc_hgnc()
len(ref_lnc_hgnc)
len(ens_linc_hgnc)
df_feature_file['HGNC ID refseq lncrna?'] = ref_lnc_hgnc
df_feature_file['HGNC ID ensembl lincrna?'] = ens_linc_hgnc
df_feature_file.head(3)
df_feature_file.to_csv('/feature_selection/check_hgnc_1/cleaned_feature_file_v1.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12674650698602793, 'fraction_numerical': 0.015968063872255488, 'mean_word_length': 7.221311475409836, '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': '160777', 'license': 'Apache-2.0', 'n_tokens_mistral': 479, 'n_tokens_neox': 461, 'n_words': 48, 'path': 'feature_selection/check_hgnc_v1/python_scripts/if_HGNC_present_in_ens(linc)_and_ref(lnc).ipynb', 'repo_name': 'EnsemblGSOC/lncRNA-analysis', 'size': '1002'}