<jupyter_start><jupyter_text>`pertinfo`<jupyter_code>pertinfo = get_full_query('pertinfo')
keys = ['pert_id', 'pert_iname', 'pert_type', 'num_gold', 'num_inst',
        'num_sig', 'in_summly', 'pert_summary', 
        'pubchem_cid', 'molecular_formula',
        'molecular_wt', 'pert_vendor', 
        'canonical_smiles', 'inchi_key', 'inchi_string']
pertinfo_df = as_dataframe(pertinfo, keys)
write_gz_tsv(pertinfo_df, 'data/pertinfo/pertinfo.tsv.gz')
pertinfo_df.head()<jupyter_output><empty_output><jupyter_text>`geneinfo`<jupyter_code>geneinfo = get_full_query('geneinfo')
keys = ['pr_id', 'pr_gene_id', 'pr_gene_symbol', 'pr_gene_title', 
        'is_lm', 'is_l1000', 'is_bing', 'pr_pool_id']
geneinfo_df = as_dataframe(geneinfo, keys)
write_gz_tsv(geneinfo_df, 'data/geneinfo/geneinfo.tsv.gz')
geneinfo_df.tail()
import collections
collections.Counter(geneinfo_df.pr_pool_id)<jupyter_output><empty_output><jupyter_text>siginfo<jupyter_code>siginfo = get_full_query('siginfo')
keys = ['sig_id', 'pert_id', 'pert_itime', 'distil_nsample', 'pert_idose',
        'cell_id', 'pert_type', 'is_gold', 'distil_ss', 'ngenes_modulated_dn_lm', 'ngenes_modulated_up_lm']
siginfo_df = as_dataframe(siginfo, keys)
write_gz_tsv(siginfo_df, 'data/siginfo/siginfo.tsv.gz')
siginfo_df.tail()<jupyter_output><empty_output><jupyter_text>cellinfo<jupyter_code>cellinfo = get_full_query('cellinfo')
keys = ['cell_id', 'cell_histology', 'cell_lineage', 'cell_source', 'cell_source_id',
        'cell_type', 'gender', 'is_from_metastasis', 'lincs_status', 'metastatic_site']
cellinfo_df = as_dataframe(cellinfo, keys)
write_gz_tsv(cellinfo_df, 'data/cellinfo/cellinfo.tsv.gz')
cellinfo_df.tail()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15582893966022263, 'fraction_numerical': 0.0023432923257176333, 'mean_word_length': 8.872832369942197, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 17, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '461286', 'license': 'CC0-1.0', 'n_tokens_mistral': 714, 'n_tokens_neox': 686, 'n_words': 80, 'path': 'api.ipynb', 'repo_name': 'dhimmel/lincs', 'size': '1707'}