def build_csv(<EOL>pipeline_files=[],<EOL>fulldata_file=None,<EOL>clean_file=None,<EOL>post_proc_rules=None,<EOL>label_rules=None,<EOL>metadata_filename="<STR_LIT>"):
save_node = {<EOL>"<STR_LIT:status>": INVALID,<EOL>"<STR_LIT>": pipeline_files,<EOL>"<STR_LIT>": post_proc_rules,<EOL>"<STR_LIT>": label_rules,<EOL>"<STR_LIT>": fulldata_file,<EOL>"<STR_LIT>": None,<EOL>"<STR_LIT>": clean_file,<EOL>"<STR_LIT>": None,<EOL>"<STR_LIT>": [],<EOL>"<STR_LIT>": None,<EOL>"<STR_LIT>": [],<EOL>"<STR_LIT>": {}<EOL>}<EOL>if not fulldata_file:<EOL><INDENT>log.error("<STR_LIT>")<EOL>save_node["<STR_LIT:status>"] = INVALID<EOL>return save_node<EOL><DEDENT>if not clean_file:<EOL><INDENT>log.error("<STR_LIT>")<EOL>save_node["<STR_LIT:status>"] = INVALID<EOL>return save_node<EOL><DEDENT>log.info("<STR_LIT>")<EOL>common_headers,headers_dict = find_all_headers(<EOL>pipeline_files=pipeline_files)<EOL>log.info(("<STR_LIT>")<EOL>.format(len(common_headers),<EOL>common_headers))<EOL>hdrs = {}<EOL>for h in common_headers:<EOL><INDENT>hdrs[h] = None<EOL><DEDENT>features_to_process = []<EOL>feature_to_predict = None<EOL>ignore_features = []<EOL>set_if_above = None<EOL>labels = []<EOL>label_values = []<EOL>if label_rules:<EOL><INDENT>set_if_above = label_rules["<STR_LIT>"]<EOL>labels = label_rules["<STR_LIT>"]<EOL>label_values = label_rules["<STR_LIT>"]<EOL><DEDENT>all_rows = []<EOL>num_done = <NUM_LIT:0><EOL>total_files = len(pipeline_files)<EOL>for c in pipeline_files:<EOL><INDENT>log.info(("<STR_LIT>")<EOL>.format(num_done,<EOL>total_files,<EOL>c))<EOL>cf = pd.read_csv(c)<EOL>log.info(("<STR_LIT>")<EOL>.format(len(cf.index)))<EOL>for index, row in cf.iterrows():<EOL><INDENT>valid_row = True<EOL>new_row = copy.deepcopy(hdrs)<EOL>new_row["<STR_LIT>"] = c<EOL>for k in hdrs:<EOL><INDENT>if k in row:<EOL><INDENT>new_row[k] = row[k]<EOL><DEDENT><DEDENT>if label_rules:<EOL><INDENT>test_rand = random.randint(<NUM_LIT:0>, <NUM_LIT:100>)<EOL>if test_rand > set_if_above:<EOL><INDENT>new_row["<STR_LIT>"] = label_values[<NUM_LIT:1>]<EOL>new_row["<STR_LIT>"] = labels[<NUM_LIT:1>]<EOL><DEDENT>else:<EOL><INDENT>new_row["<STR_LIT>"] = label_values[<NUM_LIT:0>]<EOL>new_row["<STR_LIT>"] = labels[<NUM_LIT:0>]<EOL><DEDENT><DEDENT>if valid_row:<EOL><INDENT>all_rows.append(new_row)<EOL><DEDENT><DEDENT>num_done += <NUM_LIT:1><EOL><DEDENT>log.info(("<STR_LIT>")<EOL>.format(len(all_rows)))<EOL>df = pd.DataFrame(all_rows)<EOL>log.info(("<STR_LIT>")<EOL>.format(len(df.index),<EOL>df.columns.values))<EOL>if ev("<STR_LIT>",<EOL>"<STR_LIT:0>") == "<STR_LIT:1>":<EOL><INDENT>log.info("<STR_LIT>")<EOL>save_node["<STR_LIT>"] = df.to_json()<EOL><DEDENT>if clean_file:<EOL><INDENT>log.info(("<STR_LIT>")<EOL>.format(fulldata_file))<EOL>df.to_csv(fulldata_file,<EOL>sep='<STR_LIT:U+002C>',<EOL>encoding='<STR_LIT:utf-8>',<EOL>index=False)<EOL>log.info(("<STR_LIT>")<EOL>.format(fulldata_file))<EOL>if post_proc_rules:<EOL><INDENT>clean_metadata_file = "<STR_LIT>"<EOL>feature_to_predict = "<STR_LIT>"<EOL>features_to_process = []<EOL>ignore_features = []<EOL>if label_rules:<EOL><INDENT>ignore_features = [feature_to_predict]<EOL><DEDENT>if "<STR_LIT>" in post_proc_rules:<EOL><INDENT>for p in post_proc_rules["<STR_LIT>"]:<EOL><INDENT>if p in headers_dict:<EOL><INDENT>ignore_features.append(p)<EOL><DEDENT><DEDENT>for d in df.columns.values:<EOL><INDENT>add_this_one = True<EOL>for i in ignore_features:<EOL><INDENT>if d == i:<EOL><INDENT>add_this_one = False<EOL>break<EOL><DEDENT><DEDENT>if add_this_one:<EOL><INDENT>features_to_process.append(d)<EOL><DEDENT><DEDENT>fulldata_metadata_file = "<STR_LIT>".format(<EOL>"<STR_LIT:/>".join(fulldata_file.split("<STR_LIT:/>")[:-<NUM_LIT:1>]),<EOL>metadata_filename)<EOL>log.info(("<STR_LIT>")<EOL>.format(fulldata_metadata_file))<EOL>header_data = {"<STR_LIT>": list(df.columns.values),<EOL>"<STR_LIT>": "<STR_LIT>",<EOL>"<STR_LIT>": pipeline_files,<EOL>"<STR_LIT>": post_proc_rules,<EOL>"<STR_LIT>": label_rules,<EOL>"<STR_LIT>": features_to_process,<EOL>"<STR_LIT>": feature_to_predict,<EOL>"<STR_LIT>": ignore_features,<EOL>"<STR_LIT>": rnow()}<EOL>with open(fulldata_metadata_file, "<STR_LIT:w>") as otfile:<EOL><INDENT>otfile.write(str(ppj(header_data)))<EOL><DEDENT>keep_these = features_to_process<EOL>keep_these.append(feature_to_predict)<EOL>log.info(("<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>")<EOL>.format(clean_file,<EOL>keep_these,<EOL>feature_to_predict))<EOL>clean_df = df[keep_these].dropna(<EOL>axis=<NUM_LIT:1>, how='<STR_LIT:all>').dropna()<EOL>cleaned_features = clean_df.columns.values<EOL>cleaned_to_process = []<EOL>cleaned_ignore_features = []<EOL>for c in cleaned_features:<EOL><INDENT>if c == feature_to_predict:<EOL><INDENT>cleaned_ignore_features.append(c)<EOL><DEDENT>else:<EOL><INDENT>keep_it = True<EOL>for ign in ignore_features:<EOL><INDENT>if c == ign:<EOL><INDENT>cleaned_ignore_features.append(c)<EOL>keep_it = False<EOL>break<EOL><DEDENT><DEDENT>if keep_it:<EOL><INDENT>cleaned_to_process.append(c)<EOL><DEDENT><DEDENT><DEDENT>log.info(("<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>")<EOL>.format(clean_file,<EOL>cleaned_to_process,<EOL>cleaned_ignore_features,<EOL>feature_to_predict))<EOL>write_clean_df = clean_df.drop(<EOL>columns=cleaned_ignore_features<EOL>)<EOL>log.info(("<STR_LIT>")<EOL>.format(len(write_clean_df.index)))<EOL>write_clean_df.to_csv(<EOL>clean_file,<EOL>sep='<STR_LIT:U+002C>',<EOL>encoding='<STR_LIT:utf-8>',<EOL>index=False)<EOL>clean_metadata_file = "<STR_LIT>".format(<EOL>"<STR_LIT:/>".join(clean_file.split("<STR_LIT:/>")[:-<NUM_LIT:1>]),<EOL>metadata_filename)<EOL>log.info(("<STR_LIT>")<EOL>.format(clean_metadata_file))<EOL>header_data = {"<STR_LIT>": list(write_clean_df.columns.values),<EOL>"<STR_LIT>": "<STR_LIT>",<EOL>"<STR_LIT>": pipeline_files,<EOL>"<STR_LIT>": post_proc_rules,<EOL>"<STR_LIT>": label_rules,<EOL>"<STR_LIT>": cleaned_to_process,<EOL>"<STR_LIT>": feature_to_predict,<EOL>"<STR_LIT>": cleaned_ignore_features,<EOL>"<STR_LIT>": rnow()}<EOL>with open(clean_metadata_file, "<STR_LIT:w>") as otfile:<EOL><INDENT>otfile.write(str(ppj(header_data)))<EOL><DEDENT><DEDENT>else:<EOL><INDENT>for d in df.columns.values:<EOL><INDENT>add_this_one = True<EOL>for i in ignore_features:<EOL><INDENT>if d == i:<EOL><INDENT>add_this_one = False<EOL>break<EOL><DEDENT><DEDENT>if add_this_one:<EOL><INDENT>features_to_process.append(d)<EOL><DEDENT><DEDENT>fulldata_metadata_file = "<STR_LIT>".format(<EOL>"<STR_LIT:/>".join(fulldata_file.split("<STR_LIT:/>")[:-<NUM_LIT:1>]),<EOL>metadata_filename)<EOL>log.info(("<STR_LIT>")<EOL>.format(fulldata_metadata_file))<EOL>header_data = {"<STR_LIT>": list(df.columns.values),<EOL>"<STR_LIT>": "<STR_LIT>",<EOL>"<STR_LIT>": pipeline_files,<EOL>"<STR_LIT>": post_proc_rules,<EOL>"<STR_LIT>": label_rules,<EOL>"<STR_LIT>": features_to_process,<EOL>"<STR_LIT>": feature_to_predict,<EOL>"<STR_LIT>": ignore_features,<EOL>"<STR_LIT>": rnow()}<EOL>with open(fulldata_metadata_file, "<STR_LIT:w>") as otfile:<EOL><INDENT>otfile.write(str(ppj(header_data)))<EOL><DEDENT>keep_these = features_to_process<EOL>keep_these.append(feature_to_predict)<EOL>log.info(("<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>")<EOL>.format(clean_file,<EOL>keep_these,<EOL>feature_to_predict))<EOL>clean_df = df[keep_these].dropna(<EOL>axis=<NUM_LIT:1>, how='<STR_LIT:all>').dropna()<EOL>cleaned_features = clean_df.columns.values<EOL>cleaned_to_process = []<EOL>cleaned_ignore_features = []<EOL>for c in cleaned_features:<EOL><INDENT>if c == feature_to_predict:<EOL><INDENT>cleaned_ignore_features.append(c)<EOL><DEDENT>else:<EOL><INDENT>keep_it = True<EOL>for ign in ignore_features:<EOL><INDENT>if c == ign:<EOL><INDENT>cleaned_ignore_features.append(c)<EOL>keep_it = False<EOL>break<EOL><DEDENT><DEDENT>if keep_it:<EOL><INDENT>cleaned_to_process.append(c)<EOL><DEDENT><DEDENT><DEDENT>log.info(("<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>")<EOL>.format(clean_file,<EOL>cleaned_to_process,<EOL>cleaned_ignore_features,<EOL>feature_to_predict))<EOL>write_clean_df = clean_df.drop(<EOL>columns=cleaned_ignore_features<EOL>)<EOL>log.info(("<STR_LIT>")<EOL>.format(len(write_clean_df.index)))<EOL>write_clean_df.to_csv(<EOL>clean_file,<EOL>sep='<STR_LIT:U+002C>',<EOL>encoding='<STR_LIT:utf-8>',<EOL>index=False)<EOL>clean_metadata_file = "<STR_LIT>".format(<EOL>"<STR_LIT:/>".join(clean_file.split("<STR_LIT:/>")[:-<NUM_LIT:1>]),<EOL>metadata_filename)<EOL>log.info(("<STR_LIT>")<EOL>.format(clean_metadata_file))<EOL>header_data = {"<STR_LIT>": list(write_clean_df.columns.values),<EOL>"<STR_LIT>": "<STR_LIT>",<EOL>"<STR_LIT>": pipeline_files,<EOL>"<STR_LIT>": post_proc_rules,<EOL>"<STR_LIT>": label_rules,<EOL>"<STR_LIT>": cleaned_to_process,<EOL>"<STR_LIT>": feature_to_predict,<EOL>"<STR_LIT>": cleaned_ignore_features,<EOL>"<STR_LIT>": rnow()}<EOL>with open(clean_metadata_file, "<STR_LIT:w>") as otfile:<EOL><INDENT>otfile.write(str(ppj(header_data)))<EOL><DEDENT><DEDENT>save_node["<STR_LIT>"] = clean_file<EOL>save_node["<STR_LIT>"] = clean_metadata_file<EOL>log.info(("<STR_LIT>")<EOL>.format(clean_file))<EOL><DEDENT>save_node["<STR_LIT>"] = fulldata_file<EOL>save_node["<STR_LIT>"] = fulldata_metadata_file<EOL>save_node["<STR_LIT:status>"] = VALID<EOL><DEDENT>save_node["<STR_LIT>"] = features_to_process<EOL>save_node["<STR_LIT>"] = feature_to_predict<EOL>save_node["<STR_LIT>"] = ignore_features<EOL>log.info("<STR_LIT>")<EOL>return save_node<EOL>
build_csv

    :param pipeline_files: files to process
    :param fulldata_file: output all columns to this csv file
    :param clean_file: output all numeric-ready columns to this csv file
    :param post_proc_rules: rules after building the DataFrame
    :param label_rules: labeling rules
    :param metadata_filename: metadata