The 'omop2poll' Python package offers a comprehensive solution for transforming standardized response codes from the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) survey variables into numeric values, streamlining the data preparation process. By automating the mapping and conversion of response codes, as well as the handling of missing data, it makes data analysis more accessible and reliable. The package provides a range of functions designed to help researchers and data analysts efficiently work through OMOP CDM survey data, ensuring accurate mappings of responses and effective management of data discrepancies. This package is a helpful tool for those working with OMOP CDM survey data, offering a path to more profound and accurate analyses by dramatically lowering the burden of data preprocessing.

load_survey_data(filename='survey.csv'): Loads survey data from a CSV file into a pandas DataFrame. This function takes an optional filename argument, which defaults to 'healthcare_survey.csv'. It constructs the file path by joining the current working directory with the specified filename. If the file does not exist at the constructed path, it raises a FileNotFoundError. Finally, it reads the CSV file into a DataFrame using pandas and returns this DataFrame.

map_responses(input_data): Maps numeric and text responses from a survey to their corresponding questions based on a preloaded survey data file. This function first calls load_survey_data() without arguments to load the default survey dataset. It then creates two mappings: one for numeric answers (mapping_numeric) and one for text answers (mapping_text), grouped by question_concept_id.

The function iterates over the input_data DataFrame, updating each row with the corresponding answer_numeric and answer_text based on the mappings. If an answer_text is numeric (detected as either int, float, or digit-containing string), it ensures the text remains as a string. The function returns the updated input_data DataFrame with the mapped answers.

pivot_data_numeric(filename): Pivots a dataset to structure numeric survey responses in a wide format. The function checks if the specified file exists; if not, it prints an error message and returns. It reads the data from the file into a DataFrame, then pivots this DataFrame so that each row represents a respondent and each column represents a question, with cells containing the numeric answers.

The column names are prefixed with 'q' to denote question IDs. The pivoted DataFrame is then saved to a new CSV file with a filename prefixed by 'pivot_n_'. The function concludes by printing the name of the newly saved file.

pivot_data_text(filename): Similar to pivot_data_numeric, but pivots text responses instead. It processes and pivots the dataset to structure text survey responses in a wide format, follows the same file existence check, reading, and pivoting process, but focuses on answer_text values. The output file is named with a 'pivot_t_' prefix, and the function notifies the user of the successfully saved file.

recode_missing_values(input_data): Recodes missing values in a dataset with NaN (Not a Number). This function supports both CSV and Excel files. It identifies missing values based on a predefined list ([-99, -98, -97]) and uses pandas to read the file, recoding occurrences of these values with NaN. The function raises a ValueError if the input file is not in one of the supported formats.

create_codebook(df): Generates a codebook from a DataFrame containing survey data. The function selects relevant columns (question_concept_id, question, answer_concept_id, answer_numeric, answer_text), removes duplicates, and sorts the entries by question_concept_id to create the codebook DataFrame. This DataFrame is returned for further use or inspection.

print_codebook(codebook_df): Prints the provided codebook DataFrame in a readable format. It attempts to use the tabulate library to print the DataFrame with headers, formatted as an SQL-style table. If tabulate is not installed, it falls back to pandas' built-in printing options, adjusting the display settings to ensure that all data is visible without truncation. This function is useful for displaying the codebook in a terminal or other environments where a visual representation of the DataFrame is beneficial.