CS2613/assignments/A5/readcsv.py

def read_csv(filename):
    """Read a CSV file, return list of rows"""

    import csv
    with open(filename, 'rt', newline='') as f:
        reader = csv.reader(f, skipinitialspace=True)
        return [row for row in reader]


def header_map(headers):
    """
    Read a list, and convert it to a dictionary where the key is each element of the given list and the value is
    its position in the list
    :param headers: List
    :return: Dict
    """

    header_dict = dict()
    i = 0
    for header in headers:
        header_dict[header] = i
        i = i + 1
    return header_dict


def select(table, search_items):
    """
    Read the set in the second argument and search through the table in the first argument and return the
    columns that match the query in the search items
    :param table: List
    :param search_items: List
    :return: List
    """

    header_numbers = header_map(table[0])
    ret_list = list()
    columns = list()

    for item in search_items:
        if type(item) is int:  # Convert searched elements into strings if it is a number
            columns.append(header_numbers[str(item)])
        else:
            columns.append(header_numbers[item])
    columns.sort()
    for item in table:
        lst = list()
        for number in columns:
            lst.append(item[number])
        ret_list.append(lst)
    return ret_list


def row2dict(hmap, row):
    """
    Convert a row in the second argument, given the headers in the first argument to a dictionary which uses the
    headers as keys and the row data as values
    :param hmap: Dictionary
    :param row: List
    :return: Dictionary
    """
    ret_dict = dict()
    for key in hmap:
        ret_dict[key] = row[hmap[key]]
    return ret_dict


def check_row(row, query):
    """
    Check the row in the first argument passes a query in the second argument. The second argument is a formatted
    tuple where the first element in the tuple is a column name, the second is an operation (==, <=, >=, AND,
    OR) and the third element is a condition, numeric or string matching. (ex: is age == 34, is color == blue). AND
    and OR are special in that you can pass in recursive tuples (left and right argument are tuples) that will also
    be evaluated recursively
    :param row: List
    :param query: Tuple
    :return: Boolean
    """

    def perform_operation(op, var, cond):
        if type(var) is str:
            if var.isnumeric():
                var = int(var)
        if type(cond) is str:
            if cond.isnumeric():
                cond = int(cond)

        if op == '==':
            return var == cond
        elif op == '>=':
            return var >= cond
        elif op == '<=':
            return var <= cond
        elif op == 'OR':
            return perform_operation(var[1], row[var[0]], var[2]) or perform_operation(cond[1], row[cond[0]], cond[2])
        elif op == 'AND':
            return perform_operation(var[1], row[var[0]], var[2]) and perform_operation(cond[1], row[cond[0]], cond[2])
        else:
            return False

    if type(query[0]) and type(query[2]) is tuple:
        return perform_operation(query[1], query[0], query[2])
    else:
        stringify = str(query[2])
        return perform_operation(query[1], row[str(query[0])], stringify)


def filter_table(table, query):
    """
    This function takes a table of csv values, and performs the query to filter out the rows that do not match the query
    :param table: List
    :param query: Tuple
    :return: List
    """
    header_row = header_map(table[0])
    data_rows = table[1:]
    result = list()
    result.append(table[0])
    for row in data_rows:
        data_dict = row2dict(header_row, row)
        if check_row(data_dict, query):
            result.append(row)
    return result