# Deal with Pandas boolean
df["Active"] = np.where(df["Active"] == "Y", True, False)

# Converter
def convert_percent(val):
    """
    Convert the percentage string to an actual floating point percent
    - Remove %
    - Divide by 100 to make decimal
    """
    new_val = val.replace('%', '')
    return float(new_val) / 100

# Convert dtypes when reading data
df = pd.read_csv("sales_data_types.csv",
                   dtype={'Customer Number': 'int'},
                   converters={'2016': convert_currency,
                               '2017': convert_currency,
                               'Percent Growth': convert_percent,
                               'Jan Units': lambda x: pd.to_numeric(x, errors='coerce'),
                               'Active': lambda x: np.where(x == "Y", True, False)
                              })

df.dtypes