Preserve key naming when creating a joinedload sql_query in SQLAlchemy

I am extracting a table row and the corresponding row from all referenced tables via SQLAlchemy.

Given the following object structure:

class DNAExtractionProtocol(Base):
    __tablename__ = 'dna_extraction_protocols'
    id = Column(Integer, primary_key=True)
    code = Column(String, unique=True)
    name = Column(String)
    sample_mass = Column(Float)
    mass_unit_id = Column(String, ForeignKey('measurement_units.id'))
    mass_unit = relationship("MeasurementUnit", foreign_keys=[mass_unit_id])
    digestion_buffer_id = Column(String, ForeignKey("solutions.id"))
    digestion_buffer = relationship("Solution", foreign_keys=[digestion_buffer_id])
    digestion_buffer_volume = Column(Float)
    digestion_id = Column(Integer, ForeignKey("incubations.id"))
    digestion = relationship("Incubation", foreign_keys=[digestion_id])
    lysis_buffer_id = Column(String, ForeignKey("solutions.id"))
    lysis_buffer = relationship("Solution", foreign_keys=[lysis_buffer_id])
    lysis_buffer_volume = Column(Float)
    lysis_id = Column(Integer, ForeignKey("incubations.id"))
    lysis = relationship("Incubation", foreign_keys=[lysis_id])
    proteinase_id = Column(String, ForeignKey("solutions.id"))
    proteinase = relationship("Solution", foreign_keys=[proteinase_id])
    proteinase_volume = Column(Float)
    inactivation_id = Column(Integer, ForeignKey("incubations.id"))
    inactivation = relationship("Incubation", foreign_keys=[inactivation_id])
    cooling_id = Column(Integer, ForeignKey("incubations.id"))
    cooling = relationship("Incubation", foreign_keys=[cooling_id])
    centrifugation_id = Column(Integer, ForeignKey("incubations.id"))
    centrifugation = relationship("Incubation", foreign_keys=[centrifugation_id])

    volume_unit_id = Column(String, ForeignKey('measurement_units.id'))
    volume_unit = relationship("MeasurementUnit", foreign_keys=[volume_unit_id])

I am using:

sql_query = session.query(DNAExtractionProtocol).options(Load(DNAExtractionProtocol).joinedload("*")).filter(DNAExtractionProtocol.code == code)
for item in sql_query:
    pass
mystring = str(sql_query)
mydf = pd.read_sql_query(mystring,engine,params=[code])
print(mydf.columns)

This gives me:

Index([u'dna_extraction_protocols_id', u'dna_extraction_protocols_code',
       u'dna_extraction_protocols_name',
       u'dna_extraction_protocols_sample_mass',
       u'dna_extraction_protocols_mass_unit_id',
       u'dna_extraction_protocols_digestion_buffer_id',
       u'dna_extraction_protocols_digestion_buffer_volume',
       u'dna_extraction_protocols_digestion_id',
       u'dna_extraction_protocols_lysis_buffer_id',
       u'dna_extraction_protocols_lysis_buffer_volume',
       u'dna_extraction_protocols_lysis_id',
       u'dna_extraction_protocols_proteinase_id',
       u'dna_extraction_protocols_proteinase_volume',
       u'dna_extraction_protocols_inactivation_id',
       u'dna_extraction_protocols_cooling_id',
       u'dna_extraction_protocols_centrifugation_id',
       u'dna_extraction_protocols_volume_unit_id', u'measurement_units_1_id',
       u'measurement_units_1_code', u'measurement_units_1_long_name',
       u'measurement_units_1_siunitx', u'solutions_1_id', u'solutions_1_code',
       u'solutions_1_name', u'solutions_1_supplier',
       u'solutions_1_supplier_id', u'incubations_1_id', u'incubations_1_speed',
       u'incubations_1_duration', u'incubations_1_temperature',
       u'incubations_1_movement', u'incubations_1_speed_unit_id',
       u'incubations_1_duration_unit_id', u'incubations_1_temperature_unit_id',
       u'solutions_2_id', u'solutions_2_code', u'solutions_2_name',
       u'solutions_2_supplier', u'solutions_2_supplier_id',
       u'incubations_2_id', u'incubations_2_speed', u'incubations_2_duration',
       u'incubations_2_temperature', u'incubations_2_movement',
       u'incubations_2_speed_unit_id', u'incubations_2_duration_unit_id',
       u'incubations_2_temperature_unit_id', u'solutions_3_id',
       u'solutions_3_code', u'solutions_3_name', u'solutions_3_supplier',
       u'solutions_3_supplier_id', u'incubations_3_id', u'incubations_3_speed',
       u'incubations_3_duration', u'incubations_3_temperature',
       u'incubations_3_movement', u'incubations_3_speed_unit_id',
       u'incubations_3_duration_unit_id', u'incubations_3_temperature_unit_id',
       u'incubations_4_id', u'incubations_4_speed', u'incubations_4_duration',
       u'incubations_4_temperature', u'incubations_4_movement',
       u'incubations_4_speed_unit_id', u'incubations_4_duration_unit_id',
       u'incubations_4_temperature_unit_id', u'incubations_5_id',
       u'incubations_5_speed', u'incubations_5_duration',
       u'incubations_5_temperature', u'incubations_5_movement',
       u'incubations_5_speed_unit_id', u'incubations_5_duration_unit_id',
       u'incubations_5_temperature_unit_id', u'measurement_units_2_id',
       u'measurement_units_2_code', u'measurement_units_2_long_name',
       u'measurement_units_2_siunitx', u'dna_extractions_1_id',
       u'dna_extractions_1_code', u'dna_extractions_1_protocol_id',
       u'dna_extractions_1_source_id'],
      dtype='object')

This indeed contains all the columns I want - but the naming does not help me select what I want.

Isit possible to preserve the key names from the original table in this dataframe? e.g. instead of measurement_units_1_code I would like to have mass_unit_code.