我通过SQLAlchemy从所有引用的表中提取表行和相应的行。
给定以下对象结构：
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])
我正在使用：
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)
这给了我：
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')
这确实包含了我想要的所有列 - 但命名并没有帮助我选择我想要的内容。
是否可以保留此数据框中原始表中的键名？例如而不是measurement_units_1_code我希望mass_unit_code。
答案 0 :(得分：2)
这不是joinedload应该用于的内容。在这种情况下，您想要明确join：
session.query(DNAExtractionProtocol.id.label("id"),
...,
MeasurementUnit.id.label("mass_unit_id"),
...) \
.join(DNAExtractionProtocol.mass_unit) \
.join(DNAExtractionProtocol.digestion_buffer) \
... \
.filter(...)
如果您不想输入所有这些名称，可以检查DNAExtractionProtocol类以查找所有关系并动态构建查询和标签。一个例子：
cols = []
joins = []
insp = inspect(DNAExtractionProtocol)
for name, col in insp.columns.items():
cols.append(col.label(name))
for name, rel in insp.relationships.items():
alias = aliased(rel.mapper.class_, name=name)
for col_name, col in inspect(rel.mapper).columns.items():
aliased_col = getattr(alias, col.key)
cols.append(aliased_col.label("{}_{}".format(name, col_name)))
joins.append((alias, rel.class_attribute))
query = session.query(*cols).select_from(DNAExtractionProtocol)
for join in joins:
query = query.join(*join)
编辑：根据您的数据结构，您可能需要在最后一行使用outerjoin而不是join。
您可能需要根据自己的喜好调整此项。例如，这并未考虑潜在的命名冲突，例如：对于mass_unit_id，是DNAExtractionProtocol.mass_unit_id还是MeasurementUnit.id？
此外，您可能希望执行sql_query.statement而不是str(sql_query)。 str(sql_query)用于打印目的，不用于执行。我相信如果您使用params=[code]，则不需要传递sql_query.statement，因为code已经绑定到查询中的相应参数。