def execute(self, environment):
try:
newEnv = Environment(environment, instruction.dbtemp)
instruction.envVariables.append(newEnv)
labels = []
values = {}
for i in range(len(self.params)):
v = self.params[i].execute(newEnv)
values[self.params[i].temp] = [v.value]
labels.append(self.params[i].temp)
newEnv.types[labels[i]] = v.type
newEnv.dataFrame = pd.DataFrame(values)
return [newEnv.dataFrame, newEnv.types]
except:
instruction.syntaxPostgreSQL.append(
"Error: P0001: Error en la instruccion SELECT")
def execute(self, environment):
try:
newEnv = Environment(environment, instruction.dbtemp)
instruction.envVariables.append(newEnv)
self.fromcl.execute(newEnv)
if self.wherecl != None:
self.wherecl.execute(newEnv)
if self.groupbyCl != None:
newEnv.groupCols = len(self.groupbyCl)
groupDf = None
groupEmpty = True
if self.params:
params = []
for p in self.params:
if isinstance(p, TableAll):
result = p.execute(newEnv)
for r in result:
params.append(r)
else:
params.append(p)
labels = [p.temp for p in params]
if self.groupbyCl != None:
value = []
for i in range(len(params)):
ex = params[i].execute(newEnv)
val = ex.value
newEnv.types[labels[i]] = ex.type
# Si no es columna de agrupacion
if i < len(self.groupbyCl):
if not (
isinstance(val, pd.core.series.Series)
or isinstance(val, pd.DataFrame)
):
nval = {
val: [
val for i in range(len(newEnv.dataFrame.index))
]
}
nval = pd.DataFrame(nval)
val = nval
newEnv.dataFrame = pd.concat(
[newEnv.dataFrame, val], axis=1
)
else:
if groupEmpty:
countGr = newEnv.groupCols
# Obtiene las ultimas columnas metidas (Las del group by)
df = newEnv.dataFrame.iloc[:, -countGr:]
cols = list(df.columns)
groupDf = df.groupby(cols).sum().reset_index()
groupDf = pd.concat([groupDf, val], axis=1)
groupEmpty = False
else:
groupDf = pd.concat([groupDf, val], axis=1)
if groupEmpty:
countGr = newEnv.groupCols
# Obtiene las ultimas columnas metidas (Las del group by)
df = newEnv.dataFrame.iloc[:, -countGr:]
cols = list(df.columns)
groupDf = df.groupby(cols).sum().reset_index()
groupEmpty = False
else:
value = [p.execute(newEnv) for p in params]
for j in range(len(labels)):
newEnv.types[labels[j]] = value[j].type
newEnv.dataFrame[labels[j]] = value[j].value
else:
value = [newEnv.dataFrame[p] for p in newEnv.dataFrame]
labels = [p for p in newEnv.dataFrame]
if value != []:
if self.wherecl == None:
df_ = newEnv.dataFrame
if self.orderByCl:
df_ = self.orderByCl.execute(df_, newEnv)
df_ = df_.filter(labels)
if self.limitCl:
df_ = self.limitCl.execute(df_, newEnv)
if self.distinct:
return [df_.drop_duplicates(), newEnv.types]
return [df_, newEnv.types]
df_ = newEnv.dataFrame
if self.orderByCl:
df_ = self.orderByCl.execute(df_, newEnv)
df_ = df_.filter(labels)
if self.limitCl:
df_ = self.limitCl.execute(df_, newEnv)
if self.distinct:
return [df_.drop_duplicates(), newEnv.types]
return [df_, newEnv.types]
else:
newNames = {}
i = 0
for (columnName, columnData) in groupDf.iteritems():
newNames[columnName] = labels[i]
i += 1
groupDf.rename(columns=newNames, inplace=True)
df_ = groupDf
if self.limitCl:
df_ = self.limitCl.execute(df_, newEnv)
if self.orderByCl:
df_ = self.orderByCl.execute(df_, newEnv)
if self.distinct:
return [df_.drop_duplicates(), newEnv.types]
return [df_, newEnv.types]
except:
instruction.syntaxPostgreSQL.append(
"Error: P0001: Error en la instruccion SELECT"
)