# Xlswriter 


```

Import xlswriter 


#create file (workbook) and worksheet
outWorkbook = xlsxwriter.Workbook("out.xlsx") 
outSheet = outWorkbook.add_worksheet()

#declare data 
Names = ["Oklahoma", "Non", "Mary"] 
Values = [70,80,90] 


#write headers
For item in range(len(names)):
	outSheet.write(x, y 
outSheet.write("A1", "ANMES)
outSheet.write("B1", "Scores")

#declare data to file 
outSheet.write("

outWorkbook.close()


```

Sci- kit Learn & keras: 

```
# create a function that returns a model, taking as parameters things you
# want to verify using cross-valdiation and model selection
def create_model(optimizer='adagrad',
                 kernel_initializer='glorot_uniform', 
                 dropout=0.2):
    model = Sequential()
    model.add(Dense(64,activation='relu',kernel_initializer=kernel_initializer))
    model.add(Dropout(dropout))
    model.add(Dense(1,activation='sigmoid',kernel_initializer=kernel_initializer))

    model.compile(loss='binary_crossentropy',optimizer=optimizer, metrics=['accuracy'])

    return model

# wrap the model using the function you created
clf = KerasRegressor(build_fn=create_model,verbose=0)

# just create the pipeline
pipeline = Pipeline([
    ('clf',clf)
])

pipeline.fit(X_train, y_train)

```