how to use for loop with numpy array?

Question

i have a part of code like this

#predicitng values one by one 
regr = linear_model.LinearRegression()
predicted_value = np.array([ 9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32])
predicted_value =  predicted_value.reshape(-1,1)


#mt
regr.fit(x, y)
predicted_values = regr.predict(predicted_value)
predict_outcome = regr.predict(predicted_value)
predictions = {'predicted_value': predict_outcome}
mmt = np.mean(predict_outcome)

#ht
regr.fit(x, ht)
predicted_values = regr.predict(predicted_value)
predict_outcome = regr.predict(predicted_value)
predictions = {'predicted_value': predict_outcome}
mht = np.mean(predict_outcome)

here instead of this :

predicted_value = np.array([ 9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32])

how can i set a range from 9 to 32(or x to y) so that i can avoid typing all numbers.if it is done using for loop how to apply it in this context

Hi, you could consider editing the title of your question. Currently it is somewhat misleading because the question is not necessarily about for loops. It is about initializing an array with a sequence of numbers, if I am not mistaken. — MB-F
– MB-F, Commented Mar 6, 2017 at 9:10
You could also skip the first code snippet since it is irrelevant to your actual question. — Ivan A.
– Ivan A., Commented Mar 6, 2017 at 18:15

MB-F · Accepted Answer · 2017-03-06 09:11:21Z

8

There is no need to use a loop. You can use numpy.arange([start, ]stop, [step, ]) to generate a range of numbers.

In your case:

predicted_value = np.arange(9, 33)  # Note the 33 if you want 9..32

If you really want to use a loop, there is the option of using a list comprehension:

predicted_value = np.array([i for i in range(9, 33)])

Or an explicit loop, which would be most horrible:

predicted_value = np.empty(33 - 9)
for k, i in enumerate(range(9, 33)):
    predicted_value[k] = i

edited Mar 6, 2017 at 9:11

answered Mar 6, 2017 at 8:06

MB-F

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Joe Cheng · Accepted Answer · 2017-03-06 08:09:56Z

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predicted_value = np.array([i for i in range(9, 33)])

answered Mar 6, 2017 at 8:09

Joe Cheng

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