Error in Python script "Expected 2D array, got 1D array instead:"?

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I'm following this tutorial to make this ML prediction:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style

style.use("ggplot")
from sklearn import svm

x = [1, 5, 1.5, 8, 1, 9]
y = [2, 8, 1.8, 8, 0.6, 11]

plt.scatter(x,y)
plt.show()

X = np.array([[1,2],
             [5,8],
             [1.5,1.8],
             [8,8],
             [1,0.6],
             [9,11]])

y = [0,1,0,1,0,1]
X.reshape(1, -1)

clf = svm.SVC(kernel='linear', C = 1.0)
clf.fit(X,y)

print(clf.predict([0.58,0.76]))

I'm using Python 3.6 and I get error "Expected 2D array, got 1D array instead:" I think the script is for older versions, but I don't know how to convert it to the 3.6 version.

Already try with the:

X.reshape(1, -1)
predict machine-learning python-3.x python
11 Answers

I use the below approach.

reg = linear_model.LinearRegression()
reg.fit(df[['year']],df.income)

reg.predict([[2136]])
user_102310690

I faced the same issue except that the data type of the instance I wanted to predict was a panda.Series object.

Well I just needed to predict one input instance. I took it from a slice of my data.

df = pd.DataFrame(list(BiogasPlant.objects.all()))
test = df.iloc[-1:]       # sliced it here

In this case, you'll need to convert it into a 1-D array and then reshape it.

 test2d = test.values.reshape(1,-1)

From the docs, values will convert Series into a numpy array.

user_18706850

I faced the same problem. You just have to make it an array and moreover you have to put double squared brackets to make it a single element of the 2D array as first bracket initializes the array and the second makes it an element of that array.

So simply replace the last statement by:

print(clf.predict(np.array[[0.58,0.76]]))
user_108986180

Just insert the argument between a double square bracket:

regressor.predict([[values]])

that worked for me

user_120804070

I was facing the same issue earlier but I have somehow found the solution, You can try reg.predict([[3300]]).

The API used to allow scalar value but now you need to give a 2D array.

user_121699630

With one feature my Dataframe list converts to a Series. I had to convert it back to a Dataframe list and it worked.

if type(X) is Series:
    X = X.to_frame()
user_78299770

Just enclose your numpy object with two square brackets or vice versa.

For example:

If initially your x = [8,9,12,7,5]

change it to x = [ [8,9,12,7,5] ].

That should fix the dimension issue

user_127968730

You can do it like this:

np.array(x)[:, None]

user_137941890

The X and Y matrix of Independent Variable and Dependent Variable respectively to DataFrame from int64 Type so that it gets converted from 1D array to 2D array.. i.e X=pd.DataFrame(X) and Y=pd.dataFrame(Y) where pd is of pandas class in python. and thus feature scaling in-turn doesn't lead to any error!

user_65236280
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