title: sklearn: sklearn.preprocessing DeprecationWarning for arrays
body: <p>First I looked at all the related question. 
  There are very similar problems given.<br>
  So I followed suggestions from the links, but none of them worked for me.<br>
  <a href="https://stackoverflow.com/questions/39214164/data-conversion-error-while-applying-a-function-to-each-row-in-pandas-python">Data Conversion Error while applying a function to each row in pandas Python</a><br>
  <a href="https://stackoverflow.com/questions/37798056/getting-deprecation-warning-in-sklearn-over-1d-array-despite-not-having-a-1d-ar">Getting deprecation warning in Sklearn over 1d array, despite not having a 1D array</a>  </p>
  
  <p>I also tried to follow the error message, it also didn't work.  </p>
  
  <p>The code looks like this:  </p>
  
  <pre><code># Importing the libraries
  import numpy as np
  import pandas as pd
  
  # Importing the dataset
  dataset = pd.read_csv('Position_Salaries.csv')
  X = dataset.iloc[:, 1:2].values
  y = dataset.iloc[:, 2].values
  
  # avoid DataConversionError
  X = X.astype(float)
  y = y.astype(float)
  
  
  ## Attempt to avoid DeprecationWarning for sklearn.preprocessing
  #X = X.reshape(-1,1)                  # attempt 1
  #X = np.array(X).reshape((len(X), 1)) # attempt 2
  #X = np.array([X])                    # attempt 3
  
  
  # Feature Scaling
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  sc_y = StandardScaler()
  X = sc_X.fit_transform(X)
  y = sc_y.fit_transform(y)
  
  # Fitting SVR to the dataset
  from sklearn.svm import SVR
  regressor = SVR(kernel = 'rbf')
  regressor.fit(X, y)
  
  # Predicting a new result
  y_pred = regressor.predict(sc_X.transform(np.array([6.5])))
  y_pred = sc_y.inverse_transform(y_pred)
  </code></pre>
  
  <p>The data looks like this:  </p>
  
  <pre><code>Position,Level,Salary
  Business Analyst,1,45000
  Junior Consultant,2,50000
  Senior Consultant,3,60000
  Manager,4,80000
  Country Manager,5,110000
  Region Manager,6,150000
  Partner,7,200000
  Senior Partner,8,300000
  C-level,9,500000
  CEO,10,1000000
  </code></pre>
  
  <p>The full error log goes like this:  </p>
  
  <pre><code>/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
    warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
  /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
    warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
  /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
    warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)
  /Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
    DeprecationWarning)
  </code></pre>
  
  <p>I am using only second and third column so there is no need for one hot encoding for the first column. The only problem is DeprecationWarning.</p>
  
  <p>I tried all the suggestions given but none of them worked.<br>
  So, the help will be truly appreciated.</p>