title: Can't do linear regression in scikit-Learn due to "reshaping" issue
body: <p>I have a simple CSV with two columns:</p>
  
  <ol>
  <li>ErrorWeek (a number for the week number in the year)</li>
  <li>ErrorCount (for the number of errors in a given week)</li>
  </ol>
  
  <p>I read the CSV data into a pandas dataframe, like this:</p>
  
  <pre><code>df = pd.read_csv("Errors.csv", sep=",")
  </code></pre>
  
  <p>df.head() shows:</p>
  
  <pre><code>   ErrorWeek  ErrorCount
  0          1          80
  1          2         118
  2          3         249
  3          4         397
  4          5         159
  </code></pre>
  
  <p>So far so good.</p>
  
  <p>Then, I create a test/train split, like this:</p>
  
  <pre><code>X_train, X_test, y_train, y_test = train_test_split(
      df['ErrorWeek'], df['ErrorCount'], random_state=0)
  </code></pre>
  
  <p>No errors so far.</p>
  
  <p>But, I then create a linear regression object and try to fit the data.</p>
  
  <pre><code># Create linear regression object
  regr = linear_model.LinearRegression()
  
  # Train the model using the training sets
  regr.fit(X_train, y_train)
  </code></pre>
  
  <p>Here I <strong>do</strong> get an error: "Reshape your data either using array.reshape(-1, 1)"</p>
  
  <p>--</p>
  
  <p>Looking at the shape of X_Test and y_Test, I get what looks like two one dimensional "arrays":</p>
  
  <pre><code>X_train shape: (36,)
  y_train shape: (36,)
  </code></pre>
  
  <p>--</p>
  
  <p>I have spent many hours trying to figure this out, but I'm new to Pandas, Python, and to scikit-learn. </p>
  
  <p>I'm reading in two dimensional data, but Pandas isn't seeing that way.</p>
  
  <p>What do I need to do, specifically?</p>
  
  <p>Thanks,</p>