x and y must have same first dimension numpy

Question

Im trying to plot a graph which converts dates into floats to be used in linear regression algorithm and then uses the original dates as strings for the x axis labels. When I plot the actual values from the csv file the program runs ok however when I plot the regression values i get the error raise ValueError(f"x and y must have same first dimension, but " ValueError: x and y must have same first dimension, but have shapes (60,) and (1,)

here is my code:

  scaler = StandardScaler()
  data = pd.read_csv('food.csv')

   X = data['Date'].values
   X = pd.to_datetime(X, errors="coerce")
   X = X.values.astype("float64").reshape(-1,1)

    Y = data['TOTAL'].values.reshape(-1,1)

    mean_x = np.mean(X)
    mean_y = np.mean(Y)

     m = len(X)

     numer = 0

     denom = 0

     for i in range(m):
          numer += (X[i] - mean_x) * (Y[i] - mean_y)
          denom += (X[i] - mean_x) ** 2
      m = numer / denom
     c = mean_y - (m * mean_x)

   print (f'm = {m} \nc = {c}')

  max_x = np.max(X) + 100
   min_x = np.min(X) - 100

   x = np.linspace (min_x, max_x, 100)
   y = c + m * x


  X= data['Date'].astype('str')

   x= data['Date'].astype('str')


   print(X.shape)
   print(y.shape)

   newY = Y.transpose()[0]

  newy = y.transpose()[0]


  plt.scatter(X, newY, c='#ef5423', label='data points')

  plt.plot(x, newy, color='#58b970', label='Regression Line')

   plt.show()








    

Answer 1

It's difficult to answer without data, but by the looks of your code x, and therefore y have shape (100,) (from the x = np.linspace(...) command).

So you probably don't want to pick just the 0th element in the line

newy = y.transpose()[0]

Because then newy is only a scalar value. What happens if you omit [0] and just do this?

newy = y.transpose()