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I am new to machine learning and facing some issues in converting scalar array to 2d array.
I am trying to implement polynomial regression in spyder. Here is my code, Please help!

# Polynomial Regression

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
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

# Fitting Linear Regression to the dataset
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X, y)

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)

# Predicting a new result with Linear Regression
lin_reg.predict(6.5)

# Predicting a new result with Polynomial Regression
lin_reg_2.predict(poly_reg.fit_transform(6.5))

ValueError: Expected 2D array, got scalar array instead: array=6.5.
Reshape your data either using array.reshape(-1, 1) if your data has a
single feature or array.reshape(1, -1) if it contains a single sample.

The issue with your code is linreg.predict(6.5).

If you read the error statement it says that the model requires a 2-d array , however 6.5 is scalar.
Why? If you see your X data is having 2-d so anything that you want to predict with your model should also have two 2d shape.
This can be achieved either by using .reshape(-1,1) which creates a column vector (feature vector) or .reshape(1,-1) If you have single sample.

Things to remember in order to predict I need to prepare my data in the same way as my original training data.

If you need any more info let me know.

You have to give the input as 2D array, Hence try this!

lin_reg.predict([6.5])
lin_reg_2.predict(poly_reg.fit_transform([6.5]))

You get this issue in Jupyter only.
To resolve in jupyter make the value into np array using below code.

lin_reg.predict(np.array(6.5).reshape(1,-1))
lin_reg_2.predict(poly_reg.fit_transform(np.array(6.5).reshape(1,-1)))

For spyder it work same as you expected:

lin_reg.predict(6.5)
lin_reg_2.predict(poly_reg.fit_transform(6.5))