Data Manipulation, Pandas and Distance Metrics

Write the Python code to load the CSV file from spatialkey directly into a Pandas dataframe. • You may need to familiarize yourself with the IO Tools functions of Pandas. • Turn in the fragment of code that does this. § Compute the price per square foot for all properties. Add this data back into the dataframe. • See the documentation on concatening objects. • Turn in the code that does this, and show the results of head() and tail() on the new dataframe. § Provide three data points from the original dataset that give your reason to be concerned about the some of the properties with unusual price per square foot data. • Learn about summarizing your data and sorting. • Turn in the rows of those three data points and 1 to 3 sentences discussing your concerns. § Use a scatter plot to plot the sale price to the square feet. § Do the same for number of beds to sale price. • You will need to learn about scatter plots to complete this. • For both, turn in the scatter plot &#emdash; if you're using Jupyter, just leave the data inline. § Explore the distribution of properties by number of beds. Plot this using plot() (learn about that function here). PART 2 / Distance metrics and k-Nearest Neighbor § Implement the k-Nearest Neighbor algorithm in Python using the Euclidean distance metric. • Turn on the code for this implementation in Python. • You implementation will take two vectors and return a single number. Use the template : def my_knn(vec1, vec2): # your implementation return d # the_euclidean_distance of vec1 and vec2 § Produce the distance table using your version of kNN for all properties. • To make things easier, please reduce the data vector to just beds, bath, square footage, price, latitude and longitude. • You can use the street as the index. Your final output will look something like this: