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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 This exercise will test your ability to read a data file and understand statistics about the data.
In later exercises, you will apply techniques to filter the data, build a machine learning model, and iteratively improve your model.
The course examples use data from Melbourne. To ensure you can apply these techniques on your own, you will have to apply them to a new dataset (with house prices from Iowa).
Exercises
Run the following cell to set up code-checking, which will verify your work as you go.
Step1:  Step 1
Step2:  Step 2
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Python Code:
# Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.machine_learning.ex2 import *
print("Setup Complete")
Explanation: This exercise will test your ability to read a data file and understand statistics about the data.
In later exercises, you will apply techniques to filter the data, build a machine learning model, and iteratively improve your model.
The course examples use data from Melbourne. To ensure you can apply these techniques on your own, you will have to apply them to a new dataset (with house prices from Iowa).
Exercises
Run the following cell to set up code-checking, which will verify your work as you go.
End of explanation
import pandas as pd
# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'
# Fill in the line below to read the file into a variable home_data
home_data = ____
# Check your answer
step_1.check()
#%%RM_IF(PROD)%%
# Path of the file to read
iowa_file_path = '../input/home-data-for-ml-course/train.csv'
# Fill in the line below to read the file into a variable home_data
home_data = 0
# Call line below with no argument to check that you've loaded the data correctly
step_1.assert_check_failed()
#%%RM_IF(PROD)%%
# Fill in the line below to read the file into a variable home_data
home_data = pd.DataFrame()
# Call line below with no argument to check that you've loaded the data correctly
step_1.assert_check_failed()
home_data = pd.read_csv(iowa_file_path)
step_1.assert_check_passed()
# Lines below will give you a hint or solution code
#_COMMENT_IF(PROD)_
step_1.hint()
#_COMMENT_IF(PROD)_
step_1.solution()
Explanation: Step 1: Loading Data
Read the Iowa data file into a Pandas DataFrame called home_data.
End of explanation
# Print summary statistics in next line
____
# What is the average lot size (rounded to nearest integer)?
avg_lot_size = ____
# As of today, how old is the newest home (current year - the date in which it was built)
newest_home_age = ____
# Check your answers
step_2.check()
#step_2.hint()
#step_2.solution()
Explanation: Step 2: Review The Data
Use the command you learned to view summary statistics of the data. Then fill in variables to answer the following questions
End of explanation
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