Project - EDA with Pandas Using the Ames Housing Data

Introduction

In this section, you've learned a lot about importing, cleaning up, analyzing (using descriptive statistics) and visualizing data. In this more free-form project, you'll get a chance to practice all of these skills with the Ames Housing dataset, which contains housing values in the suburbs of Ames.

Objectives

You will be able to:

• Perform a full exploratory data analysis process to gain insight about a dataset

Goals

Use your data munging and visualization skills to conduct an exploratory analysis of the dataset below. At a minimum, this should include:

• Loading the data (which is stored in the file ames_train.csv)
• Use built-in Python functions to explore measures of centrality and dispersion for at least 3 variables
• Create meaningful subsets of the data using selection operations like .loc, .iloc, or related operations. Explain why you used the chosen subsets and do this for three possible 2-way splits. State how you think the two measures of centrality and/or dispersion might be different for each subset of the data.
• Next, use histograms and scatter plots to see whether you observe differences for the subsets of the data. Make sure to use subplots so it is easy to compare the relationships.

Variable Descriptions

Look in data_description.txt for a full description of all variables.

A preview of some of the columns:

MSZoning: Identifies the general zoning classification of the sale.

   A	 Agriculture
   C	 Commercial
   FV	Floating Village Residential
   I	 Industrial
   RH	Residential High Density
   RL	Residential Low Density
   RP	Residential Low Density Park 
   RM	Residential Medium Density

OverallCond: Rates the overall condition of the house

   10	Very Excellent
   9	 Excellent
   8	 Very Good
   7	 Good
   6	 Above Average	
   5	 Average
   4	 Below Average	
   3	 Fair
   2	 Poor
   1	 Very Poor

KitchenQual: Kitchen quality

   Ex	Excellent
   Gd	Good
   TA	Typical/Average
   Fa	Fair
   Po	Poor

YrSold: Year Sold (YYYY)

SalePrice: Sale price of the house in dollars

# Let's get started importing the necessary libraries

# Loading the data

# Investigate the Data
df.info()

# Investigating Distributions using scatter_matrix

# Create a plot that shows the SalePrice Distribution

# Create a plot that shows the LotArea Distribution

# Create a plot that shows the Distribution of the overall house condition

# Create a Box Plot for SalePrice

# Perform an Exploration of home values by age

Summary

Congratulations, you've completed your first "free form" exploratory data analysis of a popular dataset!