[SOLVED] DATA51100 Assignment 7-ggregating ACS PUMS Data

Aggregating ACS PUMS Data

For this assignment, you will work again with the same ACS PUMS dataset as for assignment 6 to produce several tables which aggregate the data.

Note: For this assignment, you are free to program in any programming language of your choice; Python, R, Matlab, C++, Java,..etc. Please indicate the programming language you will use.

Requirements

You are to create a program in Python that performs the following using the pandas packages:

1. Loads the ss13hil.csv file that contains the PUMS dataset (assume it’s in the current directory) and create a DataFrame

   object from it if you use Python.

2. Create 3 tables:
   TABLE 1: Statistics of HINCP – Household income (past 12 months), grouped by HHT – Household/family type
   • Table should use the HHT types (text descriptions) as the index
   • Columns should be: mean, std, count, min, max
   • Rows should be sorted by the mean column value in descending order

     TABLE 2: HHL – Household language vs. ACCESS – Access to the Internet (Frequency Table)

   • Table should use the HHL types (text descriptions) as the index
   • Columns should be the text descriptions of ACCESS values
   • Each table entry is the sum of WGTP column for the given HHL/ACCESS combination, divided by the sum of WGTP

     values in the data. Entries need to be formatted as percentages.

   • Any rows containing NA values in HHL, ACCESS, or WGTP columns should be excluded.

     TABLE 3: Quantile Analysis of HINCP – Household income (past 12 months)

   • Rows should correspond to different quantiles of HINCP: low (0-1/3), medium (1/3-2/3), high (2/3-1)
   • Columns displayed should be: min, max, mean, household_count
   • The household_count column contains entries with the sum of WGTP values for the corresponding range of HINCP

     values (low, medium, or high)

3. Display the tables to the screen as shown in the sample output on the last page.
4. Note: For this assignment, you are free to program in any programming language of your choice; Python, R, Matlab, C++, Java,..etc. Please indicate the programming language you will use.

Additional Requirements

1. The name of your source code file should be tables.py for Python (or tables.m (for Matlab), tables.java (For Java, or tables.r (For R),..etc). All your code should be within a single file.
2. You need to use the pandas DataFrame object for storing and manipulating data (if you use Python).
3. Your code should follow good coding practices, including good use of whitespace and use of both inline and block comments.
4. You need to use meaningful identifier names that conform to standard naming conventions.
5. At the top of each file, you need to put in a block comment with the following information: your name, date, course name,

   semester, and assignment name.

6. The output should exactly match the sample output shown on the last page.

What to Turn In
You will need to turn in the single tables.py (or tables.m (for Matlab), or tables.java (For Java), or tables.r (For R),..etc) file as well as a screen shot of the created tables using BlackBoard.

HINTS

• To get the right output, use the following functions to set pandas display parameters in Python: pd.set_option(‘display.max_columns’, 500) pd.set_option(‘display.width’, 1000)
• To display entries as percentages, use the applymap method, giving it a string conversion function as input. The string conversion function should take a float value v as an input and output a string representing v as a percentage. To do this, you can use the format() method

Sample Program Output

DATA-51100, [semester] [year]
NAME: [put your name here]
PROGRAMMING ASSIGNMENT #7

*** Table 1 – Descriptive Statistics of HINCP, grouped by HHT ***

HHT – Household/family type
Married couple household
Nonfamily household:Male householder:Not living alone
Nonfamily household:Female householder:Not living alone
Other family household:Male householder, no wife present
Other family household:Female householder, no husband present 49638.428821 Nonfamily household:Male householder:Living alone 48545.356298 Nonfamily household:Female householder:Living alone 37282.245015

*** Table 2 – HHL vs. ACCESS – Frequency Table *** sum

                                             WGTP
ACCESS                             Yes w/ Subsrc.

HHL – Household language
English only 58.71% Spanish 7.83%

Yes, wo/ Subsrc.      No

           2.93%  16.87%
           0.52%   2.60%
           0.18%   1.19%
           0.06%   0.28%
           0.03%   0.14%

Other Indo-European languages
Asian and Pacific Island languages
Other language
All 75.19%

*** Table 3 – Quantile Analysis of HINCP – Household income (past 12 months) *** min max mean household_count

HINCP
low -11200 37200 19599.486904 medium 37210 81500 57613.846298 high 81530 1425000 159047.588900

1629499
1575481
1578445

5.11%
2.73%
0.80%

106790.565562
 79659.567376
 69055.725901
 64023.122122

3.73%

21.08%

mean

std

  100888.917804
   74734.380152
   63871.751863
   59398.970193
   48004.399101
   60659.516163
   44385.091076

All

 78.51%
 10.95%
  6.48%
  3.08%
  0.97%
100.00%

count min

25495 -5100 1410 0 1193 0 1998 0 5718 -5100 5835 -5100 8024 -11200

max

1425000
 625000
 645000
 610000

 609000
 681000
 676000