# [Solved] 70-511: Statistical Programming Programming Assignment 7 – Aggregating ACS PUMS Data

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# Introduction

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.

# Requirements

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

1. Loads the csv file that contains the PUMS dataset (assume it’s in the current directory) and create a DataFrame object from it.
1. 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 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.
• Table should include marginal values (‘All’ row and column).
• 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)
1. Display the tables to the screen as shown in the sample output on the last page.

1. The name of your source code file should be py. All your code should be within a single file.
2. You need to use the pandas DataFrame object for storing and manipulating data.
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 turn in the single tables.py file using BlackBoard.

# HINTS

• To get the right output, use the following functions to set pandas display parameters: 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 formatting strings or the format() method

Sample Program Output

70-511, [semester] [year]

PROGRAMMING ASSIGNMENT #7

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

mean std count min max HHT – Household/family type

Married couple household 106790.565562 100888.917804 25495 -5100 1425000

Nonfamily household:Male householder:Not living alone 79659.567376 74734.380152 1410 0 625000

Nonfamily household:Female householder:Not living alone 69055.725901 63871.751863 1193 0 645000

Other family household:Male householder, no wife present 64023.122122 59398.970193 1998 0 610000

Other family household:Female householder, no husband present 49638.428821 48004.399101 5718 -5100 609000

Nonfamily household:Male householder:Living alone 48545.356298 60659.516163 5835 -5100 681000 Nonfamily household:Female householder:Living alone 37282.245015 44385.091076 8024 -11200 676000

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

sum WGTP

ACCESS Yes w/ Subsrc. Yes, wo/ Subsrc. No All

HHL – Household language

English only 58.71% 2.93% 16.87% 78.51%

Spanish 7.83% 0.52% 2.60% 10.95%

Other Indo-European languages 5.11% 0.18% 1.19% 6.48%

Asian and Pacific Island languages 2.73% 0.06% 0.28% 3.08%

Other language 0.80% 0.03% 0.14% 0.97% All 75.19% 3.73% 21.08% 100.00%

*** Table 3 – Quantile Analysis of HINCP – Household income (past 12 months) ***

min max mean household_count HINCP low -11200 37200 19599.486904 1629499 medium 37210 81500 57613.846298 1575481 high 81530 1425000 159047.588900 1578445

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[Solved] 70-511: Statistical Programming Programming Assignment 7 – Aggregating ACS PUMS Data
30 \$