Demographic Data Analyzer code works but error

Python
1

Tell us what’s happening:
Each line of code works in Jupyter but when I run it in Repl.it, it gives me the error below:

Number of each race:
race
Amer-Indian-Eskimo 311
Asian-Pac-Islander 1039
Black 3124
Other 271
White 27816
Name: race, dtype: int64
Average age of men: 39.43354749885268
Percentage with Bachelors degrees: 0.16446055096587942%
Percentage with higher education that earn >50K: 0.46535843011613937%
Percentage without higher education that earn >50K: 0.173713601914639%
Min work time: 1 hours/week
Percentage of rich among those who work fewest hours: 0.1%
Country with highest percentage of rich: 7171
Highest percentage of rich people in country: 7171.0%
Top occupations in India: 25
EEEEE.EEEF

ERROR: test_average_age_men (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 16, in test_average_age_men
self.assertAlmostEqual(actual, expected, “Expected different value for average age of men.”)
File “/usr/lib/python3.8/unittest/case.py”, line 957, in assertAlmostEqual
if round(diff, places) == 0:
TypeError: an integer is required (got type str)

======================================================================
ERROR: test_higher_education_rich (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 26, in test_higher_education_rich
self.assertAlmostEqual(actual, expected, “Expected different value for percentage with higher education that earn >50K.”)
File “/usr/lib/python3.8/unittest/case.py”, line 957, in assertAlmostEqual
if round(diff, places) == 0:
TypeError: an integer is required (got type str)

======================================================================
ERROR: test_highest_earning_country (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 46, in test_highest_earning_country
self.assertAlmostEqual(actual, expected, “Expected different value for highest earning country.”)
File “/usr/lib/python3.8/unittest/case.py”, line 943, in assertAlmostEqual
diff = abs(first - second)
numpy.core._exceptions.UFuncTypeError: ufunc ‘subtract’ did not contain a loop with signature matching types (dtype(’<U21’), dtype(’<U21’)) -> dtype(’<U21’)

======================================================================
ERROR: test_highest_earning_country_percentage (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 51, in test_highest_earning_country_percentage
self.assertAlmostEqual(actual, expected, “Expected different value for heighest earning country percentage.”)
File “/usr/lib/python3.8/unittest/case.py”, line 957, in assertAlmostEqual
if round(diff, places) == 0:
TypeError: an integer is required (got type str)

======================================================================
ERROR: test_lower_education_rich (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 31, in test_lower_education_rich
self.assertAlmostEqual(actual, expected, “Expected different value for percentage without higher education that earn >50K.”)
File “/usr/lib/python3.8/unittest/case.py”, line 957, in assertAlmostEqual
if round(diff, places) == 0:
TypeError: an integer is required (got type str)

======================================================================
ERROR: test_percentage_bachelors (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 21, in test_percentage_bachelors
self.assertAlmostEqual(actual, expected, “Expected different value for percentage with Bachelors degrees.”)
File “/usr/lib/python3.8/unittest/case.py”, line 957, in assertAlmostEqual
if round(diff, places) == 0:
TypeError: ‘str’ object cannot be interpreted as an integer

======================================================================
ERROR: test_race_count (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 11, in test_race_count
self.assertAlmostEqual(actual, expected, “Expected race count values to be [27816, 3124, 1039, 311, 271]”)
File “/usr/lib/python3.8/unittest/case.py”, line 943, in assertAlmostEqual
diff = abs(first - second)
TypeError: unsupported operand type(s) for -: ‘list’ and ‘list’

======================================================================
ERROR: test_rich_percentage (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 41, in test_rich_percentage
self.assertAlmostEqual(actual, expected, “Expected different value for percentage of rich among those who work fewest hours.”)
File “/usr/lib/python3.8/unittest/case.py”, line 957, in assertAlmostEqual
if round(diff, places) == 0:
TypeError: an integer is required (got type str)

======================================================================
FAIL: test_top_IN_occupation (test_module.DemographicAnalyzerTestCase)

Traceback (most recent call last):
File “/home/runner/GoldenMemorableBackup/test_module.py”, line 56, in test_top_IN_occupation
self.assertEqual(actual, expected, “Expected different value for top occupations in India.”)
AssertionError: 25 != ‘Prof-specialty’ : Expected different value for top occupations in India.

Ran 10 tests in 3.927s

Your code so far
Here’s my code:

import pandas as pd
import numpy as np

def calculate_demographic_data(print_data=True):
# Read data from file
df = pd.read_csv(“adult.data.csv”)

# How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.
race_count = df.groupby('race')['race'].count()

# What is the average age of men?
average_age_men = df[df['sex'] == 'Male']['age'].mean()

# What is the percentage of people who have a Bachelor's degree?
percentage_bachelors = df[df['education'] == 'Bachelors'].shape[0] / df.shape[0]

# What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?
# What percentage of people without advanced education make more than 50K?

# with and without `Bachelors`, `Masters`, or `Doctorate`
higher_education = df[df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])]
lower_education = df[~df['education'].isin(['Bachelors', 'Masters', 'Doctorate'])]

# percentage with salary >50K
higher_education_rich = higher_education[higher_education['salary'] == '>50K']['salary'].count() / higher_education.shape[0]
lower_education_rich = lower_education[lower_education['salary'] == '>50K']['salary'].count() / lower_education.shape[0]

# What is the minimum number of hours a person works per week (hours-per-week feature)?
min_work_hours = df['hours-per-week'].min() 

# What percentage of the people who work the minimum number of hours per week have a salary of >50K?
num_min_workers = df[df['hours-per-week'] == 1]['hours-per-week'].count()

rich_percentage = df[(df['hours-per-week'] == 1) & (df['salary'] == '>50K')].shape[0] / num_min_workers

# What country has the highest percentage of people that earn >50K?
highest_earning_country = df[df['salary'] == '>50K'].groupby('native-country')['native-country'].count().max()
highest_earning_country_percentage = (highest_earning_country / df.groupby('native-country')['native-country'].count()).max()

# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = df[(df['salary'] == '>50K') & (df['native-country'] == 'India')].groupby('occupation')['occupation'].count().max()

# DO NOT MODIFY BELOW THIS LINE

if print_data:
    print("Number of each race:\n", race_count) 
    print("Average age of men:", average_age_men)
    print(f"Percentage with Bachelors degrees: {percentage_bachelors}%")
    print(f"Percentage with higher education that earn >50K: {higher_education_rich}%")
    print(f"Percentage without higher education that earn >50K: {lower_education_rich}%")
    print(f"Min work time: {min_work_hours} hours/week")
    print(f"Percentage of rich among those who work fewest hours: {rich_percentage}%")
    print("Country with highest percentage of rich:", highest_earning_country)
    print(f"Highest percentage of rich people in country: {highest_earning_country_percentage}%")
    print("Top occupations in India:", top_IN_occupation)

return {
    'race_count': race_count,
    'average_age_men': average_age_men,
    'percentage_bachelors': percentage_bachelors,
    'higher_education_rich': higher_education_rich,
    'lower_education_rich': lower_education_rich,
    'min_work_hours': min_work_hours,
    'rich_percentage': rich_percentage,
    'highest_earning_country': highest_earning_country,
    'highest_earning_country_percentage':
    highest_earning_country_percentage,
    'top_IN_occupation': top_IN_occupation
}

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Challenge: Demographic Data Analyzer

Link to the challenge:

2

For the percentage questions, try multiplying the value you obtained by 100, as the question asks for percentage and not ratio.

3

Try also rounding every value to the first decimal. As already said above, you also have to multiply by 100 when asked for percentages.
Hope it helps :slight_smile:

4

Also for the last three questions you are giving the wrong type for answer.

You give the following:

Country with highest percentage of rich: 7171
Highest percentage of rich people in country: 7171.0%
Top occupations in India: 25

For country with highest percentage you should give a country name.
That percentage for rich people is too high.
And the top occupation should be a string too.

5

try using round function as well as multiply by 100 to get the required percentage.