Demographic Data

Python
1

Hello everyone,
I think i got the code right buy for some reason all the numeric results fail the testing.

Blockquote
import pandas as pd

def calculate_demographic_data(print_data=True):
# Read data from file
df = pd.read_csv(‘adult.data.csv’, header=0)

# 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['race'].value_counts()

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

# What is the percentage of people who have a Bachelor's degree?
percentage_bachelors = df.loc[df['education'].str.contains('Bachelors') | df['education'].str.contains('Masters') | df['education'].str.contains('Doctorate') , 'education'].count()/df.count()[0]*100
# 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.loc[df['education'].str.contains('Bachelors') | df['education'].str.contains('Masters') | df['education'].str.contains('Doctorate') , ['education', 'salary']].loc[df['salary']=='<=50K' , 'education'].count()
lower_education = df.loc[df['salary']=='<=50K','education'].count()-df.loc[df['education'].str.contains('Bachelors') | df['education'].str.contains('Masters') | df['education'].str.contains('Doctorate') , ['education', 'salary']].loc[df['salary']=='<=50K' , 'education'].count()

# percentage with salary >50K
higher_education_rich = higher_education/(higher_education+lower_education)*100
lower_education_rich = lower_education/(higher_education+lower_education)*100

# 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.loc[(df['hours-per-week']==df['hours-per-week'].min()) & (df['salary']=='<=50K'), 'age'].count()

rich_percentage = num_min_workers/(num_min_workers+min_work_hours)*100

# What country has the highest percentage of people that earn >50K?
highest_earning_country = df.loc[df['salary']=='<=50K','native-country'].value_counts().index.tolist()[0]
highest_earning_country_percentage = df.loc[df['salary']=='<=50K','native-country'].value_counts()[0]/df.loc[df['salary']=='<=50K','native-country'].count()*100

# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = df.loc[(df['salary']=='<=50K') & (df['native-country']=='India'),'occupation'].value_counts().index.tolist()[0]

# 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
}

Blockquote
It returns:

Blockquote
FFFFF.F.F.

The problem seems to rely on the decimal evaluation.

2

So you need to look at the errors that are displayed by the test:

Average age of men: 39.43354749885268
...
======================================================================
ERROR: test_average_age_men (test_module.DemographicAnalyzerTestCase)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/gray/src/work/fcc-da-demographics/test_module.py", line 24, in test_average_age_men
    self.assertAlmostEqual(
  File "/usr/lib/python3.9/unittest/case.py", line 876, in assertAlmostEqual
    if round(diff, places) == 0:
TypeError: an integer is required (got type str)

This is fairly straightforward. The test wants an integer (actually one of the functions in the test; the test really wants a number, but whatever), and it got a string. You can see this if you look at the test’s code:

    def test_average_age_men(self):
        actual = self.data["average_age_men"]
        expected = 39.4
        self.assertAlmostEqual(
            actual, expected, "Expected different value for average age of men."
        )

So the test really expects the number 39.4 in average_age_men, and not a string or a long decimal. So you need to change your calculation of average_age_men so that it returns 39.4.

The other tests are similarly diagnosed. Good luck.

3

Yes, I know that. Don’t really know how to change that.

4

Have you tried rounding the numbers?

5

to start finding out how to do something you need to start googling. Knowing what to Google is half of the battle. For this, I see that the test is expecting a str and is getting an int. I googled: convert str to int in df
I found a bit of code

df['DataFrame Column'] = df['DataFrame Column'].astype(int)

you would need to change the DataFrameColumns to what the column name is and it should convert every row of the df (dataframe) to an integer.

6

sorry Jeremy meant to reply to the original post.

7

Yes, thank you, I’m new to the python (And coding outside matlab) world and getting used to it, to google you have to know what you are looking for jaja

8

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