OK, this is a hard one, because I haven't really learn pandas. Need to google a lot.
Total time: 2h
Content of this section is copied and reformatted from this repl.it, provided by FCC here.
In this challenge you must analyze demographic data using Pandas. You are given a dataset of demographic data that was extracted from the 1994 Census database. Here is a sample of what the data looks like:
| age | workclass | fnlwgt | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | salary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | State-gov | 77516 | Bachelors | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States | <=50K |
| 1 | 50 | Self-emp-not-inc | 83311 | Bachelors | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States | <=50K |
| 2 | 38 | Private | 215646 | HS-grad | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States | <=50K |
| 3 | 53 | Private | 234721 | 11th | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States | <=50K |
| 4 | 28 | Private | 338409 | Bachelors | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba | <=50K |
You must use Pandas to answer the following questions:
race column)Bachelors, Masters, or
Doctorate) make more than 50K?Use the starter code in the file demographic_data_anaylizer. Update the code
so all variables set to "None" are set to the appropriate calculation or code.
Round all decimals to the nearest tenth.
Unit tests are written for you under test_module.py.
For development, you can use main.py to test your functions. Click the "run"
button and main.py will run.
We imported the tests from test_module.py to main.py for your convenience.
The tests will run automatically whenever you hit the "run" button.
Copy your project's URL and submit it to freeCodeCamp.
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
import pandas as pd
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["race"].value_counts()
# What is the average age of men?
average_age_men = round(df.loc[df["sex"] == "Male", "age"].mean(), 1)
# What is the percentage of people who have a Bachelor's degree?
percentage_bachelors = round(
100 * df.loc[df["education"] == "Bachelors"].size / df.size,
1,
)
# 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"].isin(["Bachelors", "Masters", "Doctorate"])
]
lower_education = df.loc[
~df["education"].isin(["Bachelors", "Masters", "Doctorate"])
]
# percentage with salary >50K
higher_education_rich = round(
100
* higher_education.loc[higher_education["salary"] == ">50K"].size
/ higher_education.size,
1,
)
lower_education_rich = round(
100
* lower_education.loc[lower_education["salary"] == ">50K"].size
/ lower_education.size,
1,
)
# 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"] == min_work_hours]
rich_percentage = round(
100
* num_min_workers.loc[num_min_workers["salary"] == ">50K"].size
/ num_min_workers.size,
1,
)
# What country has the highest percentage of people that earn >50K?
highest_country = (
df[["salary", "native-country"]]
.groupby("native-country")
.apply(lambda g: g.loc[g["salary"] == ">50K"].size / g.size * 100)
)
highest_earning_country = highest_country.idxmax()
highest_earning_country_percentage = round(highest_country.max(), 1)
# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = (
df.loc[(df["native-country"] == "India") & (df["salary"] == ">50K")][
"occupation"
]
.value_counts()
.idxmax()
)
# 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,
}
calculate_demographic_data(False)
import unittest
class DemographicAnalyzerTestCase(unittest.TestCase):
def setUp(self):
self.data = calculate_demographic_data(print_data=False)
def test_race_count(self):
actual = self.data["race_count"].tolist()
expected = [27816, 3124, 1039, 311, 271]
self.assertAlmostEqual(
actual,
expected,
msg="Expected race count values to be [27816, 3124, 1039, 311, 271]",
)
def test_average_age_men(self):
actual = self.data["average_age_men"]
expected = 39.4
self.assertAlmostEqual(
actual,
expected,
msg="Expected different value for average age of men.",
)
def test_percentage_bachelors(self):
actual = self.data["percentage_bachelors"]
expected = 16.4
self.assertAlmostEqual(
actual,
expected,
msg="Expected different value for percentage with Bachelors degrees.",
)
def test_higher_education_rich(self):
actual = self.data["higher_education_rich"]
expected = 46.5
self.assertAlmostEqual(
actual,
expected,
msg="Expected different value for percentage with higher education that earn >50K.",
)
def test_lower_education_rich(self):
actual = self.data["lower_education_rich"]
expected = 17.4
self.assertAlmostEqual(
actual,
expected,
msg="Expected different value for percentage without higher education that earn >50K.",
)
def test_min_work_hours(self):
actual = self.data["min_work_hours"]
expected = 1
self.assertAlmostEqual(
actual,
expected,
msg="Expected different value for minimum work hours.",
)
def test_rich_percentage(self):
actual = self.data["rich_percentage"]
expected = 10
self.assertAlmostEqual(
actual,
expected,
msg="Expected different value for percentage of rich among those who work fewest hours.",
)
def test_highest_earning_country(self):
actual = self.data["highest_earning_country"]
expected = "Iran"
self.assertEqual(
actual,
expected,
"Expected different value for highest earning country.",
)
def test_highest_earning_country_percentage(self):
actual = self.data["highest_earning_country_percentage"]
expected = 41.9
self.assertAlmostEqual(
actual,
expected,
msg="Expected different value for heighest earning country percentage.",
)
def test_top_IN_occupation(self):
actual = self.data["top_IN_occupation"]
expected = "Prof-specialty"
self.assertEqual(
actual,
expected,
"Expected different value for top occupations in India.",
)
if __name__ == "__main__":
unittest.main(argv=["first-arg-is-ignored"], exit=False)