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language = "python3"
run = "python main.py"

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README.md
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### Assignment
# Demographic Data Analyzer
This is the boilerplate for the Demographic Data Analyzer project. Instructions for building your project can be found at https://www.freecodecamp.org/learn/data-analysis-with-python/data-analysis-with-python-projects/demographic-data-analyzer
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:
* How many people of each race are represented in this dataset? This should be a Pandas series with race names as the index labels. (`race` column)
* What is the average age of men?
* What is the percentage of people who have a Bachelor's degree?
* 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?
* What is the minimum number of hours a person works per week?
* What percentage of the people who work the minimum number of hours per week have a salary of more than 50K?
* What country has the highest percentage of people that earn >50K and what is that percentage?
* Identify the most popular occupation for those who earn >50K in India.
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`.
### Development
For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
### Testing
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.
### Submitting
Copy your project's URL and submit it to freeCodeCamp.
### Dataset Source
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

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demographic_data_analyzer.py
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def calculate_demographic_data(print_data=True):
# Read data from file
df = pd.read_csv("adult.data.csv")
df = None
# 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()
race_count = None
# What is the average age of men?
average_age_men = round(df['age'][df['sex'] == 'Male'].mean(), 1)
average_age_men = None
# What is the percentage of people who have a Bachelor's degree?
percentage_bachelors = round(df['education'].value_counts(normalize=True).mul(100).loc['Bachelors'], 1)
percentage_bachelors = None
# What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?
higher_education_rich = round(df['salary'][(df['education'] == 'Bachelors') | (df['education'] == 'Masters') | (df['education'] == 'Doctorate')].value_counts(normalize=True).mul(100).loc['>50K'], 1)
lower_education_rich = round(df['salary'][(df['education'] != 'Bachelors') & (df['education'] != 'Masters') & (df['education'] != 'Doctorate')].value_counts(normalize=True).mul(100).loc[">50K"], 1)
# What percentage of people without advanced education make more than 50K?
# with and without `Bachelors`, `Masters`, or `Doctorate`
higher_education = None
lower_education = None
# percentage with salary >50K
higher_education_rich = None
lower_education_rich = None
# What is the minimum number of hours a person works per week (hours-per-week feature)?
min_work_hours = df['hours-per-week'].min()
min_work_hours = None
# What percentage of the people who work the minimum number of hours per week have a salary of >50K?
rich_percentage = round(df['salary'][df['hours-per-week'] == df['hours-per-week'].min()].value_counts(normalize=True).mul(100).loc[">50K"], 1)
num_min_workers = None
rich_percentage = None
# What country has the highest percentage of people that earn >50K?
highest_earning_country = (df['native-country'][df['salary'] == '>50K'].value_counts()/df['native-country'].value_counts()).idxmax()
highest_earning_country_percentage = round((df['native-country'][df['salary'] == '>50K'].value_counts()/df['native-country'].value_counts()).max() * 100, 1)
highest_earning_country = None
highest_earning_country_percentage = None
# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = df['occupation'][(df['native-country'] == 'India') & (df['salary'] == '>50K')].value_counts().idxmax()
top_IN_occupation = None
# DO NOT MODIFY BELOW THIS LINE

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numpy = ["0172304e7d8d40e9e49553901903dc5f5a49a703363ed756796f5808a06fc233", "34e96e9dae65c4839bd80012023aadd6ee2ccb73ce7fdf3074c62f301e63120b", "3676abe3d621fc467c4c1469ee11e395c82b2d6b5463a9454e37fe9da07cd0d7", "3dd6823d3e04b5f223e3e265b4a1eae15f104f4366edd409e5a5e413a98f911f", "4064f53d4cce69e9ac613256dc2162e56f20a4e2d2086b1956dd2fcf77b7fac5", "4674f7d27a6c1c52a4d1aa5f0881f1eff840d2206989bae6acb1c7668c02ebfb", "7d42ab8cedd175b5ebcb39b5208b25ba104842489ed59fbb29356f671ac93583", "965df25449305092b23d5145b9bdaeb0149b6e41a77a7d728b1644b3c99277c1", "9c9d6531bc1886454f44aa8f809268bc481295cf9740827254f53c30104f074a", "a78e438db8ec26d5d9d0e584b27ef25c7afa5a182d1bf4d05e313d2d6d515271", "a7acefddf994af1aeba05bbbafe4ba983a187079f125146dc5859e6d817df824", "a87f59508c2b7ceb8631c20630118cc546f1f815e034193dc72390db038a5cb3", "ac792b385d81151bae2a5a8adb2b88261ceb4976dbfaaad9ce3a200e036753dc", "b03b2c0badeb606d1232e5f78852c102c0a7989d3a534b3129e7856a52f3d161", "b39321f1a74d1f9183bf1638a745b4fd6fe80efbb1f6b32b932a588b4bc7695f", "cae14a01a159b1ed91a324722d746523ec757357260c6804d11d6147a9e53e3f", "cd49930af1d1e49a812d987c2620ee63965b619257bd76eaaa95870ca08837cf", "e15b382603c58f24265c9c931c9a45eebf44fe2e6b4eaedbb0d025ab3255228b", "e91d31b34fc7c2c8f756b4e902f901f856ae53a93399368d9a0dc7be17ed2ca0", "ef627986941b5edd1ed74ba89ca43196ed197f1a206a3f18cc9faf2fb84fd675", "f718a7949d1c4f622ff548c572e0c03440b49b9531ff00e4ed5738b459f011e8"]
pandas = ["034185bb615dc96d08fa13aacba8862949db19d5e7804d6ee242d086f07bcc46", "0c9b7f1933e3226cc16129cf2093338d63ace5c85db7c9588e3e1ac5c1937ad5", "1f6fcf0404626ca0475715da045a878c7062ed39bc859afc4ccf0ba0a586a0aa", "1fc963ba33c299973e92d45466e576d11f28611f3549469aec4a35658ef9f4cc", "29b4cfee5df2bc885607b8f016e901e63df7ffc8f00209000471778f46cc6678", "2a8b6c28607e3f3c344fe3e9b3cd76d2bf9f59bc8c0f2e582e3728b80e1786dc", "2bc2ff52091a6ac481cc75d514f06227dc1b10887df1eb72d535475e7b825e31", "415e4d52fcfd68c3d8f1851cef4d947399232741cc994c8f6aa5e6a9f2e4b1d8", "519678882fd0587410ece91e3ff7f73ad6ded60f6fcb8aa7bcc85c1dc20ecac6", "51e0abe6e9f5096d246232b461649b0aa627f46de8f6344597ca908f2240cbaa", "698e26372dba93f3aeb09cd7da2bb6dd6ade248338cfe423792c07116297f8f4", "83af85c8e539a7876d23b78433d90f6a0e8aa913e37320785cf3888c946ee874", "982cda36d1773076a415ec62766b3c0a21cdbae84525135bdb8f460c489bb5dd", "a647e44ba1b3344ebc5991c8aafeb7cca2b930010923657a273b41d86ae225c4", "b35d625282baa7b51e82e52622c300a1ca9f786711b2af7cbe64f1e6831f4126", "bab51855f8b318ef39c2af2c11095f45a10b74cbab4e3c8199efcc5af314c648"]
python-dateutil = ["73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c", "75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"]
pytz = ["a494d53b6d39c3c6e44c3bec237336e14305e4f29bbf800b599253057fbb79ed", "c35965d010ce31b23eeb663ed3cc8c906275d6be1a34393a1d73a41febf4a048"]
six = ["30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259", "8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"]

16
renovate.json
View File

@ -1,16 +0,0 @@
{
"labels": ["renovate"],
"extends": ["config:base"],
"branchConcurrentLimit": 20,
"dependencyDashboard": true,
"major": {
"dependencyDashboardApproval": true
},
"packageRules": [
{
"matchUpdateTypes": ["minor", "patch", "pin", "digest"],
"matchCurrentVersion": "!/^0/",
"automerge": true
}
]
}

21
test_module.py
View File

@ -2,54 +2,53 @@ import unittest
import demographic_data_analyzer
class DemographicAnalyzerTestCase(unittest.TestCase):
@classmethod
def setUpClass(self):
def setUp(self):
self.data = demographic_data_analyzer.calculate_demographic_data(print_data = False)
def test_race_count(self):
actual = self.data['race_count'].tolist()
expected = [27816, 3124, 1039, 311, 271]
self.assertCountEqual(actual, expected, msg="Expected race count values to be [27816, 3124, 1039, 311, 271]")
self.assertAlmostEqual(actual, expected, "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.")
self.assertAlmostEqual(actual, expected, "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.")
self.assertAlmostEqual(actual, expected, "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.")
self.assertAlmostEqual(actual, expected, "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.")
self.assertAlmostEqual(actual, expected, "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.")
self.assertAlmostEqual(actual, expected, "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.")
self.assertAlmostEqual(actual, expected, "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.")
self.assertAlmostEqual(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 highest earning country percentage.")
self.assertAlmostEqual(actual, expected, "Expected different value for heighest earning country percentage.")
def test_top_IN_occupation(self):
actual = self.data['top_IN_occupation']