Solution

`assertAlmostEqual` is able to determine if lists are equal, but it cannot
determine if corresponding elements are almost equal. `assertCountEqual`
checks if lists have the same elements, regardless of the order.

.replit

@@ -1,2 +0,0 @@
-language = "python3"
-run = "python main.py" 

README.md

@@ -1,45 +1,3 @@
-### Assignment
-
 # 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.
+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

demographic_data_analyzer.py

@@ -3,42 +3,33 @@ import pandas as pd
 
 def calculate_demographic_data(print_data=True):
     # Read data from file
-    df = None
+    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 = None
+    race_count = df['race'].value_counts()
 
     # What is the average age of men?
-    average_age_men = None
+    average_age_men = round(df['age'][df['sex'] == 'Male'].mean(), 1)
 
     # What is the percentage of people who have a Bachelor's degree?
-    percentage_bachelors = None
+    percentage_bachelors = round(df['education'].value_counts(normalize=True).mul(100).loc['Bachelors'], 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 = None
-    lower_education = None
-
-    # percentage with salary >50K
-    higher_education_rich = None
-    lower_education_rich = None
+    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 is the minimum number of hours a person works per week (hours-per-week feature)?
-    min_work_hours = None
+    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 = None
-
-    rich_percentage = None
+    rich_percentage = round(df['salary'][df['hours-per-week'] == df['hours-per-week'].min()].value_counts(normalize=True).mul(100).loc[">50K"], 1)
 
     # What country has the highest percentage of people that earn >50K?
-    highest_earning_country = None
-    highest_earning_country_percentage = None
+    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)
 
     # Identify the most popular occupation for those who earn >50K in India.
-    top_IN_occupation = None
+    top_IN_occupation = df['occupation'][(df['native-country'] == 'India') & (df['salary'] == '>50K')].value_counts().idxmax()
 
     # DO NOT MODIFY BELOW THIS LINE
 

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-
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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"]
+content-hash = "27114271cf207dff3920111c8aa89baba75353cc23851aded0a93b193dc24770"

renovate.json

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

test_module.py

@@ -2,53 +2,54 @@ import unittest
 import demographic_data_analyzer
 
 class DemographicAnalyzerTestCase(unittest.TestCase):
-    def setUp(self):
+    @classmethod
+    def setUpClass(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.assertAlmostEqual(actual, expected, "Expected race count values to be [27816, 3124, 1039, 311, 271]")
-    
+        self.assertCountEqual(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, "Expected different value for average age of men.")
+        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, "Expected different value for percentage with Bachelors degrees.")
+        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, "Expected different value for percentage with higher education that earn >50K.")
+        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, "Expected different value for percentage without higher education that earn >50K.")
+        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, "Expected different value for minimum work hours.")     
+        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, "Expected different value for percentage of rich among those who work fewest hours.")   
+        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.assertAlmostEqual(actual, expected, "Expected different value for highest earning country.")   
+        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, "Expected different value for heighest earning country percentage.")   
+        self.assertAlmostEqual(actual, expected, msg="Expected different value for highest earning country percentage.")   
 
     def test_top_IN_occupation(self):
         actual = self.data['top_IN_occupation']