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moT01
4 years ago
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### Assignment
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# Demographic Data Analyzer
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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:
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| | age | workclass | fnlwgt | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | salary |
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|---:|------:|:-----------------|---------:|:------------|----------------:|:-------------------|:------------------|:---------------|:-------|:-------|---------------:|---------------:|-----------------:|:-----------------|:---------|
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| 0 | 39 | State-gov | 77516 | Bachelors | 13 | Never-married | Adm-clerical | Not-in-family | White | Male | 2174 | 0 | 40 | United-States | <=50K |
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| 1 | 50 | Self-emp-not-inc | 83311 | Bachelors | 13 | Married-civ-spouse | Exec-managerial | Husband | White | Male | 0 | 0 | 13 | United-States | <=50K |
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| 2 | 38 | Private | 215646 | HS-grad | 9 | Divorced | Handlers-cleaners | Not-in-family | White | Male | 0 | 0 | 40 | United-States | <=50K |
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| 3 | 53 | Private | 234721 | 11th | 7 | Married-civ-spouse | Handlers-cleaners | Husband | Black | Male | 0 | 0 | 40 | United-States | <=50K |
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| 4 | 28 | Private | 338409 | Bachelors | 13 | Married-civ-spouse | Prof-specialty | Wife | Black | Female | 0 | 0 | 40 | Cuba | <=50K |
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You must use Pandas to answer the following questions:
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* 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)
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* What is the average age of men?
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* What is the percentage of people who have a Bachelor's degree?
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* What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?
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* What percentage of people without advanced education make more than 50K?
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* What is the minimum number of hours a person works per week?
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* What percentage of the people who work the minimum number of hours per week have a salary of more than 50K?
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* What country has the highest percentage of people that earn >50K and what is that percentage?
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* Identify the most popular occupation for those who earn >50K in India.
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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.
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Unit tests are written for you under `test_module.py`.
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### Development
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For development, you can use `main.py` to test your functions. Click the "run" button and `main.py` will run.
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### Testing
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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.
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### Submitting
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Copy your project's URL and submit it to freeCodeCamp.
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### Dataset Source
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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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import pandas as pd
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def calculate_demographic_data(print_data=True):
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# Read data from file
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df = None
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# How many of each race are represented in this dataset? This should be a Pandas series with race names as the index labels.
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race_count = None
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# What is the average age of men?
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average_age_men = None
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# What is the percentage of people who have a Bachelor's degree?
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percentage_bachelors = None
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# What percentage of people with advanced education (`Bachelors`, `Masters`, or `Doctorate`) make more than 50K?
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# What percentage of people without advanced education make more than 50K?
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# with and without `Bachelors`, `Masters`, or `Doctorate`
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higher_education = None
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lower_education = None
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# percentage with salary >50K
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higher_education_rich = None
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lower_education_rich = None
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# What is the minimum number of hours a person works per week (hours-per-week feature)?
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min_work_hours = None
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# What percentage of the people who work the minimum number of hours per week have a salary of >50K?
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num_min_workers = None
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rich_percentage = None
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# What country has the highest percentage of people that earn >50K?
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highest_earning_country = None
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highest_earning_country_percentage = None
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# Identify the most popular occupation for those who earn >50K in India.
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top_IN_occupation = None
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# DO NOT MODIFY BELOW THIS LINE
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if print_data:
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print("Number of each race:\n", race_count)
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print("Average age of men:", average_age_men)
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print(f"Percentage with Bachelors degrees: {percentage_bachelors}%")
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print(f"Percentage with higher education that earn >50K: {higher_education_rich}%")
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print(f"Percentage without higher education that earn >50K: {lower_education_rich}%")
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print(f"Min work time: {min_work_hours} hours/week")
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print(f"Percentage of rich among those who work fewest hours: {rich_percentage}%")
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print("Country with highest percentage of rich:", highest_earning_country)
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print(f"Highest percentage of rich people in country: {highest_earning_country_percentage}%")
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print("Top occupations in India:", top_IN_occupation)
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return {
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'race_count': race_count,
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'average_age_men': average_age_men,
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'percentage_bachelors': percentage_bachelors,
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'higher_education_rich': higher_education_rich,
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'lower_education_rich': lower_education_rich,
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'min_work_hours': min_work_hours,
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'rich_percentage': rich_percentage,
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'highest_earning_country': highest_earning_country,
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'highest_earning_country_percentage':
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highest_earning_country_percentage,
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'top_IN_occupation': top_IN_occupation
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}
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# This entrypoint file to be used in development. Start by reading README.md
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import demographic_data_analyzer
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from unittest import main
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# Test your function by calling it here
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demographic_data_analyzer.calculate_demographic_data()
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# Run unit tests automatically
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main(module='test_module', exit=False)
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[[package]]
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category = "main"
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description = "NumPy is the fundamental package for array computing with Python."
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name = "numpy"
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optional = false
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python-versions = ">=3.5"
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version = "1.18.5"
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[[package]]
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category = "main"
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description = "Powerful data structures for data analysis, time series, and statistics"
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name = "pandas"
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optional = false
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python-versions = ">=3.6.1"
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version = "1.0.4"
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[package.dependencies]
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numpy = ">=1.13.3"
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python-dateutil = ">=2.6.1"
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pytz = ">=2017.2"
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[[package]]
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category = "main"
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description = "Extensions to the standard Python datetime module"
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name = "python-dateutil"
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optional = false
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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version = "2.8.1"
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[package.dependencies]
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six = ">=1.5"
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[[package]]
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category = "main"
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description = "World timezone definitions, modern and historical"
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name = "pytz"
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optional = false
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python-versions = "*"
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version = "2020.1"
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[[package]]
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category = "main"
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description = "Python 2 and 3 compatibility utilities"
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name = "six"
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optional = false
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
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version = "1.15.0"
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[metadata]
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content-hash = "27114271cf207dff3920111c8aa89baba75353cc23851aded0a93b193dc24770"
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python-versions = "^3.8"
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[metadata.hashes]
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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"]
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pandas = ["034185bb615dc96d08fa13aacba8862949db19d5e7804d6ee242d086f07bcc46", "0c9b7f1933e3226cc16129cf2093338d63ace5c85db7c9588e3e1ac5c1937ad5", "1f6fcf0404626ca0475715da045a878c7062ed39bc859afc4ccf0ba0a586a0aa", "1fc963ba33c299973e92d45466e576d11f28611f3549469aec4a35658ef9f4cc", "29b4cfee5df2bc885607b8f016e901e63df7ffc8f00209000471778f46cc6678", "2a8b6c28607e3f3c344fe3e9b3cd76d2bf9f59bc8c0f2e582e3728b80e1786dc", "2bc2ff52091a6ac481cc75d514f06227dc1b10887df1eb72d535475e7b825e31", "415e4d52fcfd68c3d8f1851cef4d947399232741cc994c8f6aa5e6a9f2e4b1d8", "519678882fd0587410ece91e3ff7f73ad6ded60f6fcb8aa7bcc85c1dc20ecac6", "51e0abe6e9f5096d246232b461649b0aa627f46de8f6344597ca908f2240cbaa", "698e26372dba93f3aeb09cd7da2bb6dd6ade248338cfe423792c07116297f8f4", "83af85c8e539a7876d23b78433d90f6a0e8aa913e37320785cf3888c946ee874", "982cda36d1773076a415ec62766b3c0a21cdbae84525135bdb8f460c489bb5dd", "a647e44ba1b3344ebc5991c8aafeb7cca2b930010923657a273b41d86ae225c4", "b35d625282baa7b51e82e52622c300a1ca9f786711b2af7cbe64f1e6831f4126", "bab51855f8b318ef39c2af2c11095f45a10b74cbab4e3c8199efcc5af314c648"]
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python-dateutil = ["73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c", "75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"]
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pytz = ["a494d53b6d39c3c6e44c3bec237336e14305e4f29bbf800b599253057fbb79ed", "c35965d010ce31b23eeb663ed3cc8c906275d6be1a34393a1d73a41febf4a048"]
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six = ["30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259", "8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"]
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[tool.poetry]
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name = "fcc-demographic-data-analyzer"
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version = "0.1.0"
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description = ""
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authors = ["Your Name <you@example.com>"]
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[tool.poetry.dependencies]
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python = "^3.8"
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pandas = "^1.0"
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[tool.poetry.dev-dependencies]
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[build-system]
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requires = ["poetry>=0.12"]
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build-backend = "poetry.masonry.api"
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import unittest
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import demographic_data_analyzer
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class DemographicAnalyzerTestCase(unittest.TestCase):
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def setUp(self):
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self.data = demographic_data_analyzer.calculate_demographic_data(print_data = False)
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def test_race_count(self):
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actual = self.data['race_count'].tolist()
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expected = [27816, 3124, 1039, 311, 271]
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self.assertAlmostEqual(actual, expected, "Expected race count values to be [27816, 3124, 1039, 311, 271]")
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def test_average_age_men(self):
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actual = self.data['average_age_men']
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expected = 39.4
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self.assertAlmostEqual(actual, expected, "Expected different value for average age of men.")
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def test_percentage_bachelors(self):
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actual = self.data['percentage_bachelors']
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expected = 16.4
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self.assertAlmostEqual(actual, expected, "Expected different value for percentage with Bachelors degrees.")
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def test_higher_education_rich(self):
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actual = self.data['higher_education_rich']
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expected = 46.5
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self.assertAlmostEqual(actual, expected, "Expected different value for percentage with higher education that earn >50K.")
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def test_lower_education_rich(self):
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actual = self.data['lower_education_rich']
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expected = 17.4
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self.assertAlmostEqual(actual, expected, "Expected different value for percentage without higher education that earn >50K.")
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def test_min_work_hours(self):
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actual = self.data['min_work_hours']
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expected = 1
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self.assertAlmostEqual(actual, expected, "Expected different value for minimum work hours.")
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def test_rich_percentage(self):
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actual = self.data['rich_percentage']
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expected = 10
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self.assertAlmostEqual(actual, expected, "Expected different value for percentage of rich among those who work fewest hours.")
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def test_highest_earning_country(self):
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actual = self.data['highest_earning_country']
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expected = 'Iran'
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self.assertAlmostEqual(actual, expected, "Expected different value for highest earning country.")
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def test_highest_earning_country_percentage(self):
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actual = self.data['highest_earning_country_percentage']
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expected = 41.9
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self.assertAlmostEqual(actual, expected, "Expected different value for heighest earning country percentage.")
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def test_top_IN_occupation(self):
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actual = self.data['top_IN_occupation']
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expected = 'Prof-specialty'
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self.assertEqual(actual, expected, "Expected different value for top occupations in India.")
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if __name__ == "__main__":
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unittest.main()
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Loading…
Reference in New Issue