The Following is not error free but will run
Change the .replit
file to:
# The command that runs the program. If the interpreter field is set, it will have priority and this run command will do nothing
run = "python3 main.py"
# The primary language of the repl. There can be others, though!
language = "python3"
entrypoint = "main.py"
# A list of globs that specify which files and directories should
# be hidden in the workspace.
hidden = ["venv", ".config", "**/__pycache__", "**/.mypy_cache", "**/*.pyc"]
# Specifies which nix channel to use when building the environment.
[nix]
channel = "stable-22_11"
# The command to start the interpreter.
[interpreter]
[interpreter.command]
args = [
"stderred",
"--",
"prybar-python310",
"-q",
"--ps1",
"\u0001\u001b[33m\u0002\u0001\u001b[00m\u0002 ",
"-i",
]
env = { LD_LIBRARY_PATH = "$PYTHON_LD_LIBRARY_PATH" }
[env]
VIRTUAL_ENV = "/home/runner/${REPL_SLUG}/venv"
PATH = "${VIRTUAL_ENV}/bin"
PYTHONPATH = "${VIRTUAL_ENV}/lib/python3.10/site-packages"
REPLIT_POETRY_PYPI_REPOSITORY = "https://package-proxy.replit.com/pypi/"
MPLBACKEND = "TkAgg"
POETRY_CACHE_DIR = "${HOME}/${REPL_SLUG}/.cache/pypoetry"
# Enable unit tests. This is only supported for a few languages.
[unitTest]
language = "python3"
# Add a debugger!
[debugger]
support = true
# How to start the debugger.
[debugger.interactive]
transport = "localhost:0"
startCommand = ["dap-python", "main.py"]
# How to communicate with the debugger.
[debugger.interactive.integratedAdapter]
dapTcpAddress = "localhost:0"
# How to tell the debugger to start a debugging session.
[debugger.interactive.initializeMessage]
command = "initialize"
type = "request"
[debugger.interactive.initializeMessage.arguments]
adapterID = "debugpy"
clientID = "replit"
clientName = "replit.com"
columnsStartAt1 = true
linesStartAt1 = true
pathFormat = "path"
supportsInvalidatedEvent = true
supportsProgressReporting = true
supportsRunInTerminalRequest = true
supportsVariablePaging = true
supportsVariableType = true
# How to tell the debugger to start the debuggee application.
[debugger.interactive.launchMessage]
command = "attach"
type = "request"
[debugger.interactive.launchMessage.arguments]
logging = {}
# Configures the packager.
[packager]
language = "python3"
ignoredPackages = ["unit_tests"]
[packager.features]
enabledForHosting = false
# Enable searching packages from the sidebar.
packageSearch = true
# Enable guessing what packages are needed from the code.
guessImports = false
# These are the files that need to be preserved when this
# language template is used as the base language template
# for Python repos imported from GitHub
[gitHubImport]
requiredFiles = [".replit", "replit.nix", ".config", "venv"]
[languages]
[languages.python3]
pattern = "**/*.py"
[languages.python3.languageServer]
start = "pylsp"
go to shell and run: pip install pandas
Change the demographic_data_analyzer.py
to:
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 = df[df["sex"] == [Male]]["age"].mean().round(1)
# What is the percentage of people who have a Bachelor's degree?
num_bachelors = len[df['education'] == ['Bachelors']]
total_education = len[df["education"]]
percentage_bachelors = round(num_bachelors/total_education * 100, 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[df['education'].isin[['Bachelor', 'Masters', 'Doctorate']]]
lower_education = df[~df['education'].isin[['Bachelor', 'Masters', 'Doctorate']]]
# percentage with salary >50K
non_percentage_higher = len(higher_education[higher_education.salary == ">50K"])
higher_education_rich = round(non_percentage_higher / len(higher_education) * 100, 1)
non_percentage_lower = len(lower_education[lower_education.salary == ">50K"])
lower_education_rich = round(non_percentage_lower / len(lower_education) * 100, 1)
# What is the minimum number of hours a person works per week (hours-per-week feature)?
min_work_hours = None
# 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
# What country has the highest percentage of people that earn >50K?
highest_earning_country = None
highest_earning_country_percentage = None
# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = None
# 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
}