Data Analysis with Python Projects - Demographic Data Analyzer

Python
1

am i going to load this file first. How can I get the full data. I used the read_csv function but it is also bringing out error message

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Challenge: Data Analysis with Python Projects - Demographic Data Analyzer

Link to the challenge:

2

Yes you load it with read_csv. What error message are you getting?

Can you please share your code?

3

please I’ll need your full assistance as regards the project. i don’t even get how to load it. we can use google meet or zoom. My watsapp number is +2348176125605 and mail is amanabolu@gmail.com

import pandas as pd

def demographic_data_analyzer():
“”“Analyze demographic data from the 1994 Census database.”“”

df = pd.read_csv('adult.csv')

# How many people of each race are represented in this dataset?
race_counts = df['race'].value_counts()

# What is the average age of men?
average_age_men = df.loc[df['sex'] == 'Male', 'age'].mean()

# What is the percentage of people who have a Bachelor's degree?
bachelors_degree = df['education'] == 'Bachelors'
percentage_bachelors = round(
    100 * (bachelors_degree.sum() / df.shape[0]), 1
)

# What percentage of people with advanced education (Bachelors, Masters, or Doctorate) make more than 50K?
advanced_education = ['Bachelors', 'Masters', 'Doctorate']
high_income = df['salary'] == '>50K'
percentage_high_income_advanced = round(
    100 * (high_income[advanced_education].sum() / advanced_education.sum()), 1
)

# What percentage of people without advanced education make more than 50K?
low_education = ~advanced_education
percentage_high_income_low = round(
    100 * (high_income[low_education].sum() / low_education.sum()), 1
)

# What is the minimum number of hours a person works per week?
min_hours = df['hours-per-week'].min()

# What percentage of the people who work the minimum number of hours per week have a salary of more than 50K?
percentage_high_income_min = round(
    100 * (high_income[df['hours-per-week'] == min_hours].sum() / df['hours-per-week'].value_counts()[min_hours]), 1
)

# What country has the highest percentage of people that earn >50K and what is that percentage?
country_to_percentage = (
    df.groupby('native-country')['salary'].value_counts(normalize=True) * 100
)
highest_earning_country = country_to_percentage.sort_values(ascending=False).index[0]
highest_percentage = country_to_percentage[highest_earning_country]

# Identify the most popular occupation for those who earn >50K in India.
india_high_income = df.loc[
    (df['salary'] == '>50K') & (df['native-country'] == 'India')
]
most_popular_occupation = india_high_income['occupation'].value_counts().index[0]

return (
    race_counts,
    average_age_men,
    percentage_bachelors,
    percentage_high_income_advanced,
    percentage_high_income_low,
    min_hours,
    percentage_high_income_min,
    highest_earning_country,
    highest_percentage,
    most_popular_occupation,
)

if name == ‘main’:
results = demographic_data_analyzer()
print(results)

4

Can you confirm the filename? The file with the project is called adult.data.csv

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