Add files via upload

Author: Siddhesh-Agarwal

Demographic-Data-Analyzer/demographic_data_analyzer.py

@@ -1,47 +1,97 @@
 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 = {}
+ for race in df['race']:
+ if race in race_count:
+ race_count[race] += 1
+ else:
+ race_count[race] = 1
+ race_count = pd.Series(data=race_count)
 
  # What is the average age of men?
- average_age_men = None
+ sum_age_men = 0
+ no_of_men = 0
+ for age, sex in zip(df["age"], df["sex"]):
+ if sex == "Male":
+ no_of_men += 1
+ sum_age_men += age
+ average_age_men = round(sum_age_men / no_of_men, 1)
 
  # What is the percentage of people who have a Bachelor's degree?
- percentage_bachelors = None
+ no_of_people = 0
+ no_of_bachelors = 0
+ for degree in df['education']:
+ no_of_people += 1
+ if degree == "Bachelors":
+ no_of_bachelors += 1
+ percentage_bachelors = round(no_of_bachelors / no_of_people * 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 = None
- lower_education = None
-
+ higher_education = 0
+ lower_education = 0
  # percentage with salary >50K
- higher_education_rich = None
- lower_education_rich = None
+ higher_education_rich = 0
+ lower_education_rich = 0
+ for education, salary in zip(df['education'], df['salary']):
+ if education in ('Bachelors', 'Masters', 'Doctorate'):
+ higher_education += 1
+ if salary == '>50K':
+ higher_education_rich += 1
+ else:
+ lower_education += 1
+ if salary == '>50K':
+ lower_education_rich += 1
+ lower_education_rich = round(lower_education_rich / lower_education * 100, 1)
+ higher_education_rich = round(higher_education_rich / higher_education * 100, 1)
 
  # What is the minimum number of hours a person works per week (hours-per-week feature)?
  min_work_hours = None
+ for hours in df['hours-per-week']:
+ if min_work_hours is None or hours < min_work_hours:
+ min_work_hours = hours
 
  # 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
+ num_min_workers = 0
+ min_work_hours_rich = 0
+ for hours, salary in zip(df['hours-per-week'], df['salary']):
+ if hours == min_work_hours:
+ if salary == '>50K':
+ min_work_hours_rich += 1
+ num_min_workers += 1
+ rich_percentage = round(min_work_hours_rich / num_min_workers * 100)
 
  # What country has the highest percentage of people that earn >50K?
- highest_earning_country = None
- highest_earning_country_percentage = None
+ country_earnings = {}
+ for country, salary in zip(df['native-country'], df['salary']):
+ if country in country_earnings:
+ country_earnings[country][1] += 1
+ else:
+ country_earnings[country] = [0, 1]
+ if salary == ">50K":
+ country_earnings[country][0] += 1
+
+ highest_earning_country = max(country_earnings, key=lambda x: country_earnings[x][0] / country_earnings[x][1])
+ highest_earning_country_percentage = round(country_earnings[highest_earning_country][0] / country_earnings[highest_earning_country][1] * 100, 1)
 
  # Identify the most popular occupation for those who earn >50K in India.
- top_IN_occupation = None
+ top_IN_occupation_dict = {}
+ for occupation, country, salary in zip(df["occupation"], df["native-country"], df["salary"]):
+ if country == "India" and salary == ">50K":
+ if occupation in top_IN_occupation_dict:
+ top_IN_occupation_dict[occupation] += 1
+ else:
+ top_IN_occupation_dict[occupation] = 1
+ top_IN_occupation = max(top_IN_occupation_dict, key=lambda x: top_IN_occupation_dict[x])
 
  # 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)
@@ -63,7 +113,6 @@ def calculate_demographic_data(print_data=True):
  'min_work_hours': min_work_hours,
  'rich_percentage': rich_percentage,
  'highest_earning_country': highest_earning_country,
- 'highest_earning_country_percentage':
- highest_earning_country_percentage,
+ 'highest_earning_country_percentage': highest_earning_country_percentage,
  'top_IN_occupation': top_IN_occupation
- }
+ }