Tell us what’s happening:
I receive the following error message:
ERROR: test_race_count (test_module.DemographicAnalyzerTestCase)
Traceback (most recent call last):
File “PycharmProjects\Data Analysis Projects\test_module.py”, line 10, in test_race_count
actual = self.data[‘race_count’].tolist()
File “PycharmProjects\Data Analysis Projects\venv\lib\site-packages\pandas\core\generic.py”, line 5465, in getattr
return object.getattribute(self, name)
AttributeError: ‘DataFrame’ object has no attribute ‘tolist’
I have tried using the tolist method in a separate python file and it seems to work, am puzzled as to why it does not work with the test module.
Your code so far
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.
races = {}
for race in df['race']:
if race in races:
races[race] += 1
else: # List of races
races[race] = 1
# Convert dict to df
race_count = pd.DataFrame.from_dict(races, orient='index')
race_count.columns = ['race_count']
# 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?
no_bachelor = len(df[df['education'] == 'Bachelors'])
total = len(df.index)
percentage_bachelors = round(100*(no_bachelor/total), 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 = len(df.loc[((df['education']=='Bachelors') | (df['education']=='Masters') | (df['education']=='Doctorate'))])
lower_education = total - higher_education
# percentage with salary >50K
higher_education_rich = 100*len(df.loc[((df['education']=='Bachelors') | (df['education']=='Masters') | (df['education']=='Doctorate')) & (df['salary']=='>50K')])/higher_education
higher_education_rich = round(higher_education_rich, 1)
lower_education_rich = 100*len(df.loc[~((df['education']=='Bachelors') | (df['education']=='Masters') | (df['education']=='Doctorate')) & (df['salary']== '>50K')])/lower_education
lower_education_rich = round(lower_education_rich, 1)
# What is the minimum number of hours a person works per week (hours-per-week feature)?
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 = len(df.loc[df['hours-per-week']==df['hours-per-week'].min()])
rich_percentage = 100*len(df.loc[(df['hours-per-week']==df['hours-per-week'].min())&(df['salary']=='>50K')])/num_min_workers
rich_percentage = round(rich_percentage, 1)
# What country has the highest percentage of people that earn >50K?
df = pd.read_csv('adult.data.csv')
countries = df['native-country'].unique().tolist() # np array type convert to python list
more_than_50k = []
country_total = []
percentage = []
for index, country in enumerate(countries):
more_than_50k.append(len(df.loc[(df['native-country'] == country) & (df['salary'] == '>50K')]))
country_total.append(len(df.loc[df['native-country'] == country]))
percentage.append(more_than_50k[index] / country_total[index])
df_list = [countries, more_than_50k, country_total, percentage]
df_50k = pd.DataFrame(df_list)
df_50k = df_50k.transpose()
df_50k.columns = ['Country', '>50K', 'Total', 'Percentage']
highest_earning_country = df_50k.loc[df_50k['Percentage'] == df_50k['Percentage'].max()]['Country'].item()
highest_earning_country_percentage = 100*df_50k.loc[df_50k['Percentage'] == df_50k['Percentage'].max()][
'Percentage'].item()
highest_earning_country_percentage = round(highest_earning_country_percentage, 1)
# Identify the most popular occupation for those who earn >50K in India.
top_IN_occupation = df.loc[(df['salary'] == '>50K')&(df['native-country'] == 'India')]['occupation'].mode().item()
# 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
}
Challenge: Demographic Data Analyzer
Link to the challenge:
