import numpy as np def calculate(list): n=len(list) if n<9: raise ValueError("List must contain nine numbers.") orig=np.array(list) reorg=orig.reshape(3,3) mean=[np.mean(reorg,axis=0).tolist(),np.mean(reorg,axis=1).tolist(),np.mean(reorg)] variance=[np.var(reorg,axis=0).tolist(),np.var(reorg,axis=1).tolist(),np.var(reorg)] std=[np.std(reorg,axis=0).tolist(),np.std(reorg,axis=1).tolist(),np.std(reorg)] maxv=[np.max(reorg,axis=0).tolist(),np.max(reorg,axis=1).tolist(),np.max(reorg)] minv=[np.min(reorg,axis=0).tolist(),np.min(reorg,axis=1).tolist(),np.min(reorg)] sumv=[np.sum(reorg,axis=0).tolist(),np.sum(reorg,axis=1).tolist(),np.sum(reorg)] calculations={ 'mean':mean, 'variance':variance, 'standard deviation':std, 'max':maxv, 'min':minv, 'sum':sumv } return calculations

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 = round(df[df.sex=='Male'].age.mean(),1) # What is the percentage of people who have a Bachelor's degree? percentage_bachelors = round(((df.education[df.education=="Bachelors"].count())/(df.education.count()))*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(['Bachelors', 'Masters', 'Doctorate'])] lower_education = df[~df.education.isin(['Bachelors', 'Masters', 'Doctorate'])] # percentage with salary >50K higher_education_rich = round((higher_education.salary[higher_education.salary=='>50K'].count()/higher_education.salary.count())*100,1) lower_education_rich = round((lower_education.salary[lower_education.salary=='>50K'].count()/lower_education.salary.count())*100,1) # What is the minimum number of hours a person works per week (hours-per-week feature)? min_work_hours = round(df['hours-per-week'].min(),1) # What percentage of the people who work the minimum number of hours per week have a salary of >50K? num_min_workers = round((df.loc[df['hours-per-week']==df['hours-per-week'].min(),['salary']].value_counts())['>50K'],1) rich_percentage = round((df.loc[df['hours-per-week']==df['hours-per-week'].min(),['salary']].value_counts(normalize=True)*100)['>50K'],1) # What country has the highest percentage of people that earn >50K? highest_earning_country = df.groupby(['native-country', 'salary']).size().unstack(fill_value=0).apply(lambda x: (x / x.sum()) * 100, axis=1)['>50K'].idxmax() #otra forma pd.crosstab() highest_earning_country_percentage =round(df.groupby(['native-country', 'salary']).size().unstack(fill_value=0).apply(lambda x: (x / x.sum()) * 100, axis=1)['>50K'].max(),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().iloc[0,0] # 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 }

# 1 df = pd.read_csv("medical_examination.csv") # 2 # df['overweight'] = pd.cut(df['weight']/(df['height']/100)**2,bins=[0,25,np.inf],labels=[0,1]) df['overweight']=np.where(df['weight']/(df['height']/100)**2<=25,0,1) # 3 #df['gluc']=pd.cut(df['gluc'],bins=[0,1,np.inf],labels=[0,1]) #df['cholesterol']=pd.cut(df['cholesterol'],bins=[0,1,np.inf],labels=[0,1]) df['gluc']=np.where(df['gluc'] <= 1, 0, 1) df['cholesterol']=np.where(df['cholesterol']<=1,0,1) # 4 def draw_cat_plot(): # 5 variables=['active', 'alco', 'cholesterol', 'gluc', 'overweight', 'smoke'] df_cat = pd.melt(df,id_vars=['cardio'],value_vars=variables) # 6 df_cat = df_cat.groupby(["cardio", "variable", "value"]).size().reset_index().rename(columns={0:'total'}) # 7 # 8 fig = sns.catplot(data=df_cat, x="variable", y="total", hue="value", col="cardio", kind="bar", height=4, aspect=1.5 ).figure # 9 fig.savefig('catplot.png') return fig # 10 def draw_heat_map(): f1=(df['ap_lo'] <= df['ap_hi']) f2=(df['height'] >= df['height'].quantile(0.025)) f3=(df['height'] <= df['height'].quantile(0.975)) f4=(df['weight'] >= df['weight'].quantile(0.025)) f5=(df['weight'] <= df['weight'].quantile(0.975)) # 11 df_heat = df[f1 & f2 & f3 & f4 &f5] # 12 corr = df_heat.corr() # 13 mask=np.triu(np.ones(corr.shape), 0).astype(bool) # 14 fig, ax =plt.subplots(figsize=(12, 6)) # 15 sns.heatmap(corr, mask=mask, annot=True, linewidths=0.5, ax=ax,cmap='inferno',fmt=".1f") # 16 fig.savefig('heatmap.png') return fig