Tabla de Contenidos

Certificación Obtenida

Listado de Proyectos

1- Mean-Variance-Standard Deviation Calculator

1.1- Proyecto Aprobado

1.2- Todos los tests superados

1.3- Código Creado

importnumpyasnpdefcalculate(list):n=len(list)ifn<9:raiseValueError("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    }returncalculations

2- Demographic Data Analyzer

2.1- Proyecto Aprobado

2.2- Todos los tests superados

2.3- Código Creado

defcalculate_demographic_data(print_data=True):# Read data from filedf=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 >50Khigher_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(lambdax: (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(lambdax: (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 LINEifprint_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    }

3- Medical Data Visualizer

3.1- Proyecto Aprobado

3.2- Todos los tests superados

3.3- Código Creado

# 1df=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)# 4defdraw_cat_plot():# 5variables=['active','alco','cholesterol','gluc','overweight','smoke']df_cat=pd.melt(df,id_vars=['cardio'],value_vars=variables)# 6df_cat=df_cat.groupby(["cardio","variable","value"]).size().reset_index().rename(columns={0:'total'})# 7# 8fig=sns.catplot(data=df_cat,x="variable",y="total",hue="value",col="cardio",kind="bar",height=4,aspect=1.5 ).figure# 9fig.savefig('catplot.png')returnfig# 10defdraw_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))# 11df_heat=df[f1&f2&f3&f4&f5]# 12corr=df_heat.corr()# 13mask=np.triu(np.ones(corr.shape),0).astype(bool)# 14fig,ax=plt.subplots(figsize=(12,6))# 15sns.heatmap(corr,mask=mask,annot=True,linewidths=0.5,ax=ax,cmap='inferno',fmt=".1f")# 16fig.savefig('heatmap.png')returnfig

3.4- Gráficos Generados

3.4.1- Gráfico de Columnas

3.4.2- Mapa de Calor

4- Page View Time Series Visualizer

4.1- Proyecto Aprobado

4.2- Todos los tests superados

4.3- Código Creado

importmatplotlib.pyplotaspltimportpandasaspdimportseabornassnsfrompandas.plottingimportregister_matplotlib_convertersimportcalendarimportnumpyasnpregister_matplotlib_converters()np.float=float# Import data (Make sure to parse dates. Consider setting index column to 'date'.)df=pd.read_csv("fcc-forum-pageviews.csv",index_col='date',parse_dates=['date'])# Clean dataf1=df.value>df.value.quantile(0.025)f2=df.value<df.value.quantile(0.975)df=df[f1&f2]defdraw_line_plot():# Draw line plotfig=plt.figure(figsize=(14,6))plt.plot(df.index,df.value,'r')plt.grid(True)plt.title('Daily freeCodeCamp Forum Page Views 5/2016-12/2019')plt.xlabel('Date')plt.ylabel('Page Views')# Save image and return fig (don't change this part)fig.savefig('line_plot.png')returnfigdefdraw_bar_plot():# Copy and modify data for monthly bar plotaux=df.resample('M').mean().reset_index()aux['year']=aux['date'].dt.yearaux['month']=aux['date'].dt.monthdf_bar=aux.pivot(index='year',columns='month',values='value')# Draw bar plotfig,ax=plt.subplots(figsize=(13,8))colors=plt.cm.tab20c(np.linspace(0,1,12))width=0.5/len(df_bar.columns)x=np.arange(len(df_bar.index))month_names= {i:calendar.month_name[i]foriinrange(1,13)}fori,monthinenumerate(df_bar.columns):ax.bar(x+i*width,df_bar[month],width=width,label=f'{month_names[month]}',color=colors[i]        )ax.legend(title='Months',loc='upper left')ax.set_xlabel('Years',fontsize=12)ax.set_xticks(x+width* (len(df_bar.columns)-1)/2)ax.set_xticklabels(df_bar.index,fontsize=10)ax.set_ylabel('Average Page Views',fontsize=12)plt.tight_layout()# Save image and return fig (don't change this part)fig.savefig('bar_plot.png')returnfigdefdraw_box_plot():# Prepare data for box plots (this part is done!)df_box=df.copy()df_box.reset_index(inplace=True)df_box['year']= [d.yearfordindf_box.date]df_box['month']= [d.strftime('%b')fordindf_box.date]# Draw box plots (using Seaborn)fig, (ax1,ax2)=plt.subplots(1,2,figsize=(16,7))cmap=plt.cm.tab20colors=list(cmap(np.linspace(0,1,len(df_box['year'].unique()))))flierprops=dict(marker='o',color='black',markersize=7,markerfacecolor='red')sns.boxplot(x='year',y='value',data=df_box,ax=ax1)ax1.set_title('Year-wise Box Plot (Trend)')ax1.set_xlabel('Year')ax1.set_ylabel('Page Views')ax1.grid(True,color='gray',linestyle='--',linewidth=0.5,dashes=(10,5))colors=list(cmap(np.linspace(0,1,len(df_box['month'].unique()))))months=list(calendar.month_abbr[1:])sns.boxplot(data=df_box,x='month',y='value',ax=ax2,order=months,hue='month',palette=colors,flierprops=flierprops)ax2.set_title('Month-wise Box Plot (Seasonality)')ax2.set_xlabel('Month')ax2.set_ylabel('Page Views')ax2.grid(True,color='gray',linestyle='--',linewidth=0.5,dashes=(10,5))plt.tight_layout()# Save image and return fig (don't change this part)fig.savefig('box_plot.png')returnfig

4.4- Gráficos Generados

4.4.1- Gráfico de Líneas

4.4.2- Gráfico de Barras

4.4.3- Box Plot

5- Sea Level Predictor

5.1- Proyecto Aprobado

5.2- Todos los tests superados

5.3- Código Creado

defdraw_plot():# Read data from filedf=pd.read_csv("epa-sea-level.csv")# Create scatter plotplt.figure(figsize=(12,6))plt.scatter(df['Year'],df['CSIRO Adjusted Sea Level'],alpha=0.5,c='r')# Create first line of best fitres=linregress(df['Year'],df['CSIRO Adjusted Sea Level'])years_extended=np.arange(df['Year'].min(),2051)plt.plot(years_extended,res.intercept+res.slope*years_extended,'b',label='fitted line')# Create second line of best fityears_from_2000=np.arange(2000,2051)df_2000=df[df.Year>=2000]res_2000=linregress(df_2000.Year,df_2000['CSIRO Adjusted Sea Level'])plt.plot(years_from_2000,res_2000.intercept+res_2000.slope*years_from_2000,'g',label='2000 fitted line')# Add labels and titleplt.grid(True,color='gray',linestyle='--',linewidth=0.2,dashes=(25,25))plt.xlabel('Year',fontsize=12)plt.ylabel('Sea Level (inches)',fontsize=12)plt.title('Rise in Sea Level')plt.tight_layout()y_proj_1=res.intercept+res.slope*2050y_proj_2=res_2000.intercept+res_2000.slope*2050plt.axvline(x=2050,color='purple',linestyle='--',linewidth=1,label='Projection Year (2050)')plt.axhline(y=y_proj_1,color='blue',linestyle='--',linewidth=1)plt.axhline(y=y_proj_2,color='green',linestyle='--',linewidth=1)plt.text(2040,y_proj_1*1.025,f'{y_proj_1:.2f}',color='blue',va='center',fontsize=12,fontweight='bold')plt.text(2037,y_proj_2*0.975,f'{y_proj_2:.2f}',color='green',va='center',fontsize=12,fontweight='bold')# Save plot and return data for testing (DO NOT MODIFY)plt.savefig('sea_level_plot.png')returnplt.gca()

5.4- Gráfico Generado: Scatter Plot más Proyecciones