Repositorio con el código solución a los 5 proyectos requisitos obligatorios para obtener la Data Analysis with Python Certification
- Data Analysis with Python Projects
El link para verificar la certificación es el siguiente: Verificar Certificación en Freecodecamp
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 calculationsEl archivo CSV utilizado llamado adult.data.csv NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: Link a Archivo
El código que he creado va después de los comentarios. Cada comentario se refiere a lo que se pide realizar.
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
}El archivo CSV utilizado llamado medical_examination.csv NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: Link a Archivo
La generación de cortes cambia el tipo de datos a categorico lo que provoca errores en los tests si no se modifica. Para evitar el cambio de tipo de datos he utilizado el método where.
# 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
El archivo CSV utilizado llamado fcc-forum-pageviews.csv NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: Link a Archivo
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from pandas.plotting import register_matplotlib_converters
import calendar
import numpy as np
register_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 data
f1=df.value>df.value.quantile(0.025)
f2=df.value<df.value.quantile(0.975)
df = df[f1 & f2]
def draw_line_plot():
# Draw line plot
fig=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')
return fig
def draw_bar_plot():
# Copy and modify data for monthly bar plot
aux = df.resample('M').mean().reset_index()
aux['year'] = aux['date'].dt.year
aux['month'] = aux['date'].dt.month
df_bar=aux.pivot(index='year', columns='month', values='value')
# Draw bar plot
fig, 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] for i in range(1, 13)}
for i, month in enumerate(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')
return fig
def draw_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.year for d in df_box.date]
df_box['month'] = [d.strftime('%b') for d in df_box.date]
# Draw box plots (using Seaborn)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 7))
cmap = plt.cm.tab20
colors = 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')
return fig
El archivo CSV utilizado llamado epa-sea-level.csv NO lo he cargado en mi repositorio por pesar demasiado. Sin embargo, el archivo se encuentra en la siguiente URL: Link a Archivo
def draw_plot():
# Read data from file
df=pd.read_csv("epa-sea-level.csv")
# Create scatter plot
plt.figure(figsize=(12, 6))
plt.scatter(df['Year'], df['CSIRO Adjusted Sea Level'], alpha=0.5,c='r')
# Create first line of best fit
res = 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 fit
years_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 title
plt.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 * 2050
y_proj_2 = res_2000.intercept + res_2000.slope * 2050
plt.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')
return plt.gca()