Plotando resultados da campanha (I)

Análise de campanhas de marketing com pandas

Jill Rosok

Data Scientist

Comparando taxas de conversão por idioma

import matplotlib.pyplot as plt

# Create a bar chart using channel retention DataFrame
language_conversion_rate.plot(kind = 'bar')

# Add a title and x and y-axis labels
plt.title('Conversion rate by language\n', size = 16)
plt.xlabel('Language', size = 14)
plt.ylabel('Conversion rate (%)', size = 14)

# Display the plot
plt.show()

conversão_por_idioma.png

Calculando a qualidade dos assinantes

# Group by date_subscribed and count unique users
subscribed = marketing.groupby(['date_subscribed'])['user_id']\
                     .nunique()

# Group by date_subscribed and sum conversions
retained = marketing[marketing['is_retained'] == True]\
                         .groupby(['date_subscribed'])\
                         ['user_id'].nunique()

# Calculate subscriber quality across dates
daily_retention_rate = retained/subscribed

Preparando dados para série temporal

# Reset index to turn the Series into a DataFrame
daily_retention_rate = 
    pd.DataFrame(daily_retention_rate.reset_index())

# Rename columns
daily_retention_rate.columns = ['date_subscribed', 
                               'retention_rate']

Visualizando dados ao longo do tempo

# Create a line chart using the daily_retention DataFrame
daily_retention_rate.plot('date_subscribed', 
                          'retention_rate')

# Add a title and x and y-axis labels
plt.title('Daily subscriber quality\n', size = 16)
plt.ylabel('1-month retention rate (%)', size = 14)
plt.xlabel('Date', size = 14)

# Set the y-axis to begin at 0
plt.ylim(0)
# Display the plot
plt.show()

gráfico de qualidade diária de assinantes.png

Vamos praticar!

Análise de campanhas de marketing com pandas