Python Visualization for E-commerce
Primary: plotnine (ggplot2 syntax). Secondary: plotly for interactive charts.
Setup
python
import pandas as pd
import numpy as np
from plotnine import *
import plotly.express as px
import plotly.graph_objects as go
ecom_theme = (
theme_minimal() +
theme(
figure_size=(10, 6),
plot_title=element_text(size=14, weight='bold', margin={'b': 12}),
axis_title=element_text(size=11),
axis_text=element_text(size=10),
legend_position='bottom',
panel_grid_minor=element_blank(),
)
)
COLORS = ['#2563EB', '#F59E0B', '#10B981', '#EF4444', '#8B5CF6', '#EC4899', '#6B7280']
Chart Templates
1. Funnel Chart
python
def funnel_chart(data, step_col, value_col, title="Conversion Funnel"):
data = data.copy()
data['pct'] = data[value_col] / data[value_col].iloc[0] * 100
data[step_col] = pd.Categorical(data[step_col], categories=data[step_col].tolist(), ordered=True)
return (ggplot(data, aes(x=step_col, y=value_col))
+ geom_col(fill='#2563EB', width=0.6)
+ geom_text(aes(label='pct'), format_string='{:.1f}%', va='bottom', size=10)
+ labs(title=title, x='', y='Users') + ecom_theme + coord_flip())
2. Time Series with Rolling Average
python
def time_series(data, date_col, value_col, title, window=7):
data = data.copy()
data[date_col] = pd.to_datetime(data[date_col])
data['rolling'] = data[value_col].rolling(window).mean()
return (ggplot(data, aes(x=date_col))
+ geom_line(aes(y=value_col), color='#93C5FD', alpha=0.5)
+ geom_line(aes(y='rolling'), color='#2563EB', size=1)
+ labs(title=title, x='', y=value_col, caption=f'{window}-day rolling avg') + ecom_theme)
3. A/B Test Results
python
def ab_test_chart(control_rate, variant_rate, control_ci, variant_ci, metric_name="Conversion Rate"):
df = pd.DataFrame({
'Group': ['Control', 'Variant'],
'Rate': [control_rate, variant_rate],
'CI_low': [control_ci[0], variant_ci[0]],
'CI_high': [control_ci[1], variant_ci[1]],
})
return (ggplot(df, aes(x='Group', y='Rate', fill='Group'))
+ geom_col(width=0.5) + geom_errorbar(aes(ymin='CI_low', ymax='CI_high'), width=0.15)
+ scale_fill_manual(values=['#6B7280', '#2563EB'])
+ labs(title=f'A/B Test: {metric_name}', x='', y=metric_name)
+ ecom_theme + theme(legend_position='none'))
4. Cohort Retention Heatmap
python
def cohort_heatmap(cohort_data, title="Cohort Retention"):
melted = cohort_data.melt(id_vars='cohort', var_name='period', value_name='retention')
melted['label'] = melted['retention'].apply(lambda x: f'{x:.0f}%')
return (ggplot(melted, aes(x='period', y='cohort', fill='retention'))
+ geom_tile(color='white', size=0.5) + geom_text(aes(label='label'), size=8, color='white')
+ scale_fill_gradient(low='#DBEAFE', high='#1E3A8A')
+ labs(title=title, x='Period', y='Cohort') + ecom_theme + theme(legend_position='none'))
5. KPI Dashboard
python
def kpi_sparklines(data, date_col, metrics, title="KPI Dashboard"):
melted = data.melt(id_vars=date_col, value_vars=metrics, var_name='Metric', value_name='Value')
return (ggplot(melted, aes(x=date_col, y='Value'))
+ geom_line(color='#2563EB', size=0.8) + geom_area(fill='#DBEAFE', alpha=0.3)
+ facet_wrap('~Metric', scales='free_y', ncol=2)
+ labs(title=title, x='', y='') + ecom_theme
+ theme(axis_text_x=element_text(rotation=45, ha='right')))
Export
python
ggsave(plot, 'chart.png', dpi=300, width=10, height=6) # Static
fig.write_html('chart.html', include_plotlyjs='cdn') # Interactive
Rules
- •ALWAYS inspect data before plotting:
print(df[col].unique()),print(df.head()). - •ALWAYS label axes, include title, annotate key values.
- •Use plotnine by default. Plotly only when interactivity is needed.
- •Format: commas for thousands, 1 decimal for %, currency symbols for revenue.
- •Save as PNG at 300 DPI by default.