Python: Plotly

Plotly enables creation of interactive, web-based visualizations without requiring JavaScript knowledge.

Features:
• Interactive charts: hover tooltips, zoom, pan, selection
• Web-based: works in Jupyter notebooks, web browsers
• Responsive: automatically adapts to screen size
• Export: save as PNG, SVG, or HTML
• Real-time updates: animate data changes
• Dash framework: build interactive web dashboards

Plotly supports diverse chart types for different analytical needs.

Available Charts:
• Scatter, line, bar, histogram, box plots
• 3D surface and scatter plots
• Geographic maps (choropleth, scatter-geo)
• Heatmaps and contour plots
• Sunburst and treemap hierarchical charts
• Financial charts: candlestick, OHLC
• Animations and transitions

Dash is Plotly's framework for building production web dashboards with Python.

Capabilities:
• Interactive layouts and callbacks
• Real-time data updates
• Multi-page applications
• Component library: buttons, sliders, dropdowns
• Styling with CSS and Bootstrap
• Deployment: Heroku, AWS, custom servers

Plotly and Dash are used for business intelligence, analytics, and data exploration.

Applications:
• Business dashboards and KPI monitoring
• Scientific research visualization
• Financial analysis and reporting
• Data exploration and discovery
• Real-time monitoring systems
• Interactive reports and presentations

import plotly.express as px
import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('sales.csv', parse_dates=['date'])

# ── Scatter plot ────────────────────────────────────── fig = px.scatter(df, x='sessions', y='revenue', color='region', size='orders', hover_data=['product_category'], trendline='ols', title='Revenue vs. Traffic by Region') fig.show()

# ── Animated line chart ─────────────────────────────── monthly = df.groupby(['date', 'region'])['revenue'].sum().reset_index() fig = px.line(monthly, x='date', y='revenue', color='region', animation_frame=None, title='Monthly Revenue by Region') fig.update_layout(hovermode='x unified')

# ── Faceted charts ──────────────────────────────────── fig = px.bar(df, x='month', y='revenue', color='product_category', facet_col='region', title='Revenue by Month, Category and Region', labels={'revenue': 'Revenue ($)', 'month': 'Month'}) fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1]))

# ── 3D scatter ──────────────────────────────────────── fig = px.scatter_3d(df, x='sessions', y='conversion_rate', z='revenue', color='region', opacity=0.7, title='3D Revenue Analysis') fig.show() ```

from dash import Dash, html, dcc, Input, Output, callback
import plotly.express as px
import pandas as pd

df = pd.read_csv('sales.csv')

app = Dash(__name__)

app.layout = html.Div([ html.H1("Sales Dashboard", style={"fontFamily": "sans-serif"}),

html.Div([ dcc.Dropdown( id='region-filter', options=[{'label': r, 'value': r} for r in df['region'].unique()], value=None, placeholder='Filter by region...', clearable=True ), dcc.DatePickerRange( id='date-range', start_date=df['date'].min(), end_date=df['date'].max() ), ], style={'display': 'flex', 'gap': '16px', 'marginBottom': '24px'}),

dcc.Graph(id='revenue-chart'), dcc.Graph(id='category-pie'), ])

@callback( Output('revenue-chart', 'figure'), Output('category-pie', 'figure'), Input('region-filter', 'value'), Input('date-range', 'start_date'), Input('date-range', 'end_date') ) def update_charts(region, start, end): filtered = df.copy() if region: filtered = filtered[filtered['region'] == region] filtered = filtered[ (filtered['date'] >= start) & (filtered['date'] <= end) ]

line_fig = px.line( filtered.groupby('date')['revenue'].sum().reset_index(), x='date', y='revenue', title='Revenue Over Time' ) pie_fig = px.pie( filtered.groupby('category')['revenue'].sum().reset_index(), values='revenue', names='category', title='Revenue by Category' ) return line_fig, pie_fig

if __name__ == '__main__': app.run(debug=True) ```