data-visualization

# Data Visualization ## Overview This skill focuses on creating effective data visualizations that communicate insights clearly. It covers various visualization libraries, chart selection, and design principles for impactful data presentation. ## Instructions ### 1. Understand the Data - Analyze data structure and types - Identify key metrics and dimensions - Determine the story to tell - Consider the target audience ### 2. Select Appropriate Visualization - Match chart type to data relationship - Consider data volume and complexity - Plan for interactivity needs - Account for accessibility ### 3. Design for Clarity - Choose effective color schemes - Label axes and data clearly - Remove chart junk - Highlight key insights ### 4. Implement and Iterate - Build visualization with chosen tool - Test with real data - Gather feedback - Refine based on usage ## Best Practices 1. **Right Chart for Data**: Match visualization to data type 2. **Less is More**: Remove unnecessary elements 3. **Consistent Styling**: Use coherent color schemes 4. **Accessible Design**: Consider colorblind users 5. **Clear Labels**: Descriptive titles and axis labels 6. **Context Matters**: Include reference points 7. **Interactive When Helpful**: Add tooltips and filters ## Examples ### Example 1: Python with Matplotlib/Seaborn ```python import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np # Set style for professional look plt.style.use('seaborn-v0_8-whitegrid') sns.set_palette("husl") # Create figure with subplots fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # Example 1: Line chart for time series df_sales = pd.DataFrame({ 'date': pd.date_range('2024-01-01', periods=12, freq='M'), 'revenue': [100, 120, 115, 140, 155, 170, 165, 180, 195, 210, 225, 250], 'target': [110, 115, 120, 130, 145, 160, 175, 185, 200, 215, 230, 245] }) ax1 = axes[0, 0] ax1.plot(df_sales['date'], df_sales['revenue'], marker='o', linewidth=2, label='Actual') ax1.plot(df_sales['date'], df_sales['target'], linestyle='--', linewidth=2, label='Target') ax1.fill_between(df_sales['date'], df_sales['revenue'], df_sales['target'], alpha=0.3, where=(df_sales['revenue'] >= df_sales['target']), color='green') ax1.fill_between(df_sales['date'], df_sales['revenue'], df_sales['target'], alpha=0.3, where=(df_sales['revenue'] < df_sales['target']), color='red') ax1.set_title('Monthly Revenue vs Target', fontsize=14, fontweight='bold') ax1.set_xlabel('Month') ax1.set_ylabel('Revenue ($K)') ax1.legend() ax1.tick_params(axis='x', rotation=45) # Example 2: Bar chart for comparison df_products = pd.DataFrame({ 'product': ['Product A', 'Product B', 'Product C', 'Product D', 'Product E'], 'sales': [45, 32, 28, 22, 18] }) ax2 = axes[0, 1] colors = sns.color_palette("Blues_r", len(df_products)) bars = ax2.barh(df_products['product'], df_products['sales'], color=colors) ax2.bar_label(bars, padding=3, fmt='$%.0fK') ax2.set_title('Sales by Product', fontsize=14, fontweight='bold') ax2.set_xlabel('Sales ($K)') ax2.invert_yaxis() # Example 3: Scatter plot with regression np.random.seed(42) df_scatter = pd.DataFrame({ 'ad_spend': np.random.uniform(10, 100, 50), 'conversions': lambda x: x['ad_spend'] * 2.5 + np.random.normal(0, 15, 50) }.__class__.__call__(pd.DataFrame({'ad_spend': np.random.uniform(10, 100, 50)}))) df_scatter['conversions'] = df_scatter['ad_spend'] * 2.5 + np.random.normal(0, 15, 50) ax3 = axes[1, 0] sns.regplot(data=df_scatter, x='ad_spend', y='conversions', ax=ax3, scatter_kws={'alpha': 0.6}, line_kws={'color': 'red'}) ax3.set_title('Ad Spend vs Conversions', fontsize=14, fontweight='bold') ax3.set_xlabel('Ad Spend ($K)') ax3.set_ylabel('Conversions') # Example 4: Pie/Donut chart for composition df_channels = pd.DataFrame({ 'channel': ['Organic', 'Paid Search', 'Social', 'Email', 'Direct'], 'traffic': [35, 25, 20, 12, 8] }) ax4 = axes[1, 1] wedges, texts, autotexts = ax4.pie( df_channels['traffic'], labels=df_channels['channel'], autopct='%1.1f%%', pctdistance=0.75, wedgeprops=dict(width=0.5) ) ax4.set_title('Traffic by Channel', fontsize=14, fontweight='bold') plt.tight_layout() plt.savefig('dashboard.png', dpi=150, bbox_inches='tight') plt.show() ``` ### Example 2: Interactive Visualization with Plotly ```python import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots import pandas as pd # Create interactive time series df = pd.DataFrame({ 'date': pd.date_range('2024-01-01', periods=365, freq='D'), 'value': (pd.Series(range(365)) * 0.1 + np.sin(pd.Series(range(365)) * 0.1) * 20 + np.random.normal(0, 5, 365)).cumsum() }) fig = go.Figure() fig.add_trace(go.Scatter( x=df['date'], y=df['value'], mode='lines', name='Daily Value', line=dict(color='#1f77b4', width=1.5), hovertemplate='%{x|%B %d, %Y}<br>Value: %{y:.2f}<extra></extra>' )) # Add moving average df['ma_7'] = df['value'].rolling(7).mean() fig.add_trace(go.Scatter( x=df['date'], y=df['ma_7'], mode='lines', name='7-day MA', line=dict(color='#ff7f0e', width=2, dash='dash') )) fig.update_layout( title='Daily Performance with Moving Average', xaxis_title='Date', yaxis_title='Value', hovermode='x unified', template='plotly_white', xaxis=dict( rangeselector=dict( buttons=list([ dict(count=7, label="1w", step="day", stepmode="backward"), dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=3, label="3m", step="month", stepmode="backward"), dict(step="all") ]) ), rangeslider=dict(visible=True) ) ) fig.write_html('interactive_chart.html') fig.show() ``` ### Example 3: Chart Type Selection Guide ```markdown ## Chart Selection by Data Type ### Comparison - **Bar Chart**: Compare values across categories - **Grouped Bar**: Compare multiple series across categories - **Bullet Chart**: Show performance against target ### Distribution - **Histogram**: Show frequency distribution - **Box Plot**: Show distribution summary statistics - **Violin Plot**: Show distribution shape ### Composition - **Pie/Donut Chart**: Show parts of a whole (< 6 categories) - **Stacked Bar**: Show composition across categories - **Treemap**: Show hierarchical composition ### Relationship - **Scatter Plot**: Show correlation between two variables - **Bubble Chart**: Add third dimension via size - **Heatmap**: Show correlation matrix ### Time Series - **Line Chart**: Show trends over time - **Area Chart**: Show cumulative trends - **Candlestick**: Show OHLC financial data ### Geographic - **Choropleth**: Show values by region - **Point Map**: Show locations with values - **Flow Map**: Show movement between locations ``` ### Example 4: Dashboard Layout Principles ```python # Streamlit Dashboard Example import streamlit as st import pandas as pd import plotly.express as px st.set_page_config(page_title="Sales Dashboard", layout="wide") # Header st.title("Sales Performance Dashboard") st.markdown("---") # KPI Row col1, col2, col3, col4 = st.columns(4) with col1: st.metric("Total Revenue", "$1.2M", "+12%") with col2: st.metric("Orders", "8,543", "+8%") with col3: st.metric("Avg Order Value", "$140", "+3%") with col4: st.metric("Conversion Rate", "3.2%", "-0.5%") st.markdown("---") # Filters with st.sidebar: st.header("Filters") date_range = st.date_input("Date Range", []) region = st.multiselect("Region", ["North", "South", "East", "West"]) category = st.selectbox("Category", ["All", "Electronics", "Clothing", "Home"]) # Main Charts left_col, right_col = st.columns([2, 1]) with left_col: st.subheader("Revenue Trend") # Line chart here with right_col: st.subheader("Sales by Region") # Pie chart here # Detail Table st.subheader("Recent Orders") # Data table here ```