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

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

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

## 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

# 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