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skills/seaborn/references/examples.md
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skills/seaborn/references/examples.md
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# Seaborn Common Use Cases and Examples
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This document provides practical examples for common data visualization scenarios using seaborn.
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## Exploratory Data Analysis
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### Quick Dataset Overview
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```python
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import seaborn as sns
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import matplotlib.pyplot as plt
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import pandas as pd
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# Load data
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df = pd.read_csv('data.csv')
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# Pairwise relationships for all numeric variables
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sns.pairplot(df, hue='target_variable', corner=True, diag_kind='kde')
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plt.suptitle('Dataset Overview', y=1.01)
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plt.savefig('overview.png', dpi=300, bbox_inches='tight')
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```
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### Distribution Exploration
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```python
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# Multiple distributions across categories
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g = sns.displot(
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data=df,
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x='measurement',
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hue='condition',
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col='timepoint',
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kind='kde',
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fill=True,
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height=3,
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aspect=1.5,
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col_wrap=3,
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common_norm=False
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)
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g.set_axis_labels('Measurement Value', 'Density')
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g.set_titles('{col_name}')
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```
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### Correlation Analysis
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```python
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# Compute correlation matrix
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corr = df.select_dtypes(include='number').corr()
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# Create mask for upper triangle
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mask = np.triu(np.ones_like(corr, dtype=bool))
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# Plot heatmap
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fig, ax = plt.subplots(figsize=(10, 8))
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sns.heatmap(
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corr,
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mask=mask,
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annot=True,
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fmt='.2f',
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cmap='coolwarm',
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center=0,
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square=True,
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linewidths=1,
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cbar_kws={'shrink': 0.8}
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)
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plt.title('Correlation Matrix')
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plt.tight_layout()
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```
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## Scientific Publications
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### Multi-Panel Figure with Different Plot Types
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```python
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# Set publication style
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sns.set_theme(style='ticks', context='paper', font_scale=1.1)
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sns.set_palette('colorblind')
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# Create figure with custom layout
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fig = plt.figure(figsize=(12, 8))
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gs = fig.add_gridspec(2, 3, hspace=0.3, wspace=0.3)
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# Panel A: Time series
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ax1 = fig.add_subplot(gs[0, :2])
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sns.lineplot(
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data=timeseries_df,
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x='time',
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y='expression',
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hue='gene',
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style='treatment',
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markers=True,
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dashes=False,
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ax=ax1
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)
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ax1.set_title('A. Gene Expression Over Time', loc='left', fontweight='bold')
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ax1.set_xlabel('Time (hours)')
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ax1.set_ylabel('Expression Level (AU)')
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# Panel B: Distribution comparison
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ax2 = fig.add_subplot(gs[0, 2])
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sns.violinplot(
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data=expression_df,
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x='treatment',
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y='expression',
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inner='box',
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ax=ax2
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)
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ax2.set_title('B. Expression Distribution', loc='left', fontweight='bold')
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ax2.set_xlabel('Treatment')
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ax2.set_ylabel('')
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# Panel C: Correlation
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ax3 = fig.add_subplot(gs[1, 0])
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sns.scatterplot(
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data=correlation_df,
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x='gene1',
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y='gene2',
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hue='cell_type',
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alpha=0.6,
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ax=ax3
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)
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sns.regplot(
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data=correlation_df,
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x='gene1',
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y='gene2',
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scatter=False,
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color='black',
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ax=ax3
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)
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ax3.set_title('C. Gene Correlation', loc='left', fontweight='bold')
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ax3.set_xlabel('Gene 1 Expression')
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ax3.set_ylabel('Gene 2 Expression')
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# Panel D: Heatmap
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ax4 = fig.add_subplot(gs[1, 1:])
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sns.heatmap(
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sample_matrix,
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cmap='RdBu_r',
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center=0,
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annot=True,
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fmt='.1f',
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cbar_kws={'label': 'Log2 Fold Change'},
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ax=ax4
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)
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ax4.set_title('D. Treatment Effects', loc='left', fontweight='bold')
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ax4.set_xlabel('Sample')
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ax4.set_ylabel('Gene')
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# Clean up
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sns.despine()
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plt.savefig('figure.pdf', dpi=300, bbox_inches='tight')
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plt.savefig('figure.png', dpi=300, bbox_inches='tight')
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```
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### Box Plot with Significance Annotations
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```python
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import numpy as np
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from scipy import stats
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# Create plot
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fig, ax = plt.subplots(figsize=(8, 6))
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sns.boxplot(
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data=df,
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x='treatment',
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y='response',
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order=['Control', 'Low', 'Medium', 'High'],
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palette='Set2',
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ax=ax
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)
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# Add individual points
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sns.stripplot(
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data=df,
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x='treatment',
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y='response',
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order=['Control', 'Low', 'Medium', 'High'],
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color='black',
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alpha=0.3,
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size=3,
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ax=ax
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)
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# Add significance bars
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def add_significance_bar(ax, x1, x2, y, h, text):
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ax.plot([x1, x1, x2, x2], [y, y+h, y+h, y], 'k-', lw=1.5)
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ax.text((x1+x2)/2, y+h, text, ha='center', va='bottom')
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y_max = df['response'].max()
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add_significance_bar(ax, 0, 3, y_max + 1, 0.5, '***')
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add_significance_bar(ax, 0, 1, y_max + 3, 0.5, 'ns')
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ax.set_ylabel('Response (μM)')
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ax.set_xlabel('Treatment Condition')
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ax.set_title('Treatment Response Analysis')
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sns.despine()
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```
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## Time Series Analysis
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### Multiple Time Series with Confidence Bands
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```python
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# Plot with automatic aggregation
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fig, ax = plt.subplots(figsize=(10, 6))
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sns.lineplot(
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data=timeseries_df,
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x='timestamp',
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y='value',
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hue='sensor',
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style='location',
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markers=True,
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dashes=False,
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errorbar=('ci', 95),
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ax=ax
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)
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# Customize
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ax.set_xlabel('Date')
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ax.set_ylabel('Measurement (units)')
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ax.set_title('Sensor Measurements Over Time')
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ax.legend(title='Sensor & Location', bbox_to_anchor=(1.05, 1), loc='upper left')
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# Format x-axis for dates
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import matplotlib.dates as mdates
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
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ax.xaxis.set_major_locator(mdates.DayLocator(interval=7))
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plt.xticks(rotation=45, ha='right')
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plt.tight_layout()
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```
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### Faceted Time Series
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```python
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# Create faceted time series
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g = sns.relplot(
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data=long_timeseries,
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x='date',
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y='measurement',
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hue='device',
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col='location',
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row='metric',
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kind='line',
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height=3,
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aspect=2,
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errorbar='sd',
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facet_kws={'sharex': True, 'sharey': False}
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)
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# Customize facet titles
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g.set_titles('{row_name} - {col_name}')
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g.set_axis_labels('Date', 'Value')
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# Rotate x-axis labels
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for ax in g.axes.flat:
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ax.tick_params(axis='x', rotation=45)
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g.tight_layout()
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```
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## Categorical Comparisons
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### Nested Categorical Variables
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```python
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# Create figure
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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# Left panel: Grouped bar plot
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sns.barplot(
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data=df,
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x='category',
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y='value',
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hue='subcategory',
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errorbar=('ci', 95),
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capsize=0.1,
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ax=axes[0]
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)
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axes[0].set_title('Mean Values with 95% CI')
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axes[0].set_ylabel('Value (units)')
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axes[0].legend(title='Subcategory')
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# Right panel: Strip + violin plot
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sns.violinplot(
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data=df,
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x='category',
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y='value',
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hue='subcategory',
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inner=None,
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alpha=0.3,
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ax=axes[1]
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)
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sns.stripplot(
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data=df,
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x='category',
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y='value',
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hue='subcategory',
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dodge=True,
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size=3,
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alpha=0.6,
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ax=axes[1]
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)
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axes[1].set_title('Distribution of Individual Values')
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axes[1].set_ylabel('')
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axes[1].get_legend().remove()
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plt.tight_layout()
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```
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### Point Plot for Trends
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```python
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# Show how values change across categories
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sns.pointplot(
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data=df,
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x='timepoint',
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y='score',
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hue='treatment',
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markers=['o', 's', '^'],
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linestyles=['-', '--', '-.'],
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dodge=0.3,
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capsize=0.1,
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errorbar=('ci', 95)
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)
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plt.xlabel('Timepoint')
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plt.ylabel('Performance Score')
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plt.title('Treatment Effects Over Time')
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plt.legend(title='Treatment', bbox_to_anchor=(1.05, 1), loc='upper left')
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sns.despine()
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plt.tight_layout()
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```
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## Regression and Relationships
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### Linear Regression with Facets
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```python
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# Fit separate regressions for each category
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g = sns.lmplot(
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data=df,
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x='predictor',
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y='response',
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hue='treatment',
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col='cell_line',
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height=4,
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aspect=1.2,
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scatter_kws={'alpha': 0.5, 's': 50},
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ci=95,
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palette='Set2'
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)
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g.set_axis_labels('Predictor Variable', 'Response Variable')
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g.set_titles('{col_name}')
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g.tight_layout()
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```
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### Polynomial Regression
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```python
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fig, axes = plt.subplots(1, 3, figsize=(15, 5))
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for idx, order in enumerate([1, 2, 3]):
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sns.regplot(
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data=df,
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x='x',
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y='y',
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order=order,
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scatter_kws={'alpha': 0.5},
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line_kws={'color': 'red'},
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ci=95,
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ax=axes[idx]
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)
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axes[idx].set_title(f'Order {order} Polynomial Fit')
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axes[idx].set_xlabel('X Variable')
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axes[idx].set_ylabel('Y Variable')
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plt.tight_layout()
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```
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### Residual Analysis
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```python
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fig, axes = plt.subplots(2, 2, figsize=(12, 10))
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# Main regression
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sns.regplot(data=df, x='x', y='y', ax=axes[0, 0])
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axes[0, 0].set_title('Regression Fit')
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# Residuals vs fitted
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sns.residplot(data=df, x='x', y='y', lowess=True,
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scatter_kws={'alpha': 0.5},
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line_kws={'color': 'red', 'lw': 2},
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ax=axes[0, 1])
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axes[0, 1].set_title('Residuals vs Fitted')
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axes[0, 1].axhline(0, ls='--', color='gray')
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# Q-Q plot (using scipy)
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from scipy import stats as sp_stats
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residuals = df['y'] - np.poly1d(np.polyfit(df['x'], df['y'], 1))(df['x'])
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sp_stats.probplot(residuals, dist="norm", plot=axes[1, 0])
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axes[1, 0].set_title('Q-Q Plot')
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# Histogram of residuals
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sns.histplot(residuals, kde=True, ax=axes[1, 1])
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axes[1, 1].set_title('Residual Distribution')
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axes[1, 1].set_xlabel('Residuals')
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plt.tight_layout()
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```
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## Bivariate and Joint Distributions
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### Joint Plot with Multiple Representations
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```python
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# Scatter with marginals
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g = sns.jointplot(
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data=df,
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x='var1',
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y='var2',
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hue='category',
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kind='scatter',
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height=8,
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ratio=4,
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space=0.1,
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joint_kws={'alpha': 0.5, 's': 50},
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marginal_kws={'kde': True, 'bins': 30}
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)
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# Add reference lines
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g.ax_joint.axline((0, 0), slope=1, color='r', ls='--', alpha=0.5, label='y=x')
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g.ax_joint.legend()
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g.set_axis_labels('Variable 1', 'Variable 2', fontsize=12)
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```
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### KDE Contour Plot
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```python
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fig, ax = plt.subplots(figsize=(8, 8))
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# Bivariate KDE with filled contours
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sns.kdeplot(
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data=df,
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x='x',
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y='y',
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fill=True,
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levels=10,
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cmap='viridis',
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thresh=0.05,
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ax=ax
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)
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# Overlay scatter
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sns.scatterplot(
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data=df,
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x='x',
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y='y',
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color='white',
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edgecolor='black',
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s=50,
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alpha=0.6,
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ax=ax
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)
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ax.set_xlabel('X Variable')
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ax.set_ylabel('Y Variable')
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ax.set_title('Bivariate Distribution')
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```
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### Hexbin with Marginals
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```python
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# For large datasets
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g = sns.jointplot(
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data=large_df,
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x='x',
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y='y',
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kind='hex',
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height=8,
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ratio=5,
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space=0.1,
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joint_kws={'gridsize': 30, 'cmap': 'viridis'},
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marginal_kws={'bins': 50, 'color': 'skyblue'}
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)
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g.set_axis_labels('X Variable', 'Y Variable')
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```
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## Matrix and Heatmap Visualizations
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### Hierarchical Clustering Heatmap
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```python
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# Prepare data (samples x features)
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data_matrix = df.set_index('sample_id')[feature_columns]
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# Create color annotations
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row_colors = df.set_index('sample_id')['condition'].map({
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'control': '#1f77b4',
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'treatment': '#ff7f0e'
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})
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col_colors = pd.Series(['#2ca02c' if 'gene' in col else '#d62728'
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for col in data_matrix.columns])
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# Plot
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g = sns.clustermap(
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data_matrix,
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method='ward',
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metric='euclidean',
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z_score=0, # Normalize rows
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cmap='RdBu_r',
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center=0,
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row_colors=row_colors,
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col_colors=col_colors,
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figsize=(12, 10),
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dendrogram_ratio=(0.1, 0.1),
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cbar_pos=(0.02, 0.8, 0.03, 0.15),
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linewidths=0.5
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)
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g.ax_heatmap.set_xlabel('Features')
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g.ax_heatmap.set_ylabel('Samples')
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plt.savefig('clustermap.png', dpi=300, bbox_inches='tight')
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```
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### Annotated Heatmap with Custom Colorbar
|
||||
|
||||
```python
|
||||
# Pivot data for heatmap
|
||||
pivot_data = df.pivot(index='row_var', columns='col_var', values='value')
|
||||
|
||||
# Create heatmap
|
||||
fig, ax = plt.subplots(figsize=(10, 8))
|
||||
sns.heatmap(
|
||||
pivot_data,
|
||||
annot=True,
|
||||
fmt='.1f',
|
||||
cmap='RdYlGn',
|
||||
center=pivot_data.mean().mean(),
|
||||
vmin=pivot_data.min().min(),
|
||||
vmax=pivot_data.max().max(),
|
||||
linewidths=0.5,
|
||||
linecolor='gray',
|
||||
cbar_kws={
|
||||
'label': 'Value (units)',
|
||||
'orientation': 'vertical',
|
||||
'shrink': 0.8,
|
||||
'aspect': 20
|
||||
},
|
||||
ax=ax
|
||||
)
|
||||
|
||||
ax.set_title('Variable Relationships', fontsize=14, pad=20)
|
||||
ax.set_xlabel('Column Variable', fontsize=12)
|
||||
ax.set_ylabel('Row Variable', fontsize=12)
|
||||
|
||||
plt.xticks(rotation=45, ha='right')
|
||||
plt.yticks(rotation=0)
|
||||
plt.tight_layout()
|
||||
```
|
||||
|
||||
## Statistical Comparisons
|
||||
|
||||
### Before/After Comparison
|
||||
|
||||
```python
|
||||
# Reshape data for paired comparison
|
||||
df_paired = df.melt(
|
||||
id_vars='subject',
|
||||
value_vars=['before', 'after'],
|
||||
var_name='timepoint',
|
||||
value_name='measurement'
|
||||
)
|
||||
|
||||
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
|
||||
|
||||
# Left: Individual trajectories
|
||||
for subject in df_paired['subject'].unique():
|
||||
subject_data = df_paired[df_paired['subject'] == subject]
|
||||
axes[0].plot(subject_data['timepoint'], subject_data['measurement'],
|
||||
'o-', alpha=0.3, color='gray')
|
||||
|
||||
sns.pointplot(
|
||||
data=df_paired,
|
||||
x='timepoint',
|
||||
y='measurement',
|
||||
color='red',
|
||||
markers='D',
|
||||
scale=1.5,
|
||||
errorbar=('ci', 95),
|
||||
capsize=0.2,
|
||||
ax=axes[0]
|
||||
)
|
||||
axes[0].set_title('Individual Changes')
|
||||
axes[0].set_ylabel('Measurement')
|
||||
|
||||
# Right: Distribution comparison
|
||||
sns.violinplot(
|
||||
data=df_paired,
|
||||
x='timepoint',
|
||||
y='measurement',
|
||||
inner='box',
|
||||
ax=axes[1]
|
||||
)
|
||||
sns.swarmplot(
|
||||
data=df_paired,
|
||||
x='timepoint',
|
||||
y='measurement',
|
||||
color='black',
|
||||
alpha=0.5,
|
||||
size=3,
|
||||
ax=axes[1]
|
||||
)
|
||||
axes[1].set_title('Distribution Comparison')
|
||||
axes[1].set_ylabel('')
|
||||
|
||||
plt.tight_layout()
|
||||
```
|
||||
|
||||
### Dose-Response Curve
|
||||
|
||||
```python
|
||||
# Create dose-response plot
|
||||
fig, ax = plt.subplots(figsize=(8, 6))
|
||||
|
||||
# Plot individual points
|
||||
sns.stripplot(
|
||||
data=dose_df,
|
||||
x='dose',
|
||||
y='response',
|
||||
order=sorted(dose_df['dose'].unique()),
|
||||
color='gray',
|
||||
alpha=0.3,
|
||||
jitter=0.2,
|
||||
ax=ax
|
||||
)
|
||||
|
||||
# Overlay mean with CI
|
||||
sns.pointplot(
|
||||
data=dose_df,
|
||||
x='dose',
|
||||
y='response',
|
||||
order=sorted(dose_df['dose'].unique()),
|
||||
color='blue',
|
||||
markers='o',
|
||||
scale=1.2,
|
||||
errorbar=('ci', 95),
|
||||
capsize=0.1,
|
||||
ax=ax
|
||||
)
|
||||
|
||||
# Fit sigmoid curve
|
||||
from scipy.optimize import curve_fit
|
||||
|
||||
def sigmoid(x, bottom, top, ec50, hill):
|
||||
return bottom + (top - bottom) / (1 + (ec50 / x) ** hill)
|
||||
|
||||
doses_numeric = dose_df['dose'].astype(float)
|
||||
params, _ = curve_fit(sigmoid, doses_numeric, dose_df['response'])
|
||||
|
||||
x_smooth = np.logspace(np.log10(doses_numeric.min()),
|
||||
np.log10(doses_numeric.max()), 100)
|
||||
y_smooth = sigmoid(x_smooth, *params)
|
||||
|
||||
ax.plot(range(len(sorted(dose_df['dose'].unique()))),
|
||||
sigmoid(sorted(doses_numeric.unique()), *params),
|
||||
'r-', linewidth=2, label='Sigmoid Fit')
|
||||
|
||||
ax.set_xlabel('Dose')
|
||||
ax.set_ylabel('Response')
|
||||
ax.set_title('Dose-Response Analysis')
|
||||
ax.legend()
|
||||
sns.despine()
|
||||
```
|
||||
|
||||
## Custom Styling
|
||||
|
||||
### Custom Color Palette from Hex Codes
|
||||
|
||||
```python
|
||||
# Define custom palette
|
||||
custom_palette = ['#E64B35', '#4DBBD5', '#00A087', '#3C5488', '#F39B7F']
|
||||
sns.set_palette(custom_palette)
|
||||
|
||||
# Or use for specific plot
|
||||
sns.scatterplot(
|
||||
data=df,
|
||||
x='x',
|
||||
y='y',
|
||||
hue='category',
|
||||
palette=custom_palette
|
||||
)
|
||||
```
|
||||
|
||||
### Publication-Ready Theme
|
||||
|
||||
```python
|
||||
# Set comprehensive theme
|
||||
sns.set_theme(
|
||||
context='paper',
|
||||
style='ticks',
|
||||
palette='colorblind',
|
||||
font='Arial',
|
||||
font_scale=1.1,
|
||||
rc={
|
||||
'figure.dpi': 300,
|
||||
'savefig.dpi': 300,
|
||||
'savefig.format': 'pdf',
|
||||
'axes.linewidth': 1.0,
|
||||
'axes.labelweight': 'bold',
|
||||
'xtick.major.width': 1.0,
|
||||
'ytick.major.width': 1.0,
|
||||
'xtick.direction': 'out',
|
||||
'ytick.direction': 'out',
|
||||
'legend.frameon': False,
|
||||
'pdf.fonttype': 42, # True Type fonts for PDFs
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
### Diverging Colormap Centered on Zero
|
||||
|
||||
```python
|
||||
# For data with meaningful zero point (e.g., log fold change)
|
||||
from matplotlib.colors import TwoSlopeNorm
|
||||
|
||||
# Find data range
|
||||
vmin, vmax = df['value'].min(), df['value'].max()
|
||||
vcenter = 0
|
||||
|
||||
# Create norm
|
||||
norm = TwoSlopeNorm(vmin=vmin, vcenter=vcenter, vmax=vmax)
|
||||
|
||||
# Plot
|
||||
sns.heatmap(
|
||||
pivot_data,
|
||||
cmap='RdBu_r',
|
||||
norm=norm,
|
||||
center=0,
|
||||
annot=True,
|
||||
fmt='.2f'
|
||||
)
|
||||
```
|
||||
|
||||
## Large Datasets
|
||||
|
||||
### Downsampling Strategy
|
||||
|
||||
```python
|
||||
# For very large datasets, sample intelligently
|
||||
def smart_sample(df, target_size=10000, category_col=None):
|
||||
if len(df) <= target_size:
|
||||
return df
|
||||
|
||||
if category_col:
|
||||
# Stratified sampling
|
||||
return df.groupby(category_col, group_keys=False).apply(
|
||||
lambda x: x.sample(min(len(x), target_size // df[category_col].nunique()))
|
||||
)
|
||||
else:
|
||||
# Simple random sampling
|
||||
return df.sample(target_size)
|
||||
|
||||
# Use sampled data for visualization
|
||||
df_sampled = smart_sample(large_df, target_size=5000, category_col='category')
|
||||
|
||||
sns.scatterplot(data=df_sampled, x='x', y='y', hue='category', alpha=0.5)
|
||||
```
|
||||
|
||||
### Hexbin for Dense Scatter Plots
|
||||
|
||||
```python
|
||||
# For millions of points
|
||||
fig, axes = plt.subplots(1, 2, figsize=(14, 6))
|
||||
|
||||
# Regular scatter (slow)
|
||||
axes[0].scatter(df['x'], df['y'], alpha=0.1, s=1)
|
||||
axes[0].set_title('Scatter (all points)')
|
||||
|
||||
# Hexbin (fast)
|
||||
hb = axes[1].hexbin(df['x'], df['y'], gridsize=50, cmap='viridis', mincnt=1)
|
||||
axes[1].set_title('Hexbin Aggregation')
|
||||
plt.colorbar(hb, ax=axes[1], label='Count')
|
||||
|
||||
plt.tight_layout()
|
||||
```
|
||||
|
||||
## Interactive Elements for Notebooks
|
||||
|
||||
### Adjustable Parameters
|
||||
|
||||
```python
|
||||
from ipywidgets import interact, FloatSlider
|
||||
|
||||
@interact(bandwidth=FloatSlider(min=0.1, max=3.0, step=0.1, value=1.0))
|
||||
def plot_kde(bandwidth):
|
||||
plt.figure(figsize=(10, 6))
|
||||
sns.kdeplot(data=df, x='value', hue='category',
|
||||
bw_adjust=bandwidth, fill=True)
|
||||
plt.title(f'KDE with bandwidth adjustment = {bandwidth}')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
### Dynamic Filtering
|
||||
|
||||
```python
|
||||
from ipywidgets import interact, SelectMultiple
|
||||
|
||||
categories = df['category'].unique().tolist()
|
||||
|
||||
@interact(selected=SelectMultiple(options=categories, value=[categories[0]]))
|
||||
def filtered_plot(selected):
|
||||
filtered_df = df[df['category'].isin(selected)]
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
sns.violinplot(data=filtered_df, x='category', y='value', ax=ax)
|
||||
ax.set_title(f'Showing {len(selected)} categories')
|
||||
plt.show()
|
||||
```
|
||||
Reference in New Issue
Block a user