Initial commit
This commit is contained in:
Z User
324
skills/charts/references/seaborn.md
Executable file
324
skills/charts/references/seaborn.md
Executable file
@@ -0,0 +1,324 @@
|
||||
# Seaborn Template Library
|
||||
|
||||
> **⚠️ Before writing any code, read [`_rules.md`](references/_rules.md) — three non-negotiable rules on overlap, hierarchy, and color.**
|
||||
|
||||
|
||||
Seaborn is built on top of matplotlib, specializing in **statistical visualization** — distributions, regression, categorical comparisons, etc.
|
||||
Its default styles are already much better looking than matplotlib, but still need tuning to reach professional standards.
|
||||
|
||||
## Environment Setup
|
||||
|
||||
```python
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
# ═══ Chinese Font Setup ═══
|
||||
# Font path: adjust for your system. Common locations:
|
||||
# macOS: '/System/Library/Fonts/Supplemental/SimHei.ttf'
|
||||
# Linux: '/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc'
|
||||
# Custom: './fonts/SimHei.ttf'
|
||||
import os
|
||||
SIMHEI_PATH = os.environ.get('SIMHEI_FONT', '/System/Library/Fonts/Supplemental/SimHei.ttf')
|
||||
matplotlib.font_manager.fontManager.addfont(SIMHEI_PATH)
|
||||
|
||||
# Seaborn theme + custom overrides
|
||||
sns.set_theme(style='whitegrid', font='SimHei', rc={
|
||||
'axes.unicode_minus': False,
|
||||
'figure.facecolor': '#FFFFFF',
|
||||
'axes.facecolor': '#FFFFFF',
|
||||
'axes.edgecolor': '#E5E7EB',
|
||||
'axes.linewidth': 0.8,
|
||||
'axes.spines.top': False,
|
||||
'axes.spines.right': False,
|
||||
'grid.color': '#F3F4F6',
|
||||
'grid.alpha': 0.5,
|
||||
'grid.linewidth': 0.5,
|
||||
'xtick.major.size': 0,
|
||||
'ytick.major.size': 0,
|
||||
'axes.titlesize': 16,
|
||||
'axes.titleweight': 'bold',
|
||||
'axes.titlepad': 16,
|
||||
'legend.frameon': False,
|
||||
'figure.dpi': 200,
|
||||
'savefig.dpi': 200,
|
||||
'savefig.bbox': 'tight',
|
||||
})
|
||||
|
||||
# Colors (consistent with matplotlib.md)
|
||||
COOL = ['#3B82F6', '#06B6D4', '#8B5CF6', '#F59E0B', '#EF4444', '#10B981']
|
||||
CB_SAFE = ['#0077BB', '#33BBEE', '#009988', '#EE7733', '#CC3311', '#EE3377']
|
||||
```
|
||||
|
||||
## Seaborn Palette Setup
|
||||
|
||||
```python
|
||||
# Method 1: use hex list directly
|
||||
sns.set_palette(COOL)
|
||||
|
||||
# Method 2: register custom palette
|
||||
from matplotlib.colors import ListedColormap
|
||||
bc_palette = ListedColormap(COOL, name='charts')
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Template 1: Distribution Plot (Histogram + KDE)
|
||||
|
||||
**Scenario**: View data distribution shape, detect skewness and outliers.
|
||||
|
||||
```python
|
||||
def dist_plot(data, title, xlabel, color='#3B82F6', save_path='dist.png'):
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
|
||||
sns.histplot(data, kde=True, color=color, edgecolor='white',
|
||||
linewidth=0.5, alpha=0.7, ax=ax)
|
||||
|
||||
# Bold the KDE line
|
||||
for line in ax.get_lines():
|
||||
line.set_linewidth(2.5)
|
||||
|
||||
ax.set_title(title, loc='left')
|
||||
ax.set_xlabel(xlabel)
|
||||
ax.set_ylabel('Frequency')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Template 2: Box Plot (Categorical Comparison)
|
||||
|
||||
**Scenario**: Compare distribution characteristics across groups (median, quartiles, outliers).
|
||||
|
||||
```python
|
||||
def box_compare(df, x_col, y_col, title, palette=None, save_path='box.png'):
|
||||
if palette is None:
|
||||
palette = COOL
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
|
||||
sns.boxplot(data=df, x=x_col, y=y_col, palette=palette,
|
||||
width=0.5, linewidth=1.2, fliersize=4,
|
||||
boxprops=dict(edgecolor='white'),
|
||||
medianprops=dict(color='white', linewidth=2),
|
||||
ax=ax)
|
||||
|
||||
ax.set_title(title, loc='left')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
### Box Plot + Label Avoidance (For Complex Scenarios)
|
||||
|
||||
When you need to annotate outliers, specific data points, or group names on a box plot, **label avoidance is required**:
|
||||
|
||||
```python
|
||||
def box_annotated(df, x_col, y_col, title, annotations=None,
|
||||
palette=None, save_path='box_annotated.png'):
|
||||
"""
|
||||
annotations: [{'x': 0, 'y': 45, 'text': '产线A 异常'}, ...]
|
||||
"""
|
||||
from adjustText import adjust_text
|
||||
|
||||
if palette is None:
|
||||
palette = COOL
|
||||
|
||||
fig, ax = plt.subplots(figsize=(12, 7)) # Slightly larger canvas, leave room for annotations
|
||||
|
||||
sns.boxplot(data=df, x=x_col, y=y_col, palette=palette,
|
||||
width=0.5, linewidth=1.2, fliersize=4,
|
||||
boxprops=dict(edgecolor='white'),
|
||||
medianprops=dict(color='white', linewidth=2),
|
||||
ax=ax)
|
||||
|
||||
# Annotation text — use adjustText for auto-avoidance
|
||||
if annotations:
|
||||
texts = []
|
||||
for ann in annotations:
|
||||
t = ax.text(ann['x'], ann['y'], ann['text'],
|
||||
fontsize=9, color='#374151',
|
||||
bbox=dict(boxstyle='round,pad=0.3',
|
||||
facecolor='#FFF7ED', edgecolor='#F59E0B',
|
||||
alpha=0.9))
|
||||
texts.append(t)
|
||||
|
||||
adjust_text(texts, ax=ax,
|
||||
arrowprops=dict(arrowstyle='->', color='#9CA3AF', lw=0.8),
|
||||
force_text=(0.8, 1.0),
|
||||
force_points=(0.5, 0.8))
|
||||
|
||||
ax.set_title(title, loc='left')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
### Box Plot + Colorbar (e.g., MTTR Analysis)
|
||||
|
||||
When a box plot needs to work with a colorbar (e.g., colored by dimension), you must leave enough space for the colorbar:
|
||||
|
||||
```python
|
||||
def box_with_colorbar(df, x_col, y_col, color_col, title,
|
||||
save_path='box_cbar.png'):
|
||||
import matplotlib.gridspec as gridspec
|
||||
from matplotlib.colors import Normalize
|
||||
from matplotlib.cm import ScalarMappable
|
||||
|
||||
fig = plt.figure(figsize=(14, 7), constrained_layout=True)
|
||||
gs = gridspec.GridSpec(1, 2, figure=fig, width_ratios=[1, 0.03], wspace=0.05)
|
||||
|
||||
ax = fig.add_subplot(gs[0, 0])
|
||||
cbar_ax = fig.add_subplot(gs[0, 1])
|
||||
|
||||
sns.boxplot(data=df, x=x_col, y=y_col, ax=ax,
|
||||
width=0.5, linewidth=1.2)
|
||||
|
||||
# Colorbar in its own subplot, won't obscure box plot
|
||||
norm = Normalize(vmin=df[color_col].min(), vmax=df[color_col].max())
|
||||
sm = ScalarMappable(norm=norm, cmap='Blues')
|
||||
fig.colorbar(sm, cax=cbar_ax, label=color_col)
|
||||
|
||||
ax.set_title(title, loc='left')
|
||||
fig.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Template 3: Violin Plot
|
||||
|
||||
**Scenario**: Enhanced version of box plot, showing distribution density simultaneously.
|
||||
|
||||
```python
|
||||
def violin_plot(df, x_col, y_col, title, palette=None, save_path='violin.png'):
|
||||
if palette is None:
|
||||
palette = COOL
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10, 6))
|
||||
|
||||
sns.violinplot(data=df, x=x_col, y=y_col, palette=palette,
|
||||
inner='box', linewidth=1, ax=ax)
|
||||
|
||||
ax.set_title(title, loc='left')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Template 4: Regression Scatter Plot
|
||||
|
||||
**Scenario**: Two-variable relationship + linear regression + confidence interval.
|
||||
|
||||
```python
|
||||
def reg_plot(df, x_col, y_col, title, color='#3B82F6', save_path='reg.png'):
|
||||
fig, ax = plt.subplots(figsize=(10, 7))
|
||||
|
||||
sns.regplot(data=df, x=x_col, y=y_col, color=color,
|
||||
scatter_kws={'s': 50, 'alpha': 0.6, 'edgecolor': 'white', 'linewidth': 1},
|
||||
line_kws={'linewidth': 2},
|
||||
ax=ax)
|
||||
|
||||
ax.set_title(title, loc='left')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Template 5: Correlation Heatmap
|
||||
|
||||
**Scenario**: Correlation coefficient matrix among multiple variables.
|
||||
|
||||
```python
|
||||
def corr_heatmap(df, title, cmap='RdBu_r', save_path='corr.png'):
|
||||
corr = df.corr()
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10, 8))
|
||||
|
||||
# Show only lower triangle
|
||||
mask = np.triu(np.ones_like(corr, dtype=bool))
|
||||
|
||||
sns.heatmap(corr, mask=mask, cmap=cmap, center=0,
|
||||
vmin=-1, vmax=1, annot=True, fmt='.2f',
|
||||
square=True, linewidths=1, linecolor='white',
|
||||
cbar_kws={'shrink': 0.8, 'label': 'Correlation'},
|
||||
annot_kws={'size': 9},
|
||||
ax=ax)
|
||||
|
||||
ax.set_title(title, loc='left', pad=16)
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Template 6: Pair Plot
|
||||
|
||||
**Scenario**: Overview of pairwise relationships among multiple variables (distribution on diagonal, scatter plots elsewhere).
|
||||
|
||||
```python
|
||||
def pair_plot(df, hue_col=None, palette=None, save_path='pair.png'):
|
||||
if palette is None:
|
||||
palette = COOL
|
||||
|
||||
g = sns.pairplot(df, hue=hue_col, palette=palette,
|
||||
diag_kind='kde', plot_kws={'alpha': 0.6, 's': 30},
|
||||
height=2.5, aspect=1)
|
||||
|
||||
g.figure.suptitle('Pairwise Variable Relationships', y=1.02, fontsize=16, fontweight='bold')
|
||||
|
||||
g.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Template 7: Facet Grid (FacetGrid)
|
||||
|
||||
**Scenario**: Split into multiple subplots by categorical variable for comparison.
|
||||
|
||||
```python
|
||||
def facet_hist(df, col_var, value_col, title, color='#3B82F6', save_path='facet.png'):
|
||||
g = sns.FacetGrid(df, col=col_var, col_wrap=3, height=3.5, aspect=1.3)
|
||||
g.map(sns.histplot, value_col, color=color, edgecolor='white',
|
||||
linewidth=0.5, alpha=0.7, kde=True)
|
||||
|
||||
g.set_titles('{col_name}', fontsize=12, fontweight='bold')
|
||||
g.figure.suptitle(title, y=1.02, fontsize=16, fontweight='bold')
|
||||
|
||||
g.savefig(save_path, dpi=200, facecolor='white', bbox_inches='tight')
|
||||
plt.close()
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Seaborn vs matplotlib Selection Guide
|
||||
|
||||
| What to Plot | Use Seaborn | Use matplotlib |
|
||||
|---------|-----------|--------------|
|
||||
| Histogram/KDE distribution | ✅ `histplot` / `kdeplot` | Works, but more code |
|
||||
| Box plot/Violin plot | ✅ One-liner | Works, but rougher style |
|
||||
| Regression scatter + confidence interval | ✅ `regplot` auto-calculates | Manual fitting + plotting |
|
||||
| Correlation heatmap | ✅ `heatmap` + mask | Manual `imshow` tedious |
|
||||
| Pairwise relationship matrix | ✅ `pairplot` unique | No equivalent |
|
||||
| Facet grid | ✅ `FacetGrid` | `plt.subplots` manual loop |
|
||||
| Regular bar chart | Less flexible than matplotlib | ✅ More control |
|
||||
| Line trend chart | Less than matplotlib | ✅ More control |
|
||||
| Custom annotations/arrows | Not suitable | ✅ `ax.annotate` |
|
||||
|
||||
**Principle: Use Seaborn for statistical charts, matplotlib for customized charts.**
|
||||
Reference in New Issue
Block a user