Seaborn — Complete Notes¶

CampusX: Plotting using Seaborn (Part 1 + Part 2)¶

0. Setup¶

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

Seaborn kya hai? Matplotlib ke upar bani ek high-level statistical plotting library. Kam code, zyada khoobsurat plots, aur Pandas DataFrames ke saath directly kaam karta hai.

Built-in datasets (practice ke liye)¶

tips = sns.load_dataset('tips')
iris = sns.load_dataset('iris')
titanic = sns.load_dataset('titanic')
flights = sns.load_dataset('flights')

1. Figure-level vs Axes-level Functions ⭐⭐¶

Ye Seaborn ka sabse important concept hai.

	Axes-level	Figure-level
Kya banata hai	Ek single Matplotlib `Axes`	Poora `Figure` (FacetGrid)
Matplotlib subplot mein daal sakte ho?	✅ Haan (`ax=` parameter)	❌ Nahi
Multiple subplots automatic?	❌ Nahi	✅ `col=` / `row=` se
Return karta hai	`Axes` object	`FacetGrid` object
Example	`sns.scatterplot()`	`sns.relplot()`

Seaborn ka structure (ye tree yaad kar lo)¶

FIGURE-LEVEL          AXES-LEVEL (uske andar)
──────────────────────────────────────────────
relplot()      →      scatterplot(), lineplot()
displot()      →      histplot(), kdeplot(), ecdfplot(), rugplot()
catplot()      →      stripplot(), swarmplot(), boxplot(),
                      violinplot(), boxenplot(), pointplot(),
                      barplot(), countplot()
lmplot()       →      regplot()

Alag standalone: heatmap(), clustermap(), pairplot(), jointplot()

Kab kaunsa use karein? - Sirf ek plot chahiye → axes-level (sns.scatterplot()) - Category ke hisaab se kai subplots chahiye → figure-level (sns.relplot(col='sex'))

---¶

PART 1 — RELATIONAL PLOTS¶

Maksad: Do numerical columns ke beech ka relationship dekhna.

1.1 `scatterplot()` ⭐¶

sns.scatterplot(data=tips, x='total_bill', y='tip')

`hue` — teesra (categorical) dimension ⭐¶

sns.scatterplot(data=tips, x='total_bill', y='tip', hue='sex')

`style` — chautha dimension (marker shape)¶

sns.scatterplot(data=tips, x='total_bill', y='tip', hue='sex', style='time')

`size` — panchwa dimension (point size)¶

sns.scatterplot(data=tips, x='total_bill', y='tip',
                hue='sex', style='time', size='size')

Ek scatterplot mein 5 dimensions dikha sakte ho: x, y, hue, style, size.

1.2 `lineplot()` ⭐¶

Kab? Time series / continuous trend.

gap = px.data.gapminder()      # ya koi bhi time-based dataset
temp_df = gap[gap['country'] == 'India']

sns.lineplot(data=temp_df, x='year', y='lifeExp')

Multiple lines¶

temp_df = gap[gap['country'].isin(['India', 'Pakistan', 'China'])]
sns.lineplot(data=temp_df, x='year', y='lifeExp', hue='country')
sns.lineplot(data=temp_df, x='year', y='lifeExp', hue='country', style='continent')

Note: lineplot automatically confidence interval (shaded band) bhi bana deta hai agar ek x ke multiple y values hon.

sns.lineplot(data=tips, x='size', y='total_bill', errorbar=None)  # band hatane ke liye

1.3 `relplot()` — Figure-level ⭐⭐¶

sns.relplot(data=tips, x='total_bill', y='tip', kind='scatter')
sns.relplot(data=tips, x='total_bill', y='tip', kind='line')

Facets — `col` aur `row` ⭐¶

# Har 'sex' ka alag plot (side by side)
sns.relplot(data=tips, x='total_bill', y='tip', kind='scatter', col='sex')

# Grid banao
sns.relplot(data=tips, x='total_bill', y='tip', kind='scatter',
            col='day', row='time')

# col_wrap se wrap karo
sns.relplot(data=gap, x='lifeExp', y='gdpPercap', kind='scatter',
            col='year', col_wrap=3)

Ye Seaborn ki superpower hai — ek line mein poora dashboard.

---¶

PART 2 — DISTRIBUTION PLOTS¶

Maksad: Ek ya do numerical columns ka distribution samajhna — data kaisa spread hai, normal hai ya skewed, outliers hain kya.

2.1 `histplot()` ⭐¶

sns.histplot(data=tips, x='total_bill')
sns.histplot(data=tips, x='total_bill', bins=20)
sns.histplot(data=tips, x='total_bill', hue='sex')
sns.histplot(data=tips, x='total_bill', hue='sex', multiple='stack')
sns.histplot(data=tips, x='total_bill', kde=True)     # KDE curve bhi
sns.histplot(data=tips, x='total_bill', element='step')

Categorical column pe bhi kaam karta hai:

sns.histplot(data=titanic, x='survived')

2.2 `kdeplot()` ⭐¶

Kernel Density Estimation — histogram ka smooth version.

sns.kdeplot(data=tips, x='total_bill')
sns.kdeplot(data=tips, x='total_bill', hue='sex')
sns.kdeplot(data=tips, x='total_bill', hue='sex', fill=True)
sns.kdeplot(data=tips, x='total_bill', hue='sex', multiple='stack')

Bivariate KDE (2D contour):

sns.kdeplot(data=tips, x='total_bill', y='tip')
sns.kdeplot(data=tips, x='total_bill', y='tip', fill=True, cmap='Blues')

2.3 `rugplot()`¶

Har observation ke liye chhoti si line — usually kisi aur plot ke upar.

sns.kdeplot(data=tips, x='total_bill')
sns.rugplot(data=tips, x='total_bill')

2.4 `ecdfplot()`¶

Empirical Cumulative Distribution Function.

sns.ecdfplot(data=tips, x='total_bill')

Batata hai: "kitna % data is value se kam hai".

2.5 `displot()` — Figure-level ⭐¶

sns.displot(data=tips, x='total_bill', kind='hist')
sns.displot(data=tips, x='total_bill', kind='kde')
sns.displot(data=tips, x='total_bill', kind='ecdf')

sns.displot(data=tips, x='total_bill', kind='hist', col='sex')
sns.displot(data=tips, x='total_bill', kind='kde', col='day', row='time')

Bivariate histogram (heatmap jaisa)¶

sns.displot(data=tips, x='total_bill', y='tip', kind='hist')

---¶

PART 3 — CATEGORICAL PLOTS ⭐⭐¶

Maksad: Ek categorical aur ek numerical column ka relation dekhna.

Categorical plots ke 3 sub-groups:

Group	Plots	Kya dikhate hain
Scatter type	`stripplot`, `swarmplot`	Har individual point
Distribution type	`boxplot`, `violinplot`, `boxenplot`	Distribution ka shape
Estimate type	`barplot`, `pointplot`, `countplot`	Central tendency (mean/count)

3.1 `stripplot()`¶

sns.stripplot(data=tips, x='day', y='total_bill')
sns.stripplot(data=tips, x='day', y='total_bill', jitter=0.2)
sns.stripplot(data=tips, x='day', y='total_bill', hue='sex')

Points overlap ho jaate hain — isliye jitter use karte hain.

3.2 `swarmplot()`¶

sns.swarmplot(data=tips, x='day', y='total_bill')
sns.swarmplot(data=tips, x='day', y='total_bill', hue='sex')

Points ko aise arrange karta hai ki koi overlap na ho. Bade datasets pe slow ho jaata hai.

3.3 `boxplot()` ⭐⭐¶

Sabse zyada use hone wala EDA plot.

sns.boxplot(data=tips, x='day', y='total_bill')
sns.boxplot(data=tips, x='day', y='total_bill', hue='sex')
sns.boxplot(data=tips, y='total_bill')          # sirf ek variable

Box plot kaise padhein? ⭐¶

     ┌─── Upper whisker (Q3 + 1.5*IQR)
     │
  ┌──┴──┐  ← Q3 (75th percentile)
  │─────│  ← Median (Q2, 50th percentile)
  └──┬──┘  ← Q1 (25th percentile)
     │
     └─── Lower whisker (Q1 - 1.5*IQR)

  ● ● ●    ← Outliers (whiskers ke bahar ke points)

- Box = middle 50% data (IQR = Q3 − Q1) - Line inside box = median - Dots = outliers

3.4 `violinplot()` ⭐¶

Box plot + KDE ka combination. Distribution ka shape bhi dikhta hai.

sns.violinplot(data=tips, x='day', y='total_bill')
sns.violinplot(data=tips, x='day', y='total_bill', hue='sex')
sns.violinplot(data=tips, x='day', y='total_bill', hue='sex', split=True)  # ⭐

split=True bahut useful hai — ek hi violin ke do halves.

3.5 `boxenplot()`¶

Bade datasets ke liye box plot ka enhanced version. Zyada quantiles dikhata hai.

sns.boxenplot(data=tips, x='day', y='total_bill')

3.6 `barplot()` ⭐¶

Har category ka mean (default) dikhata hai + confidence interval.

sns.barplot(data=tips, x='sex', y='total_bill')
sns.barplot(data=tips, x='sex', y='total_bill', hue='smoker')

# Estimator badlo
sns.barplot(data=tips, x='sex', y='total_bill', estimator=np.std)
sns.barplot(data=tips, x='sex', y='total_bill', estimator=np.median)

# Confidence interval hatao
sns.barplot(data=tips, x='sex', y='total_bill', errorbar=None)

⚠️ Yaad rakho: Matplotlib ka plt.bar() raw values plot karta hai; Seaborn ka barplot() aggregate (mean) plot karta hai.

3.7 `pointplot()`¶

Bar plot jaisa hi, lekin points aur lines se. Trend dekhne mein aasan.

sns.pointplot(data=tips, x='sex', y='total_bill')
sns.pointplot(data=tips, x='day', y='total_bill', hue='sex')

3.8 `countplot()` ⭐¶

Har category kitni baar aayi — value_counts() ka visual version.

sns.countplot(data=tips, x='sex')
sns.countplot(data=tips, x='day', hue='sex')
sns.countplot(data=titanic, x='pclass', hue='survived')

3.9 `catplot()` — Figure-level ⭐¶

Saare categorical plots ka figure-level version.

sns.catplot(data=tips, x='day', y='total_bill', kind='strip')
sns.catplot(data=tips, x='day', y='total_bill', kind='swarm')
sns.catplot(data=tips, x='day', y='total_bill', kind='box')
sns.catplot(data=tips, x='day', y='total_bill', kind='violin')
sns.catplot(data=tips, x='day', y='total_bill', kind='boxen')
sns.catplot(data=tips, x='day', y='total_bill', kind='bar')
sns.catplot(data=tips, x='day', y='total_bill', kind='point')
sns.catplot(data=tips, x='day', kind='count')

# Facets
sns.catplot(data=tips, x='sex', y='total_bill', kind='box', col='day')
sns.catplot(data=tips, x='sex', y='total_bill', kind='violin',
            col='day', row='time')

---¶

PART 4 — REGRESSION PLOTS¶

Maksad: Do numerical columns ke beech ka linear relationship dikhana.

4.1 `regplot()` — Axes-level¶

sns.regplot(data=tips, x='total_bill', y='tip')

Scatter + regression line + confidence band.

4.2 `lmplot()` — Figure-level ⭐¶

sns.lmplot(data=tips, x='total_bill', y='tip')
sns.lmplot(data=tips, x='total_bill', y='tip', hue='sex')
sns.lmplot(data=tips, x='total_bill', y='tip', col='sex')
sns.lmplot(data=tips, x='total_bill', y='tip', order=2)   # polynomial fit

4.3 `residplot()`¶

Regression ke residuals dikhata hai. Agar random scatter hai → linear model theek hai.

sns.residplot(data=tips, x='total_bill', y='tip')

---¶

PART 5 — MATRIX PLOTS¶

5.1 `heatmap()` ⭐⭐¶

gap = px.data.gapminder()
temp_df = gap.pivot_table(index='country', columns='year', values='lifeExp')

plt.figure(figsize=(15, 15))
sns.heatmap(temp_df)

Useful parameters¶

sns.heatmap(data, annot=True)              # numbers dikhao ⭐
sns.heatmap(data, annot=True, fmt='.1f')   # format
sns.heatmap(data, cmap='coolwarm')
sns.heatmap(data, linewidth=0.5)
sns.heatmap(data, cbar=False)
sns.heatmap(data, vmin=0, vmax=100)

Sabse common use — Correlation Matrix ⭐⭐¶

plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm', fmt='.2f')
plt.show()

5.2 `clustermap()`¶

Heatmap + hierarchical clustering (similar rows/cols ko saath rakhta hai).

sns.clustermap(iris.iloc[:, [0,1,2,3]])

---¶

PART 6 — MULTIPLOT GRIDS ⭐⭐¶

6.1 `pairplot()` ⭐⭐⭐¶

Sabse zyada use hone wala EDA tool. Saare numerical columns ke pairs ka scatter plot ek saath.

sns.pairplot(iris)
sns.pairplot(iris, hue='species')          # ⭐ super useful
sns.pairplot(iris, hue='species', diag_kind='kde')
sns.pairplot(iris, vars=['sepal_length', 'petal_length'])
sns.pairplot(iris, kind='reg')             # regression lines ke saath
sns.pairplot(iris, corner=True)            # sirf lower triangle

Diagonal pe har column ka distribution (histogram/KDE), baaki cells mein scatter plots.

6.2 `PairGrid()` — customized pairplot¶

g = sns.PairGrid(iris, hue='species')
g.map(sns.scatterplot)
g.add_legend()

# Alag alag diagonal/upper/lower
g = sns.PairGrid(iris, hue='species')
g.map_diag(sns.histplot)
g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.add_legend()

# Specific columns
g = sns.PairGrid(iris, hue='species', vars=['sepal_length', 'petal_length'])
g.map(sns.scatterplot)

6.3 `jointplot()` ⭐¶

Do variables ka relationship + dono ke individual distributions (margins pe).

sns.jointplot(data=tips, x='total_bill', y='tip')
sns.jointplot(data=tips, x='total_bill', y='tip', kind='scatter')
sns.jointplot(data=tips, x='total_bill', y='tip', kind='kde')
sns.jointplot(data=tips, x='total_bill', y='tip', kind='hex')
sns.jointplot(data=tips, x='total_bill', y='tip', kind='hist')
sns.jointplot(data=tips, x='total_bill', y='tip', kind='reg')
sns.jointplot(data=tips, x='total_bill', y='tip', hue='sex')

6.4 `JointGrid()` — customized jointplot¶

g = sns.JointGrid(data=tips, x='total_bill', y='tip')
g.plot(sns.scatterplot, sns.violinplot)

g = sns.JointGrid(data=tips, x='total_bill', y='tip')
g.plot_joint(sns.kdeplot, fill=True)
g.plot_marginals(sns.histplot, kde=True)

6.5 `FacetGrid()`¶

Manual facets banane ke liye.

g = sns.FacetGrid(data=tips, col='day', row='time')
g.map(sns.scatterplot, 'total_bill', 'tip')

g = sns.FacetGrid(data=tips, col='day', hue='sex')
g.map(sns.histplot, 'total_bill')
g.add_legend()

---¶

PART 7 — STYLING & THEMES¶

7.1 Themes¶

sns.set_theme()                        # default seaborn theme
sns.set_style('whitegrid')
sns.set_style('darkgrid')
sns.set_style('white')
sns.set_style('dark')
sns.set_style('ticks')

7.2 Context (element sizes)¶

sns.set_context('paper')       # sabse chhota
sns.set_context('notebook')    # default
sns.set_context('talk')
sns.set_context('poster')      # sabse bada

7.3 Color Palettes ⭐¶

sns.set_palette('Set2')
sns.color_palette('deep')
sns.color_palette('pastel')
sns.color_palette('bright')
sns.color_palette('dark')
sns.color_palette('colorblind')

# Har plot mein directly
sns.boxplot(data=tips, x='day', y='total_bill', palette='Set2')
sns.scatterplot(data=tips, x='total_bill', y='tip', hue='sex', palette='Dark2')

Palette types: - Qualitative (categories ke liye): Set1, Set2, Set3, Paired, Accent, tab10 - Sequential (kam se zyada): Blues, Greens, viridis, rocket, mako - Diverging (do extremes): coolwarm, RdBu, vlag, icefire

7.4 Figure size¶

# Axes-level ke liye
plt.figure(figsize=(12, 6))
sns.boxplot(...)

# Figure-level ke liye
sns.catplot(..., height=5, aspect=1.5)

7.5 Matplotlib ke saath mix karna¶

plt.figure(figsize=(10, 6))
sns.scatterplot(data=tips, x='total_bill', y='tip')
plt.title('Tips vs Total Bill')
plt.xlabel('Bill Amount')
plt.ylabel('Tip Amount')
plt.show()

Kyunki Seaborn Matplotlib pe bana hai, saari plt.* commands kaam karti hain.

---¶

PART 8 — EDA WORKFLOW (Seaborn se real analysis)¶

Ye wo sequence hai jo tum kisi bhi naye dataset pe follow kar sakte ho:

Step 1 — Univariate Analysis (ek column)¶

# Numerical column
sns.histplot(data=df, x='age', kde=True)
sns.boxplot(data=df, y='age')

# Categorical column
sns.countplot(data=df, x='sex')

Step 2 — Bivariate Analysis (do columns)¶

Column 1	Column 2	Plot
Numerical	Numerical	`scatterplot`, `regplot`, `jointplot`, `lineplot`
Numerical	Categorical	`boxplot`, `violinplot`, `barplot`, `stripplot`, `swarmplot`
Categorical	Categorical	`countplot(hue=)`, `heatmap(crosstab)`

# Num vs Num
sns.scatterplot(data=titanic, x='age', y='fare', hue='survived')

# Num vs Cat
sns.boxplot(data=titanic, x='pclass', y='age', hue='survived')

# Cat vs Cat
sns.heatmap(pd.crosstab(titanic['pclass'], titanic['survived']), annot=True, fmt='d')

Step 3 — Multivariate Analysis (3+ columns)¶

sns.pairplot(df, hue='target')
sns.heatmap(df.corr(numeric_only=True), annot=True, cmap='coolwarm')
sns.relplot(data=df, x='a', y='b', hue='c', size='d', col='e')

Titanic Dataset — Complete EDA Example ⭐¶

titanic = sns.load_dataset('titanic')

# 1. Survival breakdown
sns.countplot(data=titanic, x='survived')

# 2. Class ke hisaab se survival
sns.countplot(data=titanic, x='pclass', hue='survived')

# 3. Gender ke hisaab se survival
sns.countplot(data=titanic, x='sex', hue='survived')

# 4. Age distribution
sns.histplot(data=titanic, x='age', kde=True)

# 5. Age vs Survival
sns.boxplot(data=titanic, x='survived', y='age')
sns.violinplot(data=titanic, x='survived', y='age', hue='sex', split=True)

# 6. Fare vs Age vs Survival
sns.scatterplot(data=titanic, x='age', y='fare', hue='survived', size='pclass')

# 7. Class + Gender + Survival (3-way)
sns.catplot(data=titanic, x='sex', y='survived', col='pclass', kind='bar')

# 8. Correlation
sns.heatmap(titanic.corr(numeric_only=True), annot=True, cmap='coolwarm')

# 9. Pairplot
sns.pairplot(titanic[['survived','age','fare','pclass']], hue='survived')

Seaborn — Master Cheatsheet¶

Kya dekhna hai	Plot
Ek numerical ka distribution	`histplot`, `kdeplot`, `displot`, `boxplot`
Ek categorical ki frequency	`countplot`
Num vs Num	`scatterplot`, `regplot`, `jointplot`, `lineplot`
Num vs Cat	`boxplot`, `violinplot`, `barplot`, `stripplot`, `swarmplot`
Cat vs Cat	`countplot(hue=)`, `heatmap(crosstab)`
Time series	`lineplot`, `relplot(kind='line')`
Correlation	`heatmap(df.corr())`
Saare pairs ek saath	`pairplot`
Multiple subplots by category	`relplot`, `displot`, `catplot`, `lmplot` (col/row)
Outliers dhoondhna	`boxplot`

Universal parameters (lagbhag har plot mein kaam karte hain)¶

data=      # DataFrame
x=, y=     # columns
hue=       # color grouping
palette=   # colors
ax=        # matplotlib axes (axes-level ke liye)
col=, row= # facets (figure-level ke liye)
height=, aspect=   # size (figure-level)

Seaborn — Complete Notes¶

CampusX: Plotting using Seaborn (Part 1 + Part 2)¶

0. Setup¶

Built-in datasets (practice ke liye)¶

1. Figure-level vs Axes-level Functions ⭐⭐¶

Seaborn ka structure (ye tree yaad kar lo)¶

---¶

PART 1 — RELATIONAL PLOTS¶

1.1 scatterplot() ⭐¶

hue — teesra (categorical) dimension ⭐¶

style — chautha dimension (marker shape)¶

size — panchwa dimension (point size)¶

1.2 lineplot() ⭐¶

Multiple lines¶

1.3 relplot() — Figure-level ⭐⭐¶

Facets — col aur row ⭐¶

---¶

PART 2 — DISTRIBUTION PLOTS¶

2.1 histplot() ⭐¶

2.2 kdeplot() ⭐¶

2.3 rugplot()¶

2.4 ecdfplot()¶

2.5 displot() — Figure-level ⭐¶

Bivariate histogram (heatmap jaisa)¶

---¶

PART 3 — CATEGORICAL PLOTS ⭐⭐¶

3.1 stripplot()¶

3.2 swarmplot()¶

3.3 boxplot() ⭐⭐¶

Box plot kaise padhein? ⭐¶

3.4 violinplot() ⭐¶

3.5 boxenplot()¶

3.6 barplot() ⭐¶

3.7 pointplot()¶

3.8 countplot() ⭐¶

3.9 catplot() — Figure-level ⭐¶

---¶

PART 4 — REGRESSION PLOTS¶

4.1 regplot() — Axes-level¶

4.2 lmplot() — Figure-level ⭐¶

4.3 residplot()¶

---¶

PART 5 — MATRIX PLOTS¶

5.1 heatmap() ⭐⭐¶

Useful parameters¶

Sabse common use — Correlation Matrix ⭐⭐¶

5.2 clustermap()¶

---¶

PART 6 — MULTIPLOT GRIDS ⭐⭐¶

6.1 pairplot() ⭐⭐⭐¶

6.2 PairGrid() — customized pairplot¶

6.3 jointplot() ⭐¶

6.4 JointGrid() — customized jointplot¶

6.5 FacetGrid()¶

---¶

PART 7 — STYLING & THEMES¶

7.1 Themes¶

7.2 Context (element sizes)¶

7.3 Color Palettes ⭐¶

7.4 Figure size¶

7.5 Matplotlib ke saath mix karna¶

---¶

PART 8 — EDA WORKFLOW (Seaborn se real analysis)¶

Step 1 — Univariate Analysis (ek column)¶

Step 2 — Bivariate Analysis (do columns)¶

Step 3 — Multivariate Analysis (3+ columns)¶

Titanic Dataset — Complete EDA Example ⭐¶

Seaborn — Master Cheatsheet¶

Universal parameters (lagbhag har plot mein kaam karte hain)¶

1.1 `scatterplot()` ⭐¶

`hue` — teesra (categorical) dimension ⭐¶

`style` — chautha dimension (marker shape)¶

`size` — panchwa dimension (point size)¶

1.2 `lineplot()` ⭐¶

1.3 `relplot()` — Figure-level ⭐⭐¶

Facets — `col` aur `row` ⭐¶

2.1 `histplot()` ⭐¶

2.2 `kdeplot()` ⭐¶

2.3 `rugplot()`¶

2.4 `ecdfplot()`¶

2.5 `displot()` — Figure-level ⭐¶

3.1 `stripplot()`¶

3.2 `swarmplot()`¶

3.3 `boxplot()` ⭐⭐¶

3.4 `violinplot()` ⭐¶

3.5 `boxenplot()`¶

3.6 `barplot()` ⭐¶

3.7 `pointplot()`¶

3.8 `countplot()` ⭐¶

3.9 `catplot()` — Figure-level ⭐¶

4.1 `regplot()` — Axes-level¶

4.2 `lmplot()` — Figure-level ⭐¶

4.3 `residplot()`¶

5.1 `heatmap()` ⭐⭐¶

5.2 `clustermap()`¶

6.1 `pairplot()` ⭐⭐⭐¶

6.2 `PairGrid()` — customized pairplot¶

6.3 `jointplot()` ⭐¶

6.4 `JointGrid()` — customized jointplot¶

6.5 `FacetGrid()`¶