Seaborn-High Level Graphical Representation
Published: 2023-08-15 09:54:14

Data visualization library based on Matplotlib. Provides High Level Graphical Interface.

```
import seaborn as sns
import matplotlib.pyplot as plt
```
# loading dataset,# draw lineplot,# setting the x limit of the plot# # changing the theme to dark

```
data = sns.load_dataset("iris")
sns.lineplot(x="sepal_length", y="sepal_width", data=data)
plt.xlim(5)
sns.set_style("dark")
plt.show()
```
figure() method.
```
data = sns.load_dataset("iris")
plt.figure(figsize = (4, 4))
sns.lineplot(x="sepal_length", y="sepal_width", data=data)
sns.despine()
plt.show()
 
```

## set_context() method
# Syntax:
# set_context(context=None, font_scale=1, rc=None)
```
data = sns.load_dataset("iris")
sns.lineplot(x="sepal_length", y="sepal_width", data=data)
sns.set_context("poster")
plt.show()
```
*Subplot Already Discued in Matplotlib


# Relational Plots-->Relational plots are used for visualizing the statistical relationship between the data points
# Syntax:
# seaborn.relplot(x=None, y=None, data=None, **kwargs)
```
data = sns.load_dataset("iris")
sns.relplot(x='sepal_width', y='species', data=data)

plt.show()
```
#Scatter Plot
# Syntax:

# seaborn.scatterplot(x=None, y=None, data=None, **kwargs)
```
data = sns.load_dataset("iris")
sns.scatterplot(x='sepal_length', y='sepal_width', data=data)
plt.show()
```
# Line Plot
# Syntax:
# seaborn.lineplot(x=None, y=None, data=None, **kwargs)
```
data = sns.load_dataset("iris")
sns.lineplot(x='sepal_length', y='species', data=data)
plt.show()
```
# Bar Plot
# Syntax:
# barplot([x, y, hue, data, order, hue_order, …])
```
data = sns.load_dataset("iris")
sns.barplot(x='species', y='sepal_length', data=data)
plt.show()
```
# Count Plot
# Syntax:
# countplot([x, y, hue, data, order, …])
```
data = sns.load_dataset("iris")

sns.countplot(x='species', data=data)
plt.show()
```
# Box Plot
# Syntax:
# # boxplot([x, y, hue, data, order, hue_order, …])

```
data = sns.load_dataset("iris")

sns.boxplot(x='species', y='sepal_width', data=data)
plt.show()
```
# Violinplot
# Syntax:
# violinplot([x, y, hue, data, order, …]

```
data = sns.load_dataset("iris")

sns.violinplot(x='species', y='sepal_width', data=data)
plt.show()
```
# Stripplot
# Syntax:
# stripplot([x, y, hue, data, order, …])

```
data = sns.load_dataset("iris")

sns.stripplot(x='species', y='sepal_width', data=data)
plt.show()
```
# Swarmplot
# Syntax:
# swarmplot([x, y, hue, data, order, …])


```
data = sns.load_dataset("iris")

sns.swarmplot(x='species', y='sepal_width', data=data)
plt.show()
```
# Histogram
# Syntax:
# histplot(data=None, *, x=None, y=None, hue=None,  **kwargs)

```
data = sns.load_dataset("iris")

sns.histplot(x='species', y='sepal_width', data=data)
plt.show()
```
# Distplot
# Syntax:
# distplot(a[, bins, hist, kde, rug, fit, …])

```
data = sns.load_dataset("iris")

sns.distplot(data['sepal_width'])
plt.show()

```
# Jointplot
# Syntax:
# jointplot(x, y[, data, kind, stat_func, …])

```
data = sns.load_dataset("iris")
sns.jointplot(x='species', y='sepal_width', data=data)
plt.show()
```
# Pairplot
# Syntax:
# pairplot(data[, hue, hue_order, palette, …])

```
data = sns.load_dataset("iris")
sns.pairplot(data=data, hue='species')
plt.show()
```
# Rugplot
# Syntax:
# rugplot(a[, height, axis, ax])

```
data = sns.load_dataset("iris")
sns.rugplot(data=data)
plt.show()
```
# KDE Plot
# Syntax:
# seaborn.kdeplot(x=None, *, y=None, vertical=False, palette=None, **kwargs)

```
data = sns.load_dataset("iris")
sns.kdeplot(x='sepal_length', y='sepal_width', data=data)
plt.show()
```
# Regression Plots
# Syntax:
# seaborn.lmplot(x, y, data, hue=None, col=None, row=None, **kwargs)

```
data = sns.load_dataset("tips")
sns.lmplot(x='total_bill', y='tip', data=data)
plt.show()
```
# Regplot
# Syntax:
# seaborn.regplot( x,  y,  data=None, x_estimator=None, **kwargs)

```
data = sns.load_dataset("tips")
sns.regplot(x='total_bill', y='tip', data=data)
plt.show()
```
# Heatmap
# Syntax:
# seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, **kwargs)

```
data = sns.load_dataset("tips")
# correlation between the different parameters
tc = data.corr()
sns.heatmap(tc)
plt.show()

```