# Clustering Panels
`Tutorials` are the best documentation — [`Clustering Panels Tutorial`](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/computational/Clustering%20Notebook.ipynb)
### Introduction
Can be used to cluster any panel dataset. It is particularly useful for financial analysts, data scientists, and researchers working with time-series data across multiple entities (e.g., stocks, companies) and variables.
#### Initialization
The CustomDataFrame can be initialized using the `sov.data()` function:
```python
import sovai as sov
sov.token_auth(token="your_token_here")
df = sov.data("accounting/weekly")
```
Basic Clustering
Perform clustering on all features:
```python
df_cluster = df.cluster()
```
Feature-Specific Clustering
Cluster based on specific features:
```python
df_cluster_ebit = df.cluster(features=["ebit"])
df_cluster_multi = df.cluster(features=["total_assets", "total_debt", "ebit"])
```
#### Summary Clustering
Get a quick summary of the last 6-months data:
```python
df.cluster("summary")
```
### Visualization Methods
#### Line Plot
Visualize cluster centroids and distances:
```python
df.cluster("line_plot")
```
**Scatter Plot**
Create a scatter plot of clustered data:
```python
df.cluster("scatter_plot")
```
#### Animation Plot
Generate an animated plot of cluster evolution:
```python
df.cluster("animation_plot")
```
### Advanced Analysis
#### Distance Calculation
Calculate distances between ticker-cluster combinations:
```python
df_dist = df_cluster.drop(columns=["labels"]).distance(orient="time-series")
```
### Examples
#### Basic Clustering and Visualization
```python
import sovai as sov
sov.token_auth(token="your_token_here")
df_accounting = sov.data("accounting/weekly")
df_mega = df_accounting.select_stocks("mega").date_range("2018-01-01")
df_cluster = df_mega.cluster()
df_mega.cluster("line_plot")
```
#### Feature-Specific Clustering and Distance Analysis
```python
df_cluster_ebit = df_mega.cluster(features=["ebit"])
df_dist = df_cluster_ebit.drop(columns=["labels"]).distance(orient="time-series")
similar_to_amzn = df_dist.sort_values(["AMZN"])[["AMZN"]].T
```
