semvec/pypi-packages huggingface dataset:
eigpca 0.1.0
Principal Component Analysis via eigen-decomposition of the covariance/correlation matrix
License: MIT
Requires Python: >=3.7,<4.0
Keywords: PCA,eigen-decomposition

# eigpca
PCA via eigen-decomposition of the covariance/correlation matrix.

# Install
```bash
pip install eigpca
```

# Example
```python
from eigpca import PCA
from sklearn.datasets import load_iris

X = load_iris().data
pca = PCA()

pca.fit(X)
pca.transform(X, n_components=2)
```
### Scree Plot
```python
pca.plot(y="pov")
```
![Scree plot](examples/scree_plot_iris.png)
ghostdb 0.9
Python SDK for GhostDB Cache
License: MIT

## Example SDK usage - Python
> The below code covers a scenario for some back-end system where some process must fetch stock data from a database (e.g. MySQL) and process this data so that it is in a format required by the client to display, based on a ticker symbol and fetching and processing this data is expensive. Caching the data provides a way of speeding up this process.

```python
from GhostDB.cache import Cache

cache = Cache("my_ghostdb.conf")

def getStockData(ticker_smbl):
    # Fetch data from cache
    stock_data = cache.get(ticker_smbl)

    if not stock_data:
        # Fetch from MySQL.
        # After any processing, we can 
        # assume our computed value is stored in
        # a variable called stock_data

        # Store result in cache
        cache.put(ticker_smbl, stock_data)
    return stock_data

return getStockData("AMZN")

```