The **`load_wine` method** from the `datasets` module is used to load the wine dataset for machine learning classification problems. It is a classic and multi-class dataset.
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## Dataset overview
This dataset contains 13 different parameters for wine with 178 samples. The purpose of this wine dataset in scikit-learn is to predict the best wine class among 3 classes.

load_wine()

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> It is a new method in sklearn version $0.18$.
## Syntax
---
```python
sklearn.datasets.load_wine(*, return_X_y= False, as_frame= False)
```
---

## Parameters
* **`return_X_y`**: type=bool, default=`False`


* **`as_frame`**: type=bool, default=`False`
## Return value
* **`data`**: This is a dictionary-like object and contains the following attributes:
  * **`data`**: It will be either `ndarray` or `dataframe` of shape (178, 13). If `as_frame` is set to `True`, then the data matrix will be a pandas DataFrame. Otherwise, it will be an `ndarray`.
  * **`target`**: It will be either `ndarray` or `Series` of shape (178,). If `as_frame` is set to `True`, then target will be a pandas Series.
  * **`feature_names`**: It will be a list of the names of dataset columns as features. Otherwise, it will be an `ndarray`.
  * **`target_names`**: It will be a list of the names of target classes.
  * **`frame`**: It will be a DataFrame of shape (178, 14). If `as_frame` is set to `True`, then this field will be available.
  * **`DESCR`**: It will be a string that contains information about the dataset.
* **`(data, target)`**: It will be a tuple if `return_X_y` is set to `True`.

## Explanation
The code snippet below shows how the wine dataset looks.

# Program to load Wine Dataset
# Load useful libraries
import pandas as pd
from sklearn.datasets import load_wine
# Loading dataset
data = load_wine()
# Configuring pandas to show all features
pd.set_option("display.max_rows", None, "display.max_columns", None)
# Converting data to a dataframe to view properly
data = pd.DataFrame(data=data['data'],columns=data['feature_names'])
# Printing first 5 observations
print(data.head())


## Explanation
* **Line 6:** We load the wine dataset in a variable named `data`.
* **Line 8:** We set the length and width of the pandas data frame to its maximum so as to provide a better view to the reader.
* **Line 10:** We convert unordered data of the wine dataset to a data frame for better understanding and view.
* **Line 12:** We print the first five observations (which include 13 different features) of the wine dataset by using the `head()` method.

What is sklearn.datasets.load_wine in scikit-learn?

`load_wine` in scikit-learn loads a wine dataset with 13 features and 178 samples for classification tasks.

Name	Facts
*Classes*	3
*Features total*	13
*Total samples*	178
No. of samples per class	[59,71,48]
*Features type*	Positive and real