DataCleaner

DataCleaner(data)

Class to preprocessed object for data cleaning.

Attributes

data: pd.DataFrame of Dataset

Examples

For usage examples, please see https://jaisenbe58r.github.io/MLearner/user_guide/preprocessing/DataCleaner/

Methods

---

categorical_vs_numerical()

None

---

dtypes()

retorno del tipo de datos por columna

---

isNull()

None

---

load_data(filename, sep=';', decimal=',', params)

Loading a dataset from a csv file.

Parameters

filename: str, path object or file-like object Any valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. A local file could be: file://localhost/path/to/table.csv. If you want to pass in a path object, pandas accepts any os.PathLike. By file-like object, we refer to objects with a read() method, such as a file handler (e.g. via builtin open function) or StringIO.

seps: str Delimiter to use. If sep is None, the C engine cannot automatically detect the separator, but the Python parsing engine can, meaning the latter will be used and automatically detect the separator by Python's builtin sniffer tool, csv.Sniffer.

delimiter: str, default None Alias for sep.

Attributes

n: lenght of dataset. start: start iterator. end: end iterator. num: current iterator.

Returns

data: Pandas DataFrame, [n_samples, n_classes] Dataframe from dataset.

Examples

For usage examples, please see: https://jaisenbe58r.github.io/MLearner/user_guide/load/DataLoad/

---

load_dataframe(data)

None

---

missing_values()

Numero de valores vacios en el dataframe.

---

not_type_object()

Deteccion de de categorias con type "object"

---

reset()

None

---

type_object()

Deteccion de de categorias con type "object"

---

view_features()

Mostrar features del dataframe