Datasets

DataPrep provides a collections of datasets. You could easily load them using one line of code and explore the functionalities of dataprep on them.

List Available Datasets

You could list the name of all available datasets by calling get_dataset_names, as shown in below.

from dataprep.datasets import get_dataset_names
get_dataset_names()

['covid19',
 'wine-quality-red',
 'iris',
 'waste_hauler',
 'countries',
 'patient_info',
 'house_prices_train',
 'adult',
 'house_prices_test',
 'titanic']

Load Dataset

After you know the available dataset names from get_dataset_names. Next you could load the dataset by calling load_dataset.

from dataprep.datasets import load_dataset
df = load_dataset("titanic")
df

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

Analyze Dataset

After you get the dataset, you could try to use dataprep to explore the dataset. For example, you may want to create a profiling report of the dataset using dataprep.eda.

from dataprep.eda import create_report
report = create_report(df)
report