For quick testing, debugging, creating portable examples, and benchmarking, R has available to it a large number of data sets (in the Base R datasets
package). The command library(help="datasets")
at the R prompt describes nearly 100 historical datasets, each of which have associated descriptions and metadata.
Is there anything like this for Python?
Dataset in Python has a lot of significance and is mostly used for dealing with a huge amount of data. These datasets have a certain resemblance with the packages present as part of Python 3.6 and more. Python datasets consist of dataset object which in turn comprises metadata as part of the dataset.
Retrieving Datasets in scikit-learn and Seaborn Trivially, you may obtain those datasets by downloading them from the web, either through the browser, via command line, using the wget tool, or using network libraries such as requests in Python.
For quick testing, debugging, creating portable examples, and benchmarking, R has available to it a large number of data sets (in the Base R datasets package). The command library(help="datasets") at the R prompt describes nearly 100 historical datasets, each of which have associated descriptions and metadata.
You can use rpy2
package to access all R datasets from Python.
Set up the interface:
>>> from rpy2.robjects import r, pandas2ri >>> def data(name): ... return pandas2ri.ri2py(r[name])
Then call data()
with any dataset's name of the available datasets (just like in R
)
>>> df = data('iris') >>> df.describe() Sepal.Length Sepal.Width Petal.Length Petal.Width count 150.000000 150.000000 150.000000 150.000000 mean 5.843333 3.057333 3.758000 1.199333 std 0.828066 0.435866 1.765298 0.762238 min 4.300000 2.000000 1.000000 0.100000 25% 5.100000 2.800000 1.600000 0.300000 50% 5.800000 3.000000 4.350000 1.300000 75% 6.400000 3.300000 5.100000 1.800000 max 7.900000 4.400000 6.900000 2.500000
To see a list of the available datasets with a description for each:
>>> print(r.data())
Note: rpy2 requires R
installation with setting R_HOME
variable, and pandas
must be installed as well.
I just created PyDataset, which is a simple module to make loading a dataset from Python as easy as R
's (and it does not require R
installation, only pandas
).
To start using it, install the module:
$ pip install pydataset
Then just load up any dataset you wish (currently around 757 datasets available):
from pydataset import data titanic = data('titanic')
There are also datasets available from the Scikit-Learn library.
from sklearn import datasets
There are multiple datasets within this package. Some of the Toy Datasets are:
load_boston() Load and return the boston house-prices dataset (regression). load_iris() Load and return the iris dataset (classification). load_diabetes() Load and return the diabetes dataset (regression). load_digits([n_class]) Load and return the digits dataset (classification). load_linnerud() Load and return the linnerud dataset (multivariate regression).
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