There are in-built datasets provided in both statsmodels and sklearn packages.
In statsmodels, many R datasets can be obtained from the function sm.datasets.get_rdataset().
To view each dataset's description, use print(duncan_prestige.__doc__).
https://www.statsmodels.org/devel/datasets/index.html
import statsmodels.api as sm
prestige = sm.datasets.get_rdataset("Duncan", "car", cache=True).data
print prestige.head()
type income education prestige
accountant prof 62 86 82
pilot prof 72 76 83
architect prof 75 92 90
author prof 55 90 76
chemist prof 64 86 90There are five common toy datasets here. For others, view http://scikit-learn.org/stable/datasets/index.html.
To view each dataset's description, use print boston['DESCR'].
| load_boston([return_X_y]) | Load and return the boston house-prices dataset (regression). |
| load_iris([return_X_y]) | Load and return the iris dataset (classification). |
| load_diabetes([return_X_y]) | Load and return the diabetes dataset (regression). |
| load_digits([n_class, return_X_y]) | Load and return the digits dataset (classification). |
| load_linnerud([return_X_y]) | Load and return the linnerud dataset (multivariate regression). |
from sklearn.datasets import load_iris
# Load Iris data (https://en.wikipedia.org/wiki/Iris_flower_data_set)
iris = load_iris()
# Load iris into a dataframe and set the field names
df = pd.DataFrame(iris['data'], columns=iris['feature_names'])
df.head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
# Feature names are in .target & .target_names
>>> print iris.target_names[:5]
>>> ['setosa' 'versicolor' 'virginica']
>>> print iris.target
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
# Change target to target_names & merge with main dataframe
df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)
print df['species'].head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
0 setosa
1 setosa
2 setosa
3 setosa
4 setosa
Name: species, dtype: category
Categories (3, object): [setosa, versicolor, virginica]Not in-built but can be install via pip install vega_datasets. More at https://github.com/jakevdp/vega_datasets.
from vega_datasets import data
df = data.iris()
df.head()
petalLength petalWidth sepalLength sepalWidth species
0 1.4 0.2 5.1 3.5 setosa
1 1.4 0.2 4.9 3.0 setosa
2 1.3 0.2 4.7 3.2 setosa
3 1.5 0.2 4.6 3.1 setosa
4 1.4 0.2 5.0 3.6 setosaTo list all datasets, use list_datasets()
>>> data.list_datasets()
['7zip', 'airports', 'anscombe', 'barley', 'birdstrikes', 'budget', \
'budgets', 'burtin', 'cars', 'climate', 'co2-concentration', 'countries', \
'crimea', 'disasters', 'driving', 'earthquakes', 'ffox', 'flare', \
'flare-dependencies', 'flights-10k', 'flights-200k', 'flights-20k', \
'flights-2k', 'flights-3m', 'flights-5k', 'flights-airport', 'gapminder', \
'gapminder-health-income', 'gimp', 'github', 'graticule', 'income', 'iris', \
'jobs', 'londonBoroughs', 'londonCentroids', 'londonTubeLines', 'lookup_groups', \
'lookup_people', 'miserables', 'monarchs', 'movies', 'normal-2d', 'obesity', \
'points', 'population', 'population_engineers_hurricanes', 'seattle-temps', \
'seattle-weather', 'sf-temps', 'sp500', 'stocks', 'udistrict', 'unemployment', \
'unemployment-across-industries', 'us-10m', 'us-employment', 'us-state-capitals', \
'weather', 'weball26', 'wheat', 'world-110m', 'zipcodes']Kaggle is the most recognised online data science competition, with attractive rewards and recognition for being the top competitor. With a point system that encourages sharing, one can learnt from the top practitioners in the world.
There are 4 types of expertise medals for specific work, namely Competition, Dataset, Notebook, and Discussion medals. For expertise, it is possible to obtain bronze, silver and gold medals.
Performance Tier is an overall recognition for each of the expertise stated above, base on the number of medals accumulated. The various rankings are Novice, Contributor, Expert, Master, and Grandmaster.
More at https://www.kaggle.com/progression
Kaggle's notebook has a dedicated GPU and decent RAM for deep-learning neural networks.
For installation of new packages, check "internet" under "Settings" in the right panel first,
then in the notebook cell, !pip install package.
To read dataset, you can see the file path at the right panel for "Data".
It goes something like /kaggle/input/competition_folder_name.
To download/export the prediction for submission, we can save the prediction like
df_submission.to_csv(r'/kaggle/working/submisson.csv', index=False).
To do a direct submission, we can commit the notebook, with the output saving directly as
submission.csv, e.g., df_submission.to_csv(r'submisson.csv', index=False).