load dataset X = pd.DataFrame(data.data, columns=data.features)
from sklearn.datasets import load_iris
import pandas as pd
data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
df.head()load dataset X = pd.DataFrame(data.data, columns=data.features)
from sklearn.datasets import load_iris
import pandas as pd
data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
df.head()Create a dataset from pandas dataframe
# azureml-core of version 1.0.72 or higher is required
# azureml-dataprep[pandas] of version 1.1.34 or higher is required
from azureml.core import Workspace, Dataset
local_path = 'data/prepared.csv'
dataframe.to_csv(local_path)
# upload the local file to a datastore on the cloud
subscription_id = 'xxxxxxxxxxxxxxxxxxxxx'
resource_group = 'xxxxxx'
workspace_name = 'xxxxxxxxxxxxxxxx'
workspace = Workspace(subscription_id, resource_group, workspace_name)
# get the datastore to upload prepared data
datastore = workspace.get_default_datastore()
# upload the local file from src_dir to the target_path in datastore
datastore.upload(src_dir='data', target_path='data')
# create a dataset referencing the cloud location
dataset = Dataset.Tabular.from_delimited_files(path = [(datastore, ('data/prepared.csv'))])Copyright © 2021 Codeinu
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