How to Convert a Scikit-Learn Dataset to a Pandas Dataset

How to convert a Scikit-learn dataset to a Pandas dataset

Manually, you can use pd.DataFrame constructor, giving a numpy array (data) and a list of the names of the columns (columns).
To have everything in one DataFrame, you can concatenate the features and the target into one numpy array with np.c_[...] (note the []):

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris

# save load_iris() sklearn dataset to iris
# if you'd like to check dataset type use: type(load_iris())
# if you'd like to view list of attributes use: dir(load_iris())
iris = load_iris()

# np.c_ is the numpy concatenate function
# which is used to concat iris['data'] and iris['target'] arrays 
# for pandas column argument: concat iris['feature_names'] list
# and string list (in this case one string); you can make this anything you'd like..  
# the original dataset would probably call this ['Species']
data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                     columns= iris['feature_names'] + ['target'])

Convert sklearn diabetes dataset into pandas DataFrame

From the sklearn website. You can pass as_frame to specify a pandas dataframe.
https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html

data = load_diabetes(as_frame=True)

how to convert generated data into pandas dataframe

The first entry of the tuple contains the feature data and the the second entry contains the class labels. So if you want to make a pd.dataframe of the feature data you should use pd.DataFrame(df[0], columns=["1","2","3","4","5","6","7","8","9"]).

How do I create test and train samples from one dataframe with pandas?

I would just use numpy's randn:

In [11]: df = pd.DataFrame(np.random.randn(100, 2))

In [12]: msk = np.random.rand(len(df)) < 0.8

In [13]: train = df[msk]

In [14]: test = df[~msk]

And just to see this has worked:

In [15]: len(test)
Out[15]: 21

In [16]: len(train)
Out[16]: 79

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