How to convert a Scikit-learn dataset to a Pandas

问题:

How do I convert data from a Scikit-learn Bunch object to a Pandas DataFrame?

from sklearn.datasets import load_iris
import pandas as pd
data = load_iris()
print(type(data))
data1 = pd. # Is there a Pandas method to accomplish this?

回答1:

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'])

回答2:

from sklearn.datasets import load_iris
import pandas as pd

data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
df.head()

This tutorial maybe of interest: http://www.neural.cz/dataset-exploration-boston-house-pricing.html

回答3:

TOMDLt's solution is not generic enough for all the datasets in scikit-learn. For example it does not work for the boston housing dataset. I propose a different solution which is more universal. No need to use numpy as well.

from sklearn import datasets
import pandas as pd

boston_data = datasets.load_boston()
df_boston = pd.DataFrame(boston_data.data,columns=boston_data.feature_names)
df_boston['target'] = pd.Series(boston_data.target)
df_boston.head()

As a general function:

def sklearn_to_df(sklearn_dataset):
    df = pd.DataFrame(sklearn_dataset.data, columns=sklearn_dataset.feature_names)
    df['target'] = pd.Series(sklearn_dataset.target)
    return df

df_boston = sklearn_to_df(datasets.load_boston())

回答4:

Just as an alternative that I could wrap my head around much easier:

data = load_iris()
df = pd.DataFrame(data['data'], columns=data['feature_names'])
df['target'] = data['target']
df.head()

Basically instead of concatenating from the get go, just make a data frame with the matrix of features and then just add the target column with data['whatvername'] and grab the target values from the dataset

回答5:

This works for me.

dataFrame = pd.dataFrame(data = np.c_[ [iris['data'],iris['target'] ],
columns=iris['feature_names'].tolist() + ['target'])

回答6:

Took me 2 hours to figure this out

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

iris = load_iris()
##iris.keys()


df= pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                 columns= iris['feature_names'] + ['target'])

df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)

Get back the species for my pandas

回答7:

Other way to combine features and target variables can be using np.column_stack (details)

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

data = load_iris()
df = pd.DataFrame(np.column_stack((data.data, data.target)), columns = data.feature_names+['target'])
print(df.head())

Result:

   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)     target
0                5.1               3.5                1.4               0.2     0.0
1                4.9               3.0                1.4               0.2     0.0 
2                4.7               3.2                1.3               0.2     0.0 
3                4.6               3.1                1.5               0.2     0.0
4                5.0               3.6                1.4               0.2     0.0

If you need the string label for the target, then you can use replace by convertingtarget_names to dictionary and add a new column:

df['label'] = df.target.replace(dict(enumerate(data.target_names)))
print(df.head())

Result:

   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)     target  label 
0                5.1               3.5                1.4               0.2     0.0     setosa
1                4.9               3.0                1.4               0.2     0.0     setosa
2                4.7               3.2                1.3               0.2     0.0     setosa
3                4.6               3.1                1.5               0.2     0.0     setosa
4                5.0               3.6                1.4               0.2     0.0     setosa

回答8:

Working off the best answer and addressing my comment, here is a function for the conversion

def bunch_to_dataframe(bunch):
  fnames = bunch.feature_names
  features = fnames.tolist() if isinstance(fnames, np.ndarray) else fnames
  features += ['target']
  return pd.DataFrame(data= np.c_[bunch['data'], bunch['target']],
                 columns=features)

回答9:

There might be a better way but here is what I have done in the past and it works quite well:

items = data.items()                          #Gets all the data from this Bunch - a huge list
mydata = pd.DataFrame(items[1][1])            #Gets the Attributes
mydata[len(mydata.columns)] = items[2][1]     #Adds a column for the Target Variable
mydata.columns = items[-1][1] + [items[2][0]] #Gets the column names and updates the dataframe

Now mydata will have everything you need - attributes, target variable and columnnames

回答10:

This snippet is only syntactic sugar built upon what TomDLT and rolyat have already contributed and explained. The only differences would be that load_iris will return a tuple instead of a dictionary and the columns names are enumerated.

df = pd.DataFrame(np.c_[load_iris(return_X_y=True)])

回答11:

Whatever TomDLT answered it may not work for some of you because

data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                 columns= iris['feature_names'] + ['target'])

because iris['feature_names'] returns you a numpy array. In numpy array you can't add an array and a list ['target'] by just + operator. Hence you need to convert it into a list first and then add.

You can do

data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
                 columns= list(iris['feature_names']) + ['target'])

This will work fine tho..

回答12:

import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
X = iris['data']
y = iris['target']
iris_df = pd.DataFrame(X, columns = iris['feature_names'])
iris_df.head()