blocks|key|4407418|text|您可以手动使用pd.DataFrame构造函数，给出一个数值数组(data)和一个列名列表(columns)。要将所有内容都放在一个DataFrame中，可以使用np.c_[...]将特性和目标连接到一个numpy数组中(请注意[])：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4407419|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']+%2B+['target'])|code-block|syntax|javascript|4407420|entityMap^0|7|C|X|4|1A|7|29|A|36|2|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|L|$]]

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

<pre><code>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'])
</code></pre>

blocks|key|4407443|text|也许有一种更好的方法，但这是我过去做过的，它工作得很好：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407444|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]+%2B+[items[2][0]]+#Gets+the+column+names+and+updates+the+dataframe|code-block|syntax|javascript|4407445|现在，mydata将拥有您需要的所有内容-属性、目标变量和列名|4407446|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

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

<pre><code>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
</code></pre>

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

blocks|key|4407466|text|from+sklearn.datasets+import+load_iris
import+pandas+as+pd

data+=+load_iris()
df+=+pd.DataFrame(data.data,+columns=data.feature_names)
df.head()|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4407467|这篇教程可能会很有趣：http://www.neural.cz/dataset-exploration-boston-house-pricing.html|unstyled|offset|length|4407468|entityMap|0|LINK|mutability|MUTABLE|url|http://www.neural.cz/dataset-exploration-boston-house-pricing.html^0|0|B|1U|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|R|8|@]|9|@$G|S|H|T|1|U]]|A|$]]|$1|I|3|-4|5|F|7|V|8|@]|9|@]|A|$]]]|J|$K|$5|L|M|N|A|$O|P]]]]

<pre><code>from sklearn.datasets import load_iris
import pandas as pd

data = load_iris()
df = pd.DataFrame(data.data, columns=data.feature_names)
df.head()
</code></pre>

This tutorial maybe of interest: <a href="http://www.neural.cz/dataset-exploration-boston-house-pricing.html" rel="noreferrer">http://www.neural.cz/dataset-exploration-boston-house-pricing.html</a>

blocks|key|4407323|text|计算出最佳答案并处理我的评论，下面是一个转换函数|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407324|def+bunch_to_dataframe(bunch):
++fnames+=+bunch.feature_names
++features+=+fnames.tolist()+if+isinstance(fnames,+np.ndarray)+else+fnames
++features+%2B=+['target']
++return+pd.DataFrame(data=+np.c_[bunch['data'],+bunch['target']],
+++++++++++++++++columns=features)|code-block|syntax|javascript|4407325|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

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

<pre><code>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)
</code></pre>

blocks|key|4407476|text|这对我很有效。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407477|dataFrame+=+pd.dataFrame(data+=+np.c_[+[iris['data'],iris['target']+],
columns=iris['feature_names'].tolist()+%2B+['target'])|code-block|syntax|javascript|4407478|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This works for me.

<pre><code>dataFrame = pd.dataFrame(data = np.c_[ [iris['data'],iris['target'] ],
columns=iris['feature_names'].tolist() + ['target'])
</code></pre>

blocks|key|4407370|text|TOMDLt的解决方案对于scikit-learn中的所有数据集都不够通用。例如，它不适用于波士顿住房数据集。我提出了一个更通用的不同解决方案。也不需要使用numpy。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407371|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()|code-block|syntax|javascript|4407372|作为一般函数：|4407373|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())|4407374|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

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.

<pre><code>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()
</code></pre>

As a general function:

<pre><code>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())
</code></pre>

blocks|key|4407388|text|作为另一种选择，我可以更容易地理解：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407389|data+=+load_iris()
df+=+pd.DataFrame(data['data'],+columns=data['feature_names'])
df['target']+=+data['target']
df.head()|code-block|syntax|javascript|4407390|基本上，不是从get+go开始连接，而是使用要素矩阵创建一个数据框，然后只需添加目标列并从数据集中获取目标值|4407391|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

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

<pre><code>data = load_iris()
df = pd.DataFrame(data['data'], columns=data['feature_names'])
df['target'] = data['target']
df.head()
</code></pre>

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

blocks|key|4407550|text|我花了两个小时才弄明白|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407551|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']+%2B+['target'])

df['species']+=+pd.Categorical.from_codes(iris.target,+iris.target_names)|code-block|syntax|javascript|4407552|为我的大熊猫找回物种|4407553|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Took me 2 hours to figure this out

<pre><code>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)
</code></pre>

Get back the species for my pandas

blocks|key|4407583|text|组合特征和目标变量的其他方法可以使用np.column_stack+(details)|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4407584|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%2B['target'])
print(df.head())|code-block|syntax|javascript|4407585|结果：|4407586|+++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|4407587|如果您需要target的字符串标签，则可以通过将target_names转换为dictionary并添加新列来使用replace：|4407588|df['label']+=+df.target.replace(dict(enumerate(data.target_names)))
print(df.head())|4407589|4407590|+++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|4407591|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.column_stack.html^0|I|F|Z|7|0|0|0|0|0|5|6|O|C|13|A|1L|7|0|0|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@$9|14|A|15|B|C]]|D|@$9|16|A|17|1|18]]|E|$]]|$1|F|3|G|5|H|7|19|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1A|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1C|8|@$9|1D|A|1E|B|C]|$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1L|8|@]|D|@]|E|$I|J]]|$1|S|3|L|5|6|7|1M|8|@]|D|@]|E|$]]|$1|T|3|U|5|H|7|1N|8|@]|D|@]|E|$I|J]]|$1|V|3|-4|5|6|7|1O|8|@]|D|@]|E|$]]]|W|$X|$5|Y|Z|10|E|$11|12]]]]

Other way to combine features and target variables can be using <code>np.column_stack</code> (<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.column_stack.html" rel="nofollow noreferrer">details</a>)

<pre><code>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())
</code></pre>

Result:

<pre><code> 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
</code></pre>

If you need the string label for the <code>target</code>, then you can use <code>replace</code> by converting<code>target_names</code> to <code>dictionary</code> and add a new column:

<pre><code>df['label'] = df.target.replace(dict(enumerate(data.target_names)))
print(df.head())
</code></pre>

Result:

<pre><code> 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
</code></pre>

blocks|key|4407468|text|这段代码只是在TomDLT+and+rolyat已经贡献和解释的基础上构建的syntactic+sugar。唯一的区别是load_iris将返回一个元组而不是字典，并且列名称被枚举。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4407469|df+=+pd.DataFrame(np.c_[load_iris(return_X_y=True)])|code-block|syntax|javascript|4407470|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/38105540/5386567|1|https://en.wikipedia.org/wiki/Syntactic_sugar^0|1O|9|7|H|0|12|F|1|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]|$9|10|A|11|1|12]]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]|S|$5|N|O|P|E|$Q|T]]]]

This snippet is only <a href="https://en.wikipedia.org/wiki/Syntactic_sugar" rel="nofollow noreferrer">syntactic sugar</a> built upon what <a href="https://stackoverflow.com/a/38105540/5386567">TomDLT and rolyat</a> have already contributed and explained. The only differences would be that <code>load_iris</code> will return a tuple instead of a dictionary and the columns names are enumerated.

<pre><code>df = pd.DataFrame(np.c_[load_iris(return_X_y=True)])
</code></pre>

blocks|key|4407494|text|无论TomDLT回答了什么，它可能对你们中的一些人不起作用，因为|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407495|data1+=+pd.DataFrame(data=+np.c_[iris['data'],+iris['target']],
+++++++++++++++++columns=+iris['feature_names']+%2B+['target'])|code-block|syntax|javascript|4407496|因为iris‘’feature_name‘会返回一个numpy数组。在numpy+array中，您不能仅通过%2B运算符添加一个数组和一个列表'target‘。因此，您需要先将其转换为列表，然后再添加。|4407497|你可以做到|4407498|data1+=+pd.DataFrame(data=+np.c_[iris['data'],+iris['target']],
+++++++++++++++++columns=+list(iris['feature_names'])+%2B+['target'])|4407499|这将工作得很好，尽管..|4407500|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|T|8|@]|9|@]|A|$]]|$1|K|3|L|5|D|7|U|8|@]|9|@]|A|$E|F]]|$1|M|3|N|5|6|7|V|8|@]|9|@]|A|$]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

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

<pre><code>data1 = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
 columns= iris['feature_names'] + ['target'])
</code></pre>

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 

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

This will work fine tho..

blocks|key|4407509|text|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()|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4407510|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>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()
</code></pre>

blocks|key|4407730|text|最好的方法之一：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407731|data+=+pd.DataFrame(digits.data)|code-block|syntax|javascript|4407732|Digits是sklearn数据帧，我将其转换为pandas+dataframe|4407733|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

One of the best ways:

<pre><code>data = pd.DataFrame(digits.data)
</code></pre>

Digits is the sklearn dataframe and I converted it to a pandas DataFrame

blocks|key|4407554|text|基本上，你需要的是“数据”，并且你已经拥有了它，现在你只需要“目标”(预测)，它也在这个包中。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407555|所以只需要将这两个连接起来，就可以得到完整的数据|4407556|++data_df+=+pd.DataFrame(cancer.data,columns=cancer.feature_names)
++target_df+=+pd.DataFrame(cancer.target,columns=['target'])

++final_df+=+data_df.join(target_df)|code-block|syntax|javascript|4407557|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Basically what you need is the "data", and you have it in the scikit bunch, now you need just the "target" (prediction) which is also in the bunch.

So just need to concat these two to make the data complete 

<pre><code> data_df = pd.DataFrame(cancer.data,columns=cancer.feature_names)
 target_df = pd.DataFrame(cancer.target,columns=['target'])

 final_df = data_df.join(target_df)
</code></pre>

blocks|key|4407784|text|我从你的答案中提取了几个想法，我不知道如何让它变得简短:)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407785|import+pandas+as+pd
from+sklearn.datasets+import+load_iris
iris+=+load_iris()
df+=+pd.DataFrame(iris.data,+columns=iris['feature_names'])
df['target']+=+iris['target']|code-block|syntax|javascript|4407786|这给出了一个Pandas目标，其中feature_names加上DataFrame作为列，RangeIndex(start=0，stop=len(df)，step=1)。我想有一个更短的代码，我可以有‘目标’直接添加。|4407787|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

I took couple of ideas from your answers and I don't know how to make it shorter :)

<pre><code>import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris['feature_names'])
df['target'] = iris['target']
</code></pre>

This gives a Pandas DataFrame with feature_names plus target as columns and RangeIndex(start=0, stop=len(df), step=1).
I would like to have a shorter code where I can have 'target' added directly.

blocks|key|4407626|text|否则使用seaborn+data+sets，它是实际的pandas数据帧：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4407627|import+seaborn
iris+=+seaborn.load_dataset("iris")
type(iris)
#+<class+'pandas.core.frame.DataFrame'>|code-block|syntax|javascript|4407628|与scikit学习数据集进行比较：|4407629|from+sklearn+import+datasets
iris+=+datasets.load_iris()
type(iris)
#+<class+'sklearn.utils.Bunch'>
dir(iris)
#+['DESCR',+'data',+'feature_names',+'filename',+'target',+'target_names']|4407630|entityMap|0|LINK|mutability|MUTABLE|url|https://seaborn.pydata.org/generated/seaborn.load_dataset.html^0|4|H|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|10|8|@]|9|@]|C|$G|H]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

Otherwise use <a href="https://seaborn.pydata.org/generated/seaborn.load_dataset.html" rel="noreferrer">seaborn data sets</a> which are actual pandas data frames:

<pre><code>import seaborn
iris = seaborn.load_dataset("iris")
type(iris)
# &lt;class 'pandas.core.frame.DataFrame'&gt;
</code></pre>

Compare with scikit learn data sets:

<pre><code>from sklearn import datasets
iris = datasets.load_iris()
type(iris)
# &lt;class 'sklearn.utils.Bunch'&gt;
dir(iris)
# ['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']
</code></pre>

blocks|key|4407881|text|从0.23版本开始，您可以使用as_frame参数直接返回DataFrame。例如，加载虹膜数据集：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4407882|from+sklearn.datasets+import+load_iris
iris+=+load_iris(as_frame=True)
df+=+iris.data|code-block|syntax|javascript|4407883|在我使用provisionally+release+notes的理解中，这适用于breast_cancer，糖尿病，数字，虹膜，眼球，葡萄酒和california_houses数据集。|4407884|entityMap|0|LINK|mutability|MUTABLE|url|https://scikit-learn.org/dev/whats_new/v0.23.html^0|F|8|0|0|4|R|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|M|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

As of version 0.23, you can directly return a DataFrame using the <code>as_frame</code> argument. 
For example, loading the iris data set:

<pre><code>from sklearn.datasets import load_iris
iris = load_iris(as_frame=True)
df = iris.data
</code></pre>

In my understanding using the <a href="https://scikit-learn.org/dev/whats_new/v0.23.html" rel="nofollow noreferrer">provisionally release notes</a>, this works for the breast_cancer, diabetes, digits, iris, linnerud, wine and california_houses data sets.

blocks|key|4407928|text|该API比响应建议的要干净一些。在这里，使用as_frame，并确保也包含一个response列。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4407929|import+pandas+as+pd
from+sklearn.datasets+import+load_wine

features,+target+=+load_wine(as_frame=True).data,+load_wine(as_frame=True).target
df+=+features
df['target']+=+target

df.head(2)|code-block|syntax|javascript|4407930|entityMap^0|M|8|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

The API is a little cleaner than the responses suggested. Here, using <code>as_frame</code> and being sure to include a response column as well.

<pre class="lang-py prettyprint-override"><code>import pandas as pd
from sklearn.datasets import load_wine

features, target = load_wine(as_frame=True).data, load_wine(as_frame=True).target
df = features
df['target'] = target

df.head(2)
</code></pre>

blocks|key|4407761|text|这是另一个集成的方法示例，可能对您有所帮助。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407762|from+sklearn.datasets+import+load_iris
iris_X,+iris_y+=+load_iris(return_X_y=True,+as_frame=True)
type(iris_X),+type(iris_y)|code-block|syntax|javascript|4407763|数据iris_X作为pandas+DataFrame导入，目标iris_y作为pandas+Series导入。|4407764|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Here's another integrated method example maybe helpful.
<pre><code>from sklearn.datasets import load_iris
iris_X, iris_y = load_iris(return_X_y=True, as_frame=True)
type(iris_X), type(iris_y)
</code></pre>
The data iris_X are imported as pandas DataFrame and
the target iris_y are imported as pandas Series.

blocks|key|4407811|text|新更新|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4407812|您可以使用参数as_frame=True来获取pandas数据帧。|offset|length|style|CODE|4407813|如果as_frame参数可用(例如，load_iris)|4407814|from+sklearn+import+datasets
X,y+=+datasets.load_iris(return_X_y=True)+#+numpy+arrays

dic_data+=+datasets.load_iris(as_frame=True)
print(dic_data.keys())

df+=+dic_data['frame']+#+pandas+dataframe+data+%2B+target
df_X+=+dic_data['data']+#+pandas+dataframe+data+only
ser_y+=+dic_data['target']+#+pandas+series+target+only
dic_data['target_names']+#+numpy+array|code-block|syntax|javascript|4407815|如果as_frame参数不可用(例如，load_boston)|4407816|from+sklearn+import+datasets

fnames+=+[+i+for+i+in+dir(datasets)+if+'load_'+in+i]
print(fnames)

fname+=+'load_boston'
loader+=+getattr(datasets,fname)()
df+=+pd.DataFrame(loader['data'],columns=+loader['feature_names'])
df['target']+=+loader['target']
df.head(2)|4407817|entityMap^0|0|7|D|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@$D|W|E|X|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|Y|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|Z|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|10|8|@]|9|@]|A|$]]|$1|Q|3|R|5|L|7|11|8|@]|9|@]|A|$M|N]]|$1|S|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|T|$]]

<h1>New Update</h1>
You can use the parameter <code>as_frame=True</code> to get pandas dataframes.
<h2>If as_frame parameter available (eg. load_iris)</h2>
<pre><code>from sklearn import datasets
X,y = datasets.load_iris(return_X_y=True) # numpy arrays

dic_data = datasets.load_iris(as_frame=True)
print(dic_data.keys())

df = dic_data['frame'] # pandas dataframe data + target
df_X = dic_data['data'] # pandas dataframe data only
ser_y = dic_data['target'] # pandas series target only
dic_data['target_names'] # numpy array

</code></pre>
<h2>If as_frame parameter NOT available (eg. load_boston)</h2>
<pre><code>from sklearn import datasets

fnames = [ i for i in dir(datasets) if 'load_' in i]
print(fnames)

fname = 'load_boston'
loader = getattr(datasets,fname)()
df = pd.DataFrame(loader['data'],columns= loader['feature_names'])
df['target'] = loader['target']
df.head(2)
</code></pre>

blocks|key|4407824|text|from+sklearn.datasets+import+load_iris
import+pandas+as+pd

iris_dataset+=+load_iris()

datasets+=+pd.DataFrame(iris_dataset['data'],+columns+=+
+++++++++++iris_dataset['feature_names'])
target_val+=+pd.Series(iris_dataset['target'],+name+=+
++++++++++++'target_values')

species+=+[]
for+val+in+target_val:
++++if+val+==+0:
++++++++species.append('iris-setosa')
++++if+val+==+1:
++++++++species.append('iris-versicolor')
++++if+val+==+2:
++++++++species.append('iris-virginica')
species+=+pd.Series(species)

datasets['target']+=+target_val
datasets['target_name']+=+species
datasets.head()|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4407825|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>from sklearn.datasets import load_iris
import pandas as pd

iris_dataset = load_iris()

datasets = pd.DataFrame(iris_dataset['data'], columns = 
 iris_dataset['feature_names'])
target_val = pd.Series(iris_dataset['target'], name = 
 'target_values')

species = []
for val in target_val:
 if val == 0:
 species.append('iris-setosa')
 if val == 1:
 species.append('iris-versicolor')
 if val == 2:
 species.append('iris-virginica')
species = pd.Series(species)

datasets['target'] = target_val
datasets['target_name'] = species
datasets.head()
</code></pre>

blocks|key|4408048|text|这是一种对我有效的简单方法。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4408049|boston+=+load_boston()
boston_frame+=+pd.DataFrame(data=boston.data,+columns=boston.feature_names)
boston_frame["target"]+=+boston.target|code-block|syntax|javascript|4408050|但这也适用于load_iris。|4408051|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

This is easy method worked for me.
<pre><code>boston = load_boston()
boston_frame = pd.DataFrame(data=boston.data, columns=boston.feature_names)
boston_frame[&quot;target&quot;] = boston.target
</code></pre>
But this can applied to load_iris as well.

blocks|key|4407876|text|您可以使用pd.DataFrame构造函数，给出一个数字数组(数据)和一个列(列)的名称列表。要将所有内容都放在一个DataFrame中，可以使用np.c_...将特性和目标连接到一个numpy数组中。此外，如果在连接之前不将特征名称(iris‘feature+_name’)转换为列表，可能会遇到一些问题：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4407877|import+numpy+as+np
import+pandas+as+pd
from+sklearn.datasets+import+load_iris

iris+=+load_iris()

df+=+pd.DataFrame(data=+np.c_[iris['data'],+iris['target']],
+++++++++++++++++++++columns=+list(iris['feature_names'])+%2B+['target'])|code-block|syntax|javascript|4407878|entityMap|0|LINK|mutability|MUTABLE|url|note+the+square+brackets+and+not+parenthesis^0|26|3|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

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 square brackets and not parenthesis). Also, you can have some trouble if you don't convert the feature names (iris['feature_names']) to a list before concatenation:
<pre><code>import numpy as np
import pandas as pd
from sklearn.datasets import load_iris

iris = load_iris()

df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],
 columns= list(iris['feature_names']) + ['target'])
</code></pre>

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

<pre><code>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?
</code></pre>

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

如何将数据从Scikit-learn Bunch对象转换为Pandas DataFrame？from sklearn.datasets import load_irisimport pandas as pddata = load_iris()print(type(data))data1 = pd. # Is there...

问如何将Scikit-learn数据集转换为Pandas数据集
EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何将Scikit-learn数据集转换为Pandas数据集EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何将Scikit-learn数据集转换为Pandas数据集
EN