blocks|key|4684560|text|下面是一个易于理解的解决方案|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4684561|import+numpy+as+np
import+pandas+as+pd

#+Creating+a+2+dimensional+numpy+array
>>>+data+=+np.array([[5.8,+2.8],+[6.0,+2.2]])
>>>+print(data)
>>>+data
array([[5.8,+2.8],
+++++++[6.+,+2.2]])

#+Creating+pandas+dataframe+from+numpy+array
>>>+dataset+=+pd.DataFrame({'Column1':+data[:,+0],+'Column2':+data[:,+1]})
>>>+print(dataset)
+++Column1++Column2
0++++++5.8++++++2.8
1++++++6.0++++++2.2|code-block|syntax|javascript|4684562|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|$]]

Here is an easy to understand solution

<pre><code>import numpy as np
import pandas as pd

# Creating a 2 dimensional numpy array
&gt;&gt;&gt; data = np.array([[5.8, 2.8], [6.0, 2.2]])
&gt;&gt;&gt; print(data)
&gt;&gt;&gt; data
array([[5.8, 2.8],
 [6. , 2.2]])

# Creating pandas dataframe from numpy array
&gt;&gt;&gt; dataset = pd.DataFrame({'Column1': data[:, 0], 'Column2': data[:, 1]})
&gt;&gt;&gt; print(dataset)
 Column1 Column2
0 5.8 2.8
1 6.0 2.2
</code></pre>

blocks|key|178464|text|我同意Joris的观点；看起来你应该用不同的方式来做这件事，就像使用numpy+record+arrays一样。在this+great+answer中修改“选项2”，你可以这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|178465|import+pandas
import+numpy

dtype+=+[('Col1','int32'),+('Col2','float32'),+('Col3','float32')]
values+=+numpy.zeros(20,+dtype=dtype)
index+=+['Row'%2Bstr(i)+for+i+in+range(1,+len(values)%2B1)]

df+=+pandas.DataFrame(values,+index=index)|code-block|syntax|javascript|178466|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy-1.10.1/user/basics.rec.html|1|https://stackoverflow.com/a/21647198/943773^0|Y|J|0|1L|H|1|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|E|5|F|7|Z|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|10|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]|Q|$5|L|M|N|C|$O|R]]]]

I agree with Joris; it seems like you should be doing this differently, like with <a href="http://docs.scipy.org/doc/numpy-1.10.1/user/basics.rec.html" rel="noreferrer">numpy record arrays</a>. Modifying "option 2" from <a href="https://stackoverflow.com/a/21647198/943773">this great answer</a>, you could do it like this:

<pre><code>import pandas
import numpy

dtype = [('Col1','int32'), ('Col2','float32'), ('Col3','float32')]
values = numpy.zeros(20, dtype=dtype)
index = ['Row'+str(i) for i in range(1, len(values)+1)]

df = pandas.DataFrame(values, index=index)
</code></pre>

blocks|key|178560|text|只需使用pandas+DataFrame的from_records即可完成此操作|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|178561|import+numpy+as+np
import+pandas+as+pd
#+Creating+a+numpy+array
x+=+np.arange(1,10,1).reshape(-1,1)
dataframe+=+pd.DataFrame.from_records(x)|code-block|syntax|javascript|178562|entityMap^0|L|C|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|$]]

This can be done simply by using <code>from_records</code> of pandas DataFrame
<pre><code>import numpy as np
import pandas as pd
# Creating a numpy array
x = np.arange(1,10,1).reshape(-1,1)
dataframe = pd.DataFrame.from_records(x)
</code></pre>

blocks|key|4684705|text|++++>>import+pandas+as+pd
++++>>import+numpy+as+np
++++>>data.shape
++++(480,193)
++++>>type(data)
++++numpy.ndarray
++++>>df=pd.DataFrame(data=data[0:,0:],
++++...++++++++index=[i+for+i+in+range(data.shape[0])],
++++...++++++++columns=['f'%2Bstr(i)+for+i+in+range(data.shape[1])])
++++>>df.head()
++++[![array+to+dataframe][1]][1]|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4684706|​|unstyled|4684707|📷|atomic|offset|length|4684708|4684709|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/76e7ce3a9197c3772783c2694fd7eb33.png|imageAlt^0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|V|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|W|8|@]|9|@]|A|$]]|$1|G|3|H|5|I|7|X|8|@]|9|@$J|Y|K|Z|1|10]]|A|$]]|$1|L|3|E|5|F|7|11|8|@]|9|@]|A|$]]|$1|M|3|-4|5|F|7|12|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T|U|-4]]]]

<pre><code> &gt;&gt;import pandas as pd
 &gt;&gt;import numpy as np
 &gt;&gt;data.shape
 (480,193)
 &gt;&gt;type(data)
 numpy.ndarray
 &gt;&gt;df=pd.DataFrame(data=data[0:,0:],
 ... index=[i for i in range(data.shape[0])],
 ... columns=['f'+str(i) for i in range(data.shape[1])])
 &gt;&gt;df.head()
 [![array to dataframe][1]][1]
</code></pre>

<a href="https://i.stack.imgur.com/JziUK.png" rel="noreferrer"><img src="https://i.stack.imgur.com/JziUK.png" alt="enter image description here"></a>

blocks|key|178592|text|补充@behzad.nouri的答案-我们可以创建一个助手例程来处理这个常见的场景：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|178593|def+csvDf(dat,**kwargs):+
++from+numpy+import+array
++data+=+array(dat)
++if+data+is+None+or+len(data)==0+or+len(data[0])==0:
++++return+None
++else:
++++return+pd.DataFrame(data[1:,1:],index=data[1:,0],columns=data[0,1:],**kwargs)|code-block|syntax|javascript|178594|让我们试试看：|178595|data+=+[['','a','b','c'],['row1','row1cola','row1colb','row1colc'],
+++++['row2','row2cola','row2colb','row2colc'],['row3','row3cola','row3colb','row3colc']]
csvDf(data)

In+[61]:+csvDf(data)
Out[61]:
+++++++++++++a+++++++++b+++++++++c
row1++row1cola++row1colb++row1colc
row2++row2cola++row2colb++row2colc
row3++row3cola++row3colb++row3colc|178596|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|$]]

Adding to @behzad.nouri 's answer - we can create a helper routine to handle this common scenario:

<pre><code>def csvDf(dat,**kwargs): 
 from numpy import array
 data = array(dat)
 if data is None or len(data)==0 or len(data[0])==0:
 return None
 else:
 return pd.DataFrame(data[1:,1:],index=data[1:,0],columns=data[0,1:],**kwargs)
</code></pre>

Let's try it out:

<pre><code>data = [['','a','b','c'],['row1','row1cola','row1colb','row1colc'],
 ['row2','row2cola','row2colb','row2colc'],['row3','row3cola','row3colb','row3colc']]
csvDf(data)

In [61]: csvDf(data)
Out[61]:
 a b c
row1 row1cola row1colb row1colc
row2 row2cola row2colb row2colc
row3 row3cola row3colb row3colc
</code></pre>

blocks|key|4684806|text|这里是使用numpy数组创建pandas数据帧的简单示例。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4684807|import+numpy+as+np
import+pandas+as+pd

#+create+an+array+
var1++=+np.arange(start=1,+stop=21,+step=1).reshape(-1)
var2+=+np.random.rand(20,1).reshape(-1)
print(var1.shape)
print(var2.shape)

dataset+=+pd.DataFrame()
dataset['col1']+=+var1
dataset['col2']+=+var2
dataset.head()|code-block|syntax|javascript|4684808|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|$]]

Here simple example to create pandas dataframe by using numpy array.
<pre><code>import numpy as np
import pandas as pd

# create an array 
var1 = np.arange(start=1, stop=21, step=1).reshape(-1)
var2 = np.random.rand(20,1).reshape(-1)
print(var1.shape)
print(var2.shape)

dataset = pd.DataFrame()
dataset['col1'] = var1
dataset['col2'] = var2
dataset.head()
</code></pre>

blocks|key|178725|text|我认为这是一种简单直观的方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|178726|data+=+np.array([[0,+0],+[0,+1]+,+[1,+0]+,+[1,+1]])
reward+=+np.array([1,0,1,0])

dataset+=+pd.DataFrame()
dataset['StateAttributes']+=+data.tolist()
dataset['reward']+=+reward.tolist()

dataset|code-block|syntax|javascript|178727|返回：|178728|​|178729|📷|atomic|offset|length|178730|178731|但这里详细说明了性能方面的影响：|178732|How+to+set+the+value+of+a+pandas+column+as+list|178733|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/a191894d04f46794496a695f4aeab316.png|imageAlt|1|LINK|MUTABLE|url|https://stackoverflow.com/questions/52552198/how-to-set-the-value-of-a-pandas-column-as-list/52563718#52563718^0|0|0|0|0|0|1|0|0|0|0|0|1B|1|0^^$0|@$1|2|3|4|5|6|7|18|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|1A|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|1B|8|@]|9|@]|A|$]]|$1|K|3|L|5|M|7|1C|8|@]|9|@$N|1D|O|1E|1|1F]]|A|$]]|$1|P|3|J|5|6|7|1G|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1H|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|1I|8|@]|9|@$N|1J|O|1K|1|1L]]|A|$]]|$1|U|3|-4|5|6|7|1M|8|@]|9|@]|A|$]]]|V|$W|$5|X|Y|Z|A|$10|11|12|-4]]|13|$5|14|Y|15|A|$16|17]]]]

I think this is a simple and intuitive method:
<pre><code>data = np.array([[0, 0], [0, 1] , [1, 0] , [1, 1]])
reward = np.array([1,0,1,0])

dataset = pd.DataFrame()
dataset['StateAttributes'] = data.tolist()
dataset['reward'] = reward.tolist()

dataset
</code></pre>
returns:
<a href="https://i.stack.imgur.com/kAYPW.png" rel="noreferrer"><img src="https://i.stack.imgur.com/kAYPW.png" alt="enter image description here" /></a>
But there are performance implications detailed here:
<a href="https://stackoverflow.com/questions/52552198/how-to-set-the-value-of-a-pandas-column-as-list/52563718#52563718">How to set the value of a pandas column as list</a>

blocks|key|4684772|text|这篇文章不是很短，但也许可以帮助你。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4684773|创建数组|4684774|import+numpy+as+np
import+pandas+as+pd

data+=+np.array([['col1',+'col2'],+[4.8,+2.8],+[7.0,+1.2]])

>>>+data
array([['col1',+'col2'],
+++++++['4.8',+'2.8'],
+++++++['7.0',+'1.2']],+dtype='<U4')|code-block|syntax|javascript|4684775|创建数据框|4684776|df+=+pd.DataFrame(i+for+i+in+data).transpose()
df.drop(0,+axis=1,+inplace=True)
df.columns+=+data[0]
df

>>>+df
++col1+col2
0++4.8++7.0
1++2.8++1.2|4684777|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|Q|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|R|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|S|8|@]|9|@]|A|$G|H]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

It's not so short, but maybe can help you.
Creating Array
<pre><code>import numpy as np
import pandas as pd

data = np.array([['col1', 'col2'], [4.8, 2.8], [7.0, 1.2]])

&gt;&gt;&gt; data
array([['col1', 'col2'],
 ['4.8', '2.8'],
 ['7.0', '1.2']], dtype='&lt;U4')
</code></pre>
Creating data frame
<pre><code>df = pd.DataFrame(i for i in data).transpose()
df.drop(0, axis=1, inplace=True)
df.columns = data[0]
df

&gt;&gt;&gt; df
 col1 col2
0 4.8 7.0
1 2.8 1.2
</code></pre>

I have a Numpy array consisting of a list of lists, representing a two-dimensional array with row labels and column names as shown below:

<pre><code>data = array([['','Col1','Col2'],['Row1',1,2],['Row2',3,4]])
</code></pre>

I'd like the resulting DataFrame to have Row1 and Row2 as index values, and Col1, Col2 as header values

I can specify the index as follows:

<pre><code>df = pd.DataFrame(data,index=data[:,0]),
</code></pre>

however I am unsure how to best assign column headers.

Creating a Pandas DataFrame from a Numpy array: How do I specify the index column and column headers?

我有一个由列表列表组成的Numpy数组，表示具有行标签和列名的二维数组，如下所示：data = array([['','Col1','Col2'],['Row1',1,2],['Row2',3,4]])我希望得到的DataFrame以Row1和Row2作为索引值，以Col1和Col2作为标头值我可以按如下方式指定索引：df = pd.DataFrame(data,index=data[:,0]),

问从Numpy数组创建一个Pandas DataFrame :如何指定索引列和列标题？
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回答 8

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