添加到@behzad。Nouri的回答是——我们可以创建一个helper例程来处理这个常见的场景:

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)

让我们试一试:

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

不是很短，但也许能帮到你。

创建数组

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')

创建数据帧

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

添加到@behzad。Nouri的回答是——我们可以创建一个helper例程来处理这个常见的场景:

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)

让我们试一试:

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

这里有一个简单易懂的解决方案

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

我同意Joris的观点;似乎您应该以不同的方式执行此操作，就像使用numpy记录数组一样。修改这个答案中的“选项2”，你可以这样做:

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)

下面是使用numpy数组创建pandas数据框架的简单示例。

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()