Question

我很难创建一个无值的DataFrame。为此，我执行了几个步骤，但我相信我可以使用熊猫来获得相同的结果＆＃39;功能......

mydata = []
mydata.append([None, None, None, None])
mydata = np.array(mydata)
mydata = pd.DataFrame(mydata, columns='Start','End','Duration'])

是否有命令可以获得相同的结果？

Answer 1

我认为您需要numpy array创建的reshape list：

mydata = pd.DataFrame(np.array([None, None, None]).reshape(-1,3), 
                      columns=['Start','End','Duration'])   
print (mydata)
  Start   End Duration
0  None  None     None

使用[[]]的另一个更慢的解决方案：

mydata = pd.DataFrame([[None, None, None]], columns=['Start','End','Duration'])   
print (mydata)
  Start   End Duration
0  None  None     None

如果使用columns和index值，则所有数据均为NaN，None可能replace为print (pd.DataFrame(columns=['Start','End','Duration'], index=[0])) Start End Duration 0 NaN NaN NaN mydata = pd.DataFrame(columns=['Start','End','Duration'], index=[0]).replace({np.nan:None}) print (mydata) Start End Duration 0 None None None：

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Answer 2

另一种方法是：

pd.DataFrame({'Start':[None],'End':[None],'Duration':[None]})

Answer 3

这是一个快速的单行：

>>> pd.DataFrame(np.empty((4,3),dtype=pd.Timestamp),columns=['Start','End','Duration'])
  Start   End Duration
0  None  None     None
1  None  None     None
2  None  None     None
3  None  None     None

一般情况下，单行将采用：

>>> pd.DataFrame(np.empty((5,3),dtype=object),columns=['Start','End','Duration'])
  Start   End Duration
0  None  None     None
1  None  None     None
2  None  None     None
3  None  None     None
4  None  None     None

这是NaN单行：

>>> pd.DataFrame(np.empty((2,3))*np.nan,columns=['Start','End','Duration'])   
   Start  End  Duration
0    NaN  NaN       NaN
1    NaN  NaN       NaN

使用None初始化pandas数据帧

3 个答案: