CODEWITHSUNDEEP
pythonpandasdataframe
gabboshow
gabboshow

Reputation: 5559

initialize pandas dataframe with None

I am having an hard time to create a DataFrame with None values. To do so I execute several steps, but I believe that I can get the same results using a pandas' function... 

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

Is there a command to get the same results?

Upvotes: 2

Views: 9017

Answers (3)

ntg
ntg

Reputation: 14075

Here is a fast one-liner:

>>> 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

In general, an one-liner would go as:

>>> 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

Here is a NaN one-liner:

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

Upvotes: 2

zuku
zuku

Reputation: 667

Another method would be:

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

Upvotes: 2

jezrael
jezrael

Reputation: 862661

I think you need reshape numpy array created from 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

Another slowier solution with [[]]:

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

If use columns and index values, all data are NaN and is possible replace them to None:

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

Upvotes: 2

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