DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version warning
Question:
I appending a new row to an existing pandas dataframe as follows:
df= df.append(pd.Series(), ignore_index=True)
This is resulting in the subject DeprecationWarning.
The existing df has a mix of string, float and dateime.date datatypes (8 columns totals).
Is there a way to explicitly specify the columns types in the df.append?
I have looked here and here but I still have no solution. Please advise if there is a better way to append a row to the end of an existing dataframe without triggering this warning.
Answers:
You can try this
Type_new = pd.Series([],dtype=pd.StringDtype())
This will create a blank data frame for us.
df = df.append(pd.Series(dtype = 'object'), ignore_index=True)
You can add dtype to your code.
pd.Series(dtype='float64')
If the accepted solution still results in :
‘ValueError: No objects to concatenate’
Try this solution from FutureWarning: on `df['col'].apply(p.Series)` :
(lambda x: pd.Series(x, dtype="float"))
I appending a new row to an existing pandas dataframe as follows:
df= df.append(pd.Series(), ignore_index=True)
This is resulting in the subject DeprecationWarning.
The existing df has a mix of string, float and dateime.date datatypes (8 columns totals).
Is there a way to explicitly specify the columns types in the df.append?
I have looked here and here but I still have no solution. Please advise if there is a better way to append a row to the end of an existing dataframe without triggering this warning.
You can try this
Type_new = pd.Series([],dtype=pd.StringDtype())
This will create a blank data frame for us.
df = df.append(pd.Series(dtype = 'object'), ignore_index=True)
You can add dtype to your code.
pd.Series(dtype='float64')
If the accepted solution still results in :
‘ValueError: No objects to concatenate’
Try this solution from FutureWarning: on `df['col'].apply(p.Series)` :
(lambda x: pd.Series(x, dtype="float"))