["python","pandas","numpy","dataframe","nan"]
Elegant way to create empty pandas DataFrame with NaN of type float

I want to create a Pandas DataFrame filled with NaNs. During my research I found an answer: import pandas as pd df = pd.DataFrame(index=range(0,4),columns=['A']) This code results in a DataFrame filled with NaNs of type "object". So they cannot be used later on for example with the interpolate() method. Therefore, I created the DataFrame with this complicated code (inspired by this answer): import pandas as pd import numpy as np dummyarray = np.empty((4,1)) dummyarray[:] = np.nan df = pd.DataFrame(dummyarray) This results in a DataFrame filled with NaN of type "float", so it can be used later on with interpolate(). Is there a more elegant way to create the same result?
Simply pass the desired value as first argument, like 0, math.inf or, here, np.nan. The constructor then initializes and fills the value array to the size specified by arguments index and columns: >>> import numpy as np >>> import pandas as pd >>> df = pd.DataFrame(np.nan, index=[0, 1, 2, 3], columns=['A', 'B']) >>> df A B 0 NaN NaN 1 NaN NaN 2 NaN NaN 3 NaN NaN >>> df.dtypes A float64 B float64 dtype: object