Elegant way to create empty pandas DataFrame with NaN of type float

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

You could specify the dtype directly when constructing the DataFrame:

>>> df = pd.DataFrame(index=range(0,4),columns=['A'], dtype='float')
>>> df.dtypes
A    float64
dtype: object

Specifying the dtype forces Pandas to try creating the DataFrame with that type, rather than trying to infer it.

Hope this can help!

 pd.DataFrame(np.nan, index = np.arange(<num_rows>), columns = ['A'])

For multiple columns you can do:

df = pd.DataFrame(np.zeros([nrow, ncol])*np.nan)

You can try this line of code:

pdDataFrame = pd.DataFrame([np.nan] * 7)

This will create a pandas dataframe of size 7 with NaN of type float:

if you print pdDataFrame the output will be:

     0
0   NaN
1   NaN
2   NaN
3   NaN
4   NaN
5   NaN
6   NaN

Also the output for pdDataFrame.dtypes is:

0    float64
dtype: object