NaN-4.py

In [25]:
df = pd.DataFrame(data=[[1,         2,   np.nan,   3],
                        [np.NaN,  None,  np.NAN,  np.nan], # Notice 3 different NaNs here
                        [4,         5,    'NA',  '#$%'],
                        [6,         7,   np.nan,   8],
                        ["missing", 9,   np.nan,   10],
                        [11,       12,   np.nan,  "not available"]], 
                  index='i0,i1,i2,i3,i4,i5'.split(','), 
                  columns='c0,c1,c2,c3'.split(','))

df # Pandas has recognised "by default" None and all types of NaNs;
   # but not the strings --> ["missing”, “not available”, “NA”]

Out[25]:

	c0 	    c1 	        c2 	c3
i0 	1 	    2.0 	NaN 	3
i1 	NaN 	    NaN 	NaN 	NaN
i2 	4 	    5.0 	NA 	#$%
i3 	6 	    7.0 	NaN 	8
i4 	missing     9.0 	NaN 	10
i5 	11 	   12.0 	NaN 	not available