I have a numpy array gven below:
array([[' 0.12E'],
[' 0.00F'],
[' 0.00F'],
...,
[' 0.00G'],
[' 0.00G'],
[' 0.00G']], dtype=object)
i need to remove that E F and G and covert each element in the array to Float. How do i do it??
Option 1
Use numpy string types to automatically trim the last character. Then convert to float
If you can trust that all entries are 6 characters long and end in a character that needs to be trimmed:...
a.astype('<U5').astype(float)
array([[ 0.12],
[ 0. ],
[ 0. ],
[ 0. ],
[ 0. ],
[ 0. ]])
Option 2
Use pd.DataFrame.replace with regex=True to remove non-digits, then convert to float.
pd.DataFrame(a).replace('[^\d\.]', '', regex=True).astype(float).values
array([[ 0.12],
[ 0. ],
[ 0. ],
[ 0. ],
[ 0. ],
[ 0. ]])
Use:
s = pd.Series(a[:, 0])
a = s.str.strip().str[:-1].astype(float).values.reshape(-1, 1)
print (a)
[[ 0.12]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]
[ 0. ]]
