origin array is like:
array([nan, nan, 'hello', ..., nan, 'N', 61.0], dtype=object)
How can I remove all string from this array and get a new array with dtype float?
I know I can do this using python list:
[i for i in x if type(i) == float]
but this way will change numpy.ndarray to list, is there a way to do this in numpy?
You can try something like below.
import numpy as np
a = array([np.nan, np.nan, 'hello', ..., np.nan, 'N', 61.0], dtype=object)
a = a[[isinstance(i, float) for i in a]]
I am not seeing a way in pure numpy but if you are fine using pandas to return a numpy array:
import panadas as pd
import numpy as np
arr = np.array([np.nan, np.nan, 'hello', np.nan, 'N', 61.0], dtype=object)
pd.to_numeric(pd.Series(arr), errors='coerce').dropna().values
You can use np.fromiter():
a = np.array([np.nan, np.nan, 'hello', ..., np.nan, 'N', 61.0], dtype=object)
r = np.fromiter((x for x in a if type(x) == float), dtype=float)
print(r)
#[nan nan nan 61.]
To further remove nan values:
r = r[~np.isnan(r)]
#[61.]
np.array.numpymethods are not available for this.