Web25 feb. 2016 · Here's a simple function to create a 2D numpy array from a string: import numpy as np def str2np(strArray): lItems = [] width = None for line in … WebYou can use vectorize of numpy. It is much faster. mylen = np.vectorize(len) print mylen(arr) UPDATE 06/20: Cater for u+0000 character and non-contiguous inputs

BUG: pd.array raising for large_string/large_binary with NumPy array ...

Web2 dagen geleden · Assuming there is a reason you want to use numpy.arange (n).astype ('U'), you can wrap this call in a Series: df ['j'] = 'prefix-' + pandas.Series (numpy.arange (n).astype ('U'), index=df.index) + '-suffix'. If the goal is simply to get the final result, you can reduce your code after n = 5 to a one-line initialization of df: WebWell, if you're reading the data in as a list, just do np.array(map(float, list_of_strings)) (or equivalently, use a list comprehension). (In Python 3, you'll need to call list on the map return value if you use map, since map returns an iterator now.) However, if it's already a numpy array of strings, there's a better way. Use astype(). ifixit dust blower

NumPy - pybind11 documentation - Read the Docs

WebStarting from numpy 1.4, if one needs arrays of strings, it is recommended to use arrays of dtype object_, string_ or unicode_, and use the free functions in the numpy.char … Web21 jul. 2010 · If one needs arrays of strings, use arrays of dtype object_, string_ or unicode_, and use the free functions in the numpy.char module for fast vectorized string operations. Versus a regular Numpy array of type str or unicode, this class adds the following functionality: values automatically have whitespace removed from the end … ifixit discount