I have a numpy array:
x = np.array([[1.5,1.3],[2.5,1.5]])
What is the most pythonic way to convert this into a list of strings like this:
y = ['1.5 1.3','2.5 1.5']
What I do now is not very effective:
y = []
for i in range(x.shape[0]):
txt = ''
for j in range(x.shape[1]):
txt+=' '%.5e'%x[i,j]
y.append(txt)
Have tried with:
x = x.astype('|S10').tolist()
y = [' '.join(i) for i in x]
but if I have a lot of decimals in x, it truncates it after the 10th decimal.
You cam simply use list comprehension:
>>> x = np.array([[1.5,1.3],[2.5,1.5]])
>>> y = [' '.join('{:.5e}'.format(col) for col in line) for line in x]
>>> y
['1.50000e+00 1.30000e+00', '2.50000e+00 1.50000e+00']
You can change '{:.5}' to whatever format you want.
map (f, l) is simply syntaxic for (f(x) for x in l) in python 3.x.map.map returns a list, in python 3.x map returns a generator. Since I am sending the result to join, using a generator is better here.Here's one approach that starts off by converting the input numeric array to a string array with .astype('S4') conversion and then does one level of loop comprehension to join all strings in each row with " ".join(item), where item is each row from the string array, like so -
[" ".join(item) for item in x.astype('S4')]
If you are looking for precision control, you can change the dtype to S6, S12 or S14.
Sample run -
In [9]: x
Out[9]:
array([[ 0.62047293, 0.02957529, 0.88920602, 0.57581068],
[ 0.40903378, 0.80748886, 0.83018903, 0.22445871],
[ 0.87296866, 0.94234112, 0.70001789, 0.99333763],
[ 0.96689113, 0.35128491, 0.35775966, 0.26734985]])
In [10]: [" ".join(item) for item in x.astype('S4')]
Out[10]:
['0.62 0.02 0.88 0.57',
'0.40 0.80 0.83 0.22',
'0.87 0.94 0.70 0.99',
'0.96 0.35 0.35 0.26']
In [11]: [" ".join(item) for item in x.astype('S14')]
Out[11]:
['0.620472934011 0.029575285327 0.889206021736 0.575810682998',
'0.409033783485 0.807488858152 0.830189034897 0.224458707937',
'0.872968659668 0.942341118836 0.700017893576 0.993337626766',
'0.966891127767 0.351284905075 0.357759658063 0.267349854182']
