如何使用 numpy 将 nan 添加到数组的末尾

Question

I have a list of multiple arrays and I want them to have the same size, filling the ones with less elements with nan. I have some arrays that have integers and others that have string.

For example:

a = ['Nike']
b = [1,5,10,15,20]
c = ['Adidas']
d = [150, 2]

I have tried

max_len = max(len(a),len(b),len(c),len(d))

empty = np.empty(max_len - len(a))
a = np.asarray(a) + empty

empty = np.empty(max_len - len(b))
b = np.asarray(b) + empty

I do the same with all of the arrays, however an error occurs (TypeError: only integer scalar arrays can be converted to a scalar index)

I am doing this because I want to make a DataFrame with all of the arrays being a different columns.

Thank you in advanced

Answer 1

I'd suggest using lists since you also have strings . Here's one way using zip_longest :

from itertools import zip_longest

a, b, c, d = map(list,(zip(*zip_longest(a,b,c,d, fillvalue=float('nan')))))

---

print(a)
# ['Nike', nan, nan, nan, nan]

print(b)
# [1, 5, 10, 15, 20]

print(c)
# ['Adidas', nan, nan, nan, nan]

print(d)
# [150, 2, nan, nan, nan]

---

Another approach could be:

max_len = len(max([a,b,c,d], key=len))
a, b, c, d = [l+[float('nan')]*(max_len-len(l)) for l in [a,b,c,d]]

Answer 2

How about this?

df1 = pd.DataFrame([a,b,c,d]).T

Answer 3

You should use the numpy.append(array, value, axis) to append to an array. In you example that would be ans = np.append(a,empty) .

Answer 4

You can do that directly just like so:

>>> import pandas as pd

>>> a = ['Nike']
>>> b = [1,5,10,15,20]
>>> c = ['Adidas']
>>> d = [150, 2]

>>> pd.DataFrame([a, b, c, d])
        0    1     2     3     4
0    Nike  NaN   NaN   NaN   NaN
1       1  5.0  10.0  15.0  20.0
2  Adidas  NaN   NaN   NaN   NaN
3     150  2.0   NaN   NaN   NaN