我们大概都知道可以用 np.zeros 生成一列都是0的数组，用 np.ones生成一列都是1的数组，但是如果我想生成一列都是nan的数组呢？找了好久都没看到相应的函数，后来发现了另一个神奇的函数可以实现！
numpy.full(shape, fill_value, dtype=None, order=’C’)
Return a new array of given shape and type, filled with fill_value.
Parameters:
shape : int or sequence of ints
Shape of the new array, e.g., (2, 3) or 2.
fill_value : scalar
Fill value.
dtype : data-type, optional
The desired data-type for the array The default, None, means
np.array(fill_value).dtype.
order : {‘C’, ‘F’}, optional
Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory.
Returns:
out : ndarray
Array of fill_value with the given shape, dtype, and order.
生成5*1的数组，元素全为nan
import numpy as np
In [9]: np.full([1,5], np.nan)
Out[9]: array([[nan, nan, nan, nan, nan]])
np.full可以按照你制定的数据类型来生成数组，除了np.nan, 还可以是 数字，np.inf 等
In [10]: np.full((2, 2), np.inf)
Out[10]:
array([[inf, inf],
[inf, inf]])
In [11]: np.full((2, 2), 10)
Out[11]:
array([[10, 10],
[10, 10]])其他数组初始化赋值函数也可以记忆一下
See also
- zeros_like : Return an array of zeros with shape and type of input.
- ones_like : Return an array of ones with shape and type of input.
- empty_like: Return an empty array with shape and type of input.
- full_like : Fill an array with shape and type of input.
- zeros : Return a new array setting values to zero.
- ones : Return a new array setting values to one.
- empty: Return a new uninitialized array.