numpy数组之存取文件的实现示例

import numpy as np

a = np.array(range(20)).reshape((4, 5))
print(a)

# 后缀改为 .txt 一样
filename = 'data/a.csv'
# 写文件
np.savetxt(filename, a, fmt='%d', delimiter=',')

# 读文件
b = np.loadtxt(filename, dtype=np.int32, delimiter=',')
print(b)

import numpy as np

a = np.array(range(20)).reshape((2, 2, 5))
print(a)

filename = 'data/a.npy'
# 写文件
np.save(filename, a)

# 读文件
b = np.load(filename)
print(b)
print(b.shape)

import numpy as np

a = np.array(range(20)).reshape((2, 2, 5))
b = np.array(range(20, 44)).reshape(2, 3 ,4)
print('a:\n', a)
print('b:\n', b)

filename = 'data/a.npz'
# 写文件， 如果不指定key，那么默认key为'arr_0'、'arr_1'，一直排下去。
np.savez(filename, a, b=b)

# 读文件
c = np.load(filename)
print('keys of NpzFile c:\n', c.keys())
print("c['arr_0']:\n", c['arr_0'])
print("c['b']:\n", c['b'])

import numpy as np
import h5py

a = np.array(range(20)).reshape((2, 2, 5))
b = np.array(range(20)).reshape((1, 4, 5))
print(a)
print(b)

filename = 'data/data.h5'
# 写文件
h5f = h5py.File(filename, 'w')
h5f.create_dataset('a', data=a)
h5f.create_dataset('b', data=b)
h5f.close()

# 读文件
h5f = h5py.File(filename, 'r')
print(type(h5f))
# 通过切片得到numpy数组
print(h5f['a'][:])
print(h5f['b'][:])
h5f.close()

import numpy as np
import h5py

a = np.array(range(20)).reshape((2, 2, 5))
print(a)

filename = 'data/a.h5'
# 写文件
h5f = h5py.File(filename, 'w')
# 当数组a太大，需要切片进行操作时，可以不直接对h5f['a']进行初始化；
# 当之后不需要改变h5f['a']的shape时，可以省略maxshape参数
h5f.create_dataset('a', shape=(2, 2, 5), maxshape=(None, 2, 5), dtype=np.int32, compression='gzip')
for i in range(2):
  # 采用切片的形式赋值
  h5f['a'][i] = a[i]
h5f.close()

# 读文件
h5f = h5py.File(filename, 'r')
print(type(h5f))
print(h5f['a'])
# 通过切片得到numpy数组
print(h5f['a'][:])