Convert a numpy.ndarray to string(or bytes) and convert it back to numpy.ndarray

Imagine you have a numpy array of integers (it works with other types but you need some slight modification). You can do this:

a = np.array([0, 3, 5])
a_str = ','.join(str(x) for x in a) # '0,3,5'
a2 = np.array([int(x) for x in a_str.split(',')]) # np.array([0, 3, 5])

If you have an array of float, be sure to replace int by float in the last line.

You can also use the __repr__() method, which will have the advantage to work for multi-dimensional arrays:

from numpy import array
numpy.set_printoptions(threshold=numpy.nan)
a = array([[0,3,5],[2,3,4]])
a_str = a.__repr__() # 'array([[0, 3, 5],n       [2, 3, 4]])'
a2 = eval(a_str) # array([[0, 3, 5],
                 #        [2, 3, 4]])

If you use tostring you lose information on both shape and data type:

>>> import numpy as np
>>> a = np.arange(12).reshape(3, 4)
>>> a
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>> s = a.tostring()
>>> aa = np.fromstring(a)
>>> aa
array([  0.00000000e+000,   4.94065646e-324,   9.88131292e-324,
         1.48219694e-323,   1.97626258e-323,   2.47032823e-323,
         2.96439388e-323,   3.45845952e-323,   3.95252517e-323,
         4.44659081e-323,   4.94065646e-323,   5.43472210e-323])
>>> aa = np.fromstring(a, dtype=int)
>>> aa
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
>>> aa = np.fromstring(a, dtype=int).reshape(3, 4)
>>> aa
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

This means you have to send the metadata along with the data to the recipient. To exchange auto-consistent objects, try cPickle:

>>> import cPickle
>>> s = cPickle.dumps(a)
>>> cPickle.loads(s)
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

Imagine you have a numpy array of text like in a messenger

 >>> stex[40]
 array(['Know the famous thing ...

and you want to get statistics from the corpus (text col=11) you first must get the values from dataframe (df5) and then join all records together in one single corpus:

 >>> stex = (df5.ix[0:,[11]]).values
 >>> a_str = ','.join(str(x) for x in stex)
 >>> a_str = a_str.split()
 >>> fd2 = nltk.FreqDist(a_str)
 >>> fd2.most_common(50)

This is a slightly improvised answer to ajsp answer using XML-RPC.

On the server-side when you convert the data, convert the numpy data to a string using the
‘.tostring()’ method. This encodes the numpy ndarray as bytes string. On the client-side when you receive the data decode it using ‘.fromstring()’ method. I wrote two simple functions for this. Hope this is helpful.

1. ndarray2str — Converts numpy ndarray to bytes string.
2. str2ndarray — Converts binary str back to numpy ndarray.

    def ndarray2str(a):
        # Convert the numpy array to string 
        a = a.tostring()

        return a

On the receiver side, the data is received as a ‘xmlrpc.client.Binary’ object. You need to access the data using ‘.data‘.

    def str2ndarray(a):
        # Specify your data type, mine is numpy float64 type, so I am specifying it as np.float64
        a = np.fromstring(a.data, dtype=np.float64)
        a = np.reshape(a, new_shape)

        return a

Note: Only problem with this approach is that XML-RPC is very slow while sending large numpy arrays. It took me around 4 secs to send and receive a (10, 500, 500, 3) size numpy array for me.

I am using python 3.7.4.

This is a fast way to encode the array, the array shape and the array dtype:

def numpy_to_bytes(arr: np.array) -> str:
    arr_dtype = bytearray(str(arr.dtype), 'utf-8')
    arr_shape = bytearray(','.join([str(a) for a in arr.shape]), 'utf-8')
    sep = bytearray('|', 'utf-8')
    arr_bytes = arr.ravel().tobytes()
    to_return = arr_dtype + sep + arr_shape + sep + arr_bytes
    return to_return

def bytes_to_numpy(serialized_arr: str) -> np.array:
    sep = '|'.encode('utf-8')
    i_0 = serialized_arr.find(sep)
    i_1 = serialized_arr.find(sep, i_0 + 1)
    arr_dtype = serialized_arr[:i_0].decode('utf-8')
    arr_shape = tuple([int(a) for a in serialized_arr[i_0 + 1:i_1].decode('utf-8').split(',')])
    arr_str = serialized_arr[i_1 + 1:]
    arr = np.frombuffer(arr_str, dtype = arr_dtype).reshape(arr_shape)
    return arr

To use the functions:

a = np.ones((23, 23), dtype = 'int')
a_b = numpy_to_bytes(a)
a1 = bytes_to_numpy(a_b)
np.array_equal(a, a1) and a.shape == a1.shape and a.dtype == a1.dtype

I know, I am late but here is the correct way of doing it. using base64. This technique will convert the array to string.

import base64
import numpy as np
random_array = np.random.randn(32,32)
string_repr = base64.binascii.b2a_base64(random_array).decode("ascii")
array = np.frombuffer(base64.binascii.a2b_base64(string_repr.encode("ascii"))) 
array = array.reshape(32,32)

For array to string

Convert binary data to a line of ASCII characters in base64 coding and decode to ASCII to get string repr.

For string to array

First, encode the string in ASCII format then
Convert a block of base64 data back to binary and return the binary data.

The right answer for for numpy version >1.9

arr = np.array([1, 2, 3, 4, 5, 6])
ts = arr.tobytes()
#Reverse to array
arr = np.frombuffer(ts, dtype=arr.dtype)
print(arr)

tostring() is deprecated

You don’t need any external library (except numpy) and its there is no faster method to retrive the value!

I needed it to save the ndarray in an SQLite table.

My solution was to dump the array and convert it into hexadecimal:

array_5_str = array_5.dumps().hex()  # to save it in the table

To convert it into a ndarray again:

array_5_from_str = pickle.loads(bytes.fromhex(array_5_str))

You can compare the two ndarray with:

comparison = array_5 == array_5_from_str
equal_arrays = comparison.all()
print(equal_arrays)