Appending numpy arrays into a text file
up vote
2
down vote
favorite
I have a function which returns a 2 Dimensional numpy array and this function will be inside a loop. At each iteration, I want to append this numpy array into a file.
filename = "xyz"
for i in range(10):
np_array = function_to_get_numpy()
now append this `np_array` into filename
I can continue to append the numpy array in the script and dump once, but I want to avoid that.
Also I would prefer to store this in non-binary format.
python-3.x numpy
add a comment |
up vote
2
down vote
favorite
I have a function which returns a 2 Dimensional numpy array and this function will be inside a loop. At each iteration, I want to append this numpy array into a file.
filename = "xyz"
for i in range(10):
np_array = function_to_get_numpy()
now append this `np_array` into filename
I can continue to append the numpy array in the script and dump once, but I want to avoid that.
Also I would prefer to store this in non-binary format.
python-3.x numpy
np.savetxt
writes acsv
style output. It can take a file name, or an opened file. If you open the file in append mode, you can write to the same file multiple times. Alternatively you could format each 'row' of the array how ever you want, and use a normal file write. For text files, regular Python file writes are just fine (evenprint
with afile
parameter).
– hpaulj
Nov 11 at 21:23
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I have a function which returns a 2 Dimensional numpy array and this function will be inside a loop. At each iteration, I want to append this numpy array into a file.
filename = "xyz"
for i in range(10):
np_array = function_to_get_numpy()
now append this `np_array` into filename
I can continue to append the numpy array in the script and dump once, but I want to avoid that.
Also I would prefer to store this in non-binary format.
python-3.x numpy
I have a function which returns a 2 Dimensional numpy array and this function will be inside a loop. At each iteration, I want to append this numpy array into a file.
filename = "xyz"
for i in range(10):
np_array = function_to_get_numpy()
now append this `np_array` into filename
I can continue to append the numpy array in the script and dump once, but I want to avoid that.
Also I would prefer to store this in non-binary format.
python-3.x numpy
python-3.x numpy
asked Nov 11 at 21:07
random_28
718415
718415
np.savetxt
writes acsv
style output. It can take a file name, or an opened file. If you open the file in append mode, you can write to the same file multiple times. Alternatively you could format each 'row' of the array how ever you want, and use a normal file write. For text files, regular Python file writes are just fine (evenprint
with afile
parameter).
– hpaulj
Nov 11 at 21:23
add a comment |
np.savetxt
writes acsv
style output. It can take a file name, or an opened file. If you open the file in append mode, you can write to the same file multiple times. Alternatively you could format each 'row' of the array how ever you want, and use a normal file write. For text files, regular Python file writes are just fine (evenprint
with afile
parameter).
– hpaulj
Nov 11 at 21:23
np.savetxt
writes a csv
style output. It can take a file name, or an opened file. If you open the file in append mode, you can write to the same file multiple times. Alternatively you could format each 'row' of the array how ever you want, and use a normal file write. For text files, regular Python file writes are just fine (even print
with a file
parameter).– hpaulj
Nov 11 at 21:23
np.savetxt
writes a csv
style output. It can take a file name, or an opened file. If you open the file in append mode, you can write to the same file multiple times. Alternatively you could format each 'row' of the array how ever you want, and use a normal file write. For text files, regular Python file writes are just fine (even print
with a file
parameter).– hpaulj
Nov 11 at 21:23
add a comment |
2 Answers
2
active
oldest
votes
up vote
2
down vote
accepted
In [64]: with open('xyz','w') as f:
...: for n in range(1,4):
...: arr = np.arange(n*n).reshape(n,n)
...: np.savetxt(f, arr, fmt='%5d', delimiter=',')
...:
In [65]: cat xyz
0
0, 1
2, 3
0, 1, 2
3, 4, 5
6, 7, 8
If the number of columns varies, as it does here, it will be hard(er) to read. csv
readers like genfromtxt
won't like it.
If the number of columns is consistent, it can be loaded as one big array. Separating the writes and reloading them is possible, but more involved.
add a comment |
up vote
0
down vote
I'm going to take the opportunity to plug nppretty
, a pretty printer for numpy that I've been working on. It provides a class ArrayStream
that I think will do exactly what you need.
Install nppretty
with:
pip install nppretty
You can use ArrayStream
much like a file object. For example, this code:
from nppretty import ArrayStream
import numpy as np
arrstr = ArrayStream('arraystream.txt', name='arr2D')
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
arrstr.write(arr.reshape(2,5))
arrstr.close()
will produce a text file called arraystream.txt
with the following contents:
arr2D = [
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39],
[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59],
[60, 61, 62, 63, 64],
[65, 66, 67, 68, 69],
[70, 71, 72, 73, 74],
[75, 76, 77, 78, 79],
[80, 81, 82, 83, 84],
[85, 86, 87, 88, 89],
[90, 91, 92, 93, 94],
[95, 96, 97, 98, 99],
]
Notes on ArrayStream
ArrayStream
accepts all of the same arguments as the standard Python open
method. The one additional keyword arg is name
, which sets the name of the array in the file (name
defaults to "array"
if left blank). Just like the file object returned by a call to open
, an ArrayStream
instance can used in a with
statement. For example, the following code will produce the same output as above:
with ArrayStream('arraystream.txt', name='arr2D') as f:
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
f.write(arr.reshape(2,5))
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
accepted
In [64]: with open('xyz','w') as f:
...: for n in range(1,4):
...: arr = np.arange(n*n).reshape(n,n)
...: np.savetxt(f, arr, fmt='%5d', delimiter=',')
...:
In [65]: cat xyz
0
0, 1
2, 3
0, 1, 2
3, 4, 5
6, 7, 8
If the number of columns varies, as it does here, it will be hard(er) to read. csv
readers like genfromtxt
won't like it.
If the number of columns is consistent, it can be loaded as one big array. Separating the writes and reloading them is possible, but more involved.
add a comment |
up vote
2
down vote
accepted
In [64]: with open('xyz','w') as f:
...: for n in range(1,4):
...: arr = np.arange(n*n).reshape(n,n)
...: np.savetxt(f, arr, fmt='%5d', delimiter=',')
...:
In [65]: cat xyz
0
0, 1
2, 3
0, 1, 2
3, 4, 5
6, 7, 8
If the number of columns varies, as it does here, it will be hard(er) to read. csv
readers like genfromtxt
won't like it.
If the number of columns is consistent, it can be loaded as one big array. Separating the writes and reloading them is possible, but more involved.
add a comment |
up vote
2
down vote
accepted
up vote
2
down vote
accepted
In [64]: with open('xyz','w') as f:
...: for n in range(1,4):
...: arr = np.arange(n*n).reshape(n,n)
...: np.savetxt(f, arr, fmt='%5d', delimiter=',')
...:
In [65]: cat xyz
0
0, 1
2, 3
0, 1, 2
3, 4, 5
6, 7, 8
If the number of columns varies, as it does here, it will be hard(er) to read. csv
readers like genfromtxt
won't like it.
If the number of columns is consistent, it can be loaded as one big array. Separating the writes and reloading them is possible, but more involved.
In [64]: with open('xyz','w') as f:
...: for n in range(1,4):
...: arr = np.arange(n*n).reshape(n,n)
...: np.savetxt(f, arr, fmt='%5d', delimiter=',')
...:
In [65]: cat xyz
0
0, 1
2, 3
0, 1, 2
3, 4, 5
6, 7, 8
If the number of columns varies, as it does here, it will be hard(er) to read. csv
readers like genfromtxt
won't like it.
If the number of columns is consistent, it can be loaded as one big array. Separating the writes and reloading them is possible, but more involved.
answered Nov 12 at 3:26
hpaulj
109k674140
109k674140
add a comment |
add a comment |
up vote
0
down vote
I'm going to take the opportunity to plug nppretty
, a pretty printer for numpy that I've been working on. It provides a class ArrayStream
that I think will do exactly what you need.
Install nppretty
with:
pip install nppretty
You can use ArrayStream
much like a file object. For example, this code:
from nppretty import ArrayStream
import numpy as np
arrstr = ArrayStream('arraystream.txt', name='arr2D')
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
arrstr.write(arr.reshape(2,5))
arrstr.close()
will produce a text file called arraystream.txt
with the following contents:
arr2D = [
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39],
[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59],
[60, 61, 62, 63, 64],
[65, 66, 67, 68, 69],
[70, 71, 72, 73, 74],
[75, 76, 77, 78, 79],
[80, 81, 82, 83, 84],
[85, 86, 87, 88, 89],
[90, 91, 92, 93, 94],
[95, 96, 97, 98, 99],
]
Notes on ArrayStream
ArrayStream
accepts all of the same arguments as the standard Python open
method. The one additional keyword arg is name
, which sets the name of the array in the file (name
defaults to "array"
if left blank). Just like the file object returned by a call to open
, an ArrayStream
instance can used in a with
statement. For example, the following code will produce the same output as above:
with ArrayStream('arraystream.txt', name='arr2D') as f:
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
f.write(arr.reshape(2,5))
add a comment |
up vote
0
down vote
I'm going to take the opportunity to plug nppretty
, a pretty printer for numpy that I've been working on. It provides a class ArrayStream
that I think will do exactly what you need.
Install nppretty
with:
pip install nppretty
You can use ArrayStream
much like a file object. For example, this code:
from nppretty import ArrayStream
import numpy as np
arrstr = ArrayStream('arraystream.txt', name='arr2D')
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
arrstr.write(arr.reshape(2,5))
arrstr.close()
will produce a text file called arraystream.txt
with the following contents:
arr2D = [
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39],
[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59],
[60, 61, 62, 63, 64],
[65, 66, 67, 68, 69],
[70, 71, 72, 73, 74],
[75, 76, 77, 78, 79],
[80, 81, 82, 83, 84],
[85, 86, 87, 88, 89],
[90, 91, 92, 93, 94],
[95, 96, 97, 98, 99],
]
Notes on ArrayStream
ArrayStream
accepts all of the same arguments as the standard Python open
method. The one additional keyword arg is name
, which sets the name of the array in the file (name
defaults to "array"
if left blank). Just like the file object returned by a call to open
, an ArrayStream
instance can used in a with
statement. For example, the following code will produce the same output as above:
with ArrayStream('arraystream.txt', name='arr2D') as f:
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
f.write(arr.reshape(2,5))
add a comment |
up vote
0
down vote
up vote
0
down vote
I'm going to take the opportunity to plug nppretty
, a pretty printer for numpy that I've been working on. It provides a class ArrayStream
that I think will do exactly what you need.
Install nppretty
with:
pip install nppretty
You can use ArrayStream
much like a file object. For example, this code:
from nppretty import ArrayStream
import numpy as np
arrstr = ArrayStream('arraystream.txt', name='arr2D')
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
arrstr.write(arr.reshape(2,5))
arrstr.close()
will produce a text file called arraystream.txt
with the following contents:
arr2D = [
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39],
[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59],
[60, 61, 62, 63, 64],
[65, 66, 67, 68, 69],
[70, 71, 72, 73, 74],
[75, 76, 77, 78, 79],
[80, 81, 82, 83, 84],
[85, 86, 87, 88, 89],
[90, 91, 92, 93, 94],
[95, 96, 97, 98, 99],
]
Notes on ArrayStream
ArrayStream
accepts all of the same arguments as the standard Python open
method. The one additional keyword arg is name
, which sets the name of the array in the file (name
defaults to "array"
if left blank). Just like the file object returned by a call to open
, an ArrayStream
instance can used in a with
statement. For example, the following code will produce the same output as above:
with ArrayStream('arraystream.txt', name='arr2D') as f:
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
f.write(arr.reshape(2,5))
I'm going to take the opportunity to plug nppretty
, a pretty printer for numpy that I've been working on. It provides a class ArrayStream
that I think will do exactly what you need.
Install nppretty
with:
pip install nppretty
You can use ArrayStream
much like a file object. For example, this code:
from nppretty import ArrayStream
import numpy as np
arrstr = ArrayStream('arraystream.txt', name='arr2D')
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
arrstr.write(arr.reshape(2,5))
arrstr.close()
will produce a text file called arraystream.txt
with the following contents:
arr2D = [
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29],
[30, 31, 32, 33, 34],
[35, 36, 37, 38, 39],
[40, 41, 42, 43, 44],
[45, 46, 47, 48, 49],
[50, 51, 52, 53, 54],
[55, 56, 57, 58, 59],
[60, 61, 62, 63, 64],
[65, 66, 67, 68, 69],
[70, 71, 72, 73, 74],
[75, 76, 77, 78, 79],
[80, 81, 82, 83, 84],
[85, 86, 87, 88, 89],
[90, 91, 92, 93, 94],
[95, 96, 97, 98, 99],
]
Notes on ArrayStream
ArrayStream
accepts all of the same arguments as the standard Python open
method. The one additional keyword arg is name
, which sets the name of the array in the file (name
defaults to "array"
if left blank). Just like the file object returned by a call to open
, an ArrayStream
instance can used in a with
statement. For example, the following code will produce the same output as above:
with ArrayStream('arraystream.txt', name='arr2D') as f:
for i in range(10):
arr = np.arange(10*i, 10*(i + 1))
f.write(arr.reshape(2,5))
edited Nov 12 at 3:11
answered Nov 12 at 2:39
tel
4,61911429
4,61911429
add a comment |
add a comment |
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np.savetxt
writes acsv
style output. It can take a file name, or an opened file. If you open the file in append mode, you can write to the same file multiple times. Alternatively you could format each 'row' of the array how ever you want, and use a normal file write. For text files, regular Python file writes are just fine (evenprint
with afile
parameter).– hpaulj
Nov 11 at 21:23