How to create a numpy array of arbitrary length strings?Can't append 2 strings the way I wantHow to assign a string value to an array in numpy?H5py store list of list of stringsh5py: Store list of list of stringsCSV via NumPy genfromtxt(): dtype for variable string sizesimplementing efficient fixed size FIFO in pythonIs there a way to return an ndarray of arbitrary length strings and dtype PyObject?Assigning “list of directory” array on numpyCreate ArrayList from arrayHow do I iterate over the words of a string?How do I check if an array includes an object in JavaScript?How do I read / convert an InputStream into a String in Java?How to append something to an array?Creating multiline strings in JavaScriptHow do I make the first letter of a string uppercase in JavaScript?How to replace all occurrences of a string in JavaScriptHow to check whether a string contains a substring in JavaScript?How do I remove a particular element from an array in JavaScript?
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How to create a numpy array of arbitrary length strings?
Can't append 2 strings the way I wantHow to assign a string value to an array in numpy?H5py store list of list of stringsh5py: Store list of list of stringsCSV via NumPy genfromtxt(): dtype for variable string sizesimplementing efficient fixed size FIFO in pythonIs there a way to return an ndarray of arbitrary length strings and dtype PyObject?Assigning “list of directory” array on numpyCreate ArrayList from arrayHow do I iterate over the words of a string?How do I check if an array includes an object in JavaScript?How do I read / convert an InputStream into a String in Java?How to append something to an array?Creating multiline strings in JavaScriptHow do I make the first letter of a string uppercase in JavaScript?How to replace all occurrences of a string in JavaScriptHow to check whether a string contains a substring in JavaScript?How do I remove a particular element from an array in JavaScript?
I'm a complete rookie to Python, but it seems like a given string is able to be (effectively) arbitrary length. i.e. you can take a string str and keeping adding to it: str += "some stuff...". Is there a way to make an array of such strings?
When I try this, each element only stores a single character
strArr = numpy.empty(10, dtype='string')
for i in range(0,10)
strArr[i] = "test"
On the other hand, I know I can initialize an array of certain length strings, i.e.
strArr = numpy.empty(10, dtype='s256')
which can store 10 strings of up to 256 characters.
python arrays string numpy
edited Mar 7 at 6:36
martineau
69.3k1092186
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
add a comment |
I'm a complete rookie to Python, but it seems like a given string is able to be (effectively) arbitrary length. i.e. you can take a string str and keeping adding to it: str += "some stuff...". Is there a way to make an array of such strings?
When I try this, each element only stores a single character
strArr = numpy.empty(10, dtype='string')
for i in range(0,10)
strArr[i] = "test"
On the other hand, I know I can initialize an array of certain length strings, i.e.
strArr = numpy.empty(10, dtype='s256')
which can store 10 strings of up to 256 characters.
python arrays string numpy
edited Mar 7 at 6:36
martineau
69.3k1092186
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
add a comment |
I'm a complete rookie to Python, but it seems like a given string is able to be (effectively) arbitrary length. i.e. you can take a string str and keeping adding to it: str += "some stuff...". Is there a way to make an array of such strings?
When I try this, each element only stores a single character
strArr = numpy.empty(10, dtype='string')
for i in range(0,10)
strArr[i] = "test"
On the other hand, I know I can initialize an array of certain length strings, i.e.
strArr = numpy.empty(10, dtype='s256')
which can store 10 strings of up to 256 characters.
python arrays string numpy
edited Mar 7 at 6:36
martineau
69.3k1092186
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
I'm a complete rookie to Python, but it seems like a given string is able to be (effectively) arbitrary length. i.e. you can take a string str and keeping adding to it: str += "some stuff...". Is there a way to make an array of such strings?
When I try this, each element only stores a single character
strArr = numpy.empty(10, dtype='string')
for i in range(0,10)
strArr[i] = "test"
On the other hand, I know I can initialize an array of certain length strings, i.e.
strArr = numpy.empty(10, dtype='s256')
which can store 10 strings of up to 256 characters.
python arrays string numpy
python arrays string numpy
edited Mar 7 at 6:36
martineau
69.3k1092186
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
edited Mar 7 at 6:36
martineau
69.3k1092186
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
edited Mar 7 at 6:36
martineau
69.3k1092186
edited Mar 7 at 6:36
martineau
69.3k1092186
edited Mar 7 at 6:36
martineau
69.3k1092186
69.3k1092186
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
asked Feb 1 '13 at 3:58
DilithiumMatrixDilithiumMatrix
7,48495490
7,48495490
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
You can do so by creating an array of dtype=object. If you try to assign a long string to a normal numpy array, it truncates the string:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'])
>>> a[2] = 'bananas'
>>> a
array(['apples', 'foobar', 'banana'],
dtype='|S6')
But when you use dtype=object, you get an array of python object references. So you can have all the behaviors of python strings:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'], dtype=object)
>>> a
array([apples, foobar, cowboy], dtype=object)
>>> a[2] = 'bananas'
>>> a
array([apples, foobar, bananas], dtype=object)
Indeed, because it's an array of objects, you can assign any kind of python object to the array:
>>> a[2] = 1:2, 3:4
>>> a
array([apples, foobar, 1: 2, 3: 4], dtype=object)
However, this undoes a lot of the benefits of using numpy, which is so fast because it works on large contiguous blocks of raw memory. Working with python objects adds a lot of overhead. A simple example:
>>> a = numpy.array(['abba' for _ in range(10000)])
>>> b = numpy.array(['abba' for _ in range(10000)], dtype=object)
>>> %timeit a.copy()
100000 loops, best of 3: 2.51 us per loop
>>> %timeit b.copy()
10000 loops, best of 3: 48.4 us per loop
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
Thanks, your first example is especially helpful--I never would have guessed that behavior! I'm not worried about the speed for this object, so slower access should be fine.
– DilithiumMatrix
Feb 1 '13 at 4:25
1
Nice answer. I've incorporated a link to it with demo into a python notebook page I'm working on about numpy array creation.
– John Lockwood
Mar 20 '15 at 13:15
@senderle if an array is withobjectdtype, thennp.fromstring(arr.tostring())will fail withnumpy Cannot create an object array from a string. Any ideas to solve this?
– youkaichao
Jul 31 '18 at 13:58
@游凯超 hmm. That's a tough one. It's not a total surprise because numpy really isn't designed to work with python objects. It's more of a shortcut than a proper use of numpy. So there's no real reason for them to support corner cases like that. My approach would probably be to get the maximum length of string and use a standard fixed-width char array.
– senderle
Aug 6 '18 at 22:36
@游凯超 if your goal is to use strings as row labels or column headers you should also look into structured arrays.
– senderle
Aug 6 '18 at 22:43
add a comment |
You could use the object data type:
>>> import numpy
>>> s = numpy.array(['a', 'b', 'dude'], dtype='object')
>>> s[0] += 'bcdef'
>>> s
array([abcdef, b, dude], dtype=object)
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
add a comment |
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2 Answers
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You can do so by creating an array of dtype=object. If you try to assign a long string to a normal numpy array, it truncates the string:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'])
>>> a[2] = 'bananas'
>>> a
array(['apples', 'foobar', 'banana'],
dtype='|S6')
But when you use dtype=object, you get an array of python object references. So you can have all the behaviors of python strings:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'], dtype=object)
>>> a
array([apples, foobar, cowboy], dtype=object)
>>> a[2] = 'bananas'
>>> a
array([apples, foobar, bananas], dtype=object)
Indeed, because it's an array of objects, you can assign any kind of python object to the array:
>>> a[2] = 1:2, 3:4
>>> a
array([apples, foobar, 1: 2, 3: 4], dtype=object)
However, this undoes a lot of the benefits of using numpy, which is so fast because it works on large contiguous blocks of raw memory. Working with python objects adds a lot of overhead. A simple example:
>>> a = numpy.array(['abba' for _ in range(10000)])
>>> b = numpy.array(['abba' for _ in range(10000)], dtype=object)
>>> %timeit a.copy()
100000 loops, best of 3: 2.51 us per loop
>>> %timeit b.copy()
10000 loops, best of 3: 48.4 us per loop
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
Thanks, your first example is especially helpful--I never would have guessed that behavior! I'm not worried about the speed for this object, so slower access should be fine.
– DilithiumMatrix
Feb 1 '13 at 4:25
1
Nice answer. I've incorporated a link to it with demo into a python notebook page I'm working on about numpy array creation.
– John Lockwood
Mar 20 '15 at 13:15
@senderle if an array is withobjectdtype, thennp.fromstring(arr.tostring())will fail withnumpy Cannot create an object array from a string. Any ideas to solve this?
– youkaichao
Jul 31 '18 at 13:58
@游凯超 hmm. That's a tough one. It's not a total surprise because numpy really isn't designed to work with python objects. It's more of a shortcut than a proper use of numpy. So there's no real reason for them to support corner cases like that. My approach would probably be to get the maximum length of string and use a standard fixed-width char array.
– senderle
Aug 6 '18 at 22:36
@游凯超 if your goal is to use strings as row labels or column headers you should also look into structured arrays.
– senderle
Aug 6 '18 at 22:43
add a comment |
You can do so by creating an array of dtype=object. If you try to assign a long string to a normal numpy array, it truncates the string:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'])
>>> a[2] = 'bananas'
>>> a
array(['apples', 'foobar', 'banana'],
dtype='|S6')
But when you use dtype=object, you get an array of python object references. So you can have all the behaviors of python strings:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'], dtype=object)
>>> a
array([apples, foobar, cowboy], dtype=object)
>>> a[2] = 'bananas'
>>> a
array([apples, foobar, bananas], dtype=object)
Indeed, because it's an array of objects, you can assign any kind of python object to the array:
>>> a[2] = 1:2, 3:4
>>> a
array([apples, foobar, 1: 2, 3: 4], dtype=object)
However, this undoes a lot of the benefits of using numpy, which is so fast because it works on large contiguous blocks of raw memory. Working with python objects adds a lot of overhead. A simple example:
>>> a = numpy.array(['abba' for _ in range(10000)])
>>> b = numpy.array(['abba' for _ in range(10000)], dtype=object)
>>> %timeit a.copy()
100000 loops, best of 3: 2.51 us per loop
>>> %timeit b.copy()
10000 loops, best of 3: 48.4 us per loop
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
Thanks, your first example is especially helpful--I never would have guessed that behavior! I'm not worried about the speed for this object, so slower access should be fine.
– DilithiumMatrix
Feb 1 '13 at 4:25
1
Nice answer. I've incorporated a link to it with demo into a python notebook page I'm working on about numpy array creation.
– John Lockwood
Mar 20 '15 at 13:15
@senderle if an array is withobjectdtype, thennp.fromstring(arr.tostring())will fail withnumpy Cannot create an object array from a string. Any ideas to solve this?
– youkaichao
Jul 31 '18 at 13:58
@游凯超 hmm. That's a tough one. It's not a total surprise because numpy really isn't designed to work with python objects. It's more of a shortcut than a proper use of numpy. So there's no real reason for them to support corner cases like that. My approach would probably be to get the maximum length of string and use a standard fixed-width char array.
– senderle
Aug 6 '18 at 22:36
@游凯超 if your goal is to use strings as row labels or column headers you should also look into structured arrays.
– senderle
Aug 6 '18 at 22:43
add a comment |
You can do so by creating an array of dtype=object. If you try to assign a long string to a normal numpy array, it truncates the string:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'])
>>> a[2] = 'bananas'
>>> a
array(['apples', 'foobar', 'banana'],
dtype='|S6')
But when you use dtype=object, you get an array of python object references. So you can have all the behaviors of python strings:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'], dtype=object)
>>> a
array([apples, foobar, cowboy], dtype=object)
>>> a[2] = 'bananas'
>>> a
array([apples, foobar, bananas], dtype=object)
Indeed, because it's an array of objects, you can assign any kind of python object to the array:
>>> a[2] = 1:2, 3:4
>>> a
array([apples, foobar, 1: 2, 3: 4], dtype=object)
However, this undoes a lot of the benefits of using numpy, which is so fast because it works on large contiguous blocks of raw memory. Working with python objects adds a lot of overhead. A simple example:
>>> a = numpy.array(['abba' for _ in range(10000)])
>>> b = numpy.array(['abba' for _ in range(10000)], dtype=object)
>>> %timeit a.copy()
100000 loops, best of 3: 2.51 us per loop
>>> %timeit b.copy()
10000 loops, best of 3: 48.4 us per loop
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
You can do so by creating an array of dtype=object. If you try to assign a long string to a normal numpy array, it truncates the string:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'])
>>> a[2] = 'bananas'
>>> a
array(['apples', 'foobar', 'banana'],
dtype='|S6')
But when you use dtype=object, you get an array of python object references. So you can have all the behaviors of python strings:
>>> a = numpy.array(['apples', 'foobar', 'cowboy'], dtype=object)
>>> a
array([apples, foobar, cowboy], dtype=object)
>>> a[2] = 'bananas'
>>> a
array([apples, foobar, bananas], dtype=object)
Indeed, because it's an array of objects, you can assign any kind of python object to the array:
>>> a[2] = 1:2, 3:4
>>> a
array([apples, foobar, 1: 2, 3: 4], dtype=object)
However, this undoes a lot of the benefits of using numpy, which is so fast because it works on large contiguous blocks of raw memory. Working with python objects adds a lot of overhead. A simple example:
>>> a = numpy.array(['abba' for _ in range(10000)])
>>> b = numpy.array(['abba' for _ in range(10000)], dtype=object)
>>> %timeit a.copy()
100000 loops, best of 3: 2.51 us per loop
>>> %timeit b.copy()
10000 loops, best of 3: 48.4 us per loop
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
answered Feb 1 '13 at 4:07
senderlesenderle
94.2k21170193
94.2k21170193
Thanks, your first example is especially helpful--I never would have guessed that behavior! I'm not worried about the speed for this object, so slower access should be fine.
– DilithiumMatrix
Feb 1 '13 at 4:25
1
Nice answer. I've incorporated a link to it with demo into a python notebook page I'm working on about numpy array creation.
– John Lockwood
Mar 20 '15 at 13:15
@senderle if an array is withobjectdtype, thennp.fromstring(arr.tostring())will fail withnumpy Cannot create an object array from a string. Any ideas to solve this?
– youkaichao
Jul 31 '18 at 13:58
@游凯超 hmm. That's a tough one. It's not a total surprise because numpy really isn't designed to work with python objects. It's more of a shortcut than a proper use of numpy. So there's no real reason for them to support corner cases like that. My approach would probably be to get the maximum length of string and use a standard fixed-width char array.
– senderle
Aug 6 '18 at 22:36
@游凯超 if your goal is to use strings as row labels or column headers you should also look into structured arrays.
– senderle
Aug 6 '18 at 22:43
add a comment |
Thanks, your first example is especially helpful--I never would have guessed that behavior! I'm not worried about the speed for this object, so slower access should be fine.
– DilithiumMatrix
Feb 1 '13 at 4:25
1
Nice answer. I've incorporated a link to it with demo into a python notebook page I'm working on about numpy array creation.
– John Lockwood
Mar 20 '15 at 13:15
@senderle if an array is withobjectdtype, thennp.fromstring(arr.tostring())will fail withnumpy Cannot create an object array from a string. Any ideas to solve this?
– youkaichao
Jul 31 '18 at 13:58
@游凯超 hmm. That's a tough one. It's not a total surprise because numpy really isn't designed to work with python objects. It's more of a shortcut than a proper use of numpy. So there's no real reason for them to support corner cases like that. My approach would probably be to get the maximum length of string and use a standard fixed-width char array.
– senderle
Aug 6 '18 at 22:36
@游凯超 if your goal is to use strings as row labels or column headers you should also look into structured arrays.
– senderle
Aug 6 '18 at 22:43
Thanks, your first example is especially helpful--I never would have guessed that behavior! I'm not worried about the speed for this object, so slower access should be fine.
– DilithiumMatrix
Feb 1 '13 at 4:25
Thanks, your first example is especially helpful--I never would have guessed that behavior! I'm not worried about the speed for this object, so slower access should be fine.
– DilithiumMatrix
Feb 1 '13 at 4:25
1
1
Nice answer. I've incorporated a link to it with demo into a python notebook page I'm working on about numpy array creation.
– John Lockwood
Mar 20 '15 at 13:15
Nice answer. I've incorporated a link to it with demo into a python notebook page I'm working on about numpy array creation.
– John Lockwood
Mar 20 '15 at 13:15
@senderle if an array is with
object dtype, then np.fromstring(arr.tostring()) will fail with numpy Cannot create an object array from a string. Any ideas to solve this?– youkaichao
Jul 31 '18 at 13:58
@senderle if an array is with
object dtype, then np.fromstring(arr.tostring()) will fail with numpy Cannot create an object array from a string. Any ideas to solve this?– youkaichao
Jul 31 '18 at 13:58
@游凯超 hmm. That's a tough one. It's not a total surprise because numpy really isn't designed to work with python objects. It's more of a shortcut than a proper use of numpy. So there's no real reason for them to support corner cases like that. My approach would probably be to get the maximum length of string and use a standard fixed-width char array.
– senderle
Aug 6 '18 at 22:36
@游凯超 hmm. That's a tough one. It's not a total surprise because numpy really isn't designed to work with python objects. It's more of a shortcut than a proper use of numpy. So there's no real reason for them to support corner cases like that. My approach would probably be to get the maximum length of string and use a standard fixed-width char array.
– senderle
Aug 6 '18 at 22:36
@游凯超 if your goal is to use strings as row labels or column headers you should also look into structured arrays.
– senderle
Aug 6 '18 at 22:43
@游凯超 if your goal is to use strings as row labels or column headers you should also look into structured arrays.
– senderle
Aug 6 '18 at 22:43
add a comment |
You could use the object data type:
>>> import numpy
>>> s = numpy.array(['a', 'b', 'dude'], dtype='object')
>>> s[0] += 'bcdef'
>>> s
array([abcdef, b, dude], dtype=object)
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
add a comment |
You could use the object data type:
>>> import numpy
>>> s = numpy.array(['a', 'b', 'dude'], dtype='object')
>>> s[0] += 'bcdef'
>>> s
array([abcdef, b, dude], dtype=object)
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
add a comment |
You could use the object data type:
>>> import numpy
>>> s = numpy.array(['a', 'b', 'dude'], dtype='object')
>>> s[0] += 'bcdef'
>>> s
array([abcdef, b, dude], dtype=object)
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
You could use the object data type:
>>> import numpy
>>> s = numpy.array(['a', 'b', 'dude'], dtype='object')
>>> s[0] += 'bcdef'
>>> s
array([abcdef, b, dude], dtype=object)
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
answered Feb 1 '13 at 4:05
jterracejterrace
44.9k13120166
44.9k13120166
add a comment |
add a comment |
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