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
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
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)
python - creating a numpy array filled with a character - Stack Overflow
How to mix strings and floats in a NumPy array?
I'd consider a pandas DataFrame instead - the actual data is stored as a numpy array and so supports everything it needs to, and the headers are separate so you don't need to do awkward slicing when working on the whole data set. You can also then index rows and columns by their index. And it supports going to and from CSVs easily.
As an aside, the way to get arrays to accept mixed types is
np.array([1, 'a'], dtype=object)
but obviously you lose practically all of the benefits of using numpy in the first place.
More on reddit.comFilling NumPy array with user input
How can I create a truly empty numpy array which can be merged onto (by a recursive function)?
I'd like to manage headers as strings in the first column and first row of a matrix. The rest being float values.
The following trick will get the job done, but I don't think this is the right method.
array = numpy.zeros((rows,cols)).astype(object)
The idea is for conveinience when moving the data into a csv file.
Thank you for any advise!
I'd consider a pandas DataFrame instead - the actual data is stored as a numpy array and so supports everything it needs to, and the headers are separate so you don't need to do awkward slicing when working on the whole data set. You can also then index rows and columns by their index. And it supports going to and from CSVs easily.
As an aside, the way to get arrays to accept mixed types is
np.array([1, 'a'], dtype=object)but obviously you lose practically all of the benefits of using numpy in the first place.
There is a simpler way to do this. Numpy arrays are designed for numbers. You can put whatever you want into them, but why?
Just keep a list. e.g.
array= ... headers = ["Name", "Phone", "Address", "Email"]
Write the headers, then write the array.
Hello, everyone! I've been studying programming at school using Python for three years now, but would still consider myself very much a beginner. Earlier this year, we learned about NumPy arrays. We exclusively dealt with one-dimensional arrays filled with user input. I've been trying to figure out how to do the same for multi-dimensional arrays, but nothing I've found online resembles the method I was taught in class. Does anyone know if this method can apply to multi-dimensional arrays? If so, how?
Here's an example of it: (NumPy array filled with 4 integers fed by the user)
from numpy import array
arr=array(4*[int()])
for i in range (0,4):
arr[i]=int(input("arr["+str(i)+"]="))