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# In[1]name=['Alice','Bob','Cathy','Doug']age=[25,45,37,19]weight=[55.0,85.5,68.0,61.5]# In[2]data=np.zeros(4,dtype={'names':('name','age','weight'),'formats':('U10,'i4','f8')})print(data.dtype)# Out[2][('name', 'U10 means Unicode string of maximum length 10, i4 means 4 byte integer, and f8 means 8 byte float.We can fill the array with our lists of values.# In[3]data['name']=namedata['age']=aged..

Fast Sorting in NumpyUsing np.sort and np.argsort# In[1]x=np.array([2,1,4,3,5])np.sort(x)# Out[1]array([1, 2, 3, 4, 5])You can also use sort method.# In[2]x.sort()print(x)# Out[2][1 2 3 4 5]A related function is argsort, which instead returns the indices of the sorted elements.# In[3]x=np.array([2,1,4,3,5])i=np.argsort(x)print(i)print(x[i])# Out[3][1 0 3 2 4][1,2,3,4,5]Sorting Along Rows and Col..

Exploring Fancy Indexing# In[1]rng=np.random.default_rng(seed=1701)x=rng.integers(100,size=10)print(rng)print(x)# Out[1]Generator(PCG64)[90 40 9 30 80 67 39 15 33 79]If we want to access three different elements. We could do it like this# In[2][x[3],x[7],x[2]]# Out[2][30, 15, 9]Alternatively, we can pass a single list or array of indices to obtain the same result.# In[3]ind=[3,7,4]x[ind]# Out[3..

Comparison Operators as Ufuncs# In[1]x=np.array([1,2,3,4,5])print('x 3 :',x>3)print('x = 3 :',x>=3)print('x != 3 :',x!=3)print('x == 3 :',x==3)# Out[1]x 3 : [False False False True True]x = 3 : [False False True True True]x != 3 : [ True True False True True]x == 3 : [False False True False False]It is also possible to do an element-wise comparison of two arrays# In[2](2**x)==(x**2)..

Introducing Broadcasting# In[1]a=np.array([0,1,2])M=np.ones((3,3))M+a# Out[1]array([[1., 2., 3.], [1., 2., 3.], [1., 2., 3.]])the one-dimensional array a is stretched(broadcasted), across the second dimension in order to match the shape of M.# In[2]a=np.arange(3)b=np.arange(3)[:,np.newaxis]print(a)print(b)print(a+b)# Out[2][0 1 2][[0] [1] [2]][[0 1 2] [1 2 3] [2 3 4]]a and b both str..

Summing the Values in an ArrayPython itself can do sum of all values in an array using the built-in sum function.# In[1]L = np.random.random(100)sum(L)# Out[1]47.23340327381991# In[2]np.sum(L)# Out[2]47.23340327381991Numpy's sum function is quite similar to Python's function, and the result is the same.However, Numpy's version of the operation is computed much more quickly.# In[3]big_array=np.ra..

이 블로그에선 np.sum을 활용하여 axis 에 대해 알아보겠음.Data Type of Linear AlgebraScalar하나의 숫자만으로 이루어진 data$x∈R$Vector일반적인 일차원 배열하나의 vector를 이루는 data의 개수를 dimension이라 함.Matrix복수의 dimension을 가지는 data가 다시 여러 개 있는 경우의 data를 합쳐서 표기한 것일반적으로 2차원 배열이 행렬Tensor3차원 이상의 배열을 tensor라고 함다차원 배열의 axisvector를 위 그림에 적용해보면 vector는 x축만을 가지는 자료형1차원 배열에 해당하는 vector의 각 element는 그 자체가 rowmatrix는 x축의 row와 y축의 column을 가짐2차원 배열 matrix는 dep..

Introducing UfuncsPython's default implementation does some operations very slowly.For many types of operations, Numpy provides a convenient interface into just this kind of statically typed, compiled routine. This is known as a vertorized operationThis vectorized approach is designed to push the loop into the compiled layer that underlies Numpy, leading to much faster executionVectorized operat..

Numpy Array AttributesWe'll use Numpy's random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run:# In[1]import numpy as nprng=np.random.default_rng(seed=1701) # seed for reproducibilityx1 = rng.integers(10,size=6)x2 = rng.integers(10,size=(3,4))x3 = rng.integers(10,size=(3,4,5))print("x3 ndim: ",x3.ndim)p..

A Python Integer is more than just an IntegerC integer is essentially a label for a position in memory whose bytes encode an interger valuePython integer is a pointer to a position in memory containing all the Python object information, including the bytes that contain the integer valueA Python List is more than just a ListBecause of Python's dynamic typing, we can even create heterogeneous list..