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--- python:numpy [2021/08/04 17:33] – [ndarray] utedass
+++ python:numpy [2022/09/12 00:30] (current) – external edit 127.0.0.1
@@ Line 18: / Line 18: @@
 a.itemsize   # Size in byte of an element
 </code>
-====== Good functions ======
+===== Creation =====
+<code python>
+import numpy as np
+#### np.array(content) ####
+np.array(4)
+# array(4)
+np.array((1,2,3,4))
+# array([1, 2, 3, 4])
+np.array(((1,2,3),(4,5,6)))
+# array([[1, 2, 3],
+#        [4, 5, 6]])
+#### np.ndarray(shape) ####
+np.ndarray(4)
+# array([1.11949605e-311, 1.37961913e-306, 9.42677252e-321, 1.11829178e-311])
+np.ndarray((2,4))
+# array([[0.   , 0.03 , 0.215, 0.4  ],
+#        [0.586, 0.77 , 0.954, 1.   ]])
+</code>
 <code python>
@@ Line 32: / Line 57: @@
+</code>
+===== Indexing =====
+<code python>
+a = np.arange(10)   # a => [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+a1 = a[:6:2]        # a1 => [0,2,4]
+a[:6:2] = 30        # a => [30, 1, 30, 3, 30, 5, 6, 7, 8, 9]
+# Note that a1 holds a reference to a, changes made to a1 is reflected in a
+</code>
+<code python>
+# 2d-array
+B = A[row_start: row_stop : (row_step) , col_start : col_stop : (col_step)]
+# Note that B holds a reference to A, changes made to B is reflected in A
+</code>
+<code python>
+# Using a list
+a = np.arange(10)   # a => [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+# a list naming the indices
+l = [0, 2, 8, 9]
+a[l]
+a[[0,2,8,9]]
+# or a list of bool, masking desired elements. Must be same dimension as the array.
+bl = [False]*10
+bl[3] = True
+a[bl]            # => [3]
+# Note that this spawns a new ndarray, changes made to the resulting array is NOT reflected in the original array
+</code>
+<code python>
+# Using comparisons to create a masking list
+a = np.arange(10)   # a => [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+a[a>5]           # => [6, 7, 8, 9]
+# Note that this spawns a new ndarray, changes made to the resulting array is NOT reflected in the original array
+</code>
+====== Datatypes ======
+[[https://numpy.org/doc/stable/reference/arrays.dtypes.html|Official documentation]]
+^ Name ^ Datatype ^
+| i | integer |
+| b | byte |
+| u | unsigned integer |
+| f | float |
+| M | datetime |
+| m | timedelta |
+| O | python object |
+| S | zero terminated string |
+| U | unicode string |
+| c | complex float |
+| V | raw data (void) |
+====== Linear algebra and math ======
+[[https://numpy.org/doc/stable/reference/routines.math.html|Math functions]]
+<code python>
+np.dot(A,B)        # Dot-product, sv skalärprodukt
+np.linalg.det(A)   # Determinant
+np.transpose(A)    # Transpose
+np.identity(n)     # N-th identity matrix
+np.linalg.inv(A)   # Inverse matrix
+# Let v represent a polynomial
+np.roots(v)        # roots to the polynom
+</code>
+====== Universal functions, ufuncs ======
+[[https://numpy.org/doc/stable/reference/ufuncs.html|Official documentation]]
+Functions in the numpy library that operates on ndarrays in an element-wise manner.
+<code python>
+import numpy as np
+na = np.array([0.23, 2.3, 3.2, 4.1, 5.6])
+np.cos(na) # Returns a new ndarray of same dimensions, with the result
+np.log10(na)
+# etc..
+# Also arithmetic functions. Given nparray A and B of same size
+elementwise_sum = A+B
+elementwise_division = A/B
+elementwise_square = A**B
+elementwise_test = A != B
+elementwise_test = A > B
+</code>
+====== resize, reshape, flatten, ravel ======
+Of these four function, only resize creates a new array whilst the rest of them returns references or modify the original array.
+<code python>
+A = np.ndarray((3,4)) # 3x4 array
+B = A.flatten()       # B is a new array
+C = A.ravel()         # C is a reference to A
+A = np.arange(1,16,2)
+A.resize(2,4)      # Changes A to a 2x4 array
+B = A.reshape(4,2) # B is a reference to A
+</code>
+====== Concatenation ======
+<code python>
+np.vstack((A,B))
+np.hstack((A,B))
+np.concatenate((A,B), axis=1) # Similar to hstack
+# All of these creates new arrays
 </code>