NumPy notes,

NumPy notes,

I recently read the book "using Python for data analysis", and I will also view some information on the Internet, so I will summarize some of the more important parts, on the one hand, I will deepen my memory of some methods and facilitate my future summary.

Basic concepts:

Numpy (Numerical Python) is a basic package for high-performance scientific computing and data analysis.
NumPy ndarray: multidimensional array object, fast and flexible big dataset container
All elements must be of the same type

Refer to the official documentation Documentation for details.

Download the following source code: https://github.com/NSGUF/PythonLeaning

　Common attributes:

Print (np. version. version) # numpy version data1 = [1, 2, 4] arr1 = np. array (data1) print (arr1) data2 = [[1, 2, 3, 4], [5, 6, 7, 8] arr2 = np. array (data2) print (arr2) print ("------------ attribute ---------------") print (arr2.ndim) # print the dimension of the array (arr2.shape) # print the dimension of the array (arr2.size) # print (arr2.dtype) # print (arr2.itemsize) of the element type in the array # print (arr2.data) of the byte size of each element in the array) # buffer of the actual array element # NumPy comes with many data types, such as int16 and int32. np is available for values. int16 # NumPy is converted to print (np. int32 (12.123 ))

How to create an array:

Print ("------------ method for creating an array ------------") print (np. ones (2, 3, 4), dtype = np. int16) # print (np. empty (2, 3) # if it is an empty multi-digit group, only the memory is allocated and not initialized. Therefore, print (np) runs fast. full (4, np. pi) # initialize to the specified value print (np. random. random (2, 3) # generate a random array print (np. arange (10, 30, 5) # returns an arithmetic difference of 5 starting from 10, excluding 30 print (np. linspace (, 4) # The first number is 10, the last number is 30, and the fifth equidifference is print (np. logspace (, 5) # returns five print (np) numbers from 10 ^ 0 to 10 ^ 2. arange (4) # equivalent to the following formula: print (np. arange (0, 4, 1) print (np. arange (12 ). reshape () # change to shape 4, 3a = np. arange (24 ). reshape (2, 3, 4) # print (. resize (24) # resize is used in the same way as reshape, but calling resize will change itself, and reshape will not be B = np. arange (4) print (a) # shape is 2, 3, 4, ps: the total number must be the product of shape. Otherwise, print (a [1,...]) is returned. # equivalent to a [1] print (a [1]) # 2nd full columns print (a []) # Take the third column of the first full column print (, 1]) # 2nd columns of 2nd rows in 2nd columns, an element

Operations and operations on Arrays:

Print ("------------ operate on arrays --------------") c = a-B # All elements can be included in addition, subtraction, multiplication, division, and shape, note that when the divisor is 0, print (c) print (a ** 2) # processes each element of the array. You can also traverse print (. sum () # sum print (. min () # print (. max () # print (. cumsum () # obtain the sum of the First n of each number. For example, if the first one is 0, the first two are 0 + 1, and the first three are 0 + 1 + 2, then d = np. arange (12 ). reshape (3, 4) print (d. sum (axis =-2) # sum print (d. min (axis = 0) # print (d. max (axis =-1) # print (d. mean () # arithmetic mean print (d. std () # standard deviation print (d. var () # variance print (d. argmin () # print (d. argmax () # maximum index print (d. repeat (5) # repeat print (np. power (B, 3) # print (np. repeat (3, 4) # create a one-dimensional array with 3 elements and print (d. cumsum (axis = 1) # obtain the sum of the First n of each number. For example, the first one is 0, and the first two are 0 + 1, print (d. cumprod () # cumulative product of all elements # conclusion: When the axis parameter is not set, all elements are used by default. If the value is 0 or-2, the first number in each column is displayed, when the value is 1 or-1, the operation is performed in columns. If the value is other values, the error s = np is returned. array ([, 23]) s. sort () # sort print (np. unique (s) # locate and sort unique values

Boolean array method:

Print ("------------ Boolean value array method --------------") bools = np. array ([False, False, True, False]) print (bools. any () # whether one or more trueprints (bools. all () # all are Trueprint (. ravel () # Set the shape value to (total number of elements), which is equivalent to. reshape (. size) print (. ravel (). shape) print (np. sin (a) print (np. floor (a) # rounded up c =. copy () # Deep copy print (c is a) print ()

　How to traverse the array:

Print ("------------ traverse the array --------------") for row in a: # print (row) for element in a. flat: # traverse each element print (element)

　Input and Output of array files:

Print ("------------ array file input and output ------------") np. save ("save_a.yy", a) # convert the array. if the file name is not modified, the file name must be added to the disk np. load ("save_a.yy") np. savez ('array _ a_ B .npz', a = a, B = B) # store arch = np together with a and B. load ('array _ a_ B .npz') print (arch ['B']) # obtain array B np. savetxt ("text_ B .txt", B, delimiter = ',') # Write the txt file, with the separator arr = np. loadtxt ("text_a.txt", delimiter = ',') # Read the txt file, with the separator print (arr)

　　Some methods for linear algebra and operations on matrices:

Print ("---------------------- linear algebra -------------------------") print (np. dot (a, B) # print (. transpose () # transpose print (np. rot90 (a, 3) # select 90 degrees counterclockwise, and the second parameter is the number of rotations print (np. fliplr (a) # print (np. flipud (a) # print (np. roll (a, 1, axis = 1) # Rolling Displacement print (np. trace (a) # trace, diagonal and e = np. array ([[1, 2, 3], [1, 2, 3], [1, 2, 3]) print (np. diag (e) # returns the diagonal element print (np. vstack (np. array ([[1, 1], [1, 1]), np. array ([[], []) # merge print (np. hstack (np. array ([[1, 1], [1, 1]), np. array ([[], []) # flip after merging

Random Number Generation:

Print ("---------------------- Random Number Generation -----------------------") print (np. random. normal (size = () # a random number with a normal distribution to generate an array of 4*4. Compared with the python built-in random, np. random is much faster than print (np. random. binomial (10, 0.5, 100) # print (np. random. shuffle (B) # print (np. random. permutation (a) # returns a new arrayprint (np. random. rand (3, 2) # print (np) of evenly distributed sample values. random. randint (5, size = () # The upper limit of the first attribute indicates 0-5, print (np. random. randn () # print (np) sample value of normal distribution (Mean Value: 0, standard deviation: 1. random. beta (10, 10, size = 10) # beta distribution