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Numpy cleanup tool

Numpy common data structures

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• A common data structure used in Numpy is the NDARray format
• Created using the array function with the syntax array(list or tuple)
• It can be created using other functions such as arange, Linspace, Zeros, and so on

import numpy as np
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arr1 = np.array([-9.7.4.3])
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arr1
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array([-9,  7,  4,  3])
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type(arr1)  # n-dimensional array
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numpy.ndarray
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arr1 = np.array([-9.7.4.3], dtype='str')
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arr1
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array(['-9', '7', '4', '3'], dtype='<U2')
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arr1 = np.array([-9.7.4.3], dtype=float)
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arr1
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array([-9.,  7.,  4.,  3.])
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arr1 = np.array([-9.7.4.3], dtype=int)
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arr1
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array([-9,  7,  4,  3])
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arr2 = np.array([[1.2.3.4], [5.6.7.8], [9.10.11.12]])
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arr2
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array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])
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for i in range(1.10) :print(i)
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1, 2, 3, 4, 5, 6, 7, 8, 9Copy the code

np.arange(1.10.0.5)
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Array ([1., 1.5, 2., 2.5, 3., 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5 to 8, 8.5, 9, 9.5])Copy the code

# arithmetic array
The first parameter: the starting value
# Second argument: termination value
Number of elements
# endpoint: Whether to include a final value
np.linspace(1.10.10, endpoint=True)
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array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
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np.linspace(1.10.20, endpoint=True)
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Array ([1., 1.47368421, 1.94736842, 2.42105263, 2.89473684, 3.36842105, 3.84210526, 4.31578947, 4.78947368, 5.26315789, 5.73684211, 6.21052632, 6.68421053, 7.15789474, 7.63157895, 8.10526316, 8.57894737, 9.05263158, 9.52631579, 10.)Copy the code

9/19  # step
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0.47368421052631576
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1 + 3 * (9/19)
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2.4210526315789473
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Create an array of 4 rows and 5 columns with a value of 0
np.zeros([4.5])
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array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])
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np.zeros(4)
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array([0., 0., 0., 0.])
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Create an array of 2 rows and 3 columns with the value 1
np.ones([2.3])
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array([[1., 1., 1.],
       [1., 1., 1.]])
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Add 1 to each array element
arr2 + 1
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array([[ 2,  3,  4,  5],
       [ 6,  7,  8,  9],
       [10, 11, 12, 13]])
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Determine the dimension of the array
arr1.ndim
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1
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arr2.ndim
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2
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Determine the shape of the array
arr1.shape
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arr2.shape
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(3, 4)
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Return the number of elements in the array
arr2.size
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12
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Return the array element type
arr2.dtype
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dtype('int32')
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data2 = ((8.5.6.4.1.2.0.7), (1.5.3.5.4.7.3.9), (3.2.4.5.6.3.9), (11.2.13.4.15.6.17.8.19))
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arr3 = np.array(data2)
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arr3
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Array ([[8.5, 6, 4, 1.2, 0.7], [1.5, 3., 5.4, 7.3, 9.], [3.2, 4.5, 6., 3., 9.], [11.2, 13.4, 15.6, 17.8, 19.]])Copy the code

arr3[0]
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Array ([8.5, 6., 4., 1.2, 0.7])Copy the code

arr3[3]
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Array ([11.2, 13.4, 15.6, 17.8, 19.])Copy the code

Take the second row, the third column
arr3[1.2]
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5.4
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arr3[1] [2]
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5.4
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arr3[:, 3]
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Array ([1.2, 7.3, 3., 17.8])Copy the code

Take the second through the third columns
arr3[:, 1:3]
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Array ([[6, 4], [3., 5.4], [4.5, 6.], [13.4, 15.6]])Copy the code

arr3[3] [1]
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13.4
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$\color{red} Array subscripts start at 0 and close left and open right$

Numpy is a common data cleansing function

import numpy as np
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s = np.array([1.2.3.4.3.2.1.2.2.4.6.7.2.4.8.4.5])
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s = np.sort(s)
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s
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array([1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 5, 6, 7, 8])
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np.array(sorted(s, reverse=True))
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array([8, 7, 6, 5, 4, 4, 4, 4, 3, 3, 2, 2, 2, 2, 2, 1, 1])
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Return the sorted index
np.argsort(s)
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array([ 0,  6, 12,  7,  5,  8,  1,  2,  4,  3, 15,  9, 13, 16, 10, 11, 14],
      dtype=int64)
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arr1 = np.array([[0.1.3], [4.2.9], [4.5.9], [1, -3.4]])
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# axis=0: indicates column sorting
# axis=1: indicates row sorting
np.sort(arr1, axis=0)
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array([[ 0, -3,  3],
       [ 1,  1,  4],
       [ 4,  2,  9],
       [ 4,  5,  9]])
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np.sort(arr1, axis=1)
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array([[ 0,  1,  3],
       [ 2,  4,  9],
       [ 4,  5,  9],
       [-3,  1,  4]])
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s
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array([1, 2, 3, 4, 3, 2, 1, 2, 2, 4, 6, 7, 2, 4, 8, 4, 5])
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# The first parameter: condition
# second argument: the return value of the condition
# third argument: the return value if the condition is not met
# greater than 3 returns the element itself, and less than 3 returns -1
np.where(s>3, s, -1)
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array([-1, -1, -1,  4, -1, -1, -1, -1, -1,  4,  6,  7, -1,  4,  8,  4,  5])
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# The first parameter: condition
# second argument: the value returned
Filter elements in the array with a value greater than 3
np.extract(s > 3, s)
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array([4, 4, 6, 7, 4, 8, 4, 5])
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