How to convert matrices to lists in Python

This time brings you to python how to convert matrices to lists, Python to convert the matrix to list of considerations, the following is the actual case, take a look.

This article focuses on some of the functions in Python's numpy library and makes backups to find them.

(1) A function to convert a matrix to a list: Numpy.matrix.tolist ()

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Examples

>>> x = Np.matrix (Np.arange) reshape ((3,4)));  Xmatrix ([[0, 1, 2, 3],  [4, 5, 6, 7],  [8, 9, ten, one]]) >>> x.tolist () [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]

(2) function to convert an array to a list: Numpy.ndarray.tolist ()

Notes: (arrays can be reconstructed)

The array may be recreated, A=np.array (A.tolist ()).

Examples

>>>

>>> a = Np.array ([1, 2]) >>> a.tolist () [1, 2]>>> a = Np.array ([[1, 2], [3, 4]]) >>> list (a) [Array ([1, 2]), Array ([3, 4])]>>> a.tolist () [[1, 2], [3, 4]]

(3) Numpy.mean () computes the mean of the Matrix or array:

Examples

>>>

>>> a = Np.array ([[1, 2], [3, 4]]) #对所有元素求均值 >>> Np.mean (a) 2.5>>> Np.mean (A, axis=0) #对每一列求均值ar Ray ([2., 3.]) >>> Np.mean (A, Axis=1) #对每一行求均值array ([1.5, 3.5])

(4) NUMPY.STD () calculates the standard deviation of a matrix or array:

Examples

>>>

>>> a = Np.array ([[1, 2], [3, 4]]) #对所有元素求标准差 >>> np.std (a) 1.1180339887498949>>> np.std (A, Axi s=0) #对每一列求标准差array ([1., 1.]) >>> np.std (A, Axis=1) #对每一行求标准差array ([0.5, 0.5])

(5) Numpy.newaxis adds a dimension to an array:

Examples:

>>> A=np.array ([[1,2,3],[4,5,6],[7,8,9]]) #先输入3行2列的数组a >>> B=a[:,:2] >>> B.shape # When the rows and columns of the array are greater than 1 o'clock, do not add dimensions (3, 2) >>> c=a[:,2] >>> c.shape #可以看到, when the array has only one column, the dimension of the missing column (3,) >>> CArray ([3, 6, 9])

>>> D=a[:,2,np.newaxis] #np. Newaxis implements the dimension of adding columns >>> darray ([[3],  [6],  [9]]) >>> D.shape  #d的维度成了3行1列 (3,1) (3, 1) >>> E=a[:,2,none] #None与np. Newaxis implements the same functionality >>> Earray ([[3],  [6],  [9]]) >>> E.shape (3, 1)

(6) Numpy.random.shuffle (index): disrupts the Order of Datasets (arrays):

Examples:

>>> index = [I for I in Range] >>> index [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> Np.random.shuff Le (index) >>> Index [7, 9, 3, 0, 4, 1, 5, 2, 8, 6]

(7) Calculates the maximum minimum value for a row or column of a two-dimensional array:

>>> import NumPy as np >>> a = np.arange (0) reshape (5,3) #构造一个5行3列的二维数组 >>> a array ],   [3, 4, 5],   [6, 7, 8], [   9, ten, one],   [, +]]) >>> B = a[:,0].min () # #取第0列的最小值, other columns ;>> b 0 >>> C = A[0,:].max () # #取第0行的最大值, other lines similarly >>> C 2

(8) Adding a column to the array: Np.hstack ()

n = Np.array (Np.random.randn (4,2)) n out[153]: Array ([[0.17234, -0.01480043],   [ -0.33356669, -1.33565616],   [- 1.11680009, 0.64230761],   [ -0.51233174, -0.10359941]]) L = Np.array ([1,2,3,4]) L out[155]: Array ([1, 2, 3, 4]) L.shape OUT[156]: (4,)

As you can see, N is a two-dimensional, l is one-dimensional, and if you call Np.hstack () directly, an error occurs: The dimensions are different.

n = Np.hstack ((n,l)) Valueerror:all the input arrays must has same number of dimensions

The workaround is to change L to two-dimensional, using the method in (5):

n = Np.hstack ((n,l[:,np.newaxis)) # #注意: You must enclose the variable in () when using Np.hstack () because it accepts only one variable n out[161]: Array ([[0.17234,- 0.01480043, 1.  ],   [ -0.33356669, -1.33565616, 2.  ],   [ -1.11680009, 0.64230761, 3.  ],   [- 0.51233174, -0.10359941, 4.  ])

Here's how to add a value to an empty list by column:

n = Np.array ([[1,2,3,4,5,6],[11,22,33,44,55,66],[111,222,333,444,555,666]]) # #产生一个三行六列容易区分的数组 n out[166]: Array ([[1, 2, 3, 4, 5, 6], [One, one, one,,,]   ,   [111, 222, 333, 444, 555, 666]])  sample = [[]for I in Range (3)] # #产 Empty list of three rows and one column out[172]: [[], [], []] for I in range (0,6,2): # #每间隔一列便添加到sample中  sample = Np.hstack ((sample,n[:,i,np.newaxi S]))  sample out[170]: Array ([[1., 3., 5.], [one.], [   111., 333., 555.]]

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