Some conclusions of numerical numpy, interested friends can see

1.NumPy

1. Create an array

• 1. Create a list from python
• 1. Create other ways

2. NumPy array base

• An array of basic properties 1.numpy

  • Size: size
  • Shape: shape
  • Dimensions: ndim
  • Data Type: dtype

• 2. array index (can be written as negative): Get a single element

  • One-dimensional array: ([2])
  • Two-dimensional array: ([2,3])

• 3. array slice: acquiring sub-array

  • One-dimensional array: ([start: stop: step])
  • Two-dimensional array: ([start: stop: step], [start: stop: step])

  • How to get rows and columns?

    • Gets rows can omit the column index

  • Sub-array of non-copy View

    • It means slicing operations instead of creating a new array

  • Create a copy of the array: the changes do not alter the original data on an array of sub-arrays

    • .copy

• 4. Deformation of the array

  • .reshape
  • .newaxis
  • np.newaxis

• 5. splicing and splitting the array

  • 1. splicing

    • 1.np.concatenate([x], [y])

      • Stitching one-dimensional nothing to say

      • Two-dimensional mosaic

        • Vertical stitching: stitching along a first axis, i.e. axis = 0 (not written)
        • About splicing: splicing along the second axis, i.e., axis = 1 (write)
    • 2.np.vstack ([x], [y])
    • 3.np.hstack([x], [y])

    • 4.np.dstck stitching along the third dimension of the array

  • 2. split. Note: N split-points will be N + 1 sub-array

    • x1, x2, x3 = np.split(x, [3, 5])
    • np.hsplit use ibid
    • np.vsplit Ibid.

Calculated 3.numpy arrays: a generic function

• 1. What are generic function?

  • 1. The addition, subtraction, multiplication, division, absolute value, logical negation, index, modulo. . .

• 2. senior general function properties

  • 1. Specify output

  • 2. Polymerization

    • np.add.reduce(x)

  • 3. outer product

    • x = np.arange (1, 6), np.multiply.outer (x, x), the output is an array of 5 × 5

  • unfunc.at, ufunc.reduceat look fancy index chapter

4. Polymerization: minimum, maximum, and other values

• 1. array values ​​are summed
• 2. The minimum and maximum
• 3. Other aggregate functions

5. Calculation of the array: Broadcast

• 1. broadcasting rules
• 2. Broadcast practical application

6. comparison, and Boolean logic mask

• 1. Similar generic function and the comparison operation

• 2. Operation Boolean arrays

  • 1. The number of statistics
  • 2. Boolean operations

• 3. Boolean array as a mask

7. fancy index, the index for a plurality of values

• 1. exploration fancy index

  • 1. Single-dimensional index
  • 2. multidimensional index

• 2. The composite index

  • 1 in combination with a simple index
  • 2. Slice combination with
  • 3. The combination with the mask

• 3. The index value fancy

8. sorted array

• 1. Quick Sort

  • np.sort

  • np.argsort

    • Fancy Index
    • axis

• 2. Part Sort: partition

  • np.partition(x, 3)
  • np.argpartition

9. The structure of data: numpy structured array

• 1. Structure of generating an array of
• 2. More advanced composite type
• 3. Record Array: an array of twisted structure