Python08-numpy array finishing

numpy is a science for multidimensional array (ndarray) calculation package. Array data is the same type of element combinations arranged in a certain order.

Use array () function to create - numpy required data structure is an array

• Generating
  array of the incoming object (list, tuple, etc.). arr = np.array ([2,4,6,8]) arr = np.array ((1,4,6))

An array of fixed range np.arrang (start, stop, step)
prescribed shape array 0 np.zeros (3)
specify the shape of an array np.ones (3)
square matrix np.eye (3)
a random array np.random.rand (x, y) x row random number between 0-1 y columns
normally distributed random number np.random.randn ()
random number np.random.randint specified range (1,5,10) 10 1 a random number between 5 to
select np.random.choice () array from known
scrambled np.random.shuffle ()

• The basic properties

  The shape of the array arr.shape
  how many elements arr.size
  array type arr.dtype
  array dimension arr.ndim

• data collection

1. An interval: left and right open and closed [m, n] from m to n in which n
2. A condition: ARR [ARR> 3]
   [-1] - end value
   [3: -2] - 3 penultimate bit 2, bit 2 is not included

• Deformation
  reshape (m, n) - m rows and n columns into
  .T transpose
  .ravel () arr.ravel ()
• merge

1. concatenate ([arr1, arr2], axis = 1) axis = 1 in the row direction are combined; axis = 0 in the column direction are combined
2. hstack, vstack horizontal / vertical input np.hstack combined in the form of tuples ((arr1, arr2))
3. colum_stack, row_stack ibid.

• Dividing
  hsplit, vsplit horizontal / vertical division np.hsplit (a, (3,4)) # Split a after the third and the fourth column

• Common analytical function
  element level (acting on each element): np.abs (arr), sqrt , square, exp, log, isnan
  descriptive statistics function: .sum (), mean, std , var ( variance), min, max, argmin, (smallest index / large value) the argmax

• Conditional function
  np.where (condition, x, y) - similar in Excel if (condition, true, false)

arr=np.array([56,60,78])
np.where(arr>60,"及格","不及格")

• Set relationship
  comprising, crossing, and union, difference
  comprising: an array of values in the array which contains arr1 arr2 in np.inld (arr1, arr2)
  intersection: a common portion np.intersectld (arr1, arr2)
  and set: a collection of all elements np .unionld (arr1, arr2)
  difference set: in the presence arr1, arr2 np.setdiffld in the absence (arr1, arr2)

• Data preprocessing

  1. Type Conversion
  2. Missing values
  3. Value processing is repeated

1.astype () arr.astype (np.float64) arr.astype (np.string_)
2. missing values a. Does containing missing values, to find out b. Filling missing values
np.isnan (arr) arr [ np.isnan (ARR)] = 0
3. repeat value processing np.unique (arr)