In [1]:

    

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from pandas import DataFrame, Series


    

In [2]:

    

arr  = np.arange(10)
np.add.reduce(arr)


    

    
Out[2]:





45

In [3]:

    

arr.sum()


    

    
Out[3]:





45

In [5]:

    

from numpy.random import randn
arr = randn(5, 5)
arr[::2]


    

    
Out[5]:





array([[ 0.02187458, -0.62777294,  0.16734544,  1.43807009, -0.22187227],
       [-0.33820375,  0.13544869, -0.19165592, -0.94656592,  0.18745003],
       [ 1.39229807, -2.32765928,  0.81650944,  1.42274168,  0.46909254]])

In [6]:

    

arr[::2].sort(1) # 对部分行进行排序
arr[:, :-1] < arr[:, 1:]


    

    
Out[6]:





array([[ True,  True,  True,  True],
       [ True, False,  True, False],
       [ True,  True,  True,  True],
       [False,  True,  True,  True],
       [ True,  True,  True,  True]], dtype=bool)

In [7]:

    

np.logical_and.reduce(arr[:, :-1] < arr[:, 1:], axis=1)


    

    
Out[7]:





array([ True, False,  True, False,  True], dtype=bool)

In [8]:

    

np.all(arr[:, :-1] < arr[:, 1:], axis=1)


    

    
Out[8]:





array([ True, False,  True, False,  True], dtype=bool)

In [9]:

    

arr = np.arange(15).reshape((3, 5))
np.add.accumulate(arr, axis=1)


    

    
Out[9]:





array([[ 0,  1,  3,  6, 10],
       [ 5, 11, 18, 26, 35],
       [10, 21, 33, 46, 60]])

In [10]:

    

arr = np.arange(3).repeat([1, 2, 2])
arr


    

    
Out[10]:





array([0, 1, 1, 2, 2])

In [11]:

    

np.multiply.outer(arr, np.arange(5))


    

    
Out[11]:





array([[0, 0, 0, 0, 0],
       [0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4],
       [0, 2, 4, 6, 8],
       [0, 2, 4, 6, 8]])

In [13]:

    

result = np.subtract.outer(randn(3, 4), randn(5))
result.shape


    

    
Out[13]:





(3L, 4L, 5L)

In [15]:

    

np.subtract.outer(np.arange(5), np.arange(3))


    

    
Out[15]:





array([[ 0, -1, -2],
       [ 1,  0, -1],
       [ 2,  1,  0],
       [ 3,  2,  1],
       [ 4,  3,  2]])

In [16]:

    

arr = np.arange(10)
np.add.reduceat(arr, [0, 5, 8])


    

    
Out[16]:





array([10, 18, 17])

In [17]:

    

arr = np.multiply.outer(np.arange(4), np.arange(5))
np.add.reduceat(arr, [0, 2, 4], axis=1)


    

    
Out[17]:





array([[ 0,  0,  0],
       [ 1,  5,  4],
       [ 2, 10,  8],
       [ 3, 15, 12]])

In [18]:

    

def add_elements(x, y):
    return x + y

add_them = np.frompyfunc(add_elements, 2, 1)
add_them(np.arange(8), np.arange(8))


    

    
Out[18]:





array([0, 2, 4, 6, 8, 10, 12, 14], dtype=object)

In [19]:

    

add_them = np.vectorize(add_elements, otypes=[np.float64])
add_them(np.arange(8), np.arange(8))


    

    
Out[19]:





array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.])

In [20]:

    

arr = randn(10000)
%timeit add_them(arr, arr)


    

    




100 loops, best of 3: 2.36 ms per loop

In [21]:

    

%timeit np.add(arr, arr)


    

    




The slowest run took 5.30 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 6.62 µs per loop

In [22]:

    

dtype = [('x', np.float64), ('y', np.int32)]
sarr = np.array([(1.5, 6), (np.pi, -2)], dtype=dtype)
sarr


    

    
Out[22]:





array([(1.5, 6), (3.141592653589793, -2)], 
      dtype=[('x', '<f8'), ('y', '<i4')])

In [23]:

    

sarr[0]['y']


    

    
Out[23]:





6

In [24]:

    

sarr['x']


    

    
Out[24]:





array([ 1.5       ,  3.14159265])

In [25]:

    

dtype = [('x', np.int64, 3), ('y', np.int32)]
arr = np.zeros(4, dtype=dtype)
arr


    

    
Out[25]:





array([([0L, 0L, 0L], 0), ([0L, 0L, 0L], 0), ([0L, 0L, 0L], 0),
       ([0L, 0L, 0L], 0)], 
      dtype=[('x', '<i8', (3,)), ('y', '<i4')])

In [26]:

    

arr[0]['x']


    

    
Out[26]:





array([0, 0, 0], dtype=int64)

In [27]:

    

arr['x']


    

    
Out[27]:





array([[0, 0, 0],
       [0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]], dtype=int64)

In [28]:

    

dtype = [('x', [('a', 'f8'), ('b', 'f4')]), ('y', np.int32)]
data = np.array([((1, 2), 5), ((3, 4), 6)], dtype=dtype)
data['x']


    

    
Out[28]:





array([(1.0, 2.0), (3.0, 4.0)], 
      dtype=[('a', '<f8'), ('b', '<f4')])

In [29]:

    

data['y']


    

    
Out[29]:





array([5, 6])