In [1]:

    

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


    

In [2]:

    

np.ones((10, 5)).shape


    

    
Out[2]:





(10L, 5L)

In [3]:

    

np.ones((3, 4, 5), dtype=np.float64).strides


    

    
Out[3]:





(160L, 40L, 8L)

In [4]:

    

ints = np.ones(10, dtype=np.uint16)
floats = np.ones(10, dtype=np.float32)
np.issubdtype(ints.dtype, np.integer)


    

    
Out[4]:





True

In [5]:

    

np.issubdtype(floats.dtype, np.integer)


    

    
Out[5]:





False

In [6]:

    

np.float64.mro()


    

    
Out[6]:





[numpy.float64,
 numpy.floating,
 numpy.inexact,
 numpy.number,
 numpy.generic,
 float,
 object]

In [7]:

    

arr = np.arange(8)
arr


    

    
Out[7]:





array([0, 1, 2, 3, 4, 5, 6, 7])

In [8]:

    

arr.reshape((4, 2))


    

    
Out[8]:





array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7]])

In [9]:

    

arr.reshape((4, 2)).reshape((2, 4))


    

    
Out[9]:





array([[0, 1, 2, 3],
       [4, 5, 6, 7]])

In [10]:

    

arr = np.arange(15)
arr.reshape((5, -1))


    

    
Out[10]:





array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11],
       [12, 13, 14]])

In [12]:

    

other_arr = np.ones((3, 5))
other_arr.shape


    

    
Out[12]:





(3L, 5L)

In [13]:

    

arr.reshape(other_arr.shape)


    

    
Out[13]:





array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

In [15]:

    

arr = np.arange(15).reshape((5, 3))
arr


    

    
Out[15]:





array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11],
       [12, 13, 14]])

In [16]:

    

arr.ravel()


    

    
Out[16]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])

In [17]:

    

arr.flatten()


    

    
Out[17]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])

In [18]:

    

arr = np.arange(12).reshape((3, 4))
arr


    

    
Out[18]:





array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

In [19]:

    

arr.ravel()


    

    
Out[19]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

In [20]:

    

arr.ravel('F')


    

    
Out[20]:





array([ 0,  4,  8,  1,  5,  9,  2,  6, 10,  3,  7, 11])

In [21]:

    

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([[7, 8, 9], [10, 11, 12]])
np.concatenate([arr1, arr2], axis=0)


    

    
Out[21]:





array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 7,  8,  9],
       [10, 11, 12]])

In [22]:

    

np.concatenate([arr1, arr2], axis=1)


    

    
Out[22]:





array([[ 1,  2,  3,  7,  8,  9],
       [ 4,  5,  6, 10, 11, 12]])

In [23]:

    

np.vstack((arr1, arr2))


    

    
Out[23]:





array([[ 1,  2,  3],
       [ 4,  5,  6],
       [ 7,  8,  9],
       [10, 11, 12]])

In [24]:

    

np.hstack((arr1, arr2))


    

    
Out[24]:





array([[ 1,  2,  3,  7,  8,  9],
       [ 4,  5,  6, 10, 11, 12]])

In [25]:

    

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


    

    
Out[25]:





array([[-0.58201974,  1.40005207],
       [-0.23898917,  0.3242288 ],
       [-0.25736705, -2.04223523],
       [ 1.40643752,  1.24600034],
       [-0.28548885,  0.11601834]])

In [26]:

    

first, second, third = np.split(arr, [1, 3])
first


    

    
Out[26]:





array([[-0.58201974,  1.40005207]])

In [27]:

    

second


    

    
Out[27]:





array([[-0.23898917,  0.3242288 ],
       [-0.25736705, -2.04223523]])

In [28]:

    

third


    

    
Out[28]:





array([[ 1.40643752,  1.24600034],
       [-0.28548885,  0.11601834]])

In [30]:

    

np.split(arr, [2])


    

    
Out[30]:





[array([[-0.58201974,  1.40005207],
        [-0.23898917,  0.3242288 ]]), array([[-0.25736705, -2.04223523],
        [ 1.40643752,  1.24600034],
        [-0.28548885,  0.11601834]])]

In [31]:

    

arr = np.arange(6)
arr1 = arr.reshape((3, 2))
arr2 = randn(3, 2)
np.r_[arr1, arr2]


    

    
Out[31]:





array([[ 0.        ,  1.        ],
       [ 2.        ,  3.        ],
       [ 4.        ,  5.        ],
       [ 0.2643935 ,  0.33043113],
       [ 1.63338196,  0.0218377 ],
       [ 0.00980429, -0.2526578 ]])

In [32]:

    

np.c_[np.r_[arr1, arr2], arr]


    

    
Out[32]:





array([[ 0.        ,  1.        ,  0.        ],
       [ 2.        ,  3.        ,  1.        ],
       [ 4.        ,  5.        ,  2.        ],
       [ 0.2643935 ,  0.33043113,  3.        ],
       [ 1.63338196,  0.0218377 ,  4.        ],
       [ 0.00980429, -0.2526578 ,  5.        ]])

In [33]:

    

np.c_[1:6, -10:-5]


    

    
Out[33]:





array([[  1, -10],
       [  2,  -9],
       [  3,  -8],
       [  4,  -7],
       [  5,  -6]])

In [ ]:

    




    

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