numpy1
import numpy
world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
print(type(world_alcohol))
<class 'numpy.ndarray'>
#The numpy.array() function can take a list or list of lists as input. When we input a list, we get a one-dimensional array as a result:
vector = numpy.array([5, 10, 15, 20])
#When we input a list of lists, we get a matrix as a result:
matrix = numpy.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
print (vector)
print (matrix)
[ 5 10 15 20]
[[ 5 10 15]
[20 25 30]
[35 40 45]]
#We can use the ndarray.shape property to figure out how many elements are in the array
vector = numpy.array([1, 2, 3, 4])
print(vector.shape)
#For matrices, the shape property contains a tuple with 2 elements.
matrix = numpy.array([[5, 10, 15], [20, 25, 30]])
print(matrix.shape)
(4,)
(2, 3)
#Each value in a NumPy array has to have the same data type
#NumPy will automatically figure out an appropriate data type when reading in data or converting lists to arrays.
#You can check the data type of a NumPy array using the dtype property.
numbers = numpy.array([1, 2, 3, 4])
numbers.dtype
dtype('int64')
#When NumPy can't convert a value to a numeric data type like float or integer, it uses a special nan value that stands for Not a Number
#nan is the missing data
#1.98600000e+03 is actually 1.986 * 10 ^ 3
print(world_alcohol)
[[ nan nan nan nan nan]
[1.986e+03 nan nan nan 0.000e+00]
[1.986e+03 nan nan nan 5.000e-01]
...
[1.987e+03 nan nan nan 7.500e-01]
[1.989e+03 nan nan nan 1.500e+00]
[1.985e+03 nan nan nan 3.100e-01]]
world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",", dtype="U75", skip_header=1)
print(world_alcohol)
[['1986' 'Western Pacific' 'Viet Nam' 'Wine' '0']
['1986' 'Americas' 'Uruguay' 'Other' '0.5']
['1985' 'Africa' "Cte d'Ivoire" 'Wine' '1.62']
...
['1987' 'Africa' 'Malawi' 'Other' '0.75']
['1989' 'Americas' 'Bahamas' 'Wine' '1.5']
['1985' 'Africa' 'Malawi' 'Spirits' '0.31']]
uruguay_other_1986 = world_alcohol[1,4]
third_country = world_alcohol[2,2]
print (uruguay_other_1986)
print (third_country)
0.5
Cte d'Ivoire
vector = numpy.array([5, 10, 15, 20])
print(vector[0:3])
[ 5 10 15]
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
print(matrix[:,1])
[10 25 40]
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
print(matrix[:,0:2])
[[ 5 10]
[20 25]
[35 40]]
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
print(matrix[1:3,0:2])
[[20 25]
[35 40]]
numpy2
import numpy
#it will compare the second value to each element in the vector
# If the values are equal, the Python interpreter returns True; otherwise, it returns False
vector = numpy.array([5, 10, 15, 20])
vector == 10
array([False, True, False, False])
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
matrix == 25
array([[False, False, False],
[False, True, False],
[False, False, False]])
#Compares vector to the value 10, which generates a new Boolean vector [False, True, False, False]. It assigns this result to equal_to_ten
vector = numpy.array([5, 10, 15, 20])
equal_to_ten = (vector == 10)
print (equal_to_ten)
print(vector[equal_to_ten])
[False True False False]
[10]
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
second_column_25 = (matrix[:,1] == 25)
print (second_column_25)
print(matrix[second_column_25, :])
[False True False]
[[20 25 30]]
#We can also perform comparisons with multiple conditions
vector = numpy.array([5, 10, 15, 20])
equal_to_ten_and_five = (vector == 10) & (vector == 5)
print (equal_to_ten_and_five)
[False False False False]
vector = numpy.array([5, 10, 15, 20])
equal_to_ten_or_five = (vector == 10) | (vector == 5)
print (equal_to_ten_or_five)
[ True True False False]
vector = numpy.array([5, 10, 15, 20])
equal_to_ten_or_five = (vector == 10) | (vector == 5)
vector[equal_to_ten_or_five] = 50
print(vector)
[50 50 15 20]
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
second_column_25 = matrix[:,1] == 25
print (second_column_25)
matrix[second_column_25, 1] = 10
print (matrix)
[False True False]
[[ 5 10 15]
[20 10 30]
[35 40 45]]
#We can convert the data type of an array with the ndarray.astype() method.
vector = numpy.array(["1", "2", "3"])
print (vector.dtype)
print (vector)
vector = vector.astype(float)
print (vector.dtype)
print (vector)
<U1
['1' '2' '3']
float64
[1. 2. 3.]
vector = numpy.array([5, 10, 15, 20])
vector.sum()
50
# The axis dictates which dimension we perform the operation on
#1 means that we want to perform the operation on each row, and 0 means on each column
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
matrix.sum(axis=1)
array([ 30, 75, 120])
matrix = numpy.array([
[5, 10, 15],
[20, 25, 30],
[35, 40, 45]
])
matrix.sum(axis=0)
array([60, 75, 90])
#replace nan value with 0
world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
# print (world_alcohol)
is_value_empty = numpy.isnan(world_alcohol[:,4])
# print (is_value_empty)
world_alcohol[is_value_empty, 4] = '0'
# print (world_alcohol)
alcohol_consumption = world_alcohol[:,4]
alcohol_consumption = alcohol_consumption.astype(float)
total_alcohol = alcohol_consumption.sum()
average_alcohol = alcohol_consumption.mean()
print (total_alcohol)
print (average_alcohol)
1137.78
1.140060120240481
numpy3
import numpy as np
a = np.arange(15).reshape(3, 5)
a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
a.shape
(3, 5)
#the number of axes (dimensions) of the array
a.ndim
2
a.dtype.name
'int64'
#the total number of elements of the array
a.size
15
np.zeros ((3,4))
array([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])
np.ones( (2,3,4), dtype=np.int32 )
array([[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]],
[[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]]], dtype=int32)
#To create sequences of numbers
np.arange( 10, 30, 5 )
array([10, 15, 20, 25])
np.arange( 0, 2, 0.3 )
array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])
np.arange(12).reshape(4,3)
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
np.random.random((2,3))
array([[0.08349454, 0.82420488, 0.06979626],
[0.77967017, 0.27804427, 0.0217134 ]])
from numpy import pi
np.linspace( 0, 2*pi, 100 )
array([0. , 0.06346652, 0.12693304, 0.19039955, 0.25386607,
0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,
0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,
0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,
1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,
1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,
1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,
2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,
2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,
2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,
3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,
3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,
3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,
4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,
4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,
4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,
5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,
5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,
5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,
6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531])
np.sin(np.linspace( 0, 2*pi, 100 ))
array([ 0.00000000e+00, 6.34239197e-02, 1.26592454e-01, 1.89251244e-01,
2.51147987e-01, 3.12033446e-01, 3.71662456e-01, 4.29794912e-01,
4.86196736e-01, 5.40640817e-01, 5.92907929e-01, 6.42787610e-01,
6.90079011e-01, 7.34591709e-01, 7.76146464e-01, 8.14575952e-01,
8.49725430e-01, 8.81453363e-01, 9.09631995e-01, 9.34147860e-01,
9.54902241e-01, 9.71811568e-01, 9.84807753e-01, 9.93838464e-01,
9.98867339e-01, 9.99874128e-01, 9.96854776e-01, 9.89821442e-01,
9.78802446e-01, 9.63842159e-01, 9.45000819e-01, 9.22354294e-01,
8.95993774e-01, 8.66025404e-01, 8.32569855e-01, 7.95761841e-01,
7.55749574e-01, 7.12694171e-01, 6.66769001e-01, 6.18158986e-01,
5.67059864e-01, 5.13677392e-01, 4.58226522e-01, 4.00930535e-01,
3.42020143e-01, 2.81732557e-01, 2.20310533e-01, 1.58001396e-01,
9.50560433e-02, 3.17279335e-02, -3.17279335e-02, -9.50560433e-02,
-1.58001396e-01, -2.20310533e-01, -2.81732557e-01, -3.42020143e-01,
-4.00930535e-01, -4.58226522e-01, -5.13677392e-01, -5.67059864e-01,
-6.18158986e-01, -6.66769001e-01, -7.12694171e-01, -7.55749574e-01,
-7.95761841e-01, -8.32569855e-01, -8.66025404e-01, -8.95993774e-01,
-9.22354294e-01, -9.45000819e-01, -9.63842159e-01, -9.78802446e-01,
-9.89821442e-01, -9.96854776e-01, -9.99874128e-01, -9.98867339e-01,
-9.93838464e-01, -9.84807753e-01, -9.71811568e-01, -9.54902241e-01,
-9.34147860e-01, -9.09631995e-01, -8.81453363e-01, -8.49725430e-01,
-8.14575952e-01, -7.76146464e-01, -7.34591709e-01, -6.90079011e-01,
-6.42787610e-01, -5.92907929e-01, -5.40640817e-01, -4.86196736e-01,
-4.29794912e-01, -3.71662456e-01, -3.12033446e-01, -2.51147987e-01,
-1.89251244e-01, -1.26592454e-01, -6.34239197e-02, -2.44929360e-16])
#the product operator * operates elementwise in NumPy arrays
a = np.array( [20,30,40,50] )
b = np.arange( 4 )
print (a)
print (b)
#b
c = a-b
print (c)
b**2
#print b**2
print (a<35)
[20 30 40 50]
[0 1 2 3]
[20 29 38 47]
[ True True False False]
#The matrix product can be performed using the dot function or method
A = np.array( [[1,1],
[0,1]] )
B = np.array( [[2,0],
[3,4]] )
print (A)
print (B)
#print A*B
print (A.dot(B))
print (np.dot(A, B) )
[[1 1]
[0 1]]
[[2 0]
[3 4]]
[[5 4]
[3 4]]
[[5 4]
[3 4]]
numpy4
import numpy as np
B = np.arange(3)
print (B)
print (np.exp(B))
print (np.sqrt(B))
import numpy as np
B = np.arange(3)
print (B)
print (np.exp(B))
print (np.sqrt(B))
[0 1 2]
[1. 2.71828183 7.3890561 ]
[0. 1. 1.41421356]
#Return the floor of the input
c = np.random.random((3,4))
e = 10*c
a = np.floor(e)
print (c)
print (e)
print (a)
# a.shape
## flatten the array
print (a.ravel())
a.shape = (6, 2)
print (a)
print (a.T)
print (a.resize((2,6)))
print (a)
#If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated:
#a.reshape(3,-1)
[[0.714985 0.34000377 0.92961996 0.73992003]
[0.65911548 0.13960781 0.76232367 0.09835883]
[0.23824147 0.58918949 0.68448094 0.83094074]]
[[7.14985005 3.40003771 9.29619957 7.39920034]
[6.59115483 1.39607812 7.62323666 0.9835883 ]
[2.38241472 5.89189494 6.84480944 8.30940736]]
[[7. 3. 9. 7.]
[6. 1. 7. 0.]
[2. 5. 6. 8.]]
[7. 3. 9. 7. 6. 1. 7. 0. 2. 5. 6. 8.]
[[7. 3.]
[9. 7.]
[6. 1.]
[7. 0.]
[2. 5.]
[6. 8.]]
[[7. 9. 6. 7. 2. 6.]
[3. 7. 1. 0. 5. 8.]]
None
[[7. 3. 9. 7. 6. 1.]
[7. 0. 2. 5. 6. 8.]]
a = np.floor(10*np.random.random((2,2)))
b = np.floor(10*np.random.random((2,2)))
print (a)
print ('---')
print (b)
print ('---')
print (np.hstack((a,b)))
#np.hstack((a,b))
[[1. 7.]
[0. 6.]]
---
[[5. 0.]
[3. 4.]]
---
[[1. 7. 5. 0.]
[0. 6. 3. 4.]]
a = np.floor(10*np.random.random((2,12)))
print (a)
print (np.hsplit(a,3)) #将a按列拆分成3个数组,第二个参数表示拆分成几个数组
print (np.hsplit(a,(3,4))) # Split a after the third and the fourth column(#将a按特定列进行拆分,示例在第三列和第四列进行拆分。第二个参数表示拆分的位置)
a = np.floor(10*np.random.random((12,2)))
print (a)
np.vsplit(a,3)
[[4. 6. 4. 3. 7. 1. 6. 1. 7. 9. 3. 1.]
[4. 7. 4. 4. 3. 0. 5. 0. 0. 0. 0. 3.]]
[array([[4., 6., 4., 3.],
[4., 7., 4., 4.]]), array([[7., 1., 6., 1.],
[3., 0., 5., 0.]]), array([[7., 9., 3., 1.],
[0., 0., 0., 3.]])]
[array([[4., 6., 4.],
[4., 7., 4.]]), array([[3.],
[4.]]), array([[7., 1., 6., 1., 7., 9., 3., 1.],
[3., 0., 5., 0., 0., 0., 0., 3.]])]
[[2. 8.]
[9. 8.]
[1. 8.]
[1. 5.]
[9. 5.]
[7. 2.]
[6. 3.]
[2. 5.]
[4. 5.]
[2. 6.]
[0. 0.]
[9. 9.]]
[array([[2., 8.],
[9., 8.],
[1., 8.],
[1., 5.]]), array([[9., 5.],
[7., 2.],
[6., 3.],
[2., 5.]]), array([[4., 5.],
[2., 6.],
[0., 0.],
[9., 9.]])]
#Simple assignments make no copy of array objects or of their data.
a = np.arange(12)
b = a
# a and b are two names for the same ndarray object
b is a
b.shape = 3,4
print (a.shape)
print (id(a))
print (id(b))
(3, 4)
4838498832
4838498832
#The view method creates a new array object that looks at the same data.
c = a.view()
c is a
c.shape = 2,6
#print a.shape
c[0,4] = 1234
a
array([[ 0, 1, 2, 3],
[1234, 5, 6, 7],
[ 8, 9, 10, 11]])
#The copy method makes a complete copy of the array and its data.
d = a.copy()
d is a
d[0,0] = 9999
print (d)
print (a)
[[9999 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
[[ 0 1 2 3]
[1234 5 6 7]
[ 8 9 10 11]]
f = np.empty((6,6))
print(f)
[[1.28822975e-231 1.28822975e-231 1.38338381e-322 0.00000000e+000
0.00000000e+000 0.00000000e+000]
[0.00000000e+000 0.00000000e+000 0.00000000e+000 0.00000000e+000
0.00000000e+000 0.00000000e+000]
[0.00000000e+000 0.00000000e+000 0.00000000e+000 7.13445810e+159
1.28822975e-231 1.28822975e-231]
[9.88131292e-323 0.00000000e+000 0.00000000e+000 0.00000000e+000
0.00000000e+000 0.00000000e+000]
[0.00000000e+000 0.00000000e+000 0.00000000e+000 0.00000000e+000
0.00000000e+000 0.00000000e+000]
[0.00000000e+000 1.28822975e-231 1.28822975e-231 1.28822975e-231
5.92878775e-323 0.00000000e+000]]
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