Working with NDArray
Working with NDArray
Get shape of array
We can get shape of an array by accessing 'shape' property.
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4)])
print array.shape
print len(array.shape)
print array.shape[0]
print array.shape[1]
if __name__ == "__main__":
test_run()Here we have printed out the shape of the array.
(2, 3)
2
2
3Get size of array
Size of array is returned as sum of all cells in the n-dimension array.
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4)])
print array.size
if __name__ == "__main__":
test_run()For our example, which contains 6 numbers, it prints out 6.
6Type of array
NumPy arrays are homogenous (all the cells must have the same type) and we can get that type for an array.
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4)])
print array.dtype
if __name__ == "__main__":
test_run()Here is the type of the array.
int64Make random numbers the same for every execution
We can set seed for random number generation. Which will make sure that the random numbers are generated always the same.
import numpy as np
def test_run():
np.random.seed(100)
array = np.random.randint(0, 10, size=(3,4))
print array
if __name__ == "__main__":
test_run()So, everytime we run this code, we get the same result.
[[8 8 3 7]
[7 0 4 2]
[5 2 2 2]]Sum all elements in array
import numpy as np
def test_run():
print array
print array.sum()
if __name__ == "__main__":
test_run()Here is sum.
[[1 2 3]
[2 3 4]]
15Sum of each column and row
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4)])
print array
print array.sum(0) # sum of columns
print array.sum(1) # sum of rows
if __name__ == "__main__":
test_run()Here are the sums.
[3 5 7]
[6 9]Finding max, min and mean in an array
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4)])
print array
print array.max(0) # max of first column
print array.min(0) # min of first column
print array.mean() # mean of all elements
if __name__ == "__main__":
test_run()Her are the values.
[[1 2 3]
[2 3 4]]
[2 3 4]
[1 2 3]
2.5There are more functions available, check this page.
Find index of max value
import numpy as np
def get_max_index(a):
return np.argmax(a)
def test_run():
a = np.array([9, 6, 2, 3, 12, 14, 7, 10], dtype=np.int32) # 32-bit integer array
print "Array:", a
# Find the maximum and its index in array
print "Maximum value:", a.max()
print "Index of max.:", get_max_index(a)
if __name__ == "__main__":
test_run()Here is the output.
Array: [ 9 6 2 3 12 14 7 10]
Maximum value: 14
Index of max.: 5Glue arrays together
There are many ways to append or connect two arrays. Depends what we really need.
Append one array to another array.
In [1]: import numpy as np
In [2]: a = np.array([[1, 2, 3], [4, 5, 6]])
In [3]: b = np.array([[9, 8, 7], [6, 5, 4]])
In [4]: np.concatenate((a, b))
Out[4]:
array([[1, 2, 3],
[4, 5, 6],
[9, 8, 7],
[6, 5, 4]])Or we can do the same thing using vstack function.
In [1]: a = np.array([1, 2, 3])
In [2]: b = np.array([4, 5, 6])
In [3]: np.vstack((a, b))
Out[3]:
array([[1, 2, 3],
[4, 5, 6]])Or using hstack to do it horizontally.
a = np.array((1,2,3))
b = np.array((2,3,4))
np.hstack((a,b))
a = np.array([[1],[2],[3]])
b = np.array([[2],[3],[4]])
np.hstack((a,b))Add another column into an array.
my_data = np.random.random((210,8))
new_col = my_data.sum(1)[...,None]
new_col.shape
#(210,1)
all_data = np.append(my_data, new_col, 1)
all_data.shape
#(210,9)
# or
>>> a = np.array([[1,2,3],[2,3,4]])
>>> a
array([[1, 2, 3],
[2, 3, 4]])
>>> z = np.zeros((2,1))
>>> z
array([[ 0.],
[ 0.]])
>>> np.concatenate((a, z), axis=1)
array([[ 1., 2., 3., 0.],
[ 2., 3., 4., 0.]])Slicing array
If you want to slice data, get some specific values of array, you can access it via indices.
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4), (4, 5, 6)])
print array
print array[:, 0:3:2]
if __name__ == "__main__":
test_run()First argument ":" makes sure all rows are included. Second argument if more complex. 0 and 3 are saying what values to take. 2 is saying what will be skipped. Here is the output.
[[1 2 3]
[2 3 4]
[4 5 6]]
[[1 3]
[2 4]
[4 6]]Assigning values in array
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4), (4, 5, 6)])
print array
array[0][0] = 100
print array
if __name__ == "__main__":
test_run()Here is the output.
[[1 2 3]
[2 3 4]
[4 5 6]]
[[100 2 3]
[ 2 3 4]
[ 4 5 6]]Or you can assign value to whole row or column.
import numpy as np
def test_run():
array = np.array([(1, 2, 3), (2, 3, 4), (4, 5, 6)])
print array
array[:, 0] = 100
print array
if __name__ == "__main__":
test_run()Here is the output.
[[1 2 3]
[2 3 4]
[4 5 6]]
[[100 2 3]
[100 3 4]
[100 5 6]]Or you can set list of values to a row.
array[:, 0] = [0, 0,0]Indexing
import numpy as np
def test_run():
array = np.random.rand(5)
indices = np.array([1, 1, 2, 3])
print array
print array[indices]
if __name__ == "__main__":
test_run()Here is the output.
[ 0.22607416 0.94175441 0.45115583 0.3740176 0.53811653]
[ 0.94175441 0.94175441 0.45115583 0.3740176 ]Filtering data using conditions
Also called masking.
import numpy as np
def test_run():
array = np.array([(20, 25, 10, 5), (10, 25, 10, 5)])
print array
mean = array.mean()
print mean
print array[array < mean]
if __name__ == "__main__":
test_run()Output.
[[20 25 10 5]
[10 25 10 5]]
13.75
[10 5 10 10 5]Multiplication
import numpy as np
def test_run():
array = np.array([(20, 25, 10, 5), (10, 25, 10, 5)])
print array
print array * 2
if __name__ == "__main__":
test_run()Output.
[[20 25 10 5]
[10 25 10 5]]
[[40 50 20 10]
[20 50 20 10]]Sum arrays
import numpy as np
def test_run():
array = np.array([(20, 25, 10, 5), (10, 25, 10, 5)])
print array
print array + array
if __name__ == "__main__":
test_run()Output.
[[20 25 10 5]
[10 25 10 5]]
[[40 50 20 10]
[20 50 20 10]]Last updated
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