851 - 900 Flashcards

Question 1

Q

numpy.random.shuffle()

Answer

A

This function only shuffles the array along the first axis of a multi-dimensional array.

arr = np.arange(10)
np.random.shuffle(arr)
👉 [1 7 5 2 9 4 3 6 0 8]

arr = np.arange(9).reshape((3, 3))
np.random.shuffle(arr)

arr
👉 array([[3, 4, 5],
          [6, 7, 8],
          [0, 1, 2]])

numpy.random.shuffle

Question 2

Q

numpy.transpose(a, axes=None)

Answer

A

Reverse or permute the axes of an array and returns the modified array.

x = np.array([[0, 1],
              [2, 3]])

print(np.transpose(x))
👉 [[0 2]
    [1 3]]

my_array = numpy.array([[1,2,3],
                        [4,5,6]])

print(numpy.transpose(my_array))
👉 [[1 4]
    [2 5]
    [3 6]]

numpy.transpose

Question 3

Q

numpy.append(arr, values, axis=None)

Answer

A

Append values to the end of an array.

np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
👉 array([1, 2, 3, ..., 7, 8, 9])

numpy.append

Question 4

Q

numpy.inner(a, b, /)

Answer

A

Inner product of two arrays. Ordinary inner product of vectors for 1-D arrays.

a = np.array([1,2,3])
b = np.array([0,1,0])

print(np.inner(a, b))
👉 2

A = numpy.array([0, 1])
B = numpy.array([3, 4])

print(numpy.inner(A, B))
👉 4

A = numpy.array([2, 1])
B = numpy.array([3, 4])

print(numpy.inner(A, B))
👉 10

numpy.inner

Question 5

Q

numpy.outer(a, b, out=None)

Answer

A

Compute the outer product of two vectors.

A = numpy.array([2, 3])
B = numpy.array([3, 4])
print(numpy.outer(A, B))
👉 [[ 6  8]
    [ 9 12]]

A = numpy.array([0, 1])
B = numpy.array([3, 4])
print numpy.outer(A, B) 
👉 [[0 0]
    [3 4]]

numpy.outer

Question 6

Q

Array Broadcasting

Answer

A

describes how NumPy treats arrays with different shapes during arithmetic operations. Subject to certain constraints, the smaller array is “broadcast” across the larger array so that they have compatible shapes.

if Rows_1 == Rows_2 and Colums_1 == Colums_2:
	compatible

if (Rows_1 == 1 or Colums_1 == 1) and (Rows_2 == 1 or Colums_2 == 1):
	compatible

if (Rows_1 == 1 or Colums_1 == 1) and (Rows_1 == Rows_2 or Colums_1 == Colums_2)
	compatible

x.shape == (2, 3)

y.shape == (2, 3) --- compatible
y.shape == (2, 1) --- compatible
y.shape == (1, 3) --- compatible
y.shape == (3, )  --- compatible

y.shape == (3, 2) --- NOT_compatible
y.shape == (2,  ) --- NOT_compatible

x.shape == (1, 2, 3, 5, 1, 11, 1, 17)

y.shape ==          (1, 7, 1,  1, 17)  --- compatible

Array Broadcasting

Question 7

Q

numpy.empty(shape, dtype=float, order=’C’, *, like=None)

🎯 shape — int or tuple of int — Shape of the empty array, e.g., (2, 3) or 2.
🎯 order — {‘C’, ‘F’}, optional, default: ‘C’ — Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.

Answer

A

Return a new array of given shape and type, without initializing entries.

np.empty([2, 2])
👉 array([[ -9.74499359e+001,   6.69583040e-309],
          [  2.13182611e-314,   3.06959433e-309]])

np.empty([2, 2], dtype=int)
👉 array([[-1073741821, -1067949133],
          [  496041986,    19249760]])

Question 8

Q

numpy.diag(v, k=0)

Answer

A

Extract a diagonal or construct a diagonal array.

x = array([[0, 1, 2],
           [3, 4, 5],
           [6, 7, 8]])

np.diag(x)
👉 array([0, 4, 8])

np.diag(x, k=1)
👉 array([1, 5])

np.diag(x, k=-1)
👉 array([3, 7])

np.diag(np.diag(x))
👉 array([[0, 0, 0],
          [0, 4, 0],
          [0, 0, 8]])

numpy.diag

Question 9

Q

numpy.zeros(shape, dtype=float, order=’C’, *, like=None)

Answer

A

Return a new array of given shape and type, filled with zeros.

np.zeros(5)
👉 array([ 0.,  0.,  0.,  0.,  0.])

np.zeros((5,), dtype=int)
👉 array([0, 0, 0, 0, 0])

np.zeros((2, 1))
👉 array([[ 0.],
          [ 0.]])

s = (2,2)
np.zeros(s)
👉 array([[ 0.,  0.],
          [ 0.,  0.]])

numpy.zeros

Question 10

Q

numpy.genfromtxt()

Answer

A

Load data from a text file, with missing values handled as specified.

f = StringIO('''text,# of chars hello world,11 numpy,5''')

np.genfromtxt(f, dtype='S12,S12', delimiter=',')
👉 array([(b'text', b''), (b'hello world', b'11'), (b'numpy', b'5')], 
   dtype=[('f0', 'S12'), ('f1', 'S12')])

s = StringIO(u"1,1.3,abcde")
data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'), ('mystring','S5')], delimiter=",")

data
👉 array((1, 1.3, b'abcde'), dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', 'S5')])

numpy.genfromtxt

Question 11

Q

numpy.stack(arrays, axis=0, out=None)

Answer

A

Join a sequence of arrays along a new axis.

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

np.stack((a, b))
👉 array([[1, 2, 3],
          [4, 5, 6]])

np.stack((a, b), axis=-1)
👉 array([[1, 4],
          [2, 5],
          [3, 6]])

numpy.stack

Question 12

Q

numpy.squeeze(a, axis=None)

Answer

A

Remove axes of length.

x = np.array([[[0], [1], [2]]])

print(np.squeeze(x))
👉 [0 1 2]

x = np.array([[[0], [1], [2]]])

print(np.squeeze(x, axis=0))
👉 [[0]
    [1]
    [2]]

numpy.squeeze

Question 13

Q

pyment

🎯 pip install pyment - install
🎯 pyment -h - get the available options
🎯 python setup.py test - run the unit-tests:

Answer

A

docstrings manager (creator/converter)

To run Pyment from the command line the easiest way is to provide a Python file or a folder:

✅ will generate a patch from file
pyment example.py

✅ will generate a patch from folder
pyment folder/to/python/progs

✅ will overwrite the file
pyment -w myfile.py

To run the unit-tests:

import os
from pyment import PyComment

filename = 'test.py'

c = PyComment(filename)
c.proceed()
c.diff_to_file(os.path.basename(filename) + ".patch")
for s in c.get_output_docs():
    print(s)

pyment

Question 14

Q

min()

Answer

A

function returns the item with the lowest value, or the item with the lowest value in an iterable. If the values are strings, an alphabetically comparison is done.

x = min(5, 10)
print(x)
👉 5

# Найти минималное значение сравнив два значения
test = [1, 2, 4, 5, 6]

for i in test:
    print(min(3, i))

👉 1
👉 2
👉 3
👉 3
👉 3

min

Question 15

Q

math.Manhattan Distance

Answer

A

The distance between two points measured along axes at right angles. In a plane with p1 at (x1, y1) and p2 at (x2, y2), it is |x1 - x2| + |y1 - y2|. Lm distance.

Distance of { 1, 6 }, { 3, 5 }, { 2, 3 } from { -1, 5 }
👉 sum = (abs(1 - (-1)) + abs(6 - 5)) + (abs(3 - (-1)) + abs(5 - 5)) +
(abs(2 - (-1)) + abs(3 - 5)) = 3 + 4 + 5 = 12

Distance of { 3, 5 }, { 2, 3 } from { 1, 6 }
👉 sum = 12 + 3 + 4 = 19

Distance of { 2, 3 } from { 3, 5 }
👉 sum = 19 + 3 = 22.

def distancesum (x, y, n):
    sum = 0
		
    for i in range(n):
        for j in range(i+1,n):
            sum += (abs(x[i] - x[j]) + abs(y[i] - y[j]))
     
    return sum
 
x = [ -1, 1, 3, 2 ]
y = [ 5, 6, 5, 3 ]
n = len(x)
print(distancesum(x, y, n)

math.Manhattan Distance

Question 16

Q

What is the difference between programming and scripting?

Answer

A

Programming is used to create complex software, and it is compiled. Scripting assists programming languages and it is interpreted.

Question 17

Q

git.rmdir

Answer

A

удалить папку.

Question 18

Q

print(*objects , sep=’’ , end=’\n’ , file=sys.stdout , flush=False)

💡*objects - объекты Python
💡 sep=’’ - строка, разделитель объектов. Значение по умолчанию None
💡 end=’\n’ - строка, которой заканчивается поток. Значение по умолчанию None
💡 file=sys.stdout - объект, реализующий метод wrtite(string). Значение по умолчанию None
💡 flush=False - если True поток будет сброшен в указанный файл file принудительно.
Значение по умолчанию False

Answer

A

выводит объекты в текстовый поток, отделяя их друг от друга sep и заканчивая поток end. sep, end, file и flush, если они заданы, должны быть переданы в качестве аргументов ключевых слов.

print('Hello')
👉 Hello

print('Hello', 'how are you?')
👉 Hello how are you?

print('Hello', 'how are you?', sep='---')
👉 Hello---how are you?

lst = ['Раз', 'Два', 'Три']
for n, line in enumerate(lst, 1):
....if len(lst) == n:
........print(line)
....else:
........print(line, end='=>')

👉 Раз=>Два=>Три

print(11, 12, 13, 14, sep=';')
👉 11;12;13;14

# использование символа новой строки `\n` в переменной 
line = 'перенос строки при печати\nс помощью символа новой строки'
print(line)
👉 перенос строки при печати
👉 с помощью символа новой строки

print
print

Question 19

Q

numpy.busday_count(begindates, enddates, weekmask=’1111100’, holidays=[], busdaycal=None, out=None)

Answer

A

Counts the number of valid days between begindates and enddates, not including the day of enddates. If enddates specifies a date value that is earlier than the corresponding begindates date value, the count will be negative.

# Number of weekdays in January 2011
np.busday_count('2011-01', '2011-02')
👉 21

# Number of weekdays in 2011
np.busday_count('2011', '2012')
👉 260

# Number of Saturdays in 2011
np.busday_count('2011', '2012', weekmask='Sat')
👉 53

numpy.busday_count

Question 20

Q

os.path.commonprefix()

Answer

A

used to get longest common path prefix in a list of paths. This method returns only common prefix value in the specified list.

paths = ['/home/User/Desktop', '/home/User/Documents', '/home/User/Downloads']
prefix = os.path.commonprefix(paths)
print(prefix)

👉 /home/User/D

paths = ['/usr/local/bin', '/usr/bin']
prefix = os.path.commonprefix(paths)
print(prefix)

👉 /usr/

os.path.commonprefix

Question 21

Q

os.path.commonpath()

Answer

A

used to get the longest common sub-path in a list of paths. This method raise ValueError if the specified list of paths either contains both absolute and relative path, or is empty.

paths = ['/home/User/Desktop', '/home/User/Documents',  '/home/User/Downloads'] 
prefix = os.path.commonpath(paths)
print(prefix)

👉 /home/User

paths = ['/usr/local/bin', '/usr/bin']
prefix = os.path.commonpath(paths)
print(prefix)

👉 /usr

os.path.commonpath

Question 22

Q

numpy.random.default_rng(seed=None)

Answer

A

Construct a new Generator with the default BitGenerator (PCG64).

rng = np.random.default_rng(12345)
rfloat = rng.random()

print(rfloat)
👉 0.22733602246716966

rng = np.random.default_rng(12345)
rints = rng.integers(low=0, high=10, size=3)

print(rints)
👉 array([6, 2, 7])

If we exit and restart our Python interpreter, we’ll see that we generate the same random 
numbers again:

rng = np.random.default_rng(seed=42)
arr2 = rng.random((3, 3))

print(arr2)
👉 array([[0.77395605, 0.43887844, 0.85859792],
          [0.69736803, 0.09417735, 0.97562235],
          [0.7611397 , 0.78606431, 0.12811363]])

numpy.random.default_rng

Question 23

Q

Skewness

Answer

A

measurement of the distortion(искажение) of symmetrical distribution or asymmetry in a data set. Skewness is demonstrated on a bell curve when data points are not distributed symmetrically to the left and right sides of the median on a bell curve.

Skewness

Question 24

Q

git.nano

🎯 Ctrl+o - save the changes you’ve made to the file.
🎯 Ctrl+x - exit nano. If there are unsaved changes, you’ll be asked whether you want to save the changes.

Answer

A

open an existing file or to create a new file.

nano filename

git.nano

Question 25

Q

SQL.POW()

Answer

A

used to return a result after raising a specified exponent number to a specified base number. For example, if the base is **5 and the exponent is 2, this will return a result of 25.

SELECT POW(7, 2);
👉 49

SELECT POW(3, 3);
👉 27

SELECT POW(6, 0);
👉 1

SELECT POW(0, 4);
👉 0

SQL.POW

Question 26

Q

SQL.FORMAT()

Answer

A

used to format the specified value in the given format.

SELECT FORMAT(25, 'N')

SELECT FORMAT(1, 'P', 'en-US')AS [PERCENTAGE IN US FORMAT], 
    FORMAT(1, 'P', 'en-IN') AS [PERCENTAGE IN INDIA FORMAT];

DECLARE @d DATETIME = GETDATE();  
SELECT FORMAT( @d, 'dd/MM/yyyy', 'en-US' ) AS 'DateTime Result'

SELECT FORMAT(SYSDATETIME(), N'hh:mm tt');

SELECT 
    FORMAT(1, 'C', 'in-IN') AS 'INDIA', 
    FORMAT(1, 'C', 'ch-CH') AS 'CHINA', 
    FORMAT(1, 'C', 'sw-SW') AS 'SWITZERLAND', 
    FORMAT(1, 'C', 'us-US') AS 'USA';

SQL.FORMAT

Question 27

Q

SQL.FLOOR()

Answer

A

returns the largest integer value that is smaller than or equal to a number.

SELECT FLOOR(25.75);
👉 25

SELECT FLOOR(25.44);
👉 25

SELECT FLOOR(-21.53);
👉 -22

SQL.FLOOR

Question 28

Q

MySQL.GROUP_CONCAT()

Answer

A

used to concatenate data from multiple rows into one field.

SELECT emp_id, fname, lname, dept_id, 
GROUP_CONCAT ( strength ) as "strengths" 
FROM employee group by emp_id;

SELECT dept_id, 
GROUP_CONCAT ( DISTINCT emp_id ORDER BY emp_id  SEPARATOR', ') 
as "employees ids" 
from employee group by dept_id;

SELECT dept_id, GROUP_CONCAT ( strengths SEPARATOR '  ') as "emp-id : strengths"
FROM ( SELECT dept_id, CONCAT ( emp_id, ':', GROUP_CONCATt(strength SEPARATOR', ') )
as "strengths" FROM employee GROUP BY emp_id )as emp GROUP BY dept_id;

MySQL.GROUP_CONCAT

Question 29

Q

MySQL.COALESCE()

Answer

A

returns the first non-null value in a list.

SELECT COALESCE(NULL, NULL, NULL, 'W3Schools.com', NULL, 'Example.com');
👉 W3Schools.com

SELECT COALESCE(NULL, 1, 2, 'W3Schools.com');
👉 1

SELECT COALESCE(1, 2, Null, 'W3Schools.com');
👉 1

MySQL.COALESCE

Question 30

Q

numpy.sum(a, axis=None, dtype=None, out=None, keepdims=<no>, initial=<no>, where=<no>)</no></no></no>

Answer

A

Sum of array elements over a given axis.

np.sum([0.5, 1.5])
👉 2.0

np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
👉 1

np.sum([[0, 1], [0, 5]])
👉 6

np.sum([[0, 1], [0, 5]], axis=0)
👉 array([0, 6])

np.sum([[0, 1], [0, 5]], axis=1)
👉 array([1, 5])

np.sum([[0, 1], [np.nan, 5]], where=[False, True], axis=1)
👉 array([1., 5.])

Question 31

Q

numpy.dot(a, b, out=None)

Answer

A

Произведение двух массивов.

np.dot(3, 4)
👉 12

a = [[1, 0], [0, 1]]
b = [[4, 1], [2, 2]]

np.dot(a, b)
👉 array([[4, 1],
          [2, 2]])

numpy.dot(a, b, out=None)

Question 32

Q

numpy.random.randint(low, high=None, size=None, dtype=int)

🎯low — Lowest (signed) integers to be drawn from the distribution
🎯high — If provided, one above the largest (signed) integer to be drawn from the distribution
🎯size — Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

Answer

A

Return random integers from low (inclusive) to high (exclusive).

np.random.randint(2, size=10)
👉 array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])

np.random.randint(1, size=10)
👉 array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

np.random.randint(5, size=(2, 4))
👉 array([[4, 0, 2, 1],
          [3, 2, 2, 0]])

np.random.randint([1, 3, 5, 7], [[10], [20]], dtype=np.uint8)
👉 array([[ 8,  6,  9,  7],
          [ 1, 16,  9, 12]], dtype=uint8)

numpy.random.randint

Question 33

Q

numpy.full(shape, fill_value, dtype=None, order=’C’, *, like=None)

Answer

A

Return a new array of given shape and type, filled with fill_value.

np.full((2, 2), np.inf)
array([[inf, inf],
       [inf, inf]])

np.full((2, 2), 10)
array([[10, 10],
       [10, 10]])

np.full((2, 2), [1, 2])
array([[1, 2],
       [1, 2]])

numpy.full

Question 34

Q

numpy.reshape(a, newshape, order=’C’)

🎯a(array_like) — Array to be reshaped.
🎯newshape(int or tuple of ints) — The new shape should be compatible with the original shape.
🎯reshaped_array(ndarray) — This will be a new view object if possible; otherwise, it will
be a copy.

Answer

A

Gives a new shape to an array without changing its data.

np.reshape(a, (2, 3)) # C-like index ordering
array([[0, 1, 2],
       [3, 4, 5]])

np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
       [2, 1, 5]])

a = np.array([[1,2,3], [4,5,6]])
np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])

numpy.reshape

Question 35

Q

numpy.arange([start, ]stop, [step, ]dtype=None, *, like=None)

Answer

A

Return evenly spaced values within a given interval.

np.arange(3)
👉 array([0, 1, 2])

np.arange(3.0)
👉 array([ 0.,  1.,  2.])

np.arange(3,7)
👉 array([3, 4, 5, 6])

np.arange(3,7,2)
👉 array([3, 5])

numpy.arange

Question 36

Q

numpy.identity(n, dtype=None, *, like=None)

Answer

A

Return square array with ones on the main diagonal.

np.identity(3)
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])

numpy.identity

Question 37

Q

numpy.fromfunction(function, shape, *, dtype=<class ‘float’>, like=None, **kwargs)

Answer

A

Construct an array by executing a function over each coordinate. The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z).

np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
array([[ True, False, False],
       [False,  True, False],
       [False, False,  True]])

np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4]])

numpy.fromfunction

Question 38

Q

numpy.fromiter(iter, dtype, count=- 1, *, like=None)

Answer

A

Create a new 1-dimensional array from an iterable object.

iterable = (x*x for x in range(5))
np.fromiter(iterable, float)
👉 array([  0.,   1.,   4.,   9.,  16.])

numpy.fromiter

Question 39

Q

numpy.delete(arr, obj, axis=None)

Answer

A

Return a new array with sub-arrays along an axis deleted.

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

np.delete(arr, 1, 0)
array([[ 1,  2,  3,  4],
       [ 9, 10, 11, 12]])

np.delete(arr, np.s_[::2], 1)
array([[ 2,  4],
       [ 6,  8],
       [10, 12]])

np.delete(arr, [1,3,5], None)
array([ 1,  3,  5,  7,  8,  9, 10, 11, 12])

numpy.delete

Question 40

Q

numpy.imag(val)

Answer

A

Return the imaginary part of the complex argument.

a = np.array([1+2j, 3+4j, 5+6j])
a.imag
👉 array([2.,  4.,  6.])

np.imag(1 + 1j)
👉 1.0

numpy.imag

Question 41

Q

numpy.expand_dims(a, axis)

Answer

A

Expand the shape of an array. Insert a new axis that will appear at the axis position in the expanded array shape.

x = np.array([1, 2])

x.shape
👉 (2,)

y = np.expand_dims(x, axis=0)

y.shape
👉 (1, 2)

y = np.expand_dims(x, axis=1)

y.shape
👉 (2, 1)

numpy.expand_dims

Question 42

Q

numpy.ravel(a, order=’C’)

Answer

A

Return a contiguous flattened array. A 1-D array, containing the elements of the input, is returned. A copy is made only if needed.

x = np.array([[1, 2, 3], [4, 5, 6]])

np.ravel(x)
array([1, 2, 3, 4, 5, 6])

numpy.ravel

Question 43

Q

numpy.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=<no>, *, where=<no>)</no></no>

Answer

A

Compute the standard deviation along the specified axis. Returns the standard deviation, a measure of the spread of a distribution, of the array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis.

a = np.array([[1, 2], [3, 4]])
np.std(a)
👉 1.1180339887498949

np.std(a, axis=0)
👉 array([1.,  1.])

np.std(a, axis=1)
👉 array([0.5,  0.5])

a = np.zeros((2, 512*512), dtype=np.float32)
a[0, :] = 1.0
a[1, :] = 0.1

np.std(a)
👉 0.45000005

np.std(a, dtype=np.float64)
👉 0.44999999925494177

a = np.array([[14, 8, 11, 10], [7, 9, 10, 11], [10, 15, 5, 10]])

np.std(a)
👉 2.614064523559687

np.std(a, where=[[True], [True], [False]])
👉 2.0

numpy.std

Question 44

Q

numpy.nbytes

Answer

A

Total bytes consumed by the elements of the array.

x = np.zeros((3,5,2), dtype=np.complex128)
x.nbytes
👉 480

np.prod(x.shape) * x.itemsize
👉 480

numpy.nbytes

Question 45

Q

numpy.concatenate((a1, a2, …), axis=0, out=None, dtype=None, casting=”same_kind”)

Answer

A

Join a sequence of arrays along an existing axis.

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])

np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])

np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])

np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])

numpy.concatenate

Question 46

Q

numpy.column_stack(tup)

Answer

A

Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first.

a = np.array((1,2,3))
b = np.array((2,3,4))

np.column_stack((a,b))
array([[1, 2],
       [2, 3],
       [3, 4]])

numpy.column_stack

Question 47

Q

numpy.mean(a, axis=None, dtype=None, out=None, keepdims=<no>, *, where=<no>)</no></no>

Answer

A

Compute the arithmetic mean along the specified axis. Returns the average of the array elements.

a = np.array([[1, 2], [3, 4]])
np.mean(a)
👉 2.5

np.mean(a, axis=0)
👉 array([2., 3.])

np.mean(a, axis=1)
👉 array([1.5, 3.5])

numpy.mean

Question 48

Q

numpy.any(a, axis=None, out=None, keepdims=<no>, *, where=<no>)</no></no>

Answer

A

Test whether any array element along a given axis evaluates to True. Returns single boolean unless axis is not None.

np.any([[True, False], [True, True]])
👉 True

np.any([[True, False], [False, False]], axis=0)
👉 array([ True, False])

np.any([-1, 0, 5])
👉 True

np.any([[True, False], [False, False]], where=[[False], [True]])
👉 False

numpy.any

Question 49

Q

numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)

Answer

A

Find the unique elements of an array. Возращает array без дубликатов.

np.unique([1, 1, 2, 2, 3, 3])
👉 array([1, 2, 3])

a = np.array([[1, 1], [2, 3]])
np.unique(a)
👉 array([1, 2, 3])

a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
np.unique(a, axis=0)
👉 array([[1, 0, 0], [2, 3, 4]])

numpy.unique

Question 50

Q

numpy.hstack(tup) and numpy.vstack(tup)

Answer

A

Stack arrays in sequence horizontally (column wise). Соединить два arrays или больше.
Stack arrays in sequence vertically (row wise).

a = np.array((1,2,3))
b = np.array((4,5,6))
np.hstack((a,b))
array([1, 2, 3, 4, 5, 6])

a = np.array([[1],[2],[3]])
b = np.array([[4],[5],[6]])
np.hstack((a,b))
array([[1, 4],
       [2, 5],
       [3, 6]])

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
np.vstack((a,b))
array([[1, 2, 3],
       [4, 5, 6]])

a = np.array([[1], [2], [3]])
b = np.array([[4], [5], [6]])
np.vstack((a,b))
array([[1],
       [2],
       [3],
       [4],
       [5],
       [6]])

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