# 5.3. Array Methods¶

## 5.3.1. Copy¶

>>> import numpy as np
>>>
>>>
>>> a = np.array([1, 2, 3])
>>> b = a
>>> c = a.copy()
>>>
>>> a[0] = 99
>>>
>>> a
array([99,  2,  3])
>>>
>>> b
array([99,  2,  3])
>>>
>>> c
array([1, 2, 3])


## 5.3.2. Put¶

>>> import numpy as np


One dimensional:

>>> a = np.array([1, 2, 3, 4, 5, 6])
>>>
>>> a.put([0, 2, 5], 99)
>>> a
array([99,  2, 99,  4,  5, 99])

>>> a = np.array([1, 2, 3, 4, 5, 6])
>>> b = np.array([99, 88, 77, 66, 55, 44, 33, 22])
>>>
>>> a.put([0, 2, 5], b)
>>> a
array([99,  2, 88,  4,  5, 77])


Two dimensional:

• Equivalent to a.flat[indexes] = value

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> b = np.array([99, 88, 77, 66, 55, 44, 33, 22])
>>>
>>> a.put([0, 2, 5], b)
>>> a
array([[99,  2, 88],
[ 4,  5, 77],
[ 7,  8,  9]])


## 5.3.3. Fill¶

• Modifies inplace

Fill all:

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> a.fill(0)
>>> a
array([[0, 0, 0],
[0, 0, 0],
[0, 0, 0]])


Fill slice:

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> a[:, 0].fill(0)
>>> a
array([[0, 2, 3],
[0, 5, 6],
[0, 8, 9]])


Fill NaN (dtype=np.int64):

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]], dtype=np.int64)
>>>
>>> a[:, 0].fill(np.nan)
>>> a
array([[-9223372036854775808,                    2,                    3],
[-9223372036854775808,                    5,                    6],
[-9223372036854775808,                    8,                    9]])


Fill NaN (dtype=np.float):

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]], dtype=np.float64)
>>>
>>> a[:, 0].fill(np.nan)
>>> a
array([[nan,  2.,  3.],
[nan,  5.,  6.],
[nan,  8.,  9.]])


## 5.3.4. Transpose¶

• a.transpose() or a.T

• a.transpose() is preferred

>>> import numpy as np

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

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> a.transpose()
array([[1, 4, 7],
[2, 5, 8],
[3, 6, 9]])


## 5.3.5. Signum¶

>>> import numpy as np
>>>
>>>
>>> a = np.array([[-2, -1, 0],
...               [0, 1, 2]])
>>>
>>> np.sign(a)
array([[-1, -1,  0],
[ 0,  1,  1]])


## 5.3.6. Use Case - 0x01¶

• t1 = 230 lux

• t2 = 218 lux

• t3 = 230 lux

• t4 = 2 lux

• t5 = 0 lux

• t6 = 0 lux

• t7 = 10 lux

• t8 = 0 lux

>>> import numpy as np
>>>
>>>
>>> data = np.array([230, 218, 230, 2, 0, 0, 10, 0])
>>> np.sign(data)
array([1, 1, 1, 1, 0, 0, 1, 0])
>>>
>>> data[data<50] = 0
>>> np.sign(data)
array([1, 1, 1, 0, 0, 0, 0, 0])


## 5.3.7. Assignments¶

"""
* Assignment: Numpy Methods
* Complexity: easy
* Lines of code: 4 lines
* Time: 5 min

English:
1. Reshape result to 3x4
2. Fill last column with zeros (0)
3. Transpose result
4. Convert result to float
5. Fill first row with np.nan
6. Run doctests - all must succeed

Polish:
1. Zmień kształt na 3x4
2. Wypełnij ostatnią kolumnę zerami (0)
3. Transponuj result
4. Przekonwertuj result do float
5. Wypełnij pierwszy wiersz np.nan
6. Uruchom doctesty - wszystkie muszą się powieść

Tests:
>>> import sys; sys.tracebacklimit = 0

>>> assert result is not Ellipsis, \
'Assign result to variable: result'
>>> assert type(result) is np.ndarray, \
'Variable result has invalid type, expected: np.ndarray'

>>> result
array([[nan, nan, nan],
[47.,  9., 87.],
[64., 83., 70.],
[ 0.,  0.,  0.]])
"""

import numpy as np

DATA = np.array([[44, 47, 64, 67],
[67,  9, 83, 21],
[36, 87, 70, 88]])

result = ...