2.6. Array Import¶

2.6.1. SetUp¶

>>> import numpy as np

2.6.2. np.loadtxt()¶

>>> DATA = 'https://python.astrotech.io/_static/iris.csv'

>>> a = np.loadtxt(DATA)
Traceback (most recent call last):
ValueError: could not convert string 'sepal_length,sepal_width,petal_length,petal_width,species' to float64 at row 0, column 1.

>>> a = np.loadtxt(DATA, skiprows=1)
Traceback (most recent call last):
ValueError: could not convert string '5.4,3.9,1.3,0.4,setosa' to float64 at row 0, column 1.

>>> a = np.loadtxt(DATA, skiprows=1, delimiter=',')
Traceback (most recent call last):
ValueError: could not convert string 'setosa' to float64 at row 0, column 5.

>>> a = np.loadtxt(DATA, skiprows=1, delimiter=',', max_rows=5, usecols=(0,1,2,3))
>>> a
array([[5.4, 3.9, 1.3, 0.4],
       [5.9, 3. , 5.1, 1.8],
       [6. , 3.4, 4.5, 1.6],
       [7.3, 2.9, 6.3, 1.8],
       [5.6, 2.5, 3.9, 1.1]])

>>> header = np.loadtxt(DATA, max_rows=1, delimiter=',', dtype=str, usecols=(0,1,2,3))
>>> data = np.loadtxt(DATA, skiprows=1, max_rows=3, delimiter=',', usecols=(0,1,2,3))
>>>
>>> header  
array(['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], dtype='<U12')
>>>
>>> data
array([[5.4, 3.9, 1.3, 0.4],
       [5.9, 3. , 5.1, 1.8],
       [6. , 3.4, 4.5, 1.6]])

2.6.3. Other¶

Table 2.4. NumPy Import methods¶
Method	Data Type	Description
`np.loadtxt()`	Text	Load data from text file such as `.csv`
`np.load()`	Binary	Load data from `.npy` file
`np.loads()`	Binary	Load binary data from `pickle` string
`np.fromstring()`	Text	Load data from string
`np.fromregex()`	Text	Load data from file using regex to parse
`np.genfromtxt()`	Text	Load data with missing values handled as specified
`scipy.io.loadmat()`	Binary	reads MATLAB data files

>>> 
... data = np.loadtxt('myfile.csv', delimiter=',', usecols=1, skiprows=1, dtype=np.float16)
...
... small = (data < 1)
... medium = (data < 1) & (data < 2.0)
... large = (data < 2)
...
... np.save('/tmp/small', data[small])
... np.save('/tmp/medium', data[medium])
... np.save('/tmp/large', data[large])

2.6.4. Assignments¶

Code 2.30. Solution¶

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

English:
    1. Load text from `URL`
    2. From the first line select Iris species names and save as str to `species: np.ndarray`
    3. For other lines:
        a. Read columns with data and save as float to `features: np.ndarray`
        b. Read last column with species numbers and save as `int` to `labels: np.ndarray`
    4. Run doctests - all must succeed

Polish:
    1. Wczytaj tekst z `URL`
    2. Z pierwszej linii wybierz nazwy gatunków Irysów i zapisz rezultat jako str do `species: np.ndarray`
    3. W pozostałych linii:
        a Wczytaj kolumny z danymi i zapisz jako float do `features: np.ndarray`
        b Wczytaj ostatnią kolumnę z numerami gatunków i zapisz jako `int` do `labels: np.ndarray`
    4. Uruchom doctesty - wszystkie muszą się powieść

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

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

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

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

    >>> species
    array(['setosa', 'versicolor', 'virginica'], dtype='<U10')

    >>> len(features)
    151

    >>> features[:3]
    array([[5.4, 3.9, 1.3, 0.4],
           [5.9, 3. , 5.1, 1.8],
           [6. , 3.4, 4.5, 1.6]])

    >>> features[-3:]
    array([[4.9, 2.5, 4.5, 1.7],
           [6.3, 2.8, 5.1, 1.5],
           [6.8, 3.2, 5.9, 2.3]])

    >>> labels
    array([0, 2, 1, 2, 1, 0, 1, 1, 0, 2, 2, 0, 0, 2, 2, 1, 2, 2, 2, 1, 0, 1,
           1, 0, 0, 0, 2, 2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2, 1, 1, 1, 2, 2,
           0, 1, 1, 1, 1, 1, 2, 0, 2, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 2, 0, 0,
           0, 0, 0, 0, 1, 0, 2, 0, 0, 1, 1, 2, 2, 1, 0, 2, 1, 0, 1, 0, 2, 1,
           0, 2, 0, 2, 1, 0, 2, 1, 1, 0, 0, 1, 2, 2, 2, 1, 0, 1, 1, 1, 2, 2,
           0, 2, 2, 0, 2, 1, 2, 0, 0, 1, 0, 2, 0, 2, 1, 2, 2, 2, 1, 0, 2, 1,
           0, 0, 2, 0, 2, 1, 1, 1, 0, 1, 1, 2, 0, 1, 1, 0, 2, 2, 2])
"""

import numpy as np


DATA = 'https://python.astrotech.io/_static/iris-dirty.csv'

species = ...
features = ...
labels = ...