5.1. NumPy#
5.1.1. numpy.ravel: Flatten a NumPy Array#
If you want to get a 1-D array of a multi-dimensional array, try numpy.ravel(arr). You can either read the elements in the same row first or read the elements in the same column first.
import numpy as np
arr = np.array([[1, 2], [3, 4]])
arr
array([[1, 2],
[3, 4]])
np.ravel(arr)
array([1, 2, 3, 4])
np.ravel(arr, order="F")
array([1, 3, 2, 4])
5.1.2. np.squeeze: Remove Axes of Length One From an Array#
If one or more of your axes are of length one, you can remove those axes using np.squeeze.
import numpy as np
arr = np.array([[[1], [2]], [[3], [4]]])
arr
array([[[1],
[2]],
[[3],
[4]]])
arr.shape
(2, 2, 1)
new_arr = np.squeeze(arr)
new_arr
array([[1, 2],
[3, 4]])
new_arr.shape
(2, 2)
5.1.3. NumPy.take: Take Elements From an Array#
If you want to take elements from an array at specific indices, use numpy.take.
import numpy as np
arr = [3, 4, 1, 4, 5]
indices = [1, 3, 4]
# Get elements at indices 1, 3, and 4
np.take(arr, indices)
array([4, 4, 5])
5.1.4. Use List to Change the Positions of Rows or Columns in a NumPy Array#
If you want to change the positions of rows or columns in a NumPy array, simply use a list to specify the new positions as shown below.
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
arr
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
new_row_position = [1, 2, 0]
new_arr = arr[new_row_position, :]
new_arr
array([[4, 5, 6],
[7, 8, 9],
[1, 2, 3]])
5.1.5. Difference Between NumPy’s All and Any Methods#
If you want to get the row whose ALL values satisfy a certain condition, use NumPy’s all method.
a = np.array([[1, 2, 1], [2, 2, 5]])
# get the rows whose all values are fewer than 3
mask_all = (a < 3).all(axis=1)
a[mask_all]
array([[1, 2, 1]])
To get the row whose AT LEAST one value satisfies a certain condition, use NumPy’s any method.
mask_any = (a < 3).any(axis=1)
a[mask_any]
array([[1, 2, 1],
[2, 2, 5]])
5.1.6. Double numpy.argsort: Get Rank of Values in an Array#
If you want to get the index of the sorted list for the original list, apply numpy.argsort() twice.
a = np.array([2, 1, 4, 7, 3])
# Get rank of values in an array
a.argsort().argsort()
array([1, 0, 3, 4, 2])
In the example above, 1 is the smallest value so it is indexed 0. 2 is the second-largest value to it is indexed 1.
5.1.7. Get the Index of the Max Value in a NumPy Array#
To get the index of the max value in a NumPy array, use np.argmax. This can be helpful to get the highest probability in an array of probabilities.
a = np.array([0.2, 0.4, 0.7, 0.3])
np.argmax(a)
2
5.1.8. np.where: Replace Elements of a NumPy Array Based on a Condition#
If you want to replace elements of a NumPy array based on a condition, use numpy.where.
arr = np.array([[1, 4, 10, 15], [2, 3, 8, 9]])
# Multiply values that are less than 5 by 2
np.where(arr < 5, arr * 2, arr)
array([[ 2, 8, 10, 15],
[ 4, 6, 8, 9]])
5.1.9. array-to-latex: Turn a NumPy Array into Latex#
Show code cell content
!pip install array-to-latex
Sometimes you might want to use latex to write math. You can turn a NumPy array into latex using array-to-latex.
import array_to_latex as a2l
a = np.array([[1, 2, 3], [4, 5, 6]])
latex = a2l.to_ltx(a)
latex
\begin{bmatrix}
1.00 & 2.00 & 3.00\\
4.00 & 5.00 & 6.00
\end{bmatrix}
I copied and pasted the output of array-to-latex to the Markdown cell of Jupyter Notebook, and below is the output.
\begin{bmatrix} 1.00 & 2.00 & 3.00\ 4.00 & 5.00 & 6.00 \end{bmatrix}
5.1.10. NumPy Comparison Operators#
If you want to get elements of a NumPy array that are greater, smaller, or equal to a value or an array, simply use comparison operators such as <, <=, >, >=, ==.
a = np.array([1, 2, 3])
b = np.array([4, 1, 2])
a < 2
array([ True, False, False])
a < b
array([ True, False, False])
a[a < b]
array([1])
5.1.11. NumPy.linspace: Get Evenly Spaced Numbers Over a Specific Interval#
If you want to get evenly spaced numbers over a specific interval, use numpy.linspace(start, stop, num). The code below shows a use case of the numpy.linspace method.
import matplotlib.pyplot as plt
x = np.linspace(2, 4, num=10)
x
array([2. , 2.22222222, 2.44444444, 2.66666667, 2.88888889,
3.11111111, 3.33333333, 3.55555556, 3.77777778, 4. ])
y = np.arange(10)
plt.plot(x, y)
plt.show()
5.1.12. Check if Two NumPy Arrays Are Equal#
If you try to check whether two arrays are equal, using an assert statement will give you an error.
import numpy as np
from numpy.testing import assert_array_equal
arr1 = np.array([1, 2, 3])
arr2 = np.array([1, 2, 4])
assert arr1 == arr2
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/Users/khuyen/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter4/Numpy.ipynb Cell 48' in <cell line: 6>()
<a href='vscode-notebook-cell:/Users/khuyen/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter4/Numpy.ipynb#ch0000052?line=3'>4</a> arr1 = np.array([1, 2, 3])
<a href='vscode-notebook-cell:/Users/khuyen/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter4/Numpy.ipynb#ch0000052?line=4'>5</a> arr2 = np.array([1, 2, 4])
----> <a href='vscode-notebook-cell:/Users/khuyen/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter4/Numpy.ipynb#ch0000052?line=5'>6</a> assert arr1 == arr2
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
Use assert_array_equal instead. assert_array_equal also shows you which elements are different between two arrays.
assert_array_equal(arr1, arr2)
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
/Users/khuyen/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter4/Numpy.ipynb Cell 49' in <cell line: 1>()
----> <a href='vscode-notebook-cell:/Users/khuyen/book/Efficient_Python_tricks_and_tools_for_data_scientists/Chapter4/Numpy.ipynb#ch0000053?line=0'>1</a> assert_array_equal(arr1,arr2)
[... skipping hidden 1 frame]
File ~/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py:844, in assert_array_compare(comparison, x, y, err_msg, verbose, header, precision, equal_nan, equal_inf)
<a href='file:///Users/khuyen/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py?line=839'>840</a> err_msg += '\n' + '\n'.join(remarks)
<a href='file:///Users/khuyen/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py?line=840'>841</a> msg = build_err_msg([ox, oy], err_msg,
<a href='file:///Users/khuyen/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py?line=841'>842</a> verbose=verbose, header=header,
<a href='file:///Users/khuyen/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py?line=842'>843</a> names=('x', 'y'), precision=precision)
--> <a href='file:///Users/khuyen/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py?line=843'>844</a> raise AssertionError(msg)
<a href='file:///Users/khuyen/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py?line=844'>845</a> except ValueError:
<a href='file:///Users/khuyen/book/venv/lib/python3.8/site-packages/numpy/testing/_private/utils.py?line=845'>846</a> import traceback
AssertionError:
Arrays are not equal
Mismatched elements: 1 / 3 (33.3%)
Max absolute difference: 1
Max relative difference: 0.25
x: array([1, 2, 3])
y: array([1, 2, 4])
5.1.13. NumPy.testing.assert_almost_equal: Check if Two Arrays Are Equal up to a Certain Precision#
Sometimes, you might only want to check if two arrays are equal up to a certain precision. If so, use numpy.testing.assert_almost_equal.
import numpy as np
from numpy.testing import assert_almost_equal
a = np.array([[1.222, 2.222], [3.222, 4.222]])
test = np.array([[1.221, 2.221], [3.221, 4.221]])
assert_almost_equal(a, test, decimal=2)