# 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([[, ], [, ]])
arr

array([[,
],

[,
]])

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

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)

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#

!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()


## 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=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)