This article describes how to extract or delete elements, rows, and columns that satisfy the condition from the NumPy array ndarray.
- Extract elements that satisfy the conditions
- Extract rows and columns that satisfy the conditions
- All elements satisfy the condition:
numpy.all() - At least one element satisfies the condition:
numpy.any()
- Delete elements, rows, and columns that satisfy the conditions
- Use
~ (NOT)
- Use
numpy.delete() and numpy.where()
- Multiple conditions
See the following article for an example when ndarray contains missing values NaN.
- NumPy: Remove rows/columns with missing value (NaN) in ndarray
If you want to replace or count an element that satisfies the conditions, see the following article.
- numpy.where(): Manipulate elements depending on conditions
- NumPy: Count the number of elements satisfying the condition
If you want to extract elements that meet the condition, you can use ndarray[conditional expression].
Even if the original ndarray is a multidimensional array, a flattened one-dimensional array is returned.
import numpy as np
a = np.arange(12).reshape((3, 4))
print(a)
# [[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
print(a < 5)
# [[ True True True True]
# [ True False False False]
# [False False False False]]
print(a[a < 5])
# [0 1 2 3 4]
print(a < 10)
# [[ True True True True]
# [ True True True True]
# [ True True False False]]
print(a[a < 10])
# [0 1 2 3 4 5 6 7 8 9]
A
new ndarray is returned, and the original ndarray is unchanged. The same is true for the following examples.
b = a[a < 10]
print(b)
# [0 1 2 3 4 5 6 7 8 9]
print(a)
# [[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
It is possible to calculate the sum, average, maximum value, minimum value, standard deviation, etc., of elements that satisfy the condition.
print(a[a < 5].sum())
# 10
print(a[a < 5].mean())
# 2.0
print(a[a < 5].max())
# 4
print(a[a < 10].min())
# 0
print(a[a < 10].std())
# 2.8722813232690143
Extract rows and columns that satisfy the conditions
In the example of extracting elements, a one-dimensional array is returned, but
if you use np.all() and np.any(), you can extract rows and columns while keeping the original ndarray dimension.
All elements satisfy the condition: numpy.all()
np.all() is a function that returns True when all elements of ndarray passed to the first parameter are True and returns False otherwise.
- numpy.all — NumPy v1.16
Manual
If you specify the parameter axis, it returns True if all elements are True for each axis. In the case of a two-dimensional array, the result is for columns when axis=0 and for rows when axis=1.
print(a < 5)
# [[ True True True True]
# [ True False False False]
# [False False False False]]
print(np.all(a < 5))
# False
print(np.all(a < 5, axis=0))
# [False False False False]
print(np.all(a < 5, axis=1))
# [ True False False]
print(a < 10)
# [[ True True True True]
# [ True True True True]
# [ True True False False]]
print(np.all(a < 10, axis=0))
# [ True True False False]
print(np.all(a < 10, axis=1))
# [ True True False]
Rows and columns are extracted by giving each result to [rows, :] or [:, columns]. For [rows, :], the trailing , : can be omitted.
print(a[:, np.all(a < 10, axis=0)])
# [[0 1]
# [4 5]
# [8 9]]
print(a[np.all(a < 10, axis=1), :])
# [[0 1 2 3]
# [4 5 6 7]]
print(a[np.all(a < 10, axis=1)])
# [[0 1 2 3]
# [4 5 6 7]]
If the condition is not met, an empty ndarray is returned.
print(a[:, np.all(a < 5, axis=0)])
# []
Even
if only one row or one column is extracted, the number of dimensions does not change.
print(a[np.all(a < 5, axis=1)])
# [[0 1 2 3]]
print(a[np.all(a < 5, axis=1)].ndim)
# 2
print(a[np.all(a < 5, axis=1)].shape)
# (1, 4)
At least one element satisfies the condition: numpy.any()
np.any() is a function that returns True when ndarray passed to the first parameter contains at least one True element, and returns False otherwise.
- numpy.any — NumPy v1.16 Manual
If you specify the parameter axis, it returns True if at least one element is True for each axis. In the case of a two-dimensional array, the result is for columns when axis=0 and for rows when axis=1.
print(a < 5)
# [[ True True True True]
# [ True False False False]
# [False False False False]]
print(np.any(a < 5))
# True
print(np.any(a < 5, axis=0))
# [ True True True True]
print(np.any(a < 5, axis=1))
# [ True True False]
You can extract rows and columns that match the conditions in the same way as np.all().
print(a[:, np.any(a < 5, axis=0)])
# [[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
print(a[np.any(a < 5, axis=1)])
# [[0 1 2 3]
# [4 5 6 7]]
Delete elements, rows, and columns that satisfy the conditions
If you want to delete elements, rows, or columns instead of extracting them depending on conditions, there are the following two methods.
Use ~ (NOT)
If you add the negation operator ~ to a condition, elements, rows, and columns that do not satisfy the condition are extracted. This is equivalent to deleting elements,
rows, or columns that satisfy the condition.
print(a[~(a < 5)])
# [ 5 6 7 8 9 10 11]
print(a[:, np.all(a < 10, axis=0)])
# [[0 1]
# [4 5]
# [8 9]]
print(a[:, ~np.all(a < 10, axis=0)])
# [[ 2 3]
# [ 6 7]
# [10 11]]
print(a[np.any(a < 5, axis=1)])
# [[0 1 2 3]
# [4 5 6 7]]
print(a[~np.any(a < 5, axis=1)])
# [[ 8 9 10 11]]
Use numpy.delete() and numpy.where()
Rows and columns can also be deleted using np.delete() and np.where().
In np.delete(), set the target ndarray, the index to delete and the target axis.
In the case of a two-dimensional array, rows are deleted if axis=0 and columns are deleted if axis=1.
print(a)
# [[ 0 1 2 3]
# [ 4 5 6 7]
# [ 8 9 10 11]]
print(np.delete(a, [0, 2], axis=0))
# [[4 5 6 7]]
print(np.delete(a, [0, 2], axis=1))
# [[ 1 3]
# [ 5 7]
# [ 9 11]]
See also the following article for np.delete().
- numpy.delete(): Delete rows and columns of ndarray
np.where() returns the index of the element that satisfies the condition.
In the case of a multidimensional array, a tuple of a list of indices (row number, column number) that satisfy the condition for each dimension (row, column) is returned.
print(a < 2)
# [[ True True False False]
# [False False False False]
# [False False False False]]
print(np.where(a < 2))
# (array([0, 0]), array([0, 1]))
print(np.where(a < 2)[0])
# [0 0]
print(np.where(a < 2)[1])
# [0 1]
See also the following article for np.where().
- numpy.where(): Manipulate elements depending on conditions
By combining these two functions, you can delete the rows and columns that satisfy the condition.
print(np.delete(a, np.where(a < 2)[0], axis=0))
# [[ 4 5 6 7]
# [ 8 9 10 11]]
print(np.delete(a, np.where(a < 2)[1], axis=1))
# [[ 2 3]
# [ 6 7]
# [10 11]]
print(a == 6)
# [[False False False False]
# [False False True False]
# [False False False False]]
print(np.where(a == 6))
# (array([1]), array([2]))
print(np.delete(a, np.where(a == 6)))
# [ 0 3 4 5 6 7 8 9 10 11]
print(np.delete(a, np.where(a == 6)[0], axis=0))
# [[ 0 1 2 3]
# [ 8 9 10 11]]
print(np.delete(a, np.where(a == 6)[1], axis=1))
# [[ 0 1 3]
# [ 4 5 7]
# [ 8 9 11]]
As in the example above, the rows and columns that have at least one element satisfying the condition are deleted. This is the same as using np.any().
Multiple conditions
If you want to combine
multiple conditions, enclose each conditional expression with () and use & or |.
print(a[(a < 10) & (a % 2 == 1)])
# [1 3 5 7 9]
print(a[np.any((a == 2) | (a == 10), axis=1)])
# [[ 0 1 2 3]
# [ 8 9 10 11]]
print(a[:, ~np.any((a == 2) | (a == 10), axis=0)])
# [[ 0 1 3]
# [ 4 5 7]
# [ 8 9 11]]
How do you get the value of a two dimensional array in Python?
In Python, we can access elements of a two-dimensional array using two indices. The first index refers to the indexing of the list and the second index refers to the position of the elements. If we define only one index with an array name, it returns all the elements of 2-dimensional stored in the array.
How do I retrieve elements from a 2D array?
Accessing 2D Array Elements
In Java, when accessing the element from a 2D array using arr[first][second] , the first index can be thought of as the desired row, and the second index is used for the desired column. Just like 1D arrays, 2D arrays are indexed starting at 0 .
5 Easy Ways To Extract Elements From A Python List.
Extract Elements From A Python List Using Index. ... .
Print Items From a List Using Enumerate. ... .
Using Loops to Extract List Elements. ... .
Using Numpy To View Items From a List. ... .
Extract Elements Using The index function..
How do you find the value of 2D arrays?
To get the value in a 2D array give the name of the array followed by the row and column indicies in square brackets. The code below will get the value at row index 1 and column index 0 from ticketInfo . It will also get the value at row index 0 and column index 1 from seatingChart .
