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Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
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Accessing a value in a 2D arrayAccessing columns of a 2D arrayAccessing rows of a 2D arrayCalculating the determinant of a matrixChecking allowed values for a NumPy data typeChecking if a NumPy array is a view or copyChecking the version of NumPyChecking whether a NumPy array contains a given rowComputing Euclidean distance using NumpyConcatenating 1D arraysConverting array to lowercaseConverting type of NumPy array to stringCreating a copy of an arrayDifference between Python List and Numpy arrayDifference between the methods array_equal and array_equivDifference between the methods mod and fmodDifference between the methods power and float_powerFinding the closest value in an arrayFinding the Index of Largest Value in a Numpy ArrayFinding the Index of Smallest Value in a Numpy ArrayFinding the most frequent value in a NumPy arrayFlattening Numpy arraysGetting constant PiGetting elements from a two dimensional array using two dimensional array of indicesGetting indices of N maximum valuesGetting indices of N minimum valuesGetting the number of columns of a 2D arrayGetting the number of non-zero elements in a NumPy arrayGetting the number of rows of a 2D arrayInitializing an array of onesInitializing an array of zerosInitializing an identity matrixLimiting array values to a certain rangePerforming linear regressionPrinting full or truncated NumPy arrayPrinting large Numpy arrays without truncationRemoving rows containing NaN in a NumPy arrayReversing a NumPy arraySaving NumPy array to a fileShape of Numpy ArraysSorting value of one array according to anotherSuppressing scientific notation
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Accessing columns of a 2D NumPy array

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schedule Mar 9, 2022
Last updated
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To access particular columns of a 2D Numpy array, use the [] syntax.

Suppose we have the following 2D Numpy array:

x = np.array([[1,2,3], [4,5,6]])
x
array([[1, 2, 3],
[4, 5, 6]])

Accessing the first column

x[:,0]
array([1, 4])

The code seems cryptic, so let's break it down. The syntax of [] is as follows:

[* the_rows_you_want *, * the_columns_you_want *]

In our code snippet, we used : to denote the rows that we want. You can interpret this in the way you interpret Python's slicing syntax for lists; : alone without any numbers means "give me all the rows". This means that x[:,0] gives us all the rows of the 0th column, which in essence, is just the 0th column.

Accessing the first two columns

For your reference, we show the our 2D Numpy array x here again:

array([[1, 2, 3],
[4, 5, 6]])

To access the first two columns:

x[:,0:2]
array([[1, 2],
[4, 5]])

Note that the endpoint 2 is exclusive. You could write :2 instead of 0:2.

Accessing the last column

x[:,-1]
array([3, 6])

Accessing the last two columns

x[:,-2:]
array([[2, 3],
[5, 6]])
robocat
Published by Isshin Inada
Edited by 0 others
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