NumPy
keyboard_arrow_down 319 guides
chevron_leftCookbooks
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
check_circle
Mark as learned thumb_up
2
thumb_down
0
chat_bubble_outline
0
Comment auto_stories Bi-column layout
settings
Accessing rows of a 2D NumPy array
schedule Aug 10, 2023
Last updated local_offer
Tags Python●NumPy
tocTable of Contents
expand_more Master the mathematics behind data science with 100+ top-tier guides
Start your free 7-days trial now!
Start your free 7-days trial now!
In Numpy, we use the [] syntax to access particular rows of a 2D Numpy array.
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]])
Access the first row
x[0]
[1, 2]
Access all rows from positions 1 to 2
x[1:3]
[[3, 4], [5, 6]]
Just as reference, we show our 2D Numpy array x here again:
[[1, 2], [3, 4], [5, 6]]
Access all rows up until (exclusive) position 2:
x[:2]
[[1, 2], [3, 4]]
Access all rows from position 1 onwards
x[1:]
[[3, 4], [5, 6]]
Accessing the last row
x[-1]
[5, 6]
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
thumb_up
thumb_down
Comment
Citation
Ask a question or leave a feedback...
thumb_up
2
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!
