Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
Fancy Indexing
Python Libraries for Data Wrangling
With NumPy array fancy indexing, an array can be indexed with another NumPy array, a Python list, or a sequence of integers, whose values select elements in the indexed array.
Fancy Indexing
• With
NumPy array fancy indexing, an array can be indexed with another NumPy array, a
Python list, or a sequence of integers, whose values select elements in the
indexed array.
• Example: We
first create a NumPy array with 11 floating-point numbers and then index the
array with another NumPy array and Python list, to extract element numbers 0, 2
and 4 from the original array :
importnumpy
as np
A =
np.linspace(0, 1, 11)
print(A)
print(A[np.array([0,
2, 4])])
# The
same thing can be accomplished by indexing with a Python list
print(A[[0,
2, 4]])
Output:
[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1.0 ].
[0. 0.2 0.4]
[0. 0.2 0.4]
Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling : Tag: : Python Libraries for Data Wrangling - Fancy Indexing
Foundation of Data Science: Unit IV: Python Libraries for Data Wrangling
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Foundation of Data Science
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