🧱 NumPy Array Search – Find Values Fast with NumPy Functions

🧲 Introduction – Why Learn Array Search in NumPy?

Searching arrays is a critical operation in data analysis and machine learning workflows. Whether you’re locating specific values, checking for conditions, or filtering large datasets, NumPy’s search functions like where(), searchsorted(), and nonzero() help you identify data efficiently and precisely.

🎯 In this guide, you’ll learn:

• How to use np.where() for conditional search
• How np.searchsorted() finds insert positions in sorted arrays
• How np.nonzero() locates all non-zero values
• When to use isin() for membership tests
• Real-world use cases for each method

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🔍 Using np.where() – Condition-Based Search

import numpy as np

arr = np.array([10, 20, 30, 40, 50])
result = np.where(arr > 30)
print(result)

👉 Output:

(array([3, 4]),)

📌 where() returns indices where the condition is True
✅ Can be used for both search and value replacement

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🔄 Conditional Replacement with where()

arr = np.array([10, 20, 30, 40, 50])
new_arr = np.where(arr > 30, 1, 0)
print(new_arr)

👉 Output:

[0 0 0 1 1]

✅ All elements > 30 replaced with 1, others with 0

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🎯 Using np.searchsorted() – Search in Sorted Arrays

arr = np.array([10, 20, 30, 40])
pos = np.searchsorted(arr, 25)
print(pos)

👉 Output:

1

📌 Tells you where to insert 25 to keep array sorted
Use side='right' to insert after duplicates

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🔁 Batch Insert Positions

arr = np.array([10, 20, 30, 40])
values = [5, 15, 35]
positions = np.searchsorted(arr, values)
print(positions)

👉 Output:

[0 1 3]

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🧮 Using np.nonzero() – Locate Non-Zero Values

arr = np.array([0, 3, 0, 4, 5])
indices = np.nonzero(arr)
print(indices)

👉 Output:

(array([1, 3, 4]),)

📌 Use for sparse data or finding meaningful entries

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📌 Using np.isin() – Membership Testing

arr = np.array([10, 20, 30, 40])
test = np.isin(arr, [20, 40])
print(test)

👉 Output:

[False  True False  True]

📌 Great for filtering arrays based on membership

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🧠 Using np.argmax() and np.argmin()

arr = np.array([1, 7, 3, 9, 2])
print(np.argmax(arr))  # Output: 3 (value 9)
print(np.argmin(arr))  # Output: 0 (value 1)

✅ Quickly get index of highest or lowest value

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🔁 Multi-Dimensional where() Usage

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

result = np.where(matrix > 3)
print(result)

👉 Output:

(array([1, 1, 1]), array([0, 1, 2]))

📌 First array → row indices, second array → column indices

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📊 Search Function Comparison Table

Function	Description	Output Type
np.where()	Indices or condition-based replacement	tuple of indices
np.searchsorted()	Index to insert into sorted array	int or array of int
np.nonzero()	Indices of non-zero values	tuple of indices
np.isin()	Boolean mask of membership test	boolean array
np.argmax()	Index of max value	int
np.argmin()	Index of min value	int

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🔍 Summary – Key Takeaways

• Use where() for filtering and condition-based updates
• Use searchsorted() for insertion into sorted arrays
• Use nonzero() to locate all non-zero elements
• Use isin() to test membership quickly
• Use argmax() and argmin() for finding extremes

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⚙️ Real-World Applications

• Filter sensor values that exceed thresholds
• Search time series events with where()
• Locate anomalies in financial or medical data
• Build fast search systems using searchsorted()
• Compare datasets using isin() for joins

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❓ FAQs – NumPy Array Search

❓ How do I find all indices where a condition is met?
✅ Use np.where():

np.where(arr > 50)

❓ What’s the difference between where() and nonzero()?
✅ nonzero() only returns indices of non-zero values. where() lets you specify any condition.

❓ Can I replace values with where()?
✅ Yes:

np.where(arr > 0, 1, 0)

❓ Does searchsorted() work on unsorted arrays?
❌ No. It only gives correct results if the array is sorted.

❓ How do I check if values are in another array?
✅ Use np.isin():

np.isin(arr1, arr2)

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