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For a regular Python list, use items.index(value) to get the zero-based position of the first match. If you mean a NumPy array, compare its elements with the target and use np.where() or np.nonzero() to find matching positions. The right method depends on the array type and whether you want one match or every match.
First, identify what kind of array you have
In Python, “array” can mean a regular list, the standard-library array.array type, or a NumPy ndarray. These types do not all use the same search interface. The examples below focus on lists and NumPy arrays; Python documents its separate array module in the standard-library reference.
| Data type | Typical way to find a value | Result |
|---|---|---|
| Python list | items.index(value) |
First matching index; raises ValueError if absent |
| One-dimensional NumPy array | np.where(arr == value)[0] |
All matching indices, or an empty array |
| Multidimensional NumPy array | np.argwhere(arr == value) for coordinates; np.nonzero(arr == value) for index arrays |
One coordinate per dimension for each match |
Find the first matching index in a Python list
Call the list’s index() method with the value you want to find:
items = ["red", "blue", "green"]
position = items.index("blue")
print(position) # 1Python list indices start at zero, so the first item is at index 0. The Python 3.14.8 tutorial documents list.index(value[, start[, stop]]) as returning the zero-based index of the first occurrence. If the value is not in the searched portion, the method raises ValueError. See the Python tutorial’s list-method documentation.
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Search within part of a list
Optional start and stop arguments limit where the search looks. The returned position is still an index into the original list, not a position counted from the start of the searched slice.
items = ["red", "blue", "green", "blue"]
position = items.index("blue", 2) # 3Handle a value that might be missing
If absence is an expected outcome, check membership before calling index(), or catch the exception. Catching ValueError makes the missing case explicit:
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try:
position = items.index("purple")
except ValueError:
position = NoneUse a sentinel such as None only if it fits the rest of your code; otherwise, handle the exception at the point where a missing value matters.
Find every matching index in a list
list.index() returns only the first occurrence. To get all positions for a value, enumerate the list and keep the indices where the values match:
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items = ["red", "blue", "green", "blue"]
target = "blue"
positions = [i for i, value in enumerate(items) if value == target]
print(positions) # [1, 3]If there are no matches, the comprehension returns an empty list. This is often convenient when zero, one, or multiple matches are all ordinary outcomes.
Find matching indices in a NumPy array
NumPy arrays use elementwise comparisons. For a one-dimensional array, compare it with the target, then use np.where() to get all positions where the comparison is true:
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import numpy as np
arr = np.array([10, 20, 30, 20])
positions = np.where(arr == 20)[0]
print(positions) # [1 3]The result contains every matching index, not just the first. If no element matches, it is an empty array. NumPy uses zero-based indexing; see its documentation on array indexing and where.
Find values in a multidimensional NumPy array
In a two-dimensional array, a match has a row and column coordinate; higher-dimensional arrays have one coordinate for each axis. Choose the function based on what you need to do with those results.
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Show each match as a coordinate row
np.argwhere() returns one row of coordinates per match. For a 2D array, each row contains the row index and column index:
arr = np.array([[4, 7], [7, 9]])
coordinates = np.argwhere(arr == 7)
print(coordinates) # [[0 1]
# [1 0]]The output shape is (number_of_matches, number_of_dimensions). This format is useful for displaying or processing coordinates. NumPy’s argwhere documentation cautions that its output is not suitable for indexing an array directly.
Get index arrays for indexing
When you want to use the matching positions to index the array, use np.nonzero(). It returns a tuple with one integer index array per dimension:
index_arrays = np.nonzero(arr == 7)
print(index_arrays) # (array([0, 1]), array([1, 0]))
print(arr[index_arrays]) # [7 7]Each position in the first index array pairs with the position at the same offset in the second. NumPy’s nonzero documentation describes this per-dimension result.
Use a flat index only if you need one
A flattened index represents a location in a one-dimensional view of the array. Use one only when that single linear position is what your program needs; otherwise, retain the per-axis coordinates so the row, column, or other dimensions remain clear.
Quick Recap
Which method should you choose?
- Use
items.index(value)for the first match in a Python list when a missing value should be treated as an error. - Use a list comprehension with
enumerate()when you need all list matches or want an empty result when nothing matches. - Use
np.where(arr == value)[0]for all matches in a one-dimensional NumPy array. - Use
np.argwhere()to view multidimensional matches as coordinate rows, andnp.nonzero()when you need index arrays for NumPy indexing.
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