NumPy Filter Array
🎯 NumPy Filter Array Filtering means selecting elements based on a condition and creating a new array from matching values. NumPy uses boolean indexing to filter arrays. 1. Basic Filtering with a Boolean List...
Category: NumPy Tutorial
NumPy Sorting Arrays
🧹 NumPy Sorting Arrays Sorting helps rearrange array elements in ascending order (default) or descending using additional logic. NumPy provides: ✔ np.sort()✔ np.argsort()✔ Sorting by axis✔ Structured array sorting 1. Sorting a 1D Array...
NumPy Searching Arrays
🔍 NumPy Searching Arrays Searching arrays means finding positions (indexes) of elements that match a condition or value. NumPy provides useful methods: ✔ where()✔ searchsorted()✔ argmax(), argmin()✔ nonzero() 1. np.where() — Find Indexes of...
NumPy Splitting Array
✂️ NumPy Splitting Arrays Splitting arrays is the opposite of joining arrays — it divides one array into multiple smaller sub-arrays. NumPy provides these main split methods: ✔ array_split()✔ split()✔ hsplit()✔ vsplit()✔ dsplit() 1....
NumPy Joining Array
NumPy Joining Array NumPy Joining Array means combining two or more arrays into a single array.NumPy provides multiple functions to join arrays: concatenate() stack() hstack() vstack() dstack() column_stack() 1. Joining Using np.concatenate()
1 2 3 4 5 6 7 | import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) result = np.concatenate((arr1, arr2)) print(result) |
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NumPy Array Iterating
🔁 NumPy Array Iteration Iterating through NumPy arrays means accessing elements one-by-one using loops.NumPy provides efficient ways to iterate through 1D, 2D, 3D arrays, including vectorized methods. 1. Iterating a 1D Array
1 2 3 4 5 6 | import numpy as np arr = np.array([1, 2, 3, 4]) for x in arr: print(x) |
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NumPy Array Reshaping
1. Using reshape() You can convert a 1D array into 2D, 3D, etc. Example: Convert 1D → 2D
1 2 3 4 5 | import numpy as np arr = np.array([1, 2, 3, 4, 5, 6]) newarr = arr.reshape(2, 3) print(newarr) |
Output: [[1 2 3] [4 5 6]] 2. Reshape to 3 Dimensions
1 2 3 | arr = np.array([1,2,3,4,5,6,7,8]) newarr = arr.reshape(2,2,2) print(newarr) |
Output:...
NumPy Array Shape
📐 NumPy — Array Shape The shape of a NumPy array tells how many rows, columns, and dimensions the array has. It is represented as a tuple:
1 | (shape_of_dim1, shape_of_dim2, shape_of_dim3, ...) |
1. Checking Shape of an Array...
NumPy Array Copy vs View
🆚 NumPy — Copy vs View When working with NumPy arrays, modifying data may or may not affect the original array depending on whether you’re using a copy or a view. Feature Copy View...
NumPy Data Types
🔠 NumPy — Data Types (dtype) NumPy uses its own optimized data types rather than Python’s built-in types.These data types allow faster computation and less memory usage, especially when working with large datasets. 📌...