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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →For a plain Python grid, use a list comprehension to create a separate list for each row. For numerical work, use a NumPy array constructor such as np.zeros, np.ones or np.full. In NumPy, pass the dimensions as (rows, columns).
Choose a nested list or a NumPy array
Python’s built-in lists can represent a grid as a list of row lists. Choose this for simple grids or when ordinary Python containers are sufficient. Choose a NumPy ndarray when you need numerical array operations and want a rectangular shape with a uniform element type. NumPy also accepts a list of lists to create an array, provided the rows form a regular rectangle.
NumPy’s beginner guide describes the rectangular-shape and element-type constraints of ndarrays. Its array creation guide covers creating arrays from lists of lists and with shape-based functions.
Initialize a 2D array with Python lists
Use a nested list comprehension to create a grid of zeros:
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rows, cols = 3, 4
grid = [[0 for _ in range(cols)] for _ in range(rows)]This creates three rows, each with four entries. The inner list is constructed anew for each row, so changing a cell affects only that row.
Avoid multiplying a single row list to create the outer structure:
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grid = [[0] * cols] * rowsThat expression repeats references to the same inner list. As a result, changing a cell in one row can change the corresponding cell in every row. Use the nested comprehension when rows must be independent.
Initialize a 2D NumPy array
Install and import NumPy before using its constructors. Pass the dimensions as a tuple in row-then-column order:
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import numpy as np
rows, cols = 3, 4
zeros = np.zeros((rows, cols), dtype=int)
ones = np.ones((rows, cols), dtype=int)
filled = np.full((rows, cols), 7, dtype=int)Use dtype=int when integer values are wanted. NumPy’s zeros reference documents that np.zeros defaults to float64 when no dtype is specified.
Pick a constructor for the starting values
| Starting contents | Pattern | Important detail |
|---|---|---|
| Zeros | np.zeros((rows, cols), dtype=int) |
Without dtype, zeros defaults to float64. |
| Ones | np.ones((rows, cols), dtype=int) |
Supply the shape as a tuple. |
| One repeated value | np.full((rows, cols), value) |
Use this for a constant other than zero or one; specify dtype if needed. |
| Uninitialized storage | np.empty((rows, cols)) |
Values are not initialized. Assign every element before reading the array. |
NumPy explains that empty can be useful when every element will be overwritten, but its contents must be filled before use in calculations. See the NumPy beginner guide.
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Convert existing rows into a NumPy array
If you already have data in nested lists and each row has the same number of values, convert it with np.array:
import numpy as np
data = [[1, 2], [3, 4]]
array = np.array(data)The result is a regular 2D array because both rows have the same length. A jagged set of rows does not have the rectangular shape required for a regular 2D ndarray; NumPy documents this constraint in its beginner guide.
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