Merge pull request #698 from Rupa-Rd/numpy-datatypes

Numpy array reshape

contrib/numpy/index.md

@@ -3,6 +3,7 @@
 - [Installing NumPy](installing-numpy.md)
 - [Introduction](introduction.md)
 - [NumPy Data Types](datatypes.md)
+- [Numpy Array Shape and Reshape](reshape-array.md)
 - [Basic Mathematics](basic_math.md)
 - [Operations on Arrays in NumPy](operations-on-arrays.md)
 - [Loading Arrays from Files](loading_arrays_from_files.md)

contrib/numpy/reshape-array.md

@@ -0,0 +1,57 @@
+# Numpy Array Shape and Reshape
+
+In NumPy, the primary data structure is the ndarray (N-dimensional array). An array can have one or more dimensions, and it organizes your data efficiently.
+
+Let us create a 2D array
+
+``` python
+import numpy as np
+
+numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
+print(numbers)
+```
+
+#### Output:
+
+``` python
+array([[1, 2, 3, 4],[5, 6, 7, 8]])
+```
+
+## Changing Array Shape using `reshape()`
+
+The `reshape()` function allows you to rearrange the data within a NumPy array.
+
+It take 2 arguments, row and columns. The `reshape()` can add or remove the dimensions. For instance, array can convert a 1D array into a 2D array or vice versa.
+
+``` python
+arr_1d = np.array([1, 2, 3, 4, 5, 6]) # 1D array
+arr_2d = arr_1d.reshape(2, 3) # Reshaping with 2 rows and 3 cols
+
+print(arr_2d)
+```
+
+#### Output:
+
+``` python
+array([[1, 2, 3],[4, 5, 6]])
+```
+
+## Changing Array Shape using `resize()`
+
+The `resize()` function allows you to modify the shape of a NumPy array directly.
+
+It take 2 arguements, row and columns.
+
+``` python
+import numpy as np
+arr_1d = np.array([1, 2, 3, 4, 5, 6])
+
+arr_1d.resize((2, 3)) # 2 rows and 3 cols
+print(arr_1d)
+```
+
+#### Output:
+
+``` python
+array([[1, 2, 3],[4, 5, 6]])
+```