Intro and Basics of Numpy

Author: OmkarPathak

Numpy/P01_Introduction.py

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+# Author: OMKAR PATHAK
+
+# NumPy (Numeric Python) is a Python package used for building multi dimensional arrays and performing
+# various operations
+
+# In this program we will walk through various concepts and see available functions in the NumPy package.
+
+# For installing: pip3 install numpy
+
+import numpy as np
+
+# we have a function arange() which makes an array of the specified dimension. Example:
+myArray = np.arange(20)
+print(myArray)              # [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]
+
+# an array from 10 to 20
+myArray = np.arange(10, 20) # [10 11 12 13 14 15 16 17 18 19]
+print(myArray)
+
+# an array from 10 to 20 with 2 steps
+myArray = np.arange(10, 20, 2)
+print(myArray)              # [10 12 14 16 18]
+
+# reshape() helps to reshape our NumPy array
+myArray = np.arange(20)
+# syntax: reshape(number_of_rows, number_of_columns)
+myArray = myArray.reshape(4, 5)
+print(myArray)
+
+# [[ 0  1  2  3  4]
+#  [ 5  6  7  8  9]
+#  [10 11 12 13 14]
+#  [15 16 17 18 19]]
+
+myArray = myArray.reshape(10, 2)
+print(myArray)
+
+# [[ 0  1]
+#  [ 2  3]
+#  [ 4  5]
+#  [ 6  7]
+#  [ 8  9]
+#  [10 11]
+#  [12 13]
+#  [14 15]
+#  [16 17]
+#  [18 19]]
+
+# shape returns the shape of the array. The length of shape tuple is called as rank (or dimension)
+print(myArray.shape)        # (10, 2)
+
+# ndim returns the dimension (rank) of the array
+print(myArray.ndim)         # 2
+
+# size returns the total number of elements in the array
+print(myArray.size)         # 20
+
+# to check the data we have dtype.
+print(myArray.dtype)        # int64