Easy NumPy Tutorial Part 1

Introduction

Numerical Python or NumPy is one of the most prominent python library in the data analysis world. Whether you are a beginner data science enthusiast or an advanced data science wizard, numpy is a must.

This tutorial will teach you the most important stuff that you will be using as a data scientist/analyst.

Why NumPy?

NumPy with many other things provides data structures and algorithms for powerful and efficient numerical computing. Following points make it further clear why numpy is what it is:

1. NumPy provides an n dimensional array known as ndarray, which is not only faster and space efficient than python’s list, it also serves a way of transferring data to algorithms.
2. Provides functions for performing arithmetic operations between arrays without needing to use for loops.
3. Numpy also makes it easier to read and write data to disks.
4. Many algorithms in NumPy are written in C or C++, thus they are faster.

Importing NumPy

import numpy as np

It is a standard convention to import numpy as np.

ndarrays

The most important data structure NumPy provides is ndarray. You will find yourself using ndarray for storing, manipulating and passing data. ndarray is a multidimensional container for homogeneous data.

The easiest way of creating an ndarray is to use array function:

data1 = [1, 2, 3, 4, 5]
arr1 = np.array(data1)
arr1
# Output: array([1, 2, 3, 4, 5])

Passing nested sequences will output a multidimensional array:

np.array([[1, 2, 3], [4, 5, 6]])
# Output: array([[1, 2, 3],
#                [4, 5, 6]])

Functions like zeros and ones create arrays of 0s and 1s respectively:

np.zeros(10)
# Output: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

np.zeros((4, 5))
# Output: array([[0., 0., 0., 0., 0.],
#                [0., 0., 0., 0., 0.],
#                [0., 0., 0., 0., 0.],
#                [0., 0., 0., 0., 0.]])

arange is similar to python range function:

np.arange(10)
# Output: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

shape, dtype, ndim and astype

The attribute shape is a tuple which indicates the size of each dimension:

data = np.array([[1, 2, 3], [4, 5, 6]])
data.shape
# Output: (2, 3)

The dtype attribute stores the data type of the array:

data.dtype
# Output: dtype('int32')

You can use ndim to find out the dimensions of an ndarray:

data.ndim
# Output: 2

Using astype method you can convert an ndarray from one data type to another:

arr = np.array([1, 2, 3, 4, 5], dtype=np.int8)
arr.astype(np.int64)
# Output: array([1, 2, 3, 4, 5], dtype=int64)

ndarray arithmetic

You can do arithmetic between ndarrays without writing any for loops (vectorization). Any arithmetic operation between equally sized ndarrays is done element-wise:

arr1 = np.array([[1, 2, 3], [4, 5, 6]])
arr2 = np.array([[1, 7.6, 9], [6, 7, 2]])

# Sum
arr1 + arr2
# Output: array([[ 2. ,  9.6, 12. ],
#                [10. , 12. ,  8. ]])

# Multiplication
arr1 * arr2
# Output: array([[ 1. , 15.2, 27. ],
#                [24. , 35. , 12. ]])

Indexing and Slicing

One dimensional arrays can be indexed similar to python lists:

arr1 = np.array([0, 1, 2, 3, 4, 5])
arr1[2]
# Output: 2

arr1[2:5]
# Output: array([2, 3, 4])

For multidimensional arrays:

arr2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
arr2[1]
# Output: array([4, 5, 6])

arr2[1, 2]
# Output: 6

Continue to Part 2 and Part 3 to learn more about NumPy.