{"id":1000,"date":"2017-12-13T14:06:49","date_gmt":"2017-12-13T14:06:49","guid":{"rendered":"http:\/\/blog.cloudxlab.com\/?p=1000"},"modified":"2021-06-21T08:26:27","modified_gmt":"2021-06-21T08:26:27","slug":"numpy-pandas-introduction","status":"publish","type":"post","link":"https:\/\/cloudxlab.com\/blog\/numpy-pandas-introduction\/","title":{"rendered":"NumPy and Pandas Tutorial – Data Analysis with Python"},"content":{"rendered":"

Python is increasingly being used as a scientific language. Matrix and vector manipulations are extremely important for scientific computations. Both NumPy and Pandas have emerged to be essential libraries for any scientific computation, including machine learning, in python due to their intuitive syntax and high-performance matrix computation capabilities.<\/span><\/p>\n

In this post, we will provide an overview of the common functionalities of NumPy and Pandas. We will realize the similarity of these libraries with existing toolboxes in R and MATLAB. This similarity and added flexibility have resulted in wide acceptance of python in the scientific community lately. Topic covered in the blog are:<\/span><\/p>\n

    \n
  1. Overview of NumPy<\/span><\/li>\n
  2. Overview of Pandas<\/span><\/li>\n
  3. Using Matplotlib<\/span><\/li>\n<\/ol>\n

    This post is an excerpt from a live hands-on training conducted by CloudxLab on 25th Nov\u00a02017. It was attended by more than 100 learners around the globe. The participants were from countries namely; United States, Canada, Australia, Indonesia, India, Thailand, Philippines, Malaysia, Macao, Japan, Hong Kong, Singapore, United Kingdom, Saudi Arabia, Nepal, & New Zealand.<\/span><\/p>\n

    <\/p>\n

    What is NumPy?<\/h2>\n

    NumPy stands for \u2018Numerical Python\u2019 or \u2018Numeric Python\u2019. It is an open source module of Python which provides fast mathematical computation on arrays and matrices. Since, arrays and matrices are an essential part of the Machine Learning ecosystem, NumPy along with Machine Learning modules like Scikit-learn, Pandas, Matplotlib, TensorFlow, etc. complete the Python Machine Learning Ecosystem.<\/span><\/p>\n

    NumPy provides the essential multi-dimensional array-oriented computing functionalities designed for high-level mathematical functions and scientific computation. Numpy can be imported into the notebook using<\/span><\/p>\n

    >>> import numpy as np<\/pre>\n
    NumPy\u2019s main object is the homogeneous multidimensional array. It is a table with same type elements, i.e, integers or string or characters (homogeneous), usually integers. In NumPy, dimensions are called axes. The number of axes is called the rank.<\/span><\/p>\n
    There are several ways to create an array in NumPy like np.array, np.zeros, no.ones, etc. Each of them provides some flexibility.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n
    Command to create an array<\/span><\/td>\nExample<\/span><\/td>\n<\/tr>\n
    np.array<\/span><\/td>\n\n
    >>> a = np.array([1, 2, 3])\n>>> type(a)\n<type 'numpy.ndarray'>\n\n>>> b = np.array((3, 4, 5))\n>>> type(b)\n<type 'numpy.ndarray'><\/pre>\n<\/td>\n<\/tr>\n
    np.ones<\/span><\/td>\n\n
    >>> np.ones( (3,4), dtype=np.int16 ) \u00a0\narray([[ 1, \u00a01, \u00a01, \u00a01],\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0[ 1, \u00a01, \u00a01, \u00a01],\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0[ 1, \u00a01, \u00a01, \u00a01]])<\/pre>\n<\/td>\n<\/tr>\n
    np.full<\/span><\/td>\n\n
    >>> np.full( (3,4), 0.11 ) \u00a0\narray([[ 0.11, \u00a00.11, \u00a00.11, \u00a00.11], \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0[ 0.11, \u00a00.11, \u00a00.11, \u00a00.11], \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n\u00a0\u00a0[ 0.11, \u00a00.11, \u00a00.11, \u00a00.11]])<\/pre>\n<\/td>\n<\/tr>\n
    np.arange<\/span><\/td>\n\n
    >>> np.arange( 10, 30, 5 )\narray([10, 15, 20, 25])\n\n>>> np.arange( 0, 2, 0.3 ) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\n# it accepts float arguments\narray([ 0. , \u00a00.3, \u00a00.6, \u00a00.9, \u00a01.2, \u00a01.5, \u00a01.8])<\/pre>\n<\/td>\n<\/tr>\n
    np.linspace<\/span><\/td>\n\n
    >>> np.linspace(0, 5\/3, 6)\narray([0. , 0.33333333 , 0.66666667 , 1. , 1.33333333 \u00a01.66666667])<\/pre>\n<\/td>\n<\/tr>\n
    np.random.rand(2,3)<\/span><\/td>\n\n
    >>> np.random.rand(2,3)\narray([[ 0.55365951, \u00a00.60150511, \u00a00.36113117],\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0[ 0.5388662 , \u00a00.06929014, \u00a00.07908068]])<\/pre>\n<\/td>\n<\/tr>\n
    np.empty((2,3))<\/span><\/td>\n\n
    >>> np.empty((2,3))\narray([[ 0.21288689, \u00a00.20662218, \u00a00.78018623],\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0[ 0.35294004, \u00a00.07347101, \u00a00.54552084]])<\/pre>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
    Some of the important attributes of a NumPy object are:<\/span><\/p>\n
      \n
    1. Ndim:<\/strong> displays the dimension of the array<\/span><\/li>\n
    2. Shape:<\/strong> returns a tuple of integers indicating the size of the array<\/span><\/li>\n
    3. Size:<\/strong> returns the total number of elements in the NumPy array<\/span><\/li>\n
    4. Dtype<\/strong>: returns the type of elements in the array, i.e., int64, character<\/span><\/li>\n
    5. Itemsize:<\/strong> returns the size in bytes of each item<\/span><\/li>\n
    6. Reshape<\/strong>: Reshapes the NumPy array<\/span><\/li>\n<\/ol>\n
      NumPy array elements can be accessed using indexing. Below are some of the useful examples:<\/span><\/p>\n