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Popular Library Packages for Python

 There are various library packages available for Python language. The following are the most popular out of them.

Numpy [Numerical Python]

    - used for manipulation of homogenous array based data.

    - used for efficient storage and manipulation of dense data arrays (ndarray)

Usage:

    import numpy as np

Pandas [Panel Data]

    - used for manipulation of heterogenous and labelled data

    - used for efficient storage and manipulation of labelled or Columned data (DataFrame)

Usage:

    import pandas as pd

SciPy [Scientific Python]

    -    used for scientific computational tasks

Usage

    from scipy import linalg

    (linalg - Linear Algebra)

Matplotlib [Mathematic Plotting Library]

    -     used for quality data visualization

Usage

    import matplotlib.pypot as plt

Seaborn library also used for data visualization.

Usage

    import seaborn as sns

Scikit-Learn

    -    used for supervised and unsupervised learning algorithms.

    -    Implementing machine learning algorithms

Usage

    from sklearn import datasets

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A programmer has parallelized 99% of a program, but there is no value in increasing the problem size, i.e., the program will always be run with the same problem size regardless of the number of processors or cores used. What is the expected speedup on 20 processors? Solution As per Amdahl's law, the speedup,  N - No of processors = 20 f - % of parallel operation = 99% = 1 / (1 - 0.99) + (0.99 / 20) = 1 / 0.01 + (0.99 / 20) = 16.807 The expected speedup on 20 processors is 16.807
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 A decision tree is a flowchart-like tree structure. The topmost node in a tree is the root node. The each internal node (non-leaf node) denotes a test on an attribute and each branch represents an outcome of the test. The each leaf node (or terminal node) holds a class label. Decision trees can handle multidimensional data.  Some of the decision tree algorithms are Iterative Dichotomiser (ID3), C4.5 (a successor of ID3), Classification and Regression Trees (CART). Most algorithms for decision tree induction  follow a top-down approach.  The tree starts with a training set of tuples and their associated class labels. The algorithm is called with data partition, attribute list, and attribute selection method, where the data partition is the complete set of training tuples and their associated class labels. The splitting criterion is determined by attribute selection method which indicates the splitting attribute that may be splitting point or splitting subset. Attribu...
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Exercise 1 - Amdahl's Law

A programmer is given the job to write a program on a computer with processor having speedup factor 3.8 on 4 processors. He makes it 95% parallel and goes home dreaming of a big pay raise. Using Amdahl’s law, and assuming the problem size is the same as the serial version, and ignoring communication costs, what is the speedup factor that the programmer will get? Solution Speedup formula as per Amdahl's Law, N - no of processor = 4 f - % of parallel operation = 95% Speedup = 1 / (1 - 0.95) + (0.95/4) = 1 / 0.5 + (0.95/4) Speedup = 3.478 The programmer gets  3.478 as t he speedup factor.
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