Download Introduction to Computation and Programming Using Python by John V. Guttag PDF

By John V. Guttag

This publication introduces scholars with very little previous programming adventure to the paintings of computational challenge fixing utilizing Python and numerous Python libraries, together with PyLab. It offers scholars with abilities that might allow them to make efficient use of computational strategies, together with a few of the instruments and strategies of "data science" for utilizing computation to version and interpret information. The publication is predicated on an MIT path (which turned the most well-liked direction provided via MIT's OpenCourseWare) and used to be constructed to be used not just in a standard school room yet in in a huge open on-line direction (or MOOC) provided by means of the pioneering MIT--Harvard collaboration edX. scholars are brought to Python and the fundamentals of programming within the context of such computational recommendations and strategies as exhaustive enumeration, bisection seek, and effective approximation algorithms. The e-book doesn't require wisdom of arithmetic past highschool algebra, yet does suppose that readers are ok with rigorous pondering and never intimidated by way of mathematical ideas. even though it covers such conventional themes as computational complexity and easy algorithms, the e-book makes a speciality of quite a lot of themes now not present in such a lot introductory texts, together with details visualization, simulations to version randomness, computational suggestions to appreciate facts, and statistical options that tell (and deceive) in addition to similar yet quite complicated issues: optimization difficulties and dynamic programming.

Introduction to Computation and Programming utilizing Python can function a stepping-stone to extra complicated machine technological know-how classes, or as a uncomplicated grounding in computational challenge fixing for college kids in different disciplines.

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Extra resources for Introduction to Computation and Programming Using Python

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The last section of the output, titled “Counters,” shows the statistics that Hadoop generates for each job it runs. These are very useful for checking whether the amount of data processed is what you expected. For example, we can follow the number of records that went through the system: five map inputs produced five map outputs, then five reduce inputs in two groups produced two reduce outputs. The output was written to the output directory, which contains one output file per reducer. The job had a single reducer, so we find a single file, named part-r-00000: % cat output/part-r-00000 1949 111 1950 22 This result is the same as when we went through it by hand earlier.

We will answer this first without using Hadoop, as this information will provide a performance baseline and a useful means to check our results. The classic tool for processing line-oriented data is awk. Example 2-2 is a small script to calculate the maximum temperature for each year. Example 2-2. =9999 && q ~ /[01459]/ && temp > max) max = temp } END { print max }' done The script loops through the compressed year files, first printing the year, and then processing each file using awk. The awk script extracts two fields from the data: the air temperature and the quality code.

Example 2-3 shows the implementation of our map method. Example 2-3. write(new Text(year), new IntWritable(airTemperature)); } The Mapper class is a generic type, with four formal type parameters that specify the input key, input value, output key, and output value types of the map function. For the present example, the input key is a long integer offset, the input value is a line of text, 22 | Chapter 2: MapReduce the output key is a year, and the output value is an air temperature (an integer).

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