Coding Musings

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Category: Python (Page 1 of 2)

Computing the Minimum Number of Flight Segments

Computing the Minimum Number of Flight Segments

If we want to compute the minimum number of flight segments between a starting city and target city, we can construct an undirected graph.  In the graph the nodes represent cities and the edges represent the flight segments.  We can count the number of segments to determine the shortest distance.

The following can be applied to any situation in finding the shortest path.  It is an implementation of the breadth first search algorithm.

See code below.

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Testing for Unconnected Components in a Graph

Testing for Unconnected Components in an Undirected Graph

With a graph structure it is possible that parts of the graph will not be connected to each other.  An example of this would be with social networks, not all users are friends with other users.

The code will find the total number of connected components of the graph, or graph parts in an undirected graph.

See code below.

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Finding an Exit from a Maze

Finding an Exit from a Maze using undirected graphs.

We can think of a maze as a rectangular grid of cells with paths between adjacent cells. If we want to find if there is a path from a given cell to a given exit from the maze, where the exit is represented by a cell, you can represent the maze as an undirected graph.

The nodes of the graph are cells of the maze, and two nodes are connected an undirected edge if they are adjacent and there is no wall between them. Therefore we can surmise we just need to see if a path, series of edges connecting the nodes, to determine if the two nodes are connected.

See code below.

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Spark – Streaming Data

Spark – Streaming Data, Capturing and Querying

Today we will look at how to capture streaming data and perform some simple queries as the data is streamed. We will use the regular expressions library and the PySpak library.  The streaming data comes from a weather station that transmits different weather at different intervals.  We will need to find the correct data out of the stream and output the results.

Let’s get started.

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Spark – SQLContext

Spark – SQLContext

Today we will look at the SQLContext object from the PySpark library and how you can use it to connect to a local database.  In the example below we will:
Connect to a local PostgreSQL database and read the contents into a dataframe.
Run some simple SQL queries
And join two data frames together

Let’s get started.

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Hadoop Spark – Hello World, Word Count

Hadoop Spark – Word Count

One of the first things to do in most programming languages is create a “Hello World!” program.  The equivalent in Spark is to create a program that will read the contents of a file and count the number of occurrences of each word.

Below I will show a basic example, so let’s start counting.

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Read CSV files using Python Panda Dataframes

The great thing about the Panda’s library for Python is how easily it can manipulate data sources.  We will look at one of the first things you will want to do, read a .csv file.

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Binary Search Tree – Is BST?

Binary Search Tree

Traverse the tree in natural order and test if each node is between the previous and the next node. Properly detect duplicate values. If a wrong sub-tree is encountered immediately stop the script and indicate the error.

See code below

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Merging Tables

Simulates a sequence of merge operations with tables in a database.

See code below

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Fractional Knapsack

Standard problem of trying to maximize the values of item with a constraint.  How many items can you put in a knapsack given the value and the weight of the items.

Uses python3
goal: maximize the value of items selected from the get_optimal_value
while the items selected combined weight is less than the inputted capacity

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