Set 1 (Search and Insertion) - GeeksQuiz. The following is definition of Binary Search Tree(BST) according to Wikipedia Binary Search Tree, is a node-based binary tree data structure which has the following properties: The left subtree of a node contains only nodes with keys less than the node’s key.The right subtree of a node contains only nodes with keys greater than the node’s key.The left and right subtree each must also be a binary search tree.

There must be no duplicate nodes. The above properties of Binary Search Tree provide an ordering among keys so that the operations like search, minimum and maximum can be done fast. If there is no ordering, then we may have to compare every key to search a given key. Searching a key. Breadth First Traversal or BFS for a Graph. Breadth First Traversal (or Search) for a graph is similar to Breadth First Traversal of a tree (See method 2 of this post).

The only catch here is, unlike trees, graphs may contain cycles, so we may come to the same node again. Tree Traversals (Inorder, Preorder and Postorder) Unlike linear data structures (Array, Linked List, Queues, Stacks, etc) which have only one logical way to traverse them, trees can be traversed in different ways.

Following are the generally used ways for traversing trees. Example Tree. Solution to Brackets by codility – Code Says. Question: Question Name: Brackets Classic application of stacks.

Time series - auto.arima() equivalent for python. Pointers in Python? Autoregression Models for Time Series Forecasting With Python. Bokeh. 9.6. random — Generate pseudo-random numbers — Python 2.7.13 documentation. Source code: Lib/random.py This module implements pseudo-random number generators for various distributions.

For integers, uniform selection from a range.

Reference Guide — Bokeh 0.12.4 documentation.webloc. Python data visualization: Comparing 7 tools. The Python scientific stack is fairly mature, and there are libraries for a variety of use cases, including machine learning, and data analysis.

Data visualization is an important part of being able to explore data and communicate results, but has lagged a bit behind other tools such as R in the past. Luckily, many new Python data visualization libraries have been created in the past few years to close the gap. matplotlib has emerged as the main data visualization library, but there are also libraries such as vispy, bokeh, seaborn, pygal, folium, and networkx that either build on matplotlib or have functionality that it doesn’t support.

In this post, we’ll use a real-world dataset, and use each of these libraries to make visualizations. Python Tutorial: Hashing (Hash Tables and hashlib)- 2017. Bogotobogo.com site search: Hash Tables When we talk about hash tables, we're actually talking about dictionary. While an array can be used to construct hash tables, array indexes its elements using integers. However, if we want to store data and use keys other than integer, such as 'string', we may want to use dictionary.

Dictionaries in Python are implemented using hash tables. Python Tutorial: A Tutorial. Tutorial and Online Course Machine Learning This is a completely new and incomplete chapter of our tutorial!

We started work in January 2017! Machine learning is is the kind of programming which gives computers the capability to automatically learn from data without being explicitly programmed. Python-for-data-analysis-part-29. Visual Analytics with Tableau is the third course in the Data Visualization with Tableau Specialization offered by UC Davis on Coursera.

The course focuses on teaching Tableau nuts and bolts, including chart options, altering chart color and shapes and making tooltips. The course is front-loaded with about half the lecture content covering general topics in the first week, while each of the three following weeks focus on specific topics including dates, maps and chart calculations. Similar to the two previous courses in the specialization, assignments are locked unless you pay to register for the full course.

If you purchase the course, each week has a peer graded assignment and a quiz. A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair) – Regress to Impress. Why Even Try, Man?

I recently came upon Brian Granger and Jake VanderPlas’s Altair, a promising young visualization library. Altair seems well-suited to addressing Python’s ggplot envy, and its tie-in with JavaScript’s Vega-Lite grammar means that as the latter develops new functionality (e.g., tooltips and zooming), Altair benefits — seemingly for free! Indeed, I was so impressed by Altair that the original thesis of my post was going to be: “Yo, use Altair.” But then I began ruminating on my own Pythonic visualization habits, and — in a painful moment of self-reflection — realized I’m all over the place: I use a hodgepodge of tools and disjointed techniques depending on the task at hand (usually whichever library I first used to accomplish that task1).

This is no good. How do I write a Python dictionary to a csv file? GitHub - tqdm/tqdm: A fast, extensible progress bar for Python. Implementing the Five Most Popular Similarity Measures in Python. In a particular subset of the data science world, “similarity distance measures” has become somewhat of a buzz term.

Like all buzz terms, it has invested parties- namely math & data mining practitioners- squabbling over what the precise definition should be. As a result, the term, involved concepts and their usage can go straight over the heads of beginners. So today, I write this post to give simplified and intuitive definitions of similarity measures, as well as diving into the implementation of five of the most popular of these similarity measures. Similarity: Similarity is the measure of how much alike two data objects are. Similarity = 1 if X = Y (Where X, Y are two objects)Similarity = 0 if X ≠ Y. How to find range overlap in python? Scipy array tip sheet. Arrays are the central datatype introduced in the SciPy package. (The same array objects are accessible within the NumPy package, which is a subset of SciPy.

For consistency, we will simplify refer to to SciPy, although some of the online documentation makes reference to NumPy. And technically, array objects are of type ndarray, which stands for "n-dimensional array".) The array interface is accessible by importing the scipy module: import scipy. Python: plot a bar using matplotlib using a dictionary. PyFormat: Using % and .format() for great good! Default Parameter Values in Python. Python’s handling of default parameter values is one of a few things that tends to trip up most new Python programmers (but usually only once). What causes the confusion is the behaviour you get when you use a “mutable” object as a default value; that is, a value that can be modified in place, like a list or a dictionary. An example: >>> def function(data=[]): ... data.append(1) ... return data ... >>> function() [1] >>> function() [1, 1] >>> function() [1, 1, 1] As you can see, the list keeps getting longer and longer.

If you look at the list identity, you’ll see that the function keeps returning the same object: >>> id(function()) 12516768 >>> id(function()) 12516768 >>> id(function()) 12516768 The reason is simple: the function keeps using the same object, in each call. Python - numpy: most efficient frequency counts for unique values in an array.

A Complete Tutorial to Learn Data Science with Python from Scratch. Introduction It happened few years back. After working on SAS for more than 5 years, I decided to move out of my comfort zone. Being a data scientist, my hunt for other useful tools was ON! Fortunately, it didn’t take me long to decide, Python was my appetizer. I always had a inclination towards coding. I learned basics of Python within a week. Due to lack of resource on python for data science, I decided to create this tutorial to help many others to learn python faster. Table of Contents. Python - Underscore vs Double underscore with variables and methods. A guide to Python's function decorators. Python is rich with powerful features and expressive syntax. One of my favorites is decorators. In the context of design patterns, decorators dynamically alter the functionality of a function, method or class without having to directly use subclasses.

This is ideal when you need to extend the functionality of functions that you don't want to modify. We can implement the decorator pattern anywhere, but Python facilitates the implementation by providing much more expressive features and syntax for that. In this post I will be discussing Python's function decorators in depth, accompanied by a bunch of examples on the way to clear up the concepts.

Python - What does `if __name__ == "__main__":` do? Class - Why do we use __init__ in python classes? Python @property. Python has a great concept called property, which makes the life of an object oriented programmer much simpler. Before defining and going into details of what a property in Python is, let us first build an intuition on why it would be needed in the first place. An Example To Begin With. Python tutorial. FastLanePython. Pygame. Notes for hacking, developing, and modifying pygame.

Building pygame. See the wiki page: Compilation to figure out how to compile pygame on different platforms. Keeping up with developments. Try and discuss changes on the mailing list. How can I get a list of locally installed Python modules? How to run *.py (Python) script.