Category: Python

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Spelling Correction in Python with TextBlob

Introduction Spelling mistakes are common, and most people are used to software indicating if a mistake was made. From autocorrect on our phones, to red underlining in text editors, spell checking is an essential feature for many different products. The first program to implement spell checking was written in 1971 for the DEC PDP-10. Called SPELL, it was capable of performing only simple comparisons of words and detecting one or two letter differences. As hardware and software advanced, so have […]

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Jump Search in Python

Introduction Finding the right data we need is an age-old problem before computers. As developers, we create many search algorithms to retrieve data efficiently. Search algorithms can be divided into two broad categories: sequential and interval searches. Sequential searches check each element in a data structure. Interval searches check various points of the data (called intervals), reducing the time it takes to find an item, given a sorted dataset. In this article, you will cover Jump Search in Python – […]

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Python: Check if Key Exists in Dictionary

Introduction Dictionary (also known as ‘map’, ‘hash’ or ‘associative array’) is a built-in Python container that stores elements as a key-value pair. Just like other containers have numeric indexing, here we use keys as indexes. Keys can be numeric or string values. However, no mutable sequence or object can be used as a key, like a list. In this article, we’ll take a look at how to check if a key exists in a dictionary in Python. In the examples, […]

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Calculating Pearson Correlation Coefficient in Python with Numpy

Introduction This article is an introduction to the Pearson Correlation Coefficient, its manual calculation and its computation via Python’s numpy module. The Pearson correlation coefficient measures the linear association between variables. Its value can be interpreted like so: +1 – Complete positive correlation +0.8 – Strong positive correlation +0.6 – Moderate positive correlation 0 – no correlation whatsoever -0.6 – Moderate negative correlation -0.8 – Strong negative correlation -1 – Complete negative correlation We’ll illustrate how the correlation coefficient varies […]

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How to Merge Two Dictionaries in Python

Introduction It’s not uncommon to have two dictionaries in Python which you’d like to combine. In this article, we will take a look at various ways on how to merge two dictionaries in Python. Some solutions are not available to all Python versions, so we will examine ways to merge for selected releases too. When merging dictionaries, we have to consider what will happen when the two dictionaries have the same keys. Let’s first define what should happen when we […]

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Creating Executable Files from Python Scripts with py2exe

Introduction Executing Python scripts requires a lot of prerequisites like having Python installed, having a plethora of modules installed, using the command line, etc. while executing an .exe file is very straightforward. If you want to create a simple application and distribute it to lots of users, writing it as a short Python script is not difficult, but assumes that the users know how to run the script and have Python already installed on their machine. Examples like this show […]

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Simple NLP in Python with TextBlob: N-Grams Detection

Introduction The constant growth of data on the Internet creates a demand for a tool that could process textual information in a faster way with no effort from the ordinary user. Moreover, it’s highly important that this instrument of text analysis could implement solutions for both low and high-level NLP tasks such as counting word frequencies, calculating sentiment analysis of the texts or detecting patterns in relationships between words. TextBlob is a great lightweight library for a wide variety of […]

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Seaborn Bar Plot – Tutorial and Examples

Introduction Seaborn is one of the most widely used data visualization libraries in Python, as an extension to Matplotlib. It offers a simple, intuitive, yet highly customizable API for data visualization. In this tutorial, we’ll take a look at how to plot a Bar Plot in Seaborn. Bar graphs display numerical quantities on one axis and categorical variables on the other, letting you see how many occurrences there are for the different categories. Bar charts can be used for visualizing […]

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Reading and Writing XML Files in Python with Pandas

Introduction XML (Extensible Markup Language) is a markup language used to store structured data. The Pandas data analysis library provides functions to read/write data for most of the file types. For example, it includes read_csv() and to_csv() for interacting with CSV files. However, Pandas does not include any methods to read and write XML files. In this article, we will take a look at how we can use other modules to read data from an XML file, and load it […]

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Analytics Vidhya, Classification, Data Science, Intermediate, Libraries, NLP, Programming, Python, Supervised, Text, Unstructured Data Leave a comment

Step by step guide to building sentiment analysis model using graphlab

I have been using graph lab for quite some time now. The first Kaggle competition I used it for was Click Trough Rate (CTR) and I was amazed to see the speed at which it can crunch such big data. Over last few months, I have realised much broader applications of GraphLab. In this article I will take up the text mining capability of GraphLab and solve one of the Kaggle problems. I will be referring to this problem with […]

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