Data Inspired Insights

Tag: python (Page 2 of 3)

Pandas: Filtering and segmenting

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

One of the most common ways you will interact with a pandas DataFrame is by selecting different combinations of columns and rows. This can be done using the numerical positions of columns and rows in the DataFrame, column names and row indices, or by filtering the rows by applying some criteria to the data in the DataFrame. All of these options (and combinations of them) are available, so let’s dig in!

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Pandas: Basic data interrogation

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

Once we have our data in a pandas DataFrame, the basic table structure in pandas, the next step is how do we assess what we have? If you are coming from Excel or R Studio, you are probably used to being able to look at the data any time you want. In python/pandas, we don’t have a spreadsheet to work with, and we don’t even have an equivalent of R Studio (although Jupyter notebooks are a similar concept), but we do have several tools available that can help you get a handle on what your data looks like.

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Pandas: Reading in JSON data

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

When we are working with data in software development or when the data comes from APIs, it is often not provided in a tabular form. Instead it is provided in some combination of key-value stores and arrays broadly denoted as JavaScript Object Notation (JSON). So how do we read this type of non-tabular data into a tabular format like a pandas DataFrame?

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Pandas: Reading in tabular data

This article is part of a series of practical guides for using the Python data processing library pandas. To see view all the available parts, click here.

To get started with pandas, the first thing you are going to need to understand is how to get data into pandas. For this guide we are going to focus on reading in tabular data (i.e. data stored in a table with rows and columns). If you don’t have some data available but want to try some things out, a great place to get some data to play with is the UCI Machine Learning Repository.

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Forget SQL or NoSQL – 5 scenarios where you may not need a database at all

A while back, I attended a hackathon in Belgrade as a mentor. This hackathon was the first ‘open data’ hackathon in Serbia and focused on making applications using data that had recently been released by various ministries, government agencies, and independent bodies in Serbia. As we walked around talking to the various teams, one of the things I noticed at the time, was that almost all teams were using databases to manage their data . In most cases, the database being used was something very lightweight like SQLite3, but in some cases more serious databases (MySQL, PostgreSQL, MongoDB) were also being used.

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JSONify It – CSV to JSON Converter

Go to JSONify It

For those who have some experience in creating visualizations, particularly online visualizations using JavaScript and libraries such as D3.js, one thing that you will often come across is the need to convert your data. Typically this need will arise because the data you receive or collect will be in a human-friendly format such as an Excel spreadsheet, and in order for you to use it for the visualization you will need that data in JSON format. Annoyingly, this will often be just a one time conversion, meaning writing a stand alone script to do the conversion often seems like overkill.

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Data Science: A Kaggle Walkthrough – Creating a Model

This article is Part VI in a series looking at data science and machine learning by walking through a Kaggle competition. If you have not done so already, you are strongly encouraged to go back and read the earlier parts – (Part I, Part II, Part III, Part IV and Part V).

Continuing on the walkthrough, in this part we build the model that will predict the first booking destination country for each user based on the dataset created in the earlier parts.

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Data Science: A Kaggle Walkthrough – Adding New Data

This article is Part V in a series looking at data science and machine learning by walking through a Kaggle competition. If you have not done so already, you are strongly encouraged to go back and read the earlier parts – (Part I, Part II, Part III and Part IV).

Continuing on the walkthrough, in this part we take the data from sessions.csv that we left aside initially and add it to the transformed and expanded data from Part IV.  This part will cover, in brief, all the steps in Parts II – IV.

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