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BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app =THE GGTHEMR PACKAGE
The ggthemr package was developed by a friend of mine, Ciarán Tobin, who works with me at KillBiller and Edgetier.The package gives a quick and easy way to completely change the look and feel of your ggplot2 figures, as well as quickly create a theme based on your own, or your company’s, colour palette.. In this post, we will quickly examine some of the built in theme variations included BATCH GEOCODING WITH R AND GOOGLE MAPS I’ve recently wanted to geocode a large number of addresses (think circa 60k) in Ireland as part of a visualisation of the Irish property market. Geocoding can be simply achieved in R using the geocode() function from the ggmap library. The geocode function uses Googles Geocoding API to turn addresses from text to latitude and Batch Geocoding with R and Google maps Read More » USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. GEOCODE THE IRISH PROPERTY PRICE REGISTER USING PYTHON AND The Property Price Register. An interesting data set for all Irish data scientists is the Irish Property Price Register.The property price register (PPR) records the price, address and date of sale on all residential properties which have been purchased in Ireland since1 January 2010.
PLOT YOUR FITBIT DATA IN PYTHON (API V1.2) The Fitbit API and OAuth access. The Fitbit API is a well-documented RESTful API where all of your movement, sleep, heart rate, and food logging data (if tracked by a Fitbit device) can be programmatically accessed. To access data, you’ll need to create an “application”with
CSV DATA EXTRACTION TOOL FOR ROS BAG FILES FOR PYTHON, R umrr_driver/radar_msg (this was a type used by the CRUISE vehicle (see below)) To install the data extraction tool, download the zip file, extract it somewhere on your ROS_PACKAGE_PATH, and run rosmake data_extraction before using. The tool can be used in two different ways: 1.) Extract all compatible topics in a bag file. # Extract allmessage.
FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN PANDAS DROP: DELETE DATAFRAME ROWS & COLUMNS At the start of every analysis, data needs to be cleaned, organised, and made tidy.For every Python Pandas DataFrame, there is almost always a need to delete rows and columns to get the right selection of data for your specific analysis or visualisation.The Pandas “Drop”function is
ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
PYTHON PANDAS READ_CSV: LOAD DATA FROM CSV FILES BATCH GEOCODING WITH R AND GOOGLE MAPS I’ve recently wanted to geocode a large number of addresses (think circa 60k) in Ireland as part of a visualisation of the Irish property market. Geocoding can be simply achieved in R using the geocode() function from the ggmap library. The geocode function uses Googles Geocoding API to turn addresses from text to latitude and Batch Geocoding with R and Google maps Read More » SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN PANDAS DROP: DELETE DATAFRAME ROWS & COLUMNS At the start of every analysis, data needs to be cleaned, organised, and made tidy.For every Python Pandas DataFrame, there is almost always a need to delete rows and columns to get the right selection of data for your specific analysis or visualisation.The Pandas “Drop”function is
ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
PYTHON PANDAS READ_CSV: LOAD DATA FROM CSV FILES BATCH GEOCODING WITH R AND GOOGLE MAPS I’ve recently wanted to geocode a large number of addresses (think circa 60k) in Ireland as part of a visualisation of the Irish property market. Geocoding can be simply achieved in R using the geocode() function from the ggmap library. The geocode function uses Googles Geocoding API to turn addresses from text to latitude and Batch Geocoding with R and Google maps Read More » SELF-ORGANISING MAPS FOR CUSTOMER SEGMENTATION USING R Self-Organising Maps Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. In this post, we examine the use of R to create a SOM for customer segmentation. The figures shown here used use the 2011 Irish Census information for theTHE GGTHEMR PACKAGE
The ggthemr package was developed by a friend of mine, Ciarán Tobin, who works with me at KillBiller and Edgetier.The package gives a quick and easy way to completely change the look and feel of your ggplot2 figures, as well as quickly create a theme based on your own, or your company’s, colour palette.. In this post, we will quickly examine some of the built in theme variations included SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » BATCH GEOCODING WITH R AND GOOGLE MAPS I’ve recently wanted to geocode a large number of addresses (think circa 60k) in Ireland as part of a visualisation of the Irish property market. Geocoding can be simply achieved in R using the geocode() function from the ggmap library. The geocode function uses Googles Geocoding API to turn addresses from text to latitude and Batch Geocoding with R and Google maps Read More » PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis LIBRARIES FOR PLOTTING IN PYTHON AND PANDAS Matplotlib is the grand-daddy of Python plotting libraries. Initially launched in 2003, Matplotlib is still actively developed and maintained with over 28,000 commits on the official Matplotlib Github repository from 750+ contributors, and is the most flexible and complete data visualisation library out there. GEOCODE THE IRISH PROPERTY PRICE REGISTER USING PYTHON AND The Property Price Register. An interesting data set for all Irish data scientists is the Irish Property Price Register.The property price register (PPR) records the price, address and date of sale on all residential properties which have been purchased in Ireland since1 January 2010.
USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was PLOT YOUR FITBIT DATA IN PYTHON (API V1.2) The Fitbit API and OAuth access. The Fitbit API is a well-documented RESTful API where all of your movement, sleep, heart rate, and food logging data (if tracked by a Fitbit device) can be programmatically accessed. To access data, you’ll need to create an “application”with
SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = POSTGRESQL: FIND SLOW, LONG-RUNNING AND BLOCKED QUERIES If you run a PostgreSQL database, use pg_stat_activity to find and identify slow and blocked processes and queries, with the query text and responsible user quickly. pg_blocking_pids and pg_locks will give you everything you need to know about database locks. PYTHON PANDAS READ_CSV: LOAD DATA FROM CSV FILES WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = POSTGRESQL: FIND SLOW, LONG-RUNNING AND BLOCKED QUERIES If you run a PostgreSQL database, use pg_stat_activity to find and identify slow and blocked processes and queries, with the query text and responsible user quickly. pg_blocking_pids and pg_locks will give you everything you need to know about database locks. PYTHON PANDAS READ_CSV: LOAD DATA FROM CSV FILES WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by SELF-ORGANISING MAPS FOR CUSTOMER SEGMENTATION USING R Self-Organising Maps Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. In this post, we examine the use of R to create a SOM for customer segmentation. The figures shown here used use the 2011 Irish Census information for the PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis GEOCODE THE IRISH PROPERTY PRICE REGISTER USING PYTHON AND The Property Price Register. An interesting data set for all Irish data scientists is the Irish Property Price Register.The property price register (PPR) records the price, address and date of sale on all residential properties which have been purchased in Ireland since1 January 2010.
USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. GET BUSY WITH WORD EMBEDDINGS Get Busy with Word Embeddings – An Introduction. 8 Comments / blog, data science, python, Tutorials / By Shane. This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of naturallanguage
DATA VISUALISATION IN PYTHON The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides from that talk, along with a video recording of same. MERGE AND JOIN DATAFRAMES WITH PANDAS IN PYTHON In any real world data science situation with Python, you’ll be about 10 minutes in when you’ll need to merge or join Pandas Dataframes together to form your analysis dataset. Merging and joining dataframes is a core process that any aspiring data analyst will need to master. This blog post addresses the process of merging datasets, that is, joining two datasets together based on common PLOT YOUR FITBIT DATA IN PYTHON (API V1.2) The Fitbit API and OAuth access. The Fitbit API is a well-documented RESTful API where all of your movement, sleep, heart rate, and food logging data (if tracked by a Fitbit device) can be programmatically accessed. To access data, you’ll need to create an “application”with
SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN PANDAS DROP: DELETE DATAFRAME ROWS & COLUMNS At the start of every analysis, data needs to be cleaned, organised, and made tidy.For every Python Pandas DataFrame, there is almost always a need to delete rows and columns to get the right selection of data for your specific analysis or visualisation.The Pandas “Drop”function is
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = PLOT YOUR FITBIT DATA IN PYTHON (API V1.2) Download your sleep data. The Fitbit python library, by default, is set up to use version 1 of the Fitbit API, whereas there is better quality data available from the V1.2 API. We’re going to use the Fitbit library to manage the data requests, but we’re going to manually specify the V1.2 API URLs so USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN PANDAS DROP: DELETE DATAFRAME ROWS & COLUMNS At the start of every analysis, data needs to be cleaned, organised, and made tidy.For every Python Pandas DataFrame, there is almost always a need to delete rows and columns to get the right selection of data for your specific analysis or visualisation.The Pandas “Drop”function is
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = PLOT YOUR FITBIT DATA IN PYTHON (API V1.2) Download your sleep data. The Fitbit python library, by default, is set up to use version 1 of the Fitbit API, whereas there is better quality data available from the V1.2 API. We’re going to use the Fitbit library to manage the data requests, but we’re going to manually specify the V1.2 API URLs so USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysisTHE GGTHEMR PACKAGE
The ggthemr package was developed by a friend of mine, Ciarán Tobin, who works with me at KillBiller and Edgetier.The package gives a quick and easy way to completely change the look and feel of your ggplot2 figures, as well as quickly create a theme based on your own, or your company’s, colour palette.. In this post, we will quickly examine some of the built in theme variations included BATCH GEOCODING WITH R AND GOOGLE MAPS I’ve recently wanted to geocode a large number of addresses (think circa 60k) in Ireland as part of a visualisation of the Irish property market. Geocoding can be simply achieved in R using the geocode() function from the ggmap library. The geocode function uses Googles Geocoding API to turn addresses from text to latitude and Batch Geocoding with R and Google maps Read More » USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was LIBRARIES FOR PLOTTING IN PYTHON AND PANDAS Matplotlib is the grand-daddy of Python plotting libraries. Initially launched in 2003, Matplotlib is still actively developed and maintained with over 28,000 commits on the official Matplotlib Github repository from 750+ contributors, and is the most flexible and complete data visualisation library out there. USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » CSV DATA EXTRACTION TOOL FOR ROS BAG FILES FOR PYTHON, R umrr_driver/radar_msg (this was a type used by the CRUISE vehicle (see below)) To install the data extraction tool, download the zip file, extract it somewhere on your ROS_PACKAGE_PATH, and run rosmake data_extraction before using. The tool can be used in two different ways: 1.) Extract all compatible topics in a bag file. # Extract allmessage.
FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN PYTHON PANDAS READ_CSV: LOAD DATA FROM CSV FILES ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN PYTHON PANDAS READ_CSV: LOAD DATA FROM CSV FILES ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis SELF-ORGANISING MAPS FOR CUSTOMER SEGMENTATION USING R Self-Organising Maps Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. In this post, we examine the use of R to create a SOM for customer segmentation. The figures shown here used use the 2011 Irish Census information for the USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was GEOCODE THE IRISH PROPERTY PRICE REGISTER USING PYTHON AND The Property Price Register. An interesting data set for all Irish data scientists is the Irish Property Price Register.The property price register (PPR) records the price, address and date of sale on all residential properties which have been purchased in Ireland since1 January 2010.
SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. GET BUSY WITH WORD EMBEDDINGS Get Busy with Word Embeddings – An Introduction. 8 Comments / blog, data science, python, Tutorials / By Shane. This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of naturallanguage
FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this DATA VISUALISATION IN PYTHON The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides from that talk, along with a video recording of same. SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPSSHANE LYNN PSYCHIATRYSHANE LAWRENCE FACEBOOK 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPSSHANE LYNN PSYCHIATRYSHANE LAWRENCE FACEBOOK 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
THE GGTHEMR PACKAGE
The ggthemr package was developed by a friend of mine, Ciarán Tobin, who works with me at KillBiller and Edgetier.The package gives a quick and easy way to completely change the look and feel of your ggplot2 figures, as well as quickly create a theme based on your own, or your company’s, colour palette.. In this post, we will quickly examine some of the built in theme variations included PANDAS DROP: DELETE DATAFRAME ROWS & COLUMNS At the start of every analysis, data needs to be cleaned, organised, and made tidy.For every Python Pandas DataFrame, there is almost always a need to delete rows and columns to get the right selection of data for your specific analysis or visualisation.The Pandas “Drop”function is
GET BUSY WITH WORD EMBEDDINGS Get Busy with Word Embeddings – An Introduction. 8 Comments / blog, data science, python, Tutorials / By Shane. This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of naturallanguage
USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was GEOCODE THE IRISH PROPERTY PRICE REGISTER USING PYTHON AND The Property Price Register. An interesting data set for all Irish data scientists is the Irish Property Price Register.The property price register (PPR) records the price, address and date of sale on all residential properties which have been purchased in Ireland since1 January 2010.
SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » PLOT YOUR FITBIT DATA IN PYTHON (API V1.2) The Fitbit API and OAuth access. The Fitbit API is a well-documented RESTful API where all of your movement, sleep, heart rate, and food logging data (if tracked by a Fitbit device) can be programmatically accessed. To access data, you’ll need to create an “application”with
RAINY CYCLING COMMUTES IN IRELAND? WUNDERGROUND DATA IN PYTHON How often do you get wet cycling to work? Cycling in Ireland is taking off. The DublinBikes scheme is a massive success with over 10 million journeys, there’s large increases in people cycling in Irish cities, there’s a good cyclist community, and infrastructure is slowing improving around the country. However, Ireland is a rainy place! Itturns out that
FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPSSHANE LYNN PSYCHIATRYSHANE LAWRENCE FACEBOOK 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPSSHANE LYNN PSYCHIATRYSHANE LAWRENCE FACEBOOK 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
WUNDERGROUND DATA WITH PYTHON PANDAS & SEABORN ILOC, LOC, AND IX FOR DATA SELECTION IN PYTHON PANDAS 1. Pandas iloc data selection. The iloc indexer for Pandas Dataframe is used for integer-location based indexing / selection by position.. The iloc indexer syntax is data.iloc, which is sure to be a source of confusion for R users. “iloc” in pandas is used to select rows and columns by number, in the order that they appear in the dataframe.
BAR PLOTS IN PYTHON USING PANDAS DATAFRAMES Bar Plots – The king of plots? The ability to render a bar plot quickly and easily from data in Pandas DataFrames is a key skill for any data scientist working in Python.. Nothing beats the bar plot for fast data exploration and comparison of variable values between different groups, or building a story around how groups of data arecomposed.
PANDAS GROUPBY: SUMMARISING, AGGREGATING, GROUPING IN PYTHON Pandas – Python Data Analysis Library. I’ve recently started using Python’s excellent Pandas library as a data analysis tool, and, while finding the transition from R’s excellent data.table library frustrating at times, I’m finding my way around and finding most things work quite well.. One aspect that I’ve recently been exploring is the task of grouping large data frames by ASYNCHRONOUS UPDATES TO A WEBPAGE WITH FLASK AND SOCKET.IO Flask is an extremely lightweight and simple framework for building web applications using Python. If you haven’t used Flask before, it’s amazingly simple, and to get started serving a very simple webpage only requires a few lines of Python: # Basic Flask Python Web App. from flask import Flask. app = WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM Spacy is a natural language processing (NLP) library for Python designed to have fast performance, and with word embedding models built in, it’s perfect for a quick and easy start. Gensim is a topic modelling library for Python that provides access to Word2Vec and other word embedding algorithms for training, and it also allowspre-trained
USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES The Gamer Lifecycle. How customers are gained and lost by game developers. Source: OnGamesNData An example of a successful data-driven game is Candy Crush Saga, which is made almost $2 billion dollars in 2013 from in-game purchases.The game launched in 2012 but it’s still appearing in the top 10 grossing mobile games across the Google Play Store and the App Store. PYTHON PANDAS DATAFRAME: LOAD, EDIT, VIEW DATA Starting out with Python Pandas DataFrames. If you’re developing in data science, and moving from excel-based analysis to the world of Python, scripting, and automated analysis, you’ll come across the incredibly popular data management library, “Pandas” in Python. Pandas development started in 2008 with main developer Wes McKinney and the library has become a standard for data analysis FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope this SHANE LYNN - DATA SCIENCE, ANALYTICS, AND STARTUPS 1 Comment / blog, Data Visualisation, python, Talks / By Shane. The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides fromthat talk
THE GGTHEMR PACKAGE
The ggthemr package was developed by a friend of mine, Ciarán Tobin, who works with me at KillBiller and Edgetier.The package gives a quick and easy way to completely change the look and feel of your ggplot2 figures, as well as quickly create a theme based on your own, or your company’s, colour palette.. In this post, we will quickly examine some of the built in theme variations included PANDAS DROP: DELETE DATAFRAME ROWS & COLUMNS At the start of every analysis, data needs to be cleaned, organised, and made tidy.For every Python Pandas DataFrame, there is almost always a need to delete rows and columns to get the right selection of data for your specific analysis or visualisation.The Pandas “Drop”function is
GET BUSY WITH WORD EMBEDDINGS Get Busy with Word Embeddings – An Introduction. 8 Comments / blog, data science, python, Tutorials / By Shane. This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of naturallanguage
USING PYTHON THREADING AND RETURNING MULTIPLE RESULTS Using Python Threading and Returning Multiple Results (Tutorial) I recently had an issue with a long running web process that I needed to substantially speed up due to timeouts. The delay arose because the system needed to fetch data from a number of URLs. The total number of URLs varied from user to user, and the response time for each URL was GEOCODE THE IRISH PROPERTY PRICE REGISTER USING PYTHON AND The Property Price Register. An interesting data set for all Irish data scientists is the Irish Property Price Register.The property price register (PPR) records the price, address and date of sale on all residential properties which have been purchased in Ireland since1 January 2010.
SCRAPING DUBLIN CITY BIKES DATA USING PYTHON FAST TRACK: There is some python code that allows you to scrape bike availability from bike schemes at the bottom of this post SLOW TRACK: As a recent aside, I was interested in collecting Dublin Bikes usage data over a long time period for data visualisation and exploration purposes. The Dublinbikes scheme was launched in Scraping Dublin City Bikes Data Using Python Read More » PLOT YOUR FITBIT DATA IN PYTHON (API V1.2) The Fitbit API and OAuth access. The Fitbit API is a well-documented RESTful API where all of your movement, sleep, heart rate, and food logging data (if tracked by a Fitbit device) can be programmatically accessed. To access data, you’ll need to create an “application”with
RAINY CYCLING COMMUTES IN IRELAND? WUNDERGROUND DATA IN PYTHON How often do you get wet cycling to work? Cycling in Ireland is taking off. The DublinBikes scheme is a massive success with over 10 million journeys, there’s large increases in people cycling in Irish cities, there’s a good cyclist community, and infrastructure is slowing improving around the country. However, Ireland is a rainy place! Itturns out that
FIXING OFFICE 2016 INSTALLATION FOR MAC The Macbook in question had an “á” in the Computer name. To change the name of your computer, open up “System Preferences” by pressing Command-Space and typing “Preferences”. Alternatively, click your Apple symbol on top left and click “Preferences”. Under the “Sharing” option, you’ll find your computer name. Hope thisSkip to content
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Data science, Startups, Analytics, and Data visualisation.Main Menu
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DATA VISUALISATION IN PYTHON – PYCON DUBLIN 2018 PRESENTATION1 Comment
/ blog , Data Visualisation, python
, Talks
/ By shanelynn
The ability to explore and grasp data structures through quick and intuitive visualisation is a key skill of any data scientist. At PyConIE 2018, I presented a talk on the various libraries available for data visualisation in Dublin. This post contains the slides from that talk, along with a video recording of same. PLOTTING WITH PYTHON AND PANDAS – LIBRARIES FOR DATA VISUALISATION1 Comment
/ blog , Data Visualisation, Pandas
, python
, Tutorials
/ By shanelynn
Anyone familiar with the use of Python for data science and analysis projects has googled some combination of “plotting in python”, “data visualisation in python”, “barcharts in python” at some point. It’s not uncommon to end up lost in a sea of competing libraries, confused and alone, and just to go home again! The purpose…
Plotting with Python and Pandas – Libraries for Data VisualisationRead More »
PYTHON PANDAS READ_CSV – LOAD DATA FROM CSV FILES11 Comments
/ blog , data science, Pandas
, python
, Tutorials
/ By shanelynn
CSV (comma-separated value) files are a common file format for transferring and storing data. The ability to read, manipulate, and write data to and from CSV files using Python is a key skill to master for any data scientist or business analysis. In this post, we’ll go over what CSV files are, how to read CSV files into Pandas DataFrames, and how to write DataFrames back to CSV files post analysis. WORD EMBEDDINGS IN PYTHON WITH SPACY AND GENSIM9 Comments
/ blog , data science, Natural
Language Processing
,
python , Tutorials
/ By shanelynn
This post shows how to load, use, and make your own word embeddings using Python. Use the Gensim and Spacy libraries to load pre-trained word vector models from Google and Facebook, or train custom models using your own data and the Word2Vec algorithm. This post is a direct follow-on from the introductory Word Embeddings post, and will show you how to get started using word vectors with your own models andsystems.
GET BUSY WITH WORD EMBEDDINGS – AN INTRODUCTION8 Comments
/ blog , data science, python
, Tutorials
/ By shanelynn
This post provides an introduction to “word embeddings” or “word vectors”. Word embeddings are real-number vectors that represent words from a vocabulary, and have broad applications in the area of natural language processing (NLP). We examine training, use, and properties of word embeddings models, and look at how and why you should look to use word embeddings over older bag-of-words techniques in your data science and language modelling tasks. THE PANDAS DATAFRAME – LOADING, EDITING, AND VIEWING DATA IN PYTHON43 Comments
/ blog , data science, Data
Visualisation
, Pandas
, python
, Tutorials
/ By shanelynn
The Pandas DataFrame – this blog post covers the basics of loading, editing, and viewing data in Python, and getting to grips with the all-important data structure in Python – the Pandas Dataframe. Learn by example to load CSV files, rename columns, extract statistics, and select rows and columns. THE IRISH PROPERTY PRICE REGISTER – GEOCODED TO SMALL AREAS12 Comments
/ blog , data science, Data
Visualisation
/ By
shanelynn
In this post, geocoded data for all property price sales in Ireland from 2012-2017 is available. Data is sourced on the Irish Property Price Register and geocoded using the Google geocoding script in Python. All of the GPS latitude/longitude coordinates are further tied to census small area and electoral division boundaries. PANDAS CSV ERROR: ERROR TOKENIZING DATA. C ERROR: EOF INSIDE STRINGSTARTING AT LINE
10 Comments
/ blog , Pandas
, python
/ By shanelynn
Many precious hours have been lost to Character encoding errors and EOF character errors in CSV files being read by the Pandas read_csv file. This is an incredibly frustrating start to any analysis! Hopefully this post will save some people from the same fate! USING BIG DATA TO CREATE BETTER MOBILE VIDEO GAMES3 Comments
/ blog / By shanelynn A lot of modern-day games, especially the ones being developed for mobile, are built on business models revolving around data. Understanding how the audience thinks and responds with a product, as well as knowing how retention works in gaming, are both important in paving the way for the future of gaming. MERGE AND JOIN DATAFRAMES WITH PANDAS IN PYTHON32 Comments
/ blog , data science, Pandas
, python
, Tutorials
/ By shanelynn
Merging and Joining data sets are key activities of any data scientist or analyst. In this tutorial, we explore the process of combining datasets based on common columns quickly and easily with the Python Pandas library and it’s fast merge() functionality. Finally conquer merging and become a master with this 2-part tutorial.POSTS NAVIGATION
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