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HOME | GREG REDA
Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login formsBLOG | GREG REDA
Greg Reda Home / Blog / Talks / About Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms Feb 2020 // Feature Engineering with Time Gaps Jul 2018 // Lenny Dykstra, His Strike Zone, & Bayesian Stats Feb 2018 // Hiring Data ScientistsGREG REDA | ABOUT
Home / Blog / Talks / About. Hello. I'm Greg Reda, a software engineer and data scientist based in San Francisco. I'm currently a Machine Learning Engineer on the Delivery Logistics team at Instacart, where I work on real-time order fulfillment systems.. Previously, I built and led the data science team at Sprout Social, where I oversaw the data science, data engineering, and analytics roles DEPLOYING STATIC SITES WITH GITHUB ACTIONS A while back I wrote about deploying my site using Github and Travis CI. But recently it seems Travis CI stopped being free for open source projects.. If you're using a static site generator for your site and hosting it on it on S3, you can use Github Actions to build and deploy your site on each commit (or PR, or whatever).. Setup. If you've already set up Travis CI to deploy your site to S3 SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as USING GO AND TWILIO TO MONITOR MY EMAIL Go's strings.Builder creates an object in memory which allows strings to be written directly to it, thus minimizing any memory copying. When declaring var sb strings.Builder we're getting a block in the memory registry and then writing directly to it with each Fprintf to &sb.Calling sb.String() returns a string of whatever we've written tothe Builder.
USING TRAVIS & GITHUB TO DEPLOY STATIC SITES I’m an unabashed supporter of “Keep It Simple, Stupid” solutions - it’s the reason I use Pelican for this website and host it on S3.. However, I haven’t been completely satisfied with the process of writing a new post or making changes to my theme.It’s felt repetitive - make a change, generate site, check change, regenerate site, and eventually push to S3. WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
MORE WEB SCRAPING WITH PYTHON (AND A MAP) More web scraping with Python (and a map) This is a follow-up to my previous post about web scraping with Python. Previously, I wrote a basic intro to scraping data off of websites. Since I wanted to keep the intro fairly simple, I didn't cover storing the data. In this post, I'll cover the basics of writing the scraped data to a flat fileand
HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. INTRO TO PANDAS DATA STRUCTURES Part 1: Intro to pandas data structures, covers the basics of the library's two main data structures - Series and DataFrames. Part 2: Working with DataFrames, dives a bit deeper into the functionality of DataFrames. It shows how to inspect, select, filter, merge, combine, and group your data. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago TRANSLATING SQL TO PANDAS, PART 1 I wrote a three part pandas tutorial for SQL users that you can find here.. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. For some reason, I've always found SQL to a much more intuitive tool for exploring a tabular dataset than I have other languages (namely R and Python). JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. INTRO TO PANDAS DATA STRUCTURES Part 1: Intro to pandas data structures, covers the basics of the library's two main data structures - Series and DataFrames. Part 2: Working with DataFrames, dives a bit deeper into the functionality of DataFrames. It shows how to inspect, select, filter, merge, combine, and group your data. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago TRANSLATING SQL TO PANDAS, PART 1 I wrote a three part pandas tutorial for SQL users that you can find here.. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. For some reason, I've always found SQL to a much more intuitive tool for exploring a tabular dataset than I have other languages (namely R and Python). JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you.HOME | GREG REDA
Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login formsBLOG | GREG REDA
Greg Reda Home / Blog / Talks / About Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms Feb 2020 // Feature Engineering with Time Gaps Jul 2018 // Lenny Dykstra, His Strike Zone, & Bayesian Stats Feb 2018 // Hiring Data ScientistsGREG REDA | ABOUT
Home / Blog / Talks / About. Hello. I'm Greg Reda, a software engineer and data scientist based in San Francisco. I'm currently a Machine Learning Engineer on the Delivery Logistics team at Instacart, where I work on real-time order fulfillment systems.. Previously, I built and led the data science team at Sprout Social, where I oversaw the data science, data engineering, and analytics roles DEPLOYING STATIC SITES WITH GITHUB ACTIONS A while back I wrote about deploying my site using Github and Travis CI. But recently it seems Travis CI stopped being free for open source projects.. If you're using a static site generator for your site and hosting it on it on S3, you can use Github Actions to build and deploy your site on each commit (or PR, or whatever).. Setup. If you've already set up Travis CI to deploy your site to S3 SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as USING GO AND TWILIO TO MONITOR MY EMAIL Go's strings.Builder creates an object in memory which allows strings to be written directly to it, thus minimizing any memory copying. When declaring var sb strings.Builder we're getting a block in the memory registry and then writing directly to it with each Fprintf to &sb.Calling sb.String() returns a string of whatever we've written tothe Builder.
USING TRAVIS & GITHUB TO DEPLOY STATIC SITES I’m an unabashed supporter of “Keep It Simple, Stupid” solutions - it’s the reason I use Pelican for this website and host it on S3.. However, I haven’t been completely satisfied with the process of writing a new post or making changes to my theme.It’s felt repetitive - make a change, generate site, check change, regenerate site, and eventually push to S3. WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
MORE WEB SCRAPING WITH PYTHON (AND A MAP) More web scraping with Python (and a map) This is a follow-up to my previous post about web scraping with Python. Previously, I wrote a basic intro to scraping data off of websites. Since I wanted to keep the intro fairly simple, I didn't cover storing the data. In this post, I'll cover the basics of writing the scraped data to a flat fileand
HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you.HOME | GREG REDA
Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login formsGREG REDA | ABOUT
Home / Blog / Talks / About. Hello. I'm Greg Reda, a software engineer and data scientist based in San Francisco. I'm currently a Machine Learning Engineer on the Delivery Logistics team at Instacart, where I work on real-time order fulfillment systems.. Previously, I built and led the data science team at Sprout Social, where I oversaw the data science, data engineering, and analytics roles DEPLOYING STATIC SITES WITH GITHUB ACTIONS A while back I wrote about deploying my site using Github and Travis CI. But recently it seems Travis CI stopped being free for open source projects.. If you're using a static site generator for your site and hosting it on it on S3, you can use Github Actions to build and deploy your site on each commit (or PR, or whatever).. Setup. If you've already set up Travis CI to deploy your site to S3 SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as USING GO AND TWILIO TO MONITOR MY EMAIL Go's strings.Builder creates an object in memory which allows strings to be written directly to it, thus minimizing any memory copying. When declaring var sb strings.Builder we're getting a block in the memory registry and then writing directly to it with each Fprintf to &sb.Calling sb.String() returns a string of whatever we've written tothe Builder.
WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
MORE WEB SCRAPING WITH PYTHON (AND A MAP) More web scraping with Python (and a map) This is a follow-up to my previous post about web scraping with Python. Previously, I wrote a basic intro to scraping data off of websites. Since I wanted to keep the intro fairly simple, I didn't cover storing the data. In this post, I'll cover the basics of writing the scraped data to a flat fileand
TRANSLATING SQL TO PANDAS A few weeks ago, I gave a pandas tutorial at PyData NYC titled "Translating SQL to pandas. And back." I don't remember why I put the "And back" in there - if you can translate things one way, you can translate them the other way, too. SCRAPING CRAIGSLIST FOR SOLD OUT CONCERT TICKETS The above function takes a search_term, which is used to execute a search on Craigslist.It returns a list of dictionaries, where each dictionary represents a post found within the search results. Note the global BASE_URL variable - this is the search results URL mentioned earlier. Here, we're injecting our search term into the section of the URL that had query=. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. TRANSLATING SQL TO PANDAS, PART 1 I wrote a three part pandas tutorial for SQL users that you can find here.. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. For some reason, I've always found SQL to a much more intuitive tool for exploring a tabular dataset than I have other languages (namely R and Python). SCRAPING PAGES BEHIND LOGIN FORMS The other day a friend asked whether there was an easier way for them to get 1000+ Goodreads reviews without manually doing it one-by-one.It
USING PANDAS ON THE MOVIELENS DATASET UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. This is part three of a three part introduction to pandas, a Python library for data analysis.The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. TRANSLATING SQL TO PANDAS, PART 1 I wrote a three part pandas tutorial for SQL users that you can find here.. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. For some reason, I've always found SQL to a much more intuitive tool for exploring a tabular dataset than I have other languages (namely R and Python). SCRAPING PAGES BEHIND LOGIN FORMS The other day a friend asked whether there was an easier way for them to get 1000+ Goodreads reviews without manually doing it one-by-one.It
USING PANDAS ON THE MOVIELENS DATASET UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. This is part three of a three part introduction to pandas, a Python library for data analysis.The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you.BLOG | GREG REDA
Greg Reda Home / Blog / Talks / About Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms Feb 2020 // Feature Engineering with Time Gaps Jul 2018 // Lenny Dykstra, His Strike Zone, & Bayesian Stats Feb 2018 // Hiring Data ScientistsGREG REDA | ABOUT
Home / Blog / Talks / About. Hello. I'm Greg Reda, a software engineer and data scientist based in San Francisco. I'm currently a Machine Learning Engineer on the Delivery Logistics team at Instacart, where I work on real-time order fulfillment systems.. Previously, I built and led the data science team at Sprout Social, where I oversaw the data science, data engineering, and analytics roles DEPLOYING STATIC SITES WITH GITHUB ACTIONS A while back I wrote about deploying my site using Github and Travis CI. But recently it seems Travis CI stopped being free for open source projects.. If you're using a static site generator for your site and hosting it on it on S3, you can use Github Actions to build and deploy your site on each commit (or PR, or whatever).. Setup. If you've already set up Travis CI to deploy your site to S3GREG REDA | TALKS
Home / Blog / Talks / About. Here are some talks I've given at various conferences and meetups: Social Conversations: Where Data Science Meets Product. Data-Driven Chicago Meetup // November 2, 2017 // Video. Panel: Crunching the Numbers: How to Launch a Career in DataScience
SCRAPING PAGES BEHIND LOGIN FORMS The other day a friend asked whether there was an easier way for them to get 1000+ Goodreads reviews without manually doing it one-by-one.It
USING GO AND TWILIO TO MONITOR MY EMAIL Go's strings.Builder creates an object in memory which allows strings to be written directly to it, thus minimizing any memory copying. When declaring var sb strings.Builder we're getting a block in the memory registry and then writing directly to it with each Fprintf to &sb.Calling sb.String() returns a string of whatever we've written tothe Builder.
WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. USING TRAVIS & GITHUB TO DEPLOY STATIC SITES I’m an unabashed supporter of “Keep It Simple, Stupid” solutions - it’s the reason I use Pelican for this website and host it on S3.. However, I haven’t been completely satisfied with the process of writing a new post or making changes to my theme.It’s felt repetitive - make a change, generate site, check change, regenerate site, and eventually push to S3. USEFUL UNIX COMMANDS FOR DATA SCIENCE wc (word count). By default, wc will quickly tell you how many lines, words, and bytes are in a file. If you're looking for just the line count, you can pass the -l parameter in.. I use it most often to verify record counts between files or database tables throughout ananalysis.
MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you.HOME | GREG REDA
Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login formsGREG REDA | ABOUT
Home / Blog / Talks / About. Hello. I'm Greg Reda, a software engineer and data scientist based in San Francisco. I'm currently a Machine Learning Engineer on the Delivery Logistics team at Instacart, where I work on real-time order fulfillment systems.. Previously, I built and led the data science team at Sprout Social, where I oversaw the data science, data engineering, and analytics roles DEPLOYING STATIC SITES WITH GITHUB ACTIONS A while back I wrote about deploying my site using Github and Travis CI. But recently it seems Travis CI stopped being free for open source projects.. If you're using a static site generator for your site and hosting it on it on S3, you can use Github Actions to build and deploy your site on each commit (or PR, or whatever).. Setup. If you've already set up Travis CI to deploy your site to S3 SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as USING GO AND TWILIO TO MONITOR MY EMAIL Go's strings.Builder creates an object in memory which allows strings to be written directly to it, thus minimizing any memory copying. When declaring var sb strings.Builder we're getting a block in the memory registry and then writing directly to it with each Fprintf to &sb.Calling sb.String() returns a string of whatever we've written tothe Builder.
WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
MORE WEB SCRAPING WITH PYTHON (AND A MAP) More web scraping with Python (and a map) This is a follow-up to my previous post about web scraping with Python. Previously, I wrote a basic intro to scraping data off of websites. Since I wanted to keep the intro fairly simple, I didn't cover storing the data. In this post, I'll cover the basics of writing the scraped data to a flat fileand
TRANSLATING SQL TO PANDAS A few weeks ago, I gave a pandas tutorial at PyData NYC titled "Translating SQL to pandas. And back." I don't remember why I put the "And back" in there - if you can translate things one way, you can translate them the other way, too. SCRAPING CRAIGSLIST FOR SOLD OUT CONCERT TICKETS The above function takes a search_term, which is used to execute a search on Craigslist.It returns a list of dictionaries, where each dictionary represents a post found within the search results. Note the global BASE_URL variable - this is the search results URL mentioned earlier. Here, we're injecting our search term into the section of the URL that had query=. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. INTRO TO PANDAS DATA STRUCTURES Part 1: Intro to pandas data structures, covers the basics of the library's two main data structures - Series and DataFrames. Part 2: Working with DataFrames, dives a bit deeper into the functionality of DataFrames. It shows how to inspect, select, filter, merge, combine, and group your data. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. INTRO TO PANDAS DATA STRUCTURES Part 1: Intro to pandas data structures, covers the basics of the library's two main data structures - Series and DataFrames. Part 2: Working with DataFrames, dives a bit deeper into the functionality of DataFrames. It shows how to inspect, select, filter, merge, combine, and group your data. USING PANDAS ON THE MOVIELENS DATASET The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. In : chicago WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you.BLOG | GREG REDA
Greg Reda Home / Blog / Talks / About Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms Feb 2020 // Feature Engineering with Time Gaps Jul 2018 // Lenny Dykstra, His Strike Zone, & Bayesian Stats Feb 2018 // Hiring Data ScientistsGREG REDA | ABOUT
Home / Blog / Talks / About. Hello. I'm Greg Reda, a software engineer and data scientist based in San Francisco. I'm currently a Machine Learning Engineer on the Delivery Logistics team at Instacart, where I work on real-time order fulfillment systems.. Previously, I built and led the data science team at Sprout Social, where I oversaw the data science, data engineering, and analytics roles DEPLOYING STATIC SITES WITH GITHUB ACTIONS A while back I wrote about deploying my site using Github and Travis CI. But recently it seems Travis CI stopped being free for open source projects.. If you're using a static site generator for your site and hosting it on it on S3, you can use Github Actions to build and deploy your site on each commit (or PR, or whatever).. Setup. If you've already set up Travis CI to deploy your site to S3GREG REDA | TALKS
Home / Blog / Talks / About. Here are some talks I've given at various conferences and meetups: Social Conversations: Where Data Science Meets Product. Data-Driven Chicago Meetup // November 2, 2017 // Video. Panel: Crunching the Numbers: How to Launch a Career in DataScience
SCRAPING PAGES BEHIND LOGIN FORMS The best way to find these details is by launching your browser's developer tools inside one of the input fields (like username/email). This will bring you to the code that is responsible for the form and allow you to find the details required. Using the screenshot above as an example, we can see the form requires some user input fields and as WORKING WITH DATAFRAMES This is part two of a three part introduction to pandas, a Python library for data analysis. The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. Part 1: Intro to pandas data structures. Part 2: Working with DataFrames. Part 3: Using pandas with the MovieLens dataset. WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. USING GO AND TWILIO TO MONITOR MY EMAIL Go's strings.Builder creates an object in memory which allows strings to be written directly to it, thus minimizing any memory copying. When declaring var sb strings.Builder we're getting a block in the memory registry and then writing directly to it with each Fprintf to &sb.Calling sb.String() returns a string of whatever we've written tothe Builder.
USING TRAVIS & GITHUB TO DEPLOY STATIC SITES I’m an unabashed supporter of “Keep It Simple, Stupid” solutions - it’s the reason I use Pelican for this website and host it on S3.. However, I haven’t been completely satisfied with the process of writing a new post or making changes to my theme.It’s felt repetitive - make a change, generate site, check change, regenerate site, and eventually push to S3. MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. SCRAPING PAGES BEHIND LOGIN FORMS The other day a friend asked whether there was an easier way for them to get 1000+ Goodreads reviews without manually doing it one-by-one.It
USING PANDAS ON THE MOVIELENS DATASET UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. This is part three of a three part introduction to pandas, a Python library for data analysis.The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. TRANSLATING SQL TO PANDAS, PART 1 I wrote a three part pandas tutorial for SQL users that you can find here.. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. For some reason, I've always found SQL to a much more intuitive tool for exploring a tabular dataset than I have other languages (namely R and Python). JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you. HOME | GREG REDAABOUTBLOGSUBSCRIBE (ATOM) Home / Blog / Talks / About Nice to meet you. Greg Reda is a data scientist and software engineer who occassionally writes things on this website. Recent Articles Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms PRINCIPLES OF GOOD DATA ANALYSIS Data analysis is hard. What makes it hard is the intuitive aspect of it - knowing the direction you want to take based on the limited information you have at the moment. WEB SCRAPING 201: FINDING THE API Previously, I explained how to scrape a page where the data is rendered server-side.However, the increasing popularity of Javascript frameworks such as AngularJS coupled with RESTful APIs means that fewer sites are generated server-side and are instead being rendered client-side.. In this post, I’ll give a brief overview of the differences between the two and show how to find the INTRO TO PANDAS DATA STRUCTURES Database. pandas also has some support for reading/writing DataFrames directly from/to a database .You'll typically just need to pass a connection object or sqlalchemy engine to the read_sql or to_sql functions within the pandas.io module.. Note that to_sql executes as a series of INSERT INTO statements and thus trades speed for simplicity. If you're writing a large DataFrame to a database COHORT ANALYSIS WITH PYTHON Despite having done it countless times, I regularly forget how to build a cohort analysis with Python and pandas.I’ve decided it’s a good idea to finally write it out - step by step - so I can refer back to this post later on. SCRAPING PAGES BEHIND LOGIN FORMS The other day a friend asked whether there was an easier way for them to get 1000+ Goodreads reviews without manually doing it one-by-one.It
USING PANDAS ON THE MOVIELENS DATASET UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. This is part three of a three part introduction to pandas, a Python library for data analysis.The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. TRANSLATING SQL TO PANDAS, PART 1 I wrote a three part pandas tutorial for SQL users that you can find here.. UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. For some reason, I've always found SQL to a much more intuitive tool for exploring a tabular dataset than I have other languages (namely R and Python). JOIN VS EXISTS VS IN (SQL) For those not very familiar with SQL, this should be relatively easy to understand. We have written a subquery that will get the id for the Analyst title in tableB.Using IN, we can then grab all of the employees from tableA who have that title.. While IN statements are fairly intuitive, they're often less efficient than the same query written as a JOIN or EXISTS statement would be. HOW RANDOM IS JAVASCRIPT'S MATH.RANDOM()? A few weeks back, I was talking with my friend Molly about personal domains and realized that her nickname, Bierface, was available. The exchange basically went like this: Me: I should buy bierface.com and just put up a ridiculous picture of you.GREG REDA | ABOUT
Home / Blog / Talks / About. Hello. I'm Greg Reda, a software engineer and data scientist based in San Francisco. I'm currently a Machine Learning Engineer on the Delivery Logistics team at Instacart, where I work on real-time order fulfillment systems.. Previously, I built and led the data science team at Sprout Social, where I oversaw the data science, data engineering, and analytics rolesBLOG | GREG REDA
Greg Reda Home / Blog / Talks / About Dec 2020 // Using Go and Twilio to monitor my email Dec 2020 // Deploying static sites with Github Actions Nov 2020 // newbird: a theme for pelican Nov 2020 // Scraping pages behind login forms Feb 2020 // Feature Engineering with Time Gaps Jul 2018 // Lenny Dykstra, His Strike Zone, & Bayesian Stats Feb 2018 // Hiring Data Scientists DEPLOYING STATIC SITES WITH GITHUB ACTIONS A while back I wrote about deploying my site using Github and Travis CI. But recently it seems Travis CI stopped being free for open source projects.. If you're using a static site generator for your site and hosting it on it on S3, you can use Github Actions to build and deploy your site on each commit (or PR, or whatever).. Setup. If you've already set up Travis CI to deploy your site to S3GREG REDA | TALKS
Home / Blog / Talks / About. Here are some talks I've given at various conferences and meetups: Social Conversations: Where Data Science Meets Product. Data-Driven Chicago Meetup // November 2, 2017 // Video. Panel: Crunching the Numbers: How to Launch a Career in DataScience
SCRAPING PAGES BEHIND LOGIN FORMS The other day a friend asked whether there was an easier way for them to get 1000+ Goodreads reviews without manually doing it one-by-one.It
WORKING WITH DATAFRAMES UPDATE: If you're interested in learning pandas from a SQL perspective and would prefer to watch a video, you can find video of my 2014 PyData NYC talk here.. This is part two of a three part introduction to pandas, a Python library for data analysis.The tutorial is primarily geared towards SQL users, but is useful for anyone wanting to get started with the library. WEB SCRAPING 101 WITH PYTHON It's very similar to our last function, but let's walk through it anyway. Define a function called get_category_winner.It requires a category_url.; Lines two and three are actually exactly the same as before - we'll come back to this in the next section. USEFUL UNIX COMMANDS FOR DATA SCIENCE wc (word count). By default, wc will quickly tell you how many lines, words, and bytes are in a file. If you're looking for just the line count, you can pass the -l parameter in.. I use it most often to verify record counts between files or database tables throughout ananalysis.
MY EXPERIENCE AS A FREELANCE DATA SCIENTIST Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing(2015).
MORE WEB SCRAPING WITH PYTHON (AND A MAP) This is a follow-up to my previous post about web scraping with Python.. Previously, I wrote a basic intro to scraping data off of websites. Since I wanted to keep the intro fairly simple, IGREG REDA
Home / Blog / Talks / AboutNICE TO MEET YOU.
Greg Reda is a data scientist and software engineer who occassionally writes things on this website.RECENT ARTICLES
DEC 2020 // USING GO AND TWILIO TO MONITOR MY EMAIL DEC 2020 // DEPLOYING STATIC SITES WITH GITHUB ACTIONS NOV 2020 // NEWBIRD: A THEME FOR PELICAN NOV 2020 // SCRAPING PAGES BEHIND LOGIN FORMS FEB 2020 // FEATURE ENGINEERING WITH TIME GAPS JUL 2018 // LENNY DYKSTRA, HIS STRIKE ZONE, & BAYESIAN STATS FEB 2018 // HIRING DATA SCIENTISTS JAN 2017 // MY EXPERIENCE AS A FREELANCE DATA SCIENTIST NOV 2016 // DATA-INFORMED VS DATA-DRIVEN OCT 2016 // ASYNCHRONOUS SCRAPING WITH PYTHON ------------------------- Built with Pelican and the newbirdtheme
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