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Coding with the Yahoo_fin Package

Coding with the Yahoo_fin Package

Python, Web Scraping
Subscribe to TheAutomatic.net via the area on the right side of the page. The yahoo_fin package contains functions to scrape stock-related data from Yahoo Finance and NASDAQ. You can view the official documentation by clicking this link, but the below post will provide a few more in-depth examples. All of the functions in yahoo_fin are contained within a single module inside yahoo_fin, called stock_info. You can import all the functions at once like this: [code lang="python"] from yahoo_fin.stock_info import * [/code] Downloading price data One of the core functions available is called get_data, which retrieves historical price data for an individual stock. To call this function, just pass whatever ticker you want: [code lang="python"] get_data("nflx") # gets Netflix's data get_data("aapl") # gets Apple's data get_data("amzn") # gets Amazon's data [/code]…
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Timing Python Processes

Timing Python Processes

Python
Timing Python processes is made possible with several different packages. One of the most common ways is using the standard library package, time. Here's an example. Suppose we want to scrape the HTML from some collection of links. In this case, we're going to get a collection of URLs from Bloomberg's homepage. To do this, we'll use BeautifulSoup to get a list of full-path URLs. From the code below, this gives us a list of over 200 URLs. This first section of code should run pretty quickly; where timing a process comes in is if we wanted to cycle through some (or all) of these links and scrape the HTML from the respective pages. [code lang="Python"] # load packages import time from bs4 import BeautifulSoup import requests # get HTML…
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Underrated R Functions

Underrated R Functions

R
I wanted to write a post about a couple of handy functions in R that don't always get the recognition they deserve. This article will talk about a few functions that form part of R's core functional programming capabilities. R has thousands of functions, so this is just a short list, and I'll probably write other articles like this in the future to discuss some different R functions. Reduce Let's start with the Reduce function (note the capital "R"). Reduce takes a list or vector as input, and reduces it down to a single element. It works by applying a function to the first two elements of the vector or list, and then applying the same function to that result with the third element. This new result gets passed with…
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Vectorize Fuzzy Matching

Vectorize Fuzzy Matching

R
One of the best things about R is its ability to vectorize code. This allows you to run code much faster than you would if you were using a for or while loop. In this post, we're going to show you how to use vectorization to speed up fuzzy matching. First, a little bit of background will be covered. If you're familiar with vectorization and / or fuzzy matching, feel free to skip further down the post. What is vectorization? Vectorization works by performing operations on entire vectors, or by extension, matrices, rather than iterating through each element in a collection of objects one at a time. A basic example is adding two vectors together. This can be done like this: [code lang="R"] a <- c(3, 4, 5) b <-…
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Running R Code in Parallel

Running R Code in Parallel

R
Background Running R code in parallel can be very useful in speeding up performance. Basically, parallelization allows you to run multiple processes in your code simultaneously, rather than than iterating over a list one element at a time, or running a single process at a time. Thankfully, running R code in parallel is relatively simple using the parallel package. This package provides parallelized versions of sapply, lapply, and rapply. Parallelizing code works best when you need to call a function or perform an operation on different elements of a list or vector when doing so on any particular element of the list (or vector) has no impact on the evaluation of any other element. This could be running a large number of models across different elements of a list, scraping…
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Word Frequency Analysis

Word Frequency Analysis

Python, Web Scraping
In a previous article, we talked about using Python to scrape stock-related articles from the web. As an extension of this idea, we’re going to show you how to use the NLTK package to figure out how often different words occur in text, using scraped stock articles. Initial Setup Let's import the NLTK package, along with requests and BeautifulSoup, which we'll need to scrape the stock articles. [code language="python" style="font-size: 8px"] '''load packages''' import nltk import requests from bs4 import BeautifulSoup [/code] Pulling the data we'll need Below, we're copying code from my scraping stocks article. This gives us a function, scrape_all_articles (along with two other helper functions), which we can use to pull the actual raw text from articles linked to from NASDAQ's website. [code language="python"] def scrape_news_text(news_url): news_html…
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Running Python from the Task Scheduler

Running Python from the Task Scheduler

Python, Windows
Being able to run Python scripts from the Windows Task Scheduler is a really useful capability. It allows you to run Python in production on a Windows system, and can save countless hours of work. For instance, running code like this previous article about scraping stock articles on an automated, regular basis, could come in handy as new stock articles are posted. Before we go into how to schedule a Python script to run, you need to understand how to run Python from the command line. Just press the windows key and type cmd into the search box to make the command prompt come up. Suppose your python script is called cool_python_script.py, and is saved under C:\Users. You can run this script from the command prompt by typing the below…
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RoboBrowser: Automating Online Forms

RoboBrowser: Automating Online Forms

Python, Web Scraping
RoboBrowser is a Python 3.x package for crawling through the web and submitting online forms. It works similarly to the older Python 2.x package, mechanize. This post is going to give a simple introduction using RoboBrowser to submit a form on Wunderground for scraping historical weather data. Initial setup RoboBrowser can be installed via pip: [code lang="python"] pip install robobrowser [/code] Let's do the initial setup of the script by loading the RoboBrowser package. We'll also load pandas, as we'll be using that a little bit later. [code lang="python"] from robobrowser import RoboBrowser import pandas as pd [/code] Create RoboBrowser Object Next, we create a RoboBrowser object. This object functions similarly to an actual web browser. It allows you to navigate to different websites, fill in forms, and get HTML…
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Parsing Dates with Pandas

Parsing Dates with Pandas

Pandas, Python
The pandas package is one of the most powerful Python packages available. One useful feature of pandas is its Timestamp method. This provides functionality to convert strings in a variety of formats to dates. The problem we're trying to solve in this article is how to parse dates from strings that may contain additional text / words. We will look at this problem using pandas. In the first step, we'll load the pandas package. [code lang="python"] '''Load pandas package ''' import pandas as pd [/code] Next, let's create a sample string containing a made-up date with other text. For now, assume the dates will not contain spaces (we will re-examine this later). Taking this assumption, we use the split method, available for strings in Python, to create a list of…
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