swayson

def search_item(dataframe, name, query, na=False, case=False, regex=True):

    idx = pd.Series([False]*len(dataframe))

    # For each item in the query look for the item and collect the documents ids it pertains to
    for q in query:
        matches = dataframe[text_column].str.contains(q, na=False, case=False, regex=True)

swayson

swayson

1. Ditch Excel (for real)

    in2csv file1.xls > file1.csv

2. Conquer fixed-width formats

    in2csv -f fixed -s schema.csv data.fixed > data.csv

3. Find cells matching a regular expression

csvgrep -c phone_number -r "\d{3}-123-\d{4}" data.csv > matching.csv

swayson

---
title: 'Going deeper with dplyr: New features in 0.3 and 0.4'
output: html_document
---

## Introduction

In August 2014, I created a [40-minute video tutorial](https://www.youtube.com/watch?v=jWjqLW-u3hc) introducing the key functionality of the dplyr package in R, using dplyr version 0.2. Since then, there have been two significant updates to dplyr (0.3 and 0.4), introducing a ton of new features.

This document (created in March 2015) covers the most useful new features in 0.3 and 0.4, as well as other functionality that I didn't cover last time (though it is not necessarily new). My [new video tutorial](https://www.youtube.com/watch?v=2mh1PqfsXVI) walks through the code below in detail.

swayson

---
title: "Introduction to dplyr for Faster Data Manipulation in R"
output: html_document
---

Note: There is a 40-minute [video tutorial](https://www.youtube.com/watch?v=jWjqLW-u3hc) on YouTube that walks through this document in detail.

## Why do I use dplyr?

* Great for data exploration and transformation

swayson

Ok this was a little confusing for me but I finally realized what was happening. So I decided to give my 2 cents in hopes that it will be more clear for others and if I forget sometime in the future : ).

I was not using the name of the share I created in the VM, instead I used share or vb_share when the name of my share was wd so this had me confused for a minute.

First add your share directory in the VM Box: enter image description here

Whatever you name your share here will be the name you will need to use when mounting in the vm guest OS. i.e. I named mine "wd" for my western digital passport drive.

Next on the the guset OS make a directory to use for your mount preferably in your home directory.

swayson

// (Project -> Edit Project)
{
    "build_systems":
    [
        {
            "name": "Anaconda Python Builder",
            "selector": "source.python",
            "shell_cmd": "python -u \"$file\""
        }
    ],

swayson

library(ggplot2)
library(gtable)
 
# create example data
set.seed(42)
dataset_names <- c("Human", "Mouse", "Fly", "Worm")
datasets <- data.frame(name = factor(dataset_names, levels=dataset_names), parity = factor(c(0, 0, 1, 0)), v50 = runif(4, max=0.5), y=1:4)
data <- data.frame( dataset1 = rep(datasets$name, 4), dataset2 = rep(datasets$name, each = 4), z = runif(16,min = 0, max = 0.5) )
 
pal <- c("#dddddd", "#aaaaaa")

swayson

from multiprocessing import Pool 
from PIL import Image

SIZE = (75,75)
SAVE_DIRECTORY = 'thumbs'

def get_image_paths(folder):
  return (os.path.join(folder, f) 
      for f in os.listdir(folder) 
      if 'jpeg' in f)

swayson

Follow these steps to install the Guest Additions on your Ubuntu virtual machine:
    1. Login as ubuntu;
    2. Click on Applications/System/Terminal (or on Applications/Terminal, if you are using the 606.1 Dapper Drake release); 
    3. Update your APT database with sudo apt-get update, and typing your password, if requested; Install the latest security updates with sudo apt-get upgrade;
    4. Install required packages with sudo apt-get install build-essential module-assistant;
    5. Configure your system for building kernel modules by running sudo m-a prepare;
    6. Click on Install Guest Additions… from the Devices menu, then choose to browse the content of the CD when requested.
    7. Run sudo sh /media/cdrom/VBoxLinuxAdditions.run, and follow the instructions on screen.