joseph-allen

fs = require('fs');
moment = require('moment');

var dates = []

fs.readFile('times.js', 'utf8', function (err,data) {
  if (err) {
    return console.log(err);
  }

joseph-allen

/**
 * @fileoverview Rule to flag use of _.cloneDeep
 * @author Joseph Allen
 */

//------------------------------------------------------------------------------
// Rule Definition
//------------------------------------------------------------------------------

module.exports = function(context) {

joseph-allen

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

joseph-allen

#!/bin/bash

echo -n "GitHub User: "
read USER

echo -n "GitHub Password: "
read -s PASS

echo ""
echo -n "GitHub Repo (e.g. foo/bar): "

joseph-allen

import pandas as pd
import sys
import itertools


def permute(df):
    columns = [df[column] for column in list(df)]
    uniq_columns = [columns[x].unique() for x in range(0, len(columns))]

    return pd.DataFrame(list(itertools.product(*uniq_columns)),

joseph-allen

jupyter nbconvert --to pdf Report.ipynb  --TemplateExporter.exclude_input=True

joseph-allen

@pytest.fixture(params=[('input', 'expected')])
def get_input(request):
    return request.param


@pytest.fixture
def get_function_result(get_input):
    return test_function(get_input[0])

joseph-allen

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.tree import DecisionTreeClassifier


def plot_histograms(df, variables, n_rows, n_cols):
    fig = plt.figure(figsize=(16, 12))
    for i, var_name in enumerate(variables):
        ax = fig.add_subplot(n_rows, n_cols, i+1)

joseph-allen

# Ignore warnings
import warnings
warnings.filterwarnings('ignore')

# Handle table-like data and matrices
import numpy as np
import pandas as pd

# Modelling Algorithms
from sklearn.tree import DecisionTreeClassifier

joseph-allen

import numpy as np
from collections import Counter


def detect_outliers(df, n, features):
    """
    Takes a dataframe df of features and returns a list of the indices
    corresponding to the observations containing more than n outliers according
    to the Tukey method.
    """