dengjonathan

var gulp = require('gulp');
var browserSync = require('browser-sync')
  .create();
//var sass        = require('gulp-sass');

// Static Server + watching scss/html files
gulp.task('serve', function () {

  browserSync.init({
    server: "./"

dengjonathan

function each(collection, callback) {
  if (Array.isArray(collection)) {
    for (var i = 0; i < collection.length; i++) {
      callback(collection[i]);
    }
  } else {
    for (var j in collection) {
      callback(collection[j]);
    }
  }

dengjonathan

function flatten(array) {
  var output = [];
  // iterate through all items in argument array 
  for (var j = 0; j < array.length; j++) {
    var value = array[j];
    // base case: value is not an array, so just push to output
    if (!Array.isArray(value)) {
      output.push(value);
    } else {
      // recursive case: if value is an array, flatten it to a 1D array.

dengjonathan

def useless_columns(df):
    """Returns list of columns with less than 30 not null values"""
    columns = []
    for col in df.columns:
        # isnull returns a list-like object with True is an element in the column is NaN and False if element has value
        null_list = pd.isnull(df[col])
        real_values = len([i for i in null_list if i != True])   
        if real_values < 30:
            columns.append(col)
    return columns

dengjonathan

def csv2dataframe(fileobj):
    """Returns csv file as a pandas dataframe"""
    with open(fileobj, 'r') as f:
         return pd.read_csv(f, header=0)

# read all csv files to DataFrame objects and append to list then concatenate all dfs in list into one 'super_df'
df_list = [csv2dataframe(file) for file in os.listdir(os.getcwd()) if file.endswith('.csv')]
ranger_classes = [1, 3, 4, 5, 6, 7, 8, 9, 10, 11]
super_df = pd.concat(df_list)

dengjonathan

import numpy as np
import sklearn
import pandas as pd

dengjonathan

import numpy, sklearn
import pandas as pd
import os

# os.chdir('./Ranger Data/data')

def csv2dataframe(fileobj):
    """Returns csv file as a pandas dataframe"""
    with open(fileobj, 'r') as f:
         return pd.read_csv(f, header=0)