bbengfort

import numpy as np
import matplotlib.pyplot as plt

from yellowbrick.datasets import load_credit

from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

# Load data from yellowbrick.datasets
X, y = load_credit()

bbengfort

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as grid

from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size


def joint_plot(x, y, ax=None):
    """
    Create a square joint plot of x and y.

bbengfort

type Blast struct {
	requests  uint64          // the number of successful requests
	failures  uint64          // the number of failed requests
	started   time.Time       // the time the benchmark was started
	duration  time.Duration   // the duration of the benchmark period
	latencies []time.Duration // observed latencies in the number of requests
}

// Run N operations against the server at addr by putting a unique key with
// random values of size S. Returns a Benchmark object whose objects can be

bbengfort

%matplotlib inline 

import numpy as np
import pandas as pd 
import seaborn as sns 
import matplotlib.pyplot as plt 

from sklearn.datasets import make_regression
from sklearn.preprocessing import StandardScaler 
from matplotlib.patches import Rectangle

bbengfort

#!/usr/bin/env python3 

import argparse 
from datetime import datetime, timedelta 

try:
    from dateutil.relativedelta import relativedelta 
except ImportError: 
    relativedelta = None 

bbengfort

package main

import (
	"log"
	"os"
)

func main() {
	var (
		fobj *os.File

bbengfort

package main

import (
	"fmt"
	"strings"
	"time"
)

var (
	count   uint64

bbengfort

import csv 

from tabulate import tabulate 
from operator import itemgetter 
from collections import Counter 


def parse_inner_csv(val):
    return list(
        map(lambda s: s.strip(), val.lower().split(","))

bbengfort

import time 
import numpy as np 
import matplotlib.pyplot as plt 

from sklearn.datasets import make_regression 
from sklearn.model_selection import train_test_split as tts

from sklearn.linear_model import LinearRegression 
from yellowbrick.regressor import ResidualsPlot

bbengfort

#!/bin/bash
# Execute the reveal.js slides form of a Notebook

# Make sure the first argument to this script is a notebook
if [ "$#" -ne 1 ]; then
  echo "Usage: ./slideshow.sh Notebook.ipynb"
  exit 1
fi

# Make sure the notebook exists