matmoody

import time
import serial
import logging
from datetime import datetime

def hex_to_speed(hex_str):
    """Convert hex string to speed value"""
    try:
        # Convert from hex to decimal and add 4
        # (hex 47 = decimal 71 -> 47 mph)

matmoody

Verifying my Blockstack ID is secured with the address 1Lq55Q1TsUxhXuifeevzX2NVwL6ZqpXRSi https://explorer.blockstack.org/address/1Lq55Q1TsUxhXuifeevzX2NVwL6ZqpXRSi

matmoody

import random

import matplotlib.pyplot as plt

# Normally distributed random variable with expected value 0 and variance 1

for _ in range(10):
	if(random.random() < .5):
		print "head"
	else:

matmoody

import pandas as pd
import numpy as np

# Read in iris data set
iris = pd.read_csv("https://raw.githubusercontent.com/Thinkful-Ed/curric-data-001-data-sets/master/iris/iris.data.csv")

# Add column names
iris.columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species']

# Split data into features and target

matmoody

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
%matplotlib inline


iris = datasets.load_iris()

matmoody

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

%matplotlib inline

iris = pd.read_csv("https://raw.githubusercontent.com/Thinkful-Ed/curric-data-001-data-sets/master/iris/iris.data.csv", names = ['Sepal_l', 'Sepal_w', 'petal_l', 'petal_w', 'class'])

# Make class categorical variable

matmoody

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


iris = pd.read_csv("https://raw.githubusercontent.com/Thinkful-Ed/curric-data-001-data-sets/master/iris/iris.data.csv", names = ['Sepal_l', 'Sepal_w', 'petal_l', 'petal_w', 'class'])

# Plot iris data

matmoody

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


actid = pd.read_csv("https://raw.githubusercontent.com/Thinkful-Ed/curric-data-001-data-sets/master/ideal-weight/ideal_weight.csv")

actid.head()

matmoody

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


loan_data = pd.read_csv("loansData.csv")
loan_data.head()

matmoody

import numpy as np
import statsmodels.formula.api as smf
import pandas as pd


# Set seed for reproducible results
np.random.seed(414)

# Generate toy data
# Return evenly spaced #'x over specified interval