#datastats

Inspiration

Major League Hacking is the inspiration.

What it does

it analyzes athlete related datasets.

How we built it

I took the help of a website and started learning from it.

import pandas as pd import seaborn as sns

Part 1 functions

def mean(arr): return sum(arr) / len(arr)

def isEven (arr): return arr % 2 == 0

def half (arr): if isEven(len(arr)): return int(len(arr)/2) - 1 else: return int(len(arr)/2)

def median (arr): sortedList = sorted(arr) index = half(sortedList) if isEven(len(sortedList)): return mean([ sortedList[index], sortedList[index + 1] ]) else: return sortedList[index]

Part 2 functions

def square(x): return x * x

def sumOfSquaredDifferences (arr): xBar = mean(arr) differences = map(lambda x: x - xBar, arr) squaredDifferences = map(square, differences) return sum(squaredDifferences)

def variance (arr): n = len(arr) return sumOfSquaredDifferences(arr) / (n-1)

def sqrt (x): return x**(1/2)

def stDev (arr): return sqrt(variance(arr))

Part 3 numerator functions

def n (arr): return arr.count()

def product (x, y): return x * y

def sumXTimesY (x, y): return sum(map(product, x, y))

def numerator (x, y): return n(x) * sumXTimesY(x, y) - sum(x) * sum(y)

Part 3 denominator functions

def sumOfSquares (arr): return sum(map(square, arr))

def denominator (x, y): return sqrt((n(x) * sumOfSquares(x) - square(sum(x))) * (n(y) * sumOfSquares(y) - square(sum(y))))

Part 3 final function

def r (x, y): return numerator(x, y) / denominator(x, y)

data

data = [ {"name": "John", "distance": 5602, "high-speed-running": 504}, {"name": "Mike", "distance": 5242, "high-speed-running": 622}, {"name": "Chad", "distance": 4825, "high-speed-running": 453}, {"name": "Phil", "distance": 611, "high-speed-running": 500}, {"name": "Tyler", "distance": 5436, "high-speed-running": 409} ]

df = pd.DataFrame(data)

weekData = [ { "name": "John", "distance": 35.4, "high-speed-running": 3.65, "week": 1 }, { "name": "Mike", "distance": 32.9, "high-speed-running": 3.77, "week": 1 }, { "name": "Chad", "distance": 27.2, "high-speed-running": 3.02, "week": 1 }, { "name": "Phil", "distance": 20.5, "high-speed-running": 2.01, "week": 1 }, { "name": "Tyler", "distance": 15.3, "high-speed-running": 2.09, "week": 1 }, { "name": "John", "distance": 38.4, "high-speed-running": 4.95, "week": 2 }, { "name": "Mike", "distance": 37.9, "high-speed-running": 4.97, "week": 2 }, { "name": "Chad", "distance": 33.2, "high-speed-running": 4.42, "week": 2 }, { "name": "Phil", "distance": 25.5, "high-speed-running": 3.91, "week": 2 }, { "name": "Tyler", "distance": 19.3, "high-speed-running": 3.09, "week": 2 }, ]

weekDf = pd.DataFrame(data=weekData)