python作业3

两个列表化为字典

m= ['decision trees', 'svm', 'random forest', 'neural network']
s = [0.92, 0.94, 0.97, 0.96]
model_evaluate ={i:s[m.index(i)] for i in m}
print(model_evaluate)

前n项和

result = 0
for i in range(1,101):
    result += i
    if i==20:
        break
print(result)

字符串切片为列表

def double(s):
    if len(s)%2:
        s+='_'
    result=[s[i:i+2] for i in range(0,len(s),2)]
    return result
double('qwert')

输出真分数取整后的假分数

def fraction(num_str):
    n=num_str.split('/')
    m=int(n[0])%int(n[1])
    if m:
        answer=str(m)+'/'+n[1]
    else:
        answer=''
    return answer
fraction('36/9')

球队总得分

import pandas as pd
data = pd.read_csv('epl2016.csv')
data.head()
def score(df):
    finalscore = [] 
    for i in set(df['HomeTeam']):
        score = 0
        for j in range(len(df['HomeTeam'])):
            if i == df['HomeTeam'][j]:
                if df['FTR'][j]=='H':
                    score+=3
                elif df['FTR'][j]=='D':
                    score+=1
        for k in range(len(df['AwayTeam'])):
            if i == df['AwayTeam'][k]:
                if df['FTR'][k]=='A':
                    score+=3
                elif df['FTR'][k]=='D':
                    score+=1
        finalscore.append(score)
    return finalscore
answer = score(data)
answer

球队总进球和总失球数

import pandas as pd
data = pd.read_csv('epl2016.csv')
data.head()
def goal_and_loss(df):
    finalgoal = []
    finalloss = []
    for i in set(df['HomeTeam']):
        goal = 0 
        loss = 0
        for j in range(len(df['HomeTeam'])):
            if i == df['HomeTeam'][j]:
            	goal+=df['FTHG'][j]
            	loss+=df['FTAG'][j]
        for k in range(len(df['AwayTeam'])):
            if i == df['AwayTeam'][k]:
            	goal+=df['FTAG'][k]
            	loss+=df['FTHG'][k]
        finalgoal.append(goal)
        finalloss.append(loss)
    return [set(finalgoal),set(finalloss)]
answer = goal_and_loss(data)
answer

前n项和的列表

def triangular_n(n):
    s=[int((i+1)*i/2) for i in range(1,n+1)]
    return s
triangular_n(3)