python pandas遍历每行并累加进行条件过滤方式

a = [[0.2, 0.35, 0.45], [0.1,0.2, 0.7], [0.3, 0.5, 0.2]]
data = pd.DataFrame(a, index=['user1','user2','user3'], columns=["a", "b", "c"])

sum_df=[]
for index,row in data.iterrows():
    df = row.sort_values(ascending=False).cumsum()
    if df[0]>0.8:
        new_df = df[:1]
    else:
        new_df = df[df<=0.8]
    sum_df.append(new_df)
sum_df = pd.DataFrame(sum_df)
print(sum_df)           

d = (sum_df.notnull())*1
print(d)

final_df = d*data #将保留地特征映射到原始数据中
print(final_df)

sum_df=[]
for index,row in data.iterrows():
    df = row.sort_values(ascending=False).cumsum()
    origin = row.to_dict() #原始每个用户值
    if df[0]>0.8:
        new_df = df[:1]
    else:
        new_df = df[df<=0.8]
    name = new_df.name  #user
    tmp = new_df.to_dict()
    for key in tmp.keys(): # 原始值映射
        tmp[key] = origin[key]
    tmp['user'] = name
    sum_df.append(tmp)
sum_df = pd.DataFrame(sum_df).set_index('user').fillna(0)
print(sum_df)   

import pandas as pd
#读入数据
df = pd.read_table('d:/Users/chen_lib/Desktop/tmp.csv',sep=',', header='infer')
df.head()
 
-----------------result------------------
        mas  effectdate     num
0    371379    2019-07-15    361
1    344985    2019-07-13    77
2    425090    2019-07-01    105
3    344983    2019-02-19    339
4    432430    2019-02-21    162

for index, row in df.iterrows():
    print(index,row['mas'],row['num']) 
  
 
------------result---------------
0 371379 361
1 344985 77
2 425090 105
3 344983 339
4 432430 162

for row in df.itertuples():
    print(getattr(row, 'mas'), getattr(row, 'num')) # 输出每一行
 
 
-------------result-----------------
371379 361
344985 77
425090 105
344983 339
432430 162

for index, row in df.iteritems():
    print(index,row[0],row[1],row[2])
 
 
-------------result------------------
masterhotelid 371379 344985 425090
effectdate 2019-07-15 2019-07-13 2019-07-01
quantity 361 77 105