2017-12-27 93 views
-1

我正在重写一个csv文件,我正在寻找创建一个函数,通过列表中的项目进行比较。更清楚的是,这里是一个例子。日期值比较Python列表

我的CSV转换为表:

import csv 
with open('test.csv', 'rb') as csvfile: 
    spamreader = csv.reader(csvfile, delimiter=';', quotechar='|') 
    lista = list(spamreader) 
    print lista 

>>>[['"Fecha"', '"Cliente"', '"Subastas"', '"Impresiones_exchange"', '"Fill_rate"', '"Importe_a_pagar_a_medio"', '"ECPM_medio"'],['20/12/2017', 'Martin', '165.665', '3.777', '2,28%', '1,58', '0,42'], ['21/12/2017', 'Martin', '229.620', '18.508', '8,06%', '14,56', '0,79'], ['22/12/2017', 'Martin', '204.042', '48.526', '23,78%', '43,98', '0,91'], ['20/12/2017', 'Tom', '102.613', '20.223', '19,71%', '17,86', '0,88'], ['21/12/2017', 'Tom', '90.962', '19.186', '21,09%', '14,26', '0,74'], ['22/12/2017', 'Tom', '60.189', '12.654', '21,02%', '11,58', '0,92']]

因此,首先,我需要comparate马丁和汤姆的所有值。我的意思是,item[2] of 20/12/2017 to item[2] of 21/12/2017. item[2] of 21/12/2017 to item[2] of 22/12/2017。我需要这些用于我的清单中的所有项目(项目[2,3,4,5,6]。日期是最重要的值,因为这个想法是一天比较的。)

结果我希望是这样的:

21/12/2017 Martin 
item[2]: smaller 
item[3]: smaller 
item[4]: bigger 
item[5]: smaller 
item[6]: smaller 

22/12/2017 Martin 
item[2]: smaller 
item[3]: bigger 
item[4]: bigger 
item[5]: bigger 
item[6]: bigger 

21/12/2017 Tom 
item[2]: smaller 
item[3]: bigger 
item[4]: bigger 
item[5]: bigger 
item[6]: bigger 

22/12/2017 Tom 
item[2]: smaller 
item[3]: smaller 
item[4]: smaller 
item[5]: smaller 
item[6]: bigger 

如果我想显示的名称为“Subastas”,而不是项目[2],所有的名字太...我怎么能做到这一点

+1

也许用'pandas'模块 - 这是更强大。 – furas

+0

使用按钮'{}'来格式化列表,就像你格式化的代码一样。 – furas

回答

2

让我们开始呢?注意到你有一些数据的键是(date, name)。一个相当明显的方法是将数据存储在一个以(date, name)为关键字的字典中。

所以,把你的发布数据mylist

mylist = [['"Fecha"', '"Cliente"', '"Subastas"', '"Impresiones_exchange"', '"Fill_rate"', '"Importe_a_pagar_a_medio"', '"ECPM_medio"'],['20/12/2017', 'Martin', '165.665', '3.777', '2,28%', '1,58', '0,42'], ['21/12/2017', 'Martin', '229.620', '18.508', '8,06%', '14,56', '0,79'], ['22/12/2017', 'Martin', '204.042', '48.526', '23,78%', '43,98', '0,91'], ['20/12/2017', 'Tom', '102.613', '20.223', '19,71%', '17,86', '0,88'], ['21/12/2017', 'Tom', '90.962', '19.186', '21,09%', '14,26', '0,74'], ['22/12/2017', 'Tom', '60.189', '12.654', '21,02%', '11,58', '0,92']] 

转换它(除了第一行与列标签),以这样的词典:

import datetime 
mydict = {} 
for row in mylist[1:]: 
    date = datetime.datetime.strptime(row[0],'%d/%m/%Y') 
    name = row[1] 
    mydict[(date,name)] = row[2:] 

棘手位在这里是你的日期是形式为dd/mm/yyyy的字符串,但你稍后想要在一天和下一天之间进行比较。这并不令人意外,因为您将此问题作为您问题的主题。所以你需要把字符串日期转换成你可以进行适当比较的东西。这就是strptime()所做的。

您的数据现在看起来是这样的:

>>> mydict 
{(datetime.datetime(2017, 12, 20, 0, 0), 'Martin'): ['165.665', '3.777', '2,28%', '1,58', '0,42'], 
(datetime.datetime(2017, 12, 22, 0, 0), 'Tom'): ['60.189', '12.654', '21,02%', '11,58', '0,92'], 
(datetime.datetime(2017, 12, 21, 0, 0), 'Martin'): ['229.620', '18.508', '8,06%', '14,56', '0,79'], 
(datetime.datetime(2017, 12, 21, 0, 0), 'Tom'): ['90.962', '19.186', '21,09%', '14,26', '0,74'], 
(datetime.datetime(2017, 12, 20, 0, 0), 'Tom'): ['102.613', '20.223', '19,71%', '17,86', '0,88'], 
(datetime.datetime(2017, 12, 22, 0, 0), 'Martin'): ['204.042', '48.526', '23,78%', '43,98', '0,91']} 

下一个要观察的是,你的数据由浮点数字和百分比,但表示为字符串。这使事情变得复杂,因为你想做比较。如果你比较'165.665''229.620'第一个会更小,这是你所期望的

['165.665', '3.777', ... 
    ['229.620', '18.508', ... 

:先取2个数据点的马丁。但是,如果您将'3.777''18.508'进行比较,则第一个将会更大:不是您所期望的。这是因为字符串按字母顺序进行比较,31之后。

更糟糕的是,您的数据有时以小数点表示逗号,有时不表示。

所以你需要一个函数来对字符串进行数值转换。这里是一个天真的一个为你的数据的作品,但很可能需要进行在现实生活中更稳健:

def convert(n): 
    n = n.replace(",",".").replace("%","") 
    try: 
     return float(n) 
    except ValueError: 
     return 0e0 

现在你在一个位置做比较:

for (day, name) in mydict: 
    previous_day = day - datetime.timedelta(days=1) 
    if (previous_day,name) in mydict: 
     print datetime.datetime.strftime(day,"%d/%m/%Y"), name 
     day2_values = mydict[(day, name)] 
     day1_values = mydict[(previous_day, name)] 
     comparer = zip(day2_values, day1_values) 
     for n,value in enumerate(comparer): 
      print "item[%d]:" % (n+2,), 
      if convert(value[1]) < convert(value[0]): 
       print value[1], "smaller than", value[0] 
      else: 
       print value[1], "bigger than", value[0] 
     print 

我有使消息更加明确,例如,item[2]: 165.665 smaller than 229.620。这样,您就可以轻松验证程序是否正确,而无需重新查看数据,这很容易出错且乏味。如果你愿意,你可以随时让这些信息不那么明确。

22/12/2017 Tom 
item[2]: 90.962 bigger than 60.189 
item[3]: 19.186 bigger than 12.654 
item[4]: 21,09% bigger than 21,02% 
item[5]: 14,26 bigger than 11,58 
item[6]: 0,74 smaller than 0,92 

21/12/2017 Martin 
item[2]: 165.665 smaller than 229.620 
item[3]: 3.777 smaller than 18.508 
item[4]: 2,28% smaller than 8,06% 
item[5]: 1,58 smaller than 14,56 
item[6]: 0,42 smaller than 0,79 

21/12/2017 Tom 
item[2]: 102.613 bigger than 90.962 
item[3]: 20.223 bigger than 19.186 
item[4]: 19,71% smaller than 21,09% 
item[5]: 17,86 bigger than 14,26 
item[6]: 0,88 bigger than 0,74 

22/12/2017 Martin 
item[2]: 229.620 bigger than 204.042 
item[3]: 18.508 smaller than 48.526 
item[4]: 8,06% smaller than 23,78% 
item[5]: 14,56 smaller than 43,98 
item[6]: 0,79 smaller than 0,91 

要显示"Subastas",而不是item[2],记得,列标签是在mylist的第一个元素:

>>> mylist[0] 
['"Fecha"', '"Cliente"', '"Subastas"', '"Impresiones_exchange"', '"Fill_rate"', '"Importe_a_pagar_a_medio"', '"ECPM_medio"'] 

所以将它们包括在输出中,你需要改变这一行:

print "item[%d]:" % (n+2,), 

print mylist[0][n+2] + ":", 
+0

对于努力+1 _ –

+0

,如果我想显示名称为“Subastas”的项目[2] instaed和所有名字太...我该怎么做呢? –

+0

我需要帮助解决我的问题。 https://stackoverflow.com/questions/48001004/data-csv-dashboard-python。这是非常相似的!谢谢 –

0

您可以加载LISTA成数据帧,然后从那里执行比较:

import pandas as pd 
import numpy as np 

headers = lista.pop(0) 

df = pd.DataFrame(lista, columns = headers) 

martin = df[df['"Cliente"'] == 'Martin'] 
tom = df[df['"Cliente"'] == 'Tom'] 

merge = pd.merge(martin, tom, on = '"Fecha"') 

stats = headers[2:] 
compare = ['"Fecha"'] 

for index, row in merge.iterrows(): 
    for x in stats: 
     merge[x+'_compare'] = np.where(row[x+'_x'] > row[x+'_y'], 'Martin', 'Tom') 
     if x+'_compare' not in compare: 
      compare.append(x+'_compare') 

print(merge[compare]) 

#output 
"Fecha" "Subastas"_compare "Impresiones_exchange"_compare "Fill_rate"_compare "Importe_a_pagar_a_medio"_compare "ECPM_medio"_compare 
20/12/2017 Tom Martin Martin Martin Tom 
21/12/2017 Tom Martin Martin Martin Tom 
22/12/2017 Tom Martin Martin Martin Tom