Question

我刚刚开始学习pandas我正在使用Dataframe和数据结构进行学习，所以当我为Dictionary和Index提供硬编码值时，它工作正常，但我想要这个用户输入基于用户可以输入的输入，那些vaue可以存储在Dictionary上，并且基于它可以产生预期的结果：

以下示例代码在其中的硬编码值运行良好字典＆amp;索引..

import pandas as pd
import numpy as np
###########  computation by numpy vectorisation method #######
purchae_1 = pd.Series({'Name': 'Karn',
                       'Item Purchased': 'Dog Food',
                       'Cost': 22.50})

purchae_2 = pd.Series({'Name': 'Renu',
                       'Item Purchased': 'Kitty Letter',
                       'Cost': 2.50})

purchae_3 = pd.Series({'Name': 'Rigved',
                       'Item Purchased': 'Foot Ball',
                       'Cost': 12.50})

#df = pd.DataFrame([purchae_1,purchae_2,purchae_3], index = ['Store1', 'Store2', 'Store3'])
df = pd.DataFrame([purchae_1,purchae_2,purchae_3], index = ['Store1', 'Store2', 'Store3'])
print(df.head())

   bash-4.1$ ./pythonDatafram.py
            Cost Item Purchased    Name
    Store1  22.5       Dog Food    Karn
    Store2   2.5   Kitty Letter    Renu
    Store3  12.5      Foot Ball  Rigved

虽然在下面的例子中，我试图以这样的方式构建它，所以，它将询问用户的输入并基于该Dtaframe将被创建结果可以是yeilded，但有人不能正常工作

import pandas as pd
import numpy as np

User_Name  = input('Name ')
Item_Purchased = input('Item Purchased ')
Item_Cost = input('Cost ')

purchae_1 = pd.Series( {'Name ': User_Name,
                        'Item_Purchased ' : Item_Purchased,
                        'Item_Cost ' : Item_Cost})

purchae_2 = pd.Series({'Name ': User_Name,
                       'Item Purchased ': Item_Purchased,
                       'Cost ': Item_Cost})

purchae_3 = pd.Series({'Name ': User_Name,
                       'Item Purchased ': Item_Purchased,
                       'Cost ': Item_Cost})

df = pd.DataFrame([purchae_1,purchae_2,purchae_3], index = ['Store1', 'Store2', 'Store3'])
print(df.head())

所以，当我执行它时，它显示以下结果..请帮助我理解我需要做什么才能让它运行其他序列... 因为我已经定义了Variable purchase_1，buy_2＆amp; purchase_3只有pics第一个并跳过其余的......

bash-4.1$ ./pythonDatafram.py
Name Karn
Item Purchased Dog Food
Cost 22.50
        Cost  Item Purchased  Item_Cost  Item_Purchased  Name
Store1    NaN             NaN      22.50        Dog Food  Karn
Store2  22.50        Dog Food        NaN             NaN  Karn
Store3  22.50        Dog Food        NaN             NaN  Karn
bash-4.1$

Answer 1

在您给出的最后一个示例中，purchae_1中有不同的列名。您使用Cost两次。和Item_Cost一次。在purchae_1中将Item_Cost更改为Cost和Item_Purchased更改为Item Purchased实质上，此问题正在发生，因为列名称不同。一个非常简单的修复！

import pandas as pd
import numpy as np

User_Name  = input('Name ')
Item_Purchased = input('Item Purchased ')
Item_Cost = input('Cost ')

purchae_1 = pd.Series( {'Name ': User_Name,
                        'Item Purchased ' : Item_Purchased, #<--- change is here
                        'Cost ' : Item_Cost}) #<--- change is here

purchae_2 = pd.Series({'Name ': User_Name,
                       'Item Purchased ': Item_Purchased,
                       'Cost ': Item_Cost})

purchae_3 = pd.Series({'Name ': User_Name,
                       'Item Purchased ': Item_Purchased,
                       'Cost ': Item_Cost})

df = pd.DataFrame([purchae_1,purchae_2,purchae_3], index = ['Store1', 'Store2', 'Store3'])
print(df.head())

Answer 2

我认为你的问题来自错误标签。有时您使用'成本'，有时使用'Item_Cost'; 'Item_Purchased'和'Item Purchased'

同样的事情

如果你继续使用相同的标签，它应该有效：

purchae_1 = pd.Series( {'Name ': User_Name,
                    'Item Purchased ' : Item_Purchased,
                    'Cost ' : Item_Cost})

purchae_2 = pd.Series({'Name ': User_Name,
                   'Item Purchased ': Item_Purchased,
                   'Cost ': Item_Cost})

purchae_3 = pd.Series({'Name ': User_Name,
                   'Item Purchased ': Item_Purchased,
                   'Cost ': Item_Cost})

python pandas具有用户级输入的Dataframe

2 个答案: