将日期（系列）列从一个DataFrame添加到其他Pandas，Python [英] Adding Dates (Series) column from one DataFrame to the other Pandas, Python

问题描述

我正在尝试从df1到df2广播一个日期列。

I am trying to 'broadcast' a date column from df1 to df2.

在df1中，我有所有用户的名字及其基本信息。
在df2我有一个由用户进行的购买清单。

In df1 I have the names of all the users and their basic information. In df2 I have a list of purchases made by the users.

df1和df2代码

假设我有一个更大的数据集（以上为样本创建）我可以添加（！）df1 ['DoB']列到df2？

我尝试过concat（）和merge（）但没有他们似乎工作：

I have tried both concat() and merge() but none of them seem to work:

代码和错误

只有将df1和df2合并在一起，然后才删除不需要的列，唯一的方法似乎是工作。但是如果我有几十个不需要的列，这将是非常有问题的。

The only way it seems to work is only if I merge both df1 and df2 together and then just delete the columns I don't need. But if I have tens of unwanted columns, it is going to be very problematic.

完整的代码（包括引发错误的行）：

The full code (including the lines that throw an error):

import pandas as pd
df1 = pd.DataFrame(columns=['Name','Age','DoB','HomeTown'])

df1['Name'] = ['John', 'Jack', 'Wendy','Paul']
df1['Age'] = [25,23,30,31]
df1['DoB'] = pd.to_datetime(['04-01-2012', '03-02-1991', '04-10-1986', '06-03-1985'], dayfirst=True)
df1['HomeTown'] = ['London', 'Brighton', 'Manchester', 'Jersey']

df2 = pd.DataFrame(columns=['Name','Purchase'])
df2['Name'] = ['John','Wendy','John','Jack','Wendy','Jack','John','John']
df2['Purchase'] = ['fridge','coffee','washingmachine','tickets','iPhone','stove','notebook','laptop']

df2 = df2.concat(df1) # error

df2 = df2.merge(df1['DoB'], on='Name', how='left') #error

df2 = df2.merge(df1, on='Name', how='left')
del df2['Age'], df2['HomeTown']
df2 #that's how i want it to look like 

任何帮助将不胜感激。谢谢：）

Any help would be much appreciated. Thank you :)




推荐答案

我想你需要 合并 与子集 [['Name'，'DoB']] - 需要code>名称列匹配：

I think you need merge with subset [['Name','DoB']] - need Name column for matching:

print (df1[['Name','DoB']])
    Name        DoB
0   John 2012-01-04
1   Jack 1991-02-03
2  Wendy 1986-10-04
3   Paul 1985-03-06

df2 = df2.merge(df1[['Name','DoB']], on='Name', how='left')
print (df2)
    Name        Purchase        DoB
0   John          fridge 2012-01-04
1  Wendy          coffee 1986-10-04
2   John  washingmachine 2012-01-04
3   Jack         tickets 1991-02-03
4  Wendy          iPhone 1986-10-04
5   Jack           stove 1991-02-03
6   John        notebook 2012-01-04
7   John          laptop 2012-01-04

---

另一个解决方案是使用 map 按系列 s ：

s = df1.set_index('Name')['DoB']
print (s)
Name
John    2012-01-04
Jack    1991-02-03
Wendy   1986-10-04
Paul    1985-03-06
Name: DoB, dtype: datetime64[ns]

df2['DoB'] = df2.Name.map(s)
print (df2)
    Name        Purchase        DoB
0   John          fridge 2012-01-04
1  Wendy          coffee 1986-10-04
2   John  washingmachine 2012-01-04
3   Jack         tickets 1991-02-03
4  Wendy          iPhone 1986-10-04
5   Jack           stove 1991-02-03
6   John        notebook 2012-01-04
7   John          laptop 2012-01-04

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