如何比较2个不同的数据框列，如果在Python中相等，则添加1列？

发布于 2025-01-19 19:19:01 字数 1659 浏览 0 评论 0原文

我有 2 个数据框 Table1 & Table2

Table1 示例输出：

CustomerID
CUST_3849502
CUST_3935123

Table2 示例输出：

CustomerID	AccountID	TimeCreated
CUST_3849502	3823479@store	2022-04-07T21:38:13.195641Z
CUST_3935123	343950347@store	2022-04-07T21:38:13.647964Z
CUST_4566768	876876465@store	2022-02-08T15:55:13.857347Z

我正在尝试添加Table2["AccountID"] &如果 Table1["CustomerID"] 位于 Table2["CustomerID"] 中，则 Table2["TimeCreated"] 到 Table1 >

所以 Table1 所需的输出是：

CustomerID	AccountID	TimeCreated
CUST_3849502	3823479@store	2022-04-07T21:38:13.195641Z
CUST_3935123	343950347@store	2022-04-07T21:38:13.647964Z

我已经尝试过：

for x in Table1["CustomerID"]:
   if Table2["CustomerID"] in x:
     Table1["AccountID"] = Table2["AccountID"]
     Table1["TimeCreated"] = Table2["TimeCreated"]

但不断收到 TypeError: 'in'第二行需要字符串作为左操作数，而不是 Series

两列都是类型 pandas.core.series.Series 所以不确定这里的问题是什么，请帮助

原文

I have 2 dataframes Table1 & Table2

Table1 example output:

CustomerID
CUST_3849502
CUST_3935123

Table2 example output:

CustomerID	AccountID	TimeCreated
CUST_3849502	3823479@store	2022-04-07T21:38:13.195641Z
CUST_3935123	343950347@store	2022-04-07T21:38:13.647964Z
CUST_4566768	876876465@store	2022-02-08T15:55:13.857347Z

I'm trying to add Table2["AccountID"] & Table2["TimeCreated"] to Table1 if Table1["CustomerID"] is in Table2["CustomerID"]

So desired output of Table1 is:

CustomerID	AccountID	TimeCreated
CUST_3849502	3823479@store	2022-04-07T21:38:13.195641Z
CUST_3935123	343950347@store	2022-04-07T21:38:13.647964Z

I've tried:

for x in Table1["CustomerID"]:
   if Table2["CustomerID"] in x:
     Table1["AccountID"] = Table2["AccountID"]
     Table1["TimeCreated"] = Table2["TimeCreated"]

But keep getting TypeError: 'in <string>' requires string as left operand, not Series for the 2nd line

Both columns are type pandas.core.series.Series so not sure what the issue is here, please help

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甜宝宝 2025-01-26 19:19:01

您会发现下面的代码实现了您的预期目标。

import pandas as pd

df = pd.DataFrame()
df['CustomerID']=['CUST_3849502','CUST_3935123']

df2 = pd.DataFrame()

df2['CustomerID']=['CUST_3849502','CUST_3935123','trash']
df2['AccountID']=['3823479@store','343950347@store','yeet']
df2['TimeCreated']=['2022-04-07T21:38:13.195641Z','2022-04-07T21:38:13.647964Z','yams']

df3 = pd.merge(df,df2,'left')
print(df3)

我注意到您试图将数据框列数据作为列表解析。可以使用这可以实现这一点

df["Your Column Name"].to_list()

如果您不想使用“ pd.merge”，，则可以'构建'列表并将其添加到您的数据框架中（只要列表的长度等于dataframe中的行数量...否则它将抛出索引）。

You'll find the code below accomplishes your intended goal.

import pandas as pd

df = pd.DataFrame()
df['CustomerID']=['CUST_3849502','CUST_3935123']

df2 = pd.DataFrame()

df2['CustomerID']=['CUST_3849502','CUST_3935123','trash']
df2['AccountID']=['3823479@store','343950347@store','yeet']
df2['TimeCreated']=['2022-04-07T21:38:13.195641Z','2022-04-07T21:38:13.647964Z','yams']

df3 = pd.merge(df,df2,'left')
print(df3)

I noticed you were trying to parse the dataframe column data as a list. This can be accomplished using

df["Your Column Name"].to_list()

If you didn't want to use "pd.merge", you can 'build out' your lists and add them onto your dataframe (SO LONG AS THE LENGTH OF THE LIST EQUALS THE AMOUNT OF ROWS IN THE DATAFRAME... Otherwise it will throw an IndexError).

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