vlookup 相当于使用 Pandas 连接 2 个表
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vlookup equivalent to join 2 tables using pandas
提问by Justin Pines
Scenario:
设想：
- df1 has all possible auction ID values
- df2 has a subset of possible auction ID values
- df2 contains the object ID present in the auction (can be 0 or positive int)
- df1 具有所有可能的拍卖 ID 值
- df2 有一个可能的拍卖 ID 值的子集
- df2 包含拍卖中存在的对象 ID（可以是 0 或正整数）
Goal:
目标：
Create a new df1 column "object_id" populated with corresponding values in df2
创建一个新的 df1 列“object_id”，用 df2 中的相应值填充
Example...
例子...
df1 = pd.DataFrame(columns=['auction_id'], data=[1,2,3,4,5,6,7,8,9])
auction_id
1
2
3
4
5
6
7
8
9
df2 = pd.DataFrame({'auction_id':[1,2,4,6,7,8,9], 'object_id': [640,0,332,332,0,58,332]})
auction_id object_id
1 640
2 0
4 332
6 332
7 0
8 58
9 332
My best effort so far
迄今为止我的最大努力
using vlookup in Pandas using join):
df1['object_id'] = df1.auction_id.map(df2.object_id)
Which yields:
其中产生：
df1
auction_id object_id
1 0
2 332
3 332
4 0
5 58
6 332
7 NaN
8 NaN
9 NaN
回答by cerod
You can use the merge function from the pandas library as follows:
您可以使用 pandas 库中的合并功能，如下所示：
import pandas as pd
df1 = pd.DataFrame(columns=['auction_id'], data=[1,2,3,4,5,6,7,8,9])
df2 = pd.DataFrame({'auction_id':[1,2,4,6,7,8,9], 'object_id': [640,0,332,332,0,58,332]})
new_df = pd.merge(df1,df2,how='left',on='auction_id')
You can read about the merge function here: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html
您可以在此处阅读有关合并功能的信息：http: //pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html
