基于另一个数据帧的列值筛选数据帧

更新

你可以用一个简单的面具:

m = df2.SKU.isin(df1.SKU)
df2 = df2[m]

您正在寻找内部连接。试试这个:

df3 = df1.merge(df2, on=['SKU','Sales'], how='inner')

#  SKU  Sales
#0   A    100
#1   B    200
#2   C    300

或者这个:

df3 = df1.merge(df2, on='SKU', how='inner')

#  SKU  Sales_x  Sales_y
#0   A      100      100
#1   B      200      200
#2   C      300      300

解决方案1:

# First identify the common SKU's    
temp = list(set(list(df1.SKU)).intersection(set(list(df2.SKU))))

# Filter df2 using the list of common SKU's
df3 = df2[df2.SKU.isin(temp)]
print(df3)

   SKU  Sales
0   A   400
1   B   300
2   C   900

解决方案2:单线解决方案

df3 = df2[df2.SKU.isin(list(df1.SKU))]

编辑1:更新问题的解决方案(不是最佳的解决方法,而是回答您的问题)

# reading data for df1
df1= pd.read_clipboard(sep='\\s+')
df1
    Company SKU Sales
0   Walmart A   100
1   Total   A   200
2   Walmart B   200
3   Total   B   300
4   Walmart C   400
5   Walmart D   500

# reading data for df2
df2= pd.read_clipboard(sep='\\s+')
df2
Company SKU Sales
0   Walmart A   400
1   Total   B   300
2   Walmart C   900
3   Walmart F   400
4   Total   G   500

# Using intersect and zip to create a list of tuples matching in the data frames
temp = list(set(list(zip(df1.Company,df1.SKU))).intersection(set(list(zip(df2.Company,df2.SKU)))))
temp
[('Walmart', 'A'), ('Walmart', 'C'), ('Total', 'B')]

# Creating a helper variable in df2 to lookup in the temp list
df2["temp"] = list(zip(df2.Company,df2.SKU))
df2= df2[df2["temp"].isin(temp)]
del(df2["temp"])
df2
    Company SKU Sales
0   Walmart A   400
1   Total   B   300
2   Walmart C   900

欢迎提出改进此代码的建议

一种方法是对齐索引,然后使用掩码。

# align indices
df1 = df1.set_index(['Company',  'SKU'])
df2 = df2.set_index(['Company',  'SKU'])

# calculate & apply mask
df2 = df2[df2.index.isin(df1.index)].reset_index()

不需要重置索引,但需要提升 Company 和 SKU 到列。