熊貓在兩個數據框之間進行比較，標記匹配的內容

Question

我必須要數據幀df和df1

df低於

Facility    Category ID   Part  Text
Centennial  History 11111   A   Drain
Centennial  History 11111   B   Read
Centennial  History 11111   C   EKG
Centennial  History 11111   D   Assistant 
Centennial  History 11111   E   Primary

df1在下面（僅包含一個小樣本問題，實際上是50,000行）

Facility    Category  ID      Part   Text
Centennial  History  11111    D      Assistant 

基本上，我想比較數據框之間的行，如果行在兩個數據框之間匹配，則在第一個數據框df創建另一個列，列標題為['MatchingFlag']

我的最終結果數據框如下所示，因為我擔心那些不匹配的數據框。

Facility    Category  ID    Part    Text      MatchingFlag
Centennial  History  11111  A     Drain         No
Centennial  History  11111  B     Read          No
Centennial  History  11111  C     EKG           No
Centennial  History  11111  D     Assistant     Yes
Centennial  History  11111  E     Primary       No

有什么幫助嗎？ 我試過合並df = pd.merge(df1, df, how='left', on=['Facility', 'Category', 'ID', 'Part', 'Text'])然后根據空白或NaN值創建一個標志，但這並沒有達到我的期望。

Answer 1

在要匹配的列上設置索引，然后使用該索引來排序匹配的行可能是有意義的

columns = ['Facility', 'Category', 'ID', 'Part', 'Text']

# It's always a good idea to sort after creating a MultiIndex like this
df = df.set_index(columns).sortlevel()
df1 = df1.set_index(columns).sortlevel()

# You don't have to use Yes here, anything will do
# The boolean True might be more appropriate
df['MatchingFlag'] = "Yes"
df1['MatchingFlag'] = "Yes"

# Add them together, matching rows will have the value "YesYes"
# Non-matches will be nan
result = df + df1

# If you'd rather not have NaN's 
result.loc[:,'MatchingFlag'] = result.loc[:,'MatchingFlag'].replace('YesYes','Yes')
result.loc[:,'MatchingFlag'] = result['MatchingFlag'].fillna('No')