合并DataFrame时缺少的条目数

Question

在一个练习中，我被要求通过内部联接（df1 + df2 + df3 = mergedDf）合并3个DataFrame，然后在另一个问题中，我被要求告诉我在执行这种3向合并时丢失了多少个条目

#DataFrame1
df1 = pd.DataFrame(columns=["Goals","Medals"],data=[[5,2],[1,0],[3,1]])
df1.index = ['Argentina','Angola','Bolivia']
print(df1)
            Goals    Medals
Argentina       5         2
Angola          1         0
Bolivia         3         1

#DataFrame2
df2 = pd.DataFrame(columns=["Dates","Medals"],data=[[1,0],[2,1],[2,2])
df2.index = ['Venezuela','Africa']
print(df2)
            Dates    Medals
Venezuela       1         0
Africa          2         1
Argentina       2         2

#DataFrame3
df3 = pd.DataFrame(columns=["Players","Goals"],data=[[11,5],[11,1],[10,0]])
df3.index = ['Argentina','Australia','Belgica']
print(df3)
           Players    Goals
Argentina       11        5
Australia       11        1
Spain           10        0

#mergedDf
mergedDf = pd.merge(df1,df2,how='inner',left_index=True, right_index=True)
mergedDf = pd.merge(mergedDf,df3,how='inner',left_index=True, right_index=True)
print(mergedDF)
           Goals_X  Medals_X  Dates  Medals_Y  Players  Goals_Y
Argentina        5         2      2         2       11        2

#Calculate number of lost entries by code

我尝试将所有内容与外部联接合并，然后减去mergedDf，但是我不知道该怎么做，有人可以帮助我吗？

Answer 1

具有外部联接和参数指示符的解决方案，根据both值的总和，在两个指示符列a和b中没有True的最后一个计数行（过程类似{{ 1}} s）：

1

另一种解决方案是使用内部联接和每个与mergedDf = pd.merge(df1,df2,how='outer',left_index=True, right_index=True, indicator='a') mergedDf = pd.merge(mergedDf,df3,how='outer',left_index=True, right_index=True, indicator='b') print(mergedDf) Goals_x Medals_x Dates Medals_y a Players Goals_y \ Africa NaN NaN 2.0 1.0 right_only NaN NaN Angola 1.0 0.0 NaN NaN left_only NaN NaN Argentina 5.0 2.0 2.0 2.0 both 11.0 5.0 Australia NaN NaN NaN NaN NaN 11.0 1.0 Belgica NaN NaN NaN NaN NaN 10.0 0.0 Bolivia 3.0 1.0 NaN NaN left_only NaN NaN Venezuela NaN NaN 1.0 0.0 right_only NaN NaN b Africa left_only Angola left_only Argentina both Australia right_only Belgica right_only Bolivia left_only Venezuela left_only missing = ((mergedDf['a'] != 'both') & (mergedDf['b'] != 'both')).sum() print (missing) 6 不匹配的索引的sum过滤值：

mergedDf.index

如果每个索引中都有唯一值，请注意：

mergedDf = pd.merge(df1,df2,how='inner',left_index=True, right_index=True)
mergedDf = pd.merge(mergedDf,df3,how='inner',left_index=True, right_index=True)
vals = mergedDf.index
print (vals)
Index(['Argentina'], dtype='object')

dfs = [df1, df2, df3]
missing = sum((~x.index.isin(vals)).sum() for x in dfs)
print (missing)
6

Answer 2

您可以将True传递给合并中的create_blob_from_path()

indicator

然后与df1=pd.DataFrame({'A':[1,2,3],'B':[1,1,1]}) df2=pd.DataFrame({'A':[2,3],'B':[1,1]}) df1.merge(df2,on='A',how='inner') Out[257]: A B_x B_y 0 2 1 1 1 3 1 1 df1.merge(df2,on='A',how='outer',indicator =True) Out[258]: A B_x B_y _merge 0 1 1 NaN left_only 1 2 1 1.0 both 2 3 1 1.0 both mergedf=df1.merge(df2,on='A',how='outer',indicator =True) 一起知道您在value_counts时损失了多少，因为只有inner会在both时保留

how='inner'

对于3 df并使用两个合并列进行过滤的单词为mergedf['_merge'].value_counts() Out[260]: both 2 left_only 1 right_only 0 Name: _merge, dtype: int64

both

Answer 3

我找到了一个简单但有效的解决方案：

合并内部和外部的3个数据框：

df1 = Df1()
df2 = Df2()
df3 = Df3()
inner = pd.merge(pd.merge(df1,df2,on='<Common column>',how='inner'),df3,on='<Common column>',how='inner')
outer = pd.merge(pd.merge(df1,df2,on='<Common column>',how='outer'),df3,on='<Common column>',how='outer')

现在，错过的条目（行）数为：

return (len(outer)-len(inner))