python - 合并在 pandas 中是如何工作的

Question

我发布了一个问题earlier并得到了答案。

您能解释一下 join final_df = pd.merge(df, temp_df.reset_index(), how="left").fillna(0) 的工作原理吗？我得到了正确的结果，但我不明白连接是如何发生的。 df 和 temp_df 之间没有公共(public)列。

工作代码如下:

    d = {'emp': ['a',   'a',    'a',    'a',    'b',    'b',    'b',    'c',    'c',    'c',    'c' ], 
     'date': ['1',  '1',    '1',    '1',    '2',    '2',    '2',    '3',    '3',    '3',    '3' ], 
     'usd':[1,  2,  3,  4,  5,  6,  7,  8,  9,  10,     11 ], 
     'expense type':['Car Mileage',     'Car Rental',   'Car Rental - Gas',     'food',     'Car Rental',   'Car Rental - Gas',     'food',     'Car Mileage',  'Car Rental',   'food',     'wine' ],
     'zflag':['1',  '1', '1',   ' ',' ',' ',' ','2','2',' ',' ' ]
     }

    df = pd.DataFrame(data=d)



        df
    Out[253]: 
       date emp      expense type  usd zflag
    0     1   a       Car Mileage    1     1
    1     1   a        Car Rental    2     1
    2     1   a  Car Rental - Gas    3     1
    3     1   a              food    4      
    4     2   b        Car Rental    5      
    5     2   b  Car Rental - Gas    6      
    6     2   b              food    7      
    7     3   c       Car Mileage    8     2
    8     3   c        Car Rental    9     2
    9     3   c              food   10      
    10    3   c              wine   11   


temp_df = df.groupby(["emp", "date"], axis=0)["expense type"].apply(lambda x: 1 if "Car Mileage" in x.values and any([k in x.values for k in ["Car Rental", "Car Rental - Gas"]]) else 0).rename("zzflag")
temp_df = temp_df.loc[temp_df!=0,:].cumsum()
final_df = pd.merge(df, temp_df.reset_index(), how="left").fillna(0)

更新 1:

temp_df 没有索引，它是一个系列。所以我不确定如何按照评论中的建议在索引上进行连接。

temp_df
Out[335]: 
emp  date
a    1       1
c    3       2
Name: zzflag, dtype: int64

Answer 1

不带 on 或 index 参数的

pd.merge 正在加入公共(public)列名称:

Per Docs in pandas API on pd.merge查看“on”参数:

on : label or list Field names to join on. Must be found in both DataFrames. If on is None and not merging on indexes, then it merges on the intersection of the columns by default.

d = {'emp': ['a',   'a',    'a',    'a',    'b',    'b',    'b',    'c',    'c',    'c',    'c' ], 
     'date': ['1',  '1',    '1',    '1',    '2',    '2',    '2',    '3',    '3',    '3',    '3' ], 
     'usd':[1,  2,  3,  4,  5,  6,  7,  8,  9,  10,     11 ], 
     'expense type':['Car Mileage',     'Car Rental',   'Car Rental - Gas',     'food',     'Car Rental',   'Car Rental - Gas',     'food',     'Car Mileage',  'Car Rental',   'food',     'wine' ],
     'zflag':['1',  '1', '1',   ' ',' ',' ',' ','2','2',' ',' ' ]
     }

df = pd.DataFrame(data=d)
temp_df = df.groupby(["emp", "date"], axis=0)["expense type"].apply(lambda x: 1 if "Car Mileage" in x.values and any([k in x.values for k in ["Car Rental", "Car Rental - Gas"]]) else 0).rename("zzflag")
temp_df = temp_df.loc[temp_df!=0,:].cumsum()
a = temp_df.reset_index()

all(pd.merge(df, a) == pd.merge(df, a, on=['emp','date']))

输出:

True