What is the difference between join and merge in Pandas

Question

Suppose I have two DataFrames like so   left   pd DataFrame   key1     foo    bar     lval    1  2     right   pd DataFrame   key2     foo    bar     rval    4  5      I want to merge them  so I try something like this   pd merge left  right  left on  key1   right on  key2     And I m happy      key1    lval    key2    rval 0   foo     1       foo     4 1   bar     2       bar     5   But I m trying to use the join method  which I ve been lead to believe is pretty similar    left join right  on   key1    key2      And I get this     anaconda lib python2 7 site-packages pandas tools merge pyc in  validate specification self      406             if self right index      407                 if not   len self left on     self right index nlevels    -- gt  408                     raise AssertionError       409                 self right on    None    n     410         elif self right on is not None   AssertionError     What am I missing

User · Answer

From this documentation

pandas provides a single function, merge, as the entry point for all standard database join operations between DataFrame objects:
merge(left, right, how='inner', on=None, left_on=None, right_on=None,
      left_index=False, right_index=False, sort=True,
      suffixes=('_x', '_y'), copy=True, indicator=False)

And :

DataFrame.join is a convenient method for combining the columns of two potentially differently-indexed DataFrames into a single result DataFrame. Here is a very basic example: The data alignment here is on the indexes (row labels). This same behavior can be achieved using merge plus additional arguments instructing it to use the indexes:
result = pd.merge(left, right, left_index=True, right_index=True,
how='outer')

User · Answer

To put it analogously to SQL  Pandas merge is to outer inner join and Pandas join is to natural join   Hence when you use merge in pandas  you want to specify which kind of sqlish join you want to use whereas when you use pandas join  you really want to have a matching column label to ensure it joins

User · Answer

Join  Default Index  If any same column name then it will throw an error in default mode because u have not defined lsuffix or rsuffix     df 1 join df 2     Merge  Default Same Column Names  If no same column name it will throw an error in default mode    df 1 merge df 2     on parameter has different meaning in both cases   df 1 merge df 2  on  column 1    df 1 join df 2  on  column 1      It will throw error df 1 join df 2 set index  column 1    on  column 1

User · Answer

I always use join on indices   import pandas as pd left   pd DataFrame   key     foo    bar     val    1  2    set index  key   right   pd DataFrame   key     foo    bar     val    4  5    set index  key   left join right  lsuffix   l   rsuffix   r         val l  val r key             foo      1      4 bar      2      5   The same functionality can be had by using merge on the columns follows   left   pd DataFrame   key     foo    bar     val    1  2    right   pd DataFrame   key     foo    bar     val    4  5    left merge right  on   key    suffixes    l     r        key  val l  val r 0  foo      1      4 1  bar      2      5

User · Answer

I believe that join   is just a convenience method  Try df1 merge df2  instead  which allows you to specify left on and right on   In  30   left merge right  left on  key1   right on  key2   Out 30      key1  lval key2  rval 0  foo     1  foo     4 1  bar     2  bar     5

User · Answer

pandas merge   is the underlying function used for all merge join behavior    DataFrames provide the pandas DataFrame merge   and pandas DataFrame join   methods as a convenient way to access the capabilities of pandas merge    For example  df1 merge right df2       is equivalent to pandas merge left df1  right df2          These are the main differences between df join   and df merge      lookup on right table  df1 join df2  always joins via the index of df2  but df1 merge df2  can join to one or more columns of df2  default  or to the index of df2  with right index True    lookup on left table  by default  df1 join df2  uses the index of df1 and df1 merge df2  uses column s  of df1  That can be overridden by specifying df1 join df2  on key or keys  or df1 merge df2  left index True    left vs inner join  df1 join df2  does a left join by default  keeps all rows of df1   but df merge does an inner join by default  returns only matching rows of df1 and df2     So  the generic approach is to use pandas merge df1  df2  or df1 merge df2   But for a number of common situations  keeping all rows of df1 and joining to  an index in df2   you can save some typing by using df1 join df2  instead   Some notes on these issues from the documentation at http   pandas pydata org pandas-docs stable merging html database-style-dataframe-joining-merging      merge is a function in the pandas namespace  and it is also   available as a DataFrame instance method  with the calling DataFrame   being implicitly considered the left object in the join       The related DataFrame join method  uses merge internally for the   index-on-index and index-on-column s  joins  but joins on indexes by   default rather than trying to join on common columns  the default   behavior for merge   If you are joining on index  you may wish to   use DataFrame join to save yourself some typing            These two function calls are completely equivalent   left join right  on key or keys  pd merge left  right  left on key or keys  right index True  how  left   sort False

User · Answer

One of the difference is that merge is creating a new index  and join is keeping the left side index  It can have a big consequence on your later transformations if you wrongly assume that your index isn t changed with merge    For example   import pandas as pd  df1   pd DataFrame   org index    101  102  103  104                        date    201801  201801  201802  201802                        val    1  2  3  4    index  101  102  103  104   df1         date  org index  val 101  201801        101    1 102  201801        102    2 103  201802        103    3 104  201802        104    4   -  df2   pd DataFrame   date    201801  201802    dateval     A    B     set index  date   df2         dateval date           201801       A 201802       B   -  df1 merge df2  on  date         date  org index  val dateval 0  201801        101    1       A 1  201801        102    2       A 2  201802        103    3       B 3  201802        104    4       B   -  df1 join df2  on  date          date  org index  val dateval 101  201801        101    1       A 102  201801        102    2       A 103  201802        103    3       B 104  201802        104    4       B

[python] What is the difference between join and merge in Pandas?

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