What is the difference between join and merge in Pandas?

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)

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

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')

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

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

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')

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