Python Pandas - Merging/Joining

Pandas has full-featured, high performance in-memory join operations idiomatically very similar to relational databases like SQL.
Pandas provides a single function, merge, as the entry point for all standard database join operations between DataFrame objects −
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=True)

Here, we have used the following parameters −
left − A DataFrame object.
left − A DataFrame object.
right − Another DataFrame object.
right − Another DataFrame object.
on − Columns (names) to join on. Must be found in both the left and right DataFrame
objects.
on − Columns (names) to join on. Must be found in both the left and right DataFrame
objects.
left_on − Columns from the left DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame.
left_on − Columns from the left DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame.
right_on − Columns from the right DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame.
right_on − Columns from the right DataFrame to use as keys. Can either be column names or arrays with length equal to the length of the DataFrame.
left_index − If True, use the index (row labels) from the left DataFrame as its join key(s). In case of a DataFrame with a MultiIndex (hierarchical), the number of levels must match the number of join keys from the right DataFrame.
left_index − If True, use the index (row labels) from the left DataFrame as its join key(s). In case of a DataFrame with a MultiIndex (hierarchical), the number of levels must match the number of join keys from the right DataFrame.
right_index − Same usage as left_index for the right DataFrame.
right_index − Same usage as left_index for the right DataFrame.
how − One of 'left', 'right', 'outer', 'inner'. Defaults to inner. Each method has been described below.
how − One of 'left', 'right', 'outer', 'inner'. Defaults to inner. Each method has been described below.
sort − Sort the result DataFrame by the join keys in lexicographical order. Defaults to True, setting to False will improve the performance substantially in many cases.
sort − Sort the result DataFrame by the join keys in lexicographical order. Defaults to True, setting to False will improve the performance substantially in many cases.
Let us now create two different DataFrames and perform the merging operations on it.
# import the pandas library
import pandas as pd
left = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
   'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame(
   {'id':[1,2,3,4,5],
   'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
   'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print left
print right
Its output is as follows −
    Name  id   subject_id
0   Alex   1         sub1
1    Amy   2         sub2
2  Allen   3         sub4
3  Alice   4         sub6
4  Ayoung  5         sub5

    Name  id   subject_id
0  Billy   1         sub2
1  Brian   2         sub4
2  Bran    3         sub3
3  Bryce   4         sub6
4  Betty   5         sub5

import pandas as pd
left = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
   'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame({
	'id':[1,2,3,4,5],
   'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
   'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print pd.merge(left,right,on='id')
Its output is as follows −
   Name_x   id  subject_id_x   Name_y   subject_id_y
0  Alex      1          sub1    Billy           sub2
1  Amy       2          sub2    Brian           sub4
2  Allen     3          sub4     Bran           sub3
3  Alice     4          sub6    Bryce           sub6
4  Ayoung    5          sub5    Betty           sub5

import pandas as pd
left = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
   'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame({
	'id':[1,2,3,4,5],
   'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
   'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print pd.merge(left,right,on=['id','subject_id'])
Its output is as follows −
    Name_x   id   subject_id   Name_y
0    Alice    4         sub6    Bryce
1   Ayoung    5         sub5    Betty

The how argument to merge specifies how to determine which keys are to be included in the resulting table. If a key combination does not appear in either the left or the right tables, the values in the joined table will be NA.
Here is a summary of the how options and their SQL equivalent names −
import pandas as pd
left = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
   'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
   'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print pd.merge(left, right, on='subject_id', how='left')
Its output is as follows −
    Name_x   id_x   subject_id   Name_y   id_y
0     Alex      1         sub1      NaN    NaN
1      Amy      2         sub2    Billy    1.0
2    Allen      3         sub4    Brian    2.0
3    Alice      4         sub6    Bryce    4.0
4   Ayoung      5         sub5    Betty    5.0

import pandas as pd
left = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
   'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
   'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print pd.merge(left, right, on='subject_id', how='right')
Its output is as follows −
    Name_x  id_x   subject_id   Name_y   id_y
0      Amy   2.0         sub2    Billy      1
1    Allen   3.0         sub4    Brian      2
2    Alice   4.0         sub6    Bryce      4
3   Ayoung   5.0         sub5    Betty      5
4      NaN   NaN         sub3     Bran      3

import pandas as pd
left = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
   'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
   'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print pd.merge(left, right, how='outer', on='subject_id')
Its output is as follows −
    Name_x  id_x   subject_id   Name_y   id_y
0     Alex   1.0         sub1      NaN    NaN
1      Amy   2.0         sub2    Billy    1.0
2    Allen   3.0         sub4    Brian    2.0
3    Alice   4.0         sub6    Bryce    4.0
4   Ayoung   5.0         sub5    Betty    5.0
5      NaN   NaN         sub3     Bran    3.0

Joining will be performed on index. Join operation honors the object on which it is called.
So, a.join(b) is not equal to b.join(a).
import pandas as pd
left = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'],
   'subject_id':['sub1','sub2','sub4','sub6','sub5']})
right = pd.DataFrame({
   'id':[1,2,3,4,5],
   'Name': ['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
   'subject_id':['sub2','sub4','sub3','sub6','sub5']})
print pd.merge(left, right, on='subject_id', how='inner')
Its output is as follows −
    Name_x   id_x   subject_id   Name_y   id_y
0      Amy      2         sub2    Billy      1
1    Allen      3         sub4    Brian      2
2    Alice      4         sub6    Bryce      4
3   Ayoung      5         sub5    Betty      5


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