Pandas Merging 101

• How can I perform a (INNER| (LEFT|RIGHT|FULL) OUTER) JOIN with pandas?

• How do I add NaNs for missing rows after a merge?

• How do I get rid of NaNs after merging?

• Can I merge on the index?

• How do I merge multiple DataFrames?

• Cross join with pandas

• merge? join? concat? update? Who? What? Why?!

... and more. I've seen these recurring questions asking about various facets of the pandas merge functionality. Most of the information regarding merge and its various use cases today is fragmented across dozens of badly worded, unsearchable posts. The aim here is to collate some of the more important points for posterity.

This Q&A is meant to be the next installment in a series of helpful user guides on common pandas idioms (see this post on pivoting, and this post on concatenation, which I will be touching on, later).

Please note that this post is not meant to be a replacement for the documentation, so please read that as well! Some of the examples are taken from there.

Table of Contents

For ease of access.

-
Merging basics - basic types of joins (read this first)

-
Index-based joins

-
Generalizing to multiple DataFrames

-
Cross join

This post aims to give readers a primer on SQL-flavored merging with Pandas, how to use it, and when not to use it.

In particular, here's what this post will go through:

-
The basics - types of joins (LEFT, RIGHT, OUTER, INNER)

• merging with different column names

• merging with multiple columns

• avoiding duplicate merge key column in output

What this post (and other posts by me on this thread) will not go through:

• Performance-related discussions and timings (for now). Mostly notable mentions of better alternatives, wherever appropriate.

• Handling suffixes, removing extra columns, renaming outputs, and other specific use cases. There are other (read: better) posts that deal with that, so figure it out!

Note
Most examples default to INNER JOIN operations while demonstrating various features, unless otherwise specified.

Furthermore, all the DataFrames here can be copied and replicated so
you can play with them. Also, see this
post
on how to read DataFrames from your clipboard.

Lastly, all visual representation of JOIN operations have been hand-drawn using Google Drawings. Inspiration from here.

Enough talk - just show me how to use merge!
Setup & Basics

np.random.seed(0)
left = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': np.random.randn(4)})
right = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'value': np.random.randn(4)})

left

  key     value
0   A  1.764052
1   B  0.400157
2   C  0.978738
3   D  2.240893

right

  key     value
0   B  1.867558
1   D -0.977278
2   E  0.950088
3   F -0.151357

For the sake of simplicity, the key column has the same name (for now).

An INNER JOIN is represented by

Note
This, along with the forthcoming figures all follow this convention:

• blue indicates rows that are present in the merge result

• red indicates rows that are excluded from the result (i.e., removed)

• green indicates missing values that are replaced with NaNs in the result

To perform an INNER JOIN, call merge on the left DataFrame, specifying the right DataFrame and the join key (at the very least) as arguments.

left.merge(right, on='key')
# Or, if you want to be explicit
# left.merge(right, on='key', how='inner')

  key   value_x   value_y
0   B  0.400157  1.867558
1   D  2.240893 -0.977278

This returns only rows from left and right which share a common key (in this example, "B" and "D).

A LEFT OUTER JOIN, or LEFT JOIN is represented by

This can be performed by specifying how='left'.

left.merge(right, on='key', how='left')

  key   value_x   value_y
0   A  1.764052       NaN
1   B  0.400157  1.867558
2   C  0.978738       NaN
3   D  2.240893 -0.977278

Carefully note the placement of NaNs here. If you specify how='left', then only keys from left are used, and missing data from right is replaced by NaN.

And similarly, for a RIGHT OUTER JOIN, or RIGHT JOIN which is...

...specify how='right':

left.merge(right, on='key', how='right')

  key   value_x   value_y
0   B  0.400157  1.867558
1   D  2.240893 -0.977278
2   E       NaN  0.950088
3   F       NaN -0.151357

Here, keys from right are used, and missing data from left is replaced by NaN.

Finally, for the FULL OUTER JOIN, given by

specify how='outer'.

left.merge(right, on='key', how='outer')

  key   value_x   value_y
0   A  1.764052       NaN
1   B  0.400157  1.867558
2   C  0.978738       NaN
3   D  2.240893 -0.977278
4   E       NaN  0.950088
5   F       NaN -0.151357

This uses the keys from both frames, and NaNs are inserted for missing rows in both.

The documentation summarizes these various merges nicely:

Other JOINs - LEFT-Excluding, RIGHT-Excluding, and FULL-Excluding/ANTI JOINs

If you need LEFT-Excluding JOINs and RIGHT-Excluding JOINs in two steps.

For LEFT-Excluding JOIN, represented as

Start by performing a LEFT OUTER JOIN and then filtering to rows coming from left only (excluding everything from the right),

(left.merge(right, on='key', how='left', indicator=True)
     .query('_merge == "left_only"')
     .drop('_merge', axis=1))

  key   value_x  value_y
0   A  1.764052      NaN
2   C  0.978738      NaN

Where,

left.merge(right, on='key', how='left', indicator=True)

  key   value_x   value_y     _merge
0   A  1.764052       NaN  left_only
1   B  0.400157  1.867558       both
2   C  0.978738       NaN  left_only
3   D  2.240893 -0.977278       both

And similarly, for a RIGHT-Excluding JOIN,

(left.merge(right, on='key', how='right', indicator=True)
     .query('_merge == "right_only"')
     .drop('_merge', axis=1))

  key  value_x   value_y
2   E      NaN  0.950088
3   F      NaN -0.151357

Lastly, if you are required to do a merge that only retains keys from the left or right, but not both (IOW, performing an ANTI-JOIN),

You can do this in similar fashion—

```
(left.merge(right, on='key', how='outer', indicator=True)
.query('_merge != "both"')
.drop('_merge', axis=1))

key value_x value_y

Stack Overflow Q#53645882, score: 1303