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
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:
https://i.stack.imgur.com/5qDIy.png
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', 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', 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', 1))
key value_x value_y
0 A 1.764052 NaN
2 C 0.978738 NaN
4 E NaN 0.950088
5 F NaN -0.151357
Different names for key columns
If the key columns are named differently—for example, left
has keyLeft
, and right
has keyRight
instead of key
—then you will have to specify left_on
and right_on
as arguments instead of on
:
left2 = left.rename({'key':'keyLeft'}, axis=1)
right2 = right.rename({'key':'keyRight'}, axis=1)
left2
keyLeft value
0 A 1.764052
1 B 0.400157
2 C 0.978738
3 D 2.240893
right2
keyRight value
0 B 1.867558
1 D -0.977278
2 E 0.950088
3 F -0.151357
left2.merge(right2, left_on='keyLeft', right_on='keyRight', how='inner')
keyLeft value_x keyRight value_y
0 B 0.400157 B 1.867558
1 D 2.240893 D -0.977278
Avoiding duplicate key column in output
When merging on keyLeft
from left
and keyRight
from right
, if you only want either of the keyLeft
or keyRight
(but not both) in the output, you can start by setting the index as a preliminary step.
left3 = left2.set_index('keyLeft')
left3.merge(right2, left_index=True, right_on='keyRight')
value_x keyRight value_y
0 0.400157 B 1.867558
1 2.240893 D -0.977278
Contrast this with the output of the command just before (that is, the output of left2.merge(right2, left_on='keyLeft', right_on='keyRight', how='inner')
), you'll notice keyLeft
is missing. You can figure out what column to keep based on which frame's index is set as the key. This may matter when, say, performing some OUTER JOIN operation.
Merging only a single column from one of the DataFrames
For example, consider
right3 = right.assign(newcol=np.arange(len(right)))
right3
key value newcol
0 B 1.867558 0
1 D -0.977278 1
2 E 0.950088 2
3 F -0.151357 3
If you are required to merge only "newcol" (without any of the other columns), you can usually just subset columns before merging:
left.merge(right3[['key', 'newcol']], on='key')
key value newcol
0 B 0.400157 0
1 D 2.240893 1
If you're doing a LEFT OUTER JOIN, a more performant solution would involve map
:
# left['newcol'] = left['key'].map(right3.set_index('key')['newcol']))
left.assign(newcol=left['key'].map(right3.set_index('key')['newcol']))
key value newcol
0 A 1.764052 NaN
1 B 0.400157 0.0
2 C 0.978738 NaN
3 D 2.240893 1.0
As mentioned, this is similar to, but faster than
left.merge(right3[['key', 'newcol']], on='key', how='left')
key value newcol
0 A 1.764052 NaN
1 B 0.400157 0.0
2 C 0.978738 NaN
3 D 2.240893 1.0
Merging on multiple columns
To join on more than one column, specify a list for on
(or left_on
and right_on
, as appropriate).
left.merge(right, on=['key1', 'key2'] ...)
Or, in the event the names are different,
left.merge(right, left_on=['lkey1', 'lkey2'], right_on=['rkey1', 'rkey2'])
Other useful merge* operations and functions
Merging a DataFrame with Series on index: See this answer.
Besides merge, DataFrame.update and DataFrame.combine_first are also used in certain cases to update one DataFrame with another.
pd.merge_ordered is a useful function for ordered JOINs.
pd.merge_asof (read: merge_asOf) is useful for approximate joins.
This section only covers the very basics, and is designed to only whet your appetite. For more examples and cases, see the documentation on merge
, join
, and concat
as well as the links to the function specifications.
Continue Reading
Jump to other topics in Pandas Merging 101 to continue learning:
Merging basics - basic types of joins *
Index-based joins
Generalizing to multiple DataFrames
Cross join
*You are here.
A supplemental visual view of pd.concat([df0, df1], kwargs)
. Notice that, kwarg axis=0
or axis=1
's meaning is not as intuitive as df.mean()
or df.apply(func)
https://i.stack.imgur.com/1rb1R.jpg
concat
and merge
with a direction parameter being horizontal
or vertical
.
axis=1
and axis=0
is?
merge
and concat
and axis and whatever. However, as @eliu shows, it's all just the same concept of merge with "left" and "right" and "horizontal" or "vertical". I, personally, have to look into the documentation every time I have to remember which "axis" is 0
and which is 1
.
Joins 101
These animations might be better to explain you visually. Credits: Garrick Aden-Buie tidyexplain repo
Inner Join
https://i.stack.imgur.com/3qpXx.gif
Outer Join or Full Join
https://i.stack.imgur.com/dG8mw.gif
Right Join
https://i.stack.imgur.com/JpPRH.gif
Left Join
https://i.stack.imgur.com/s5hgJ.gif
In this answer, I will consider practical examples.
The first one, is of pandas.concat
.
The second one, of merging dataframes from the index of one and the column of another one.
Considering the following DataFrames
with the same column names:
Preco2018 with size (8784, 5)
https://i.stack.imgur.com/rsqan.png
Preco 2019 with size (8760, 5)
https://i.stack.imgur.com/uZoyW.png
That have the same column names.
You can combine them using pandas.concat
, by simply
import pandas as pd
frames = [Preco2018, Preco2019]
df_merged = pd.concat(frames)
Which results in a DataFrame with the following size (17544, 5)
https://i.stack.imgur.com/8gAVG.png
If you want to visualize, it ends up working like this
https://i.stack.imgur.com/3KFNZ.png
(Source)
2. Merge by Column and Index
In this part, I will consider a specific case: If one wants to merge the index of one dataframe and the column of another dataframe.
Let's say one has the dataframe Geo
with 54 columns, being one of the columns the Date Data
, which is of type datetime64[ns]
.
https://i.stack.imgur.com/gvSIB.png
And the dataframe Price
that has one column with the price and the index corresponds to the dates
https://i.stack.imgur.com/Dp7Jm.png
In this specific case, to merge them, one uses pd.merge
merged = pd.merge(Price, Geo, left_index=True, right_on='Data')
Which results in the following dataframe
https://i.stack.imgur.com/yJVD3.png
This post will go through the following topics:
Merging with index under different conditions options for index-based joins: merge, join, concat merging on indexes merging on index of one, column of other
options for index-based joins: merge, join, concat
merging on indexes
merging on index of one, column of other
effectively using named indexes to simplify merging syntax
Index-based joins
TL;DR
There are a few options, some simpler than others depending on the use case. DataFrame.merge with left_index and right_index (or left_on and right_on using named indexes) supports inner/left/right/full can only join two at a time supports column-column, index-column, index-index joins DataFrame.join (join on index) supports inner/left (default)/right/full can join multiple DataFrames at a time supports index-index joins pd.concat (joins on index) supports inner/full (default) can join multiple DataFrames at a time supports index-index joins
Index to index joins
Setup & Basics
import pandas as pd
import numpy as np
np.random.seed([3, 14])
left = pd.DataFrame(data={'value': np.random.randn(4)},
index=['A', 'B', 'C', 'D'])
right = pd.DataFrame(data={'value': np.random.randn(4)},
index=['B', 'D', 'E', 'F'])
left.index.name = right.index.name = 'idxkey'
left
value
idxkey
A -0.602923
B -0.402655
C 0.302329
D -0.524349
right
value
idxkey
B 0.543843
D 0.013135
E -0.326498
F 1.385076
Typically, an inner join on index would look like this:
left.merge(right, left_index=True, right_index=True)
value_x value_y
idxkey
B -0.402655 0.543843
D -0.524349 0.013135
Other joins follow similar syntax.
Notable Alternatives
DataFrame.join defaults to joins on the index. DataFrame.join does a LEFT OUTER JOIN by default, so how='inner' is necessary here. left.join(right, how='inner', lsuffix='_x', rsuffix='_y') value_x value_y idxkey B -0.402655 0.543843 D -0.524349 0.013135 Note that I needed to specify the lsuffix and rsuffix arguments since join would otherwise error out: left.join(right) ValueError: columns overlap but no suffix specified: Index(['value'], dtype='object') Since the column names are the same. This would not be a problem if they were differently named. left.rename(columns={'value':'leftvalue'}).join(right, how='inner') leftvalue value idxkey B -0.402655 0.543843 D -0.524349 0.013135 pd.concat joins on the index and can join two or more DataFrames at once. It does a full outer join by default, so how='inner' is required here.. pd.concat([left, right], axis=1, sort=False, join='inner') value value idxkey B -0.402655 0.543843 D -0.524349 0.013135 For more information on concat, see this post.
Index to Column joins
To perform an inner join using index of left, column of right, you will use DataFrame.merge
a combination of left_index=True
and right_on=...
.
right2 = right.reset_index().rename({'idxkey' : 'colkey'}, axis=1)
right2
colkey value
0 B 0.543843
1 D 0.013135
2 E -0.326498
3 F 1.385076
left.merge(right2, left_index=True, right_on='colkey')
value_x colkey value_y
0 -0.402655 B 0.543843
1 -0.524349 D 0.013135
Other joins follow a similar structure. Note that only merge
can perform index to column joins. You can join on multiple columns, provided the number of index levels on the left equals the number of columns on the right.
join
and concat
are not capable of mixed merges. You will need to set the index as a pre-step using DataFrame.set_index
.
Effectively using Named Index [pandas >= 0.23]
If your index is named, then from pandas >= 0.23, DataFrame.merge
allows you to specify the index name to on
(or left_on
and right_on
as necessary).
left.merge(right, on='idxkey')
value_x value_y
idxkey
B -0.402655 0.543843
D -0.524349 0.013135
For the previous example of merging with the index of left, column of right, you can use left_on
with the index name of left:
left.merge(right2, left_on='idxkey', right_on='colkey')
value_x colkey value_y
0 -0.402655 B 0.543843
1 -0.524349 D 0.013135
Continue Reading
Jump to other topics in Pandas Merging 101 to continue learning:
Merging basics - basic types of joins
Index-based joins*
Generalizing to multiple DataFrames
Cross join
* you are here
This post will go through the following topics:
how to correctly generalize to multiple DataFrames (and why merge has shortcomings here)
merging on unique keys
merging on non-unqiue keys
Generalizing to multiple DataFrames
Oftentimes, the situation arises when multiple DataFrames are to be merged together. Naively, this can be done by chaining merge
calls:
df1.merge(df2, ...).merge(df3, ...)
However, this quickly gets out of hand for many DataFrames. Furthermore, it may be necessary to generalise for an unknown number of DataFrames.
Here I introduce pd.concat
for multi-way joins on unique keys, and DataFrame.join
for multi-way joins on non-unique keys. First, the setup.
# Setup.
np.random.seed(0)
A = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'valueA': np.random.randn(4)})
B = pd.DataFrame({'key': ['B', 'D', 'E', 'F'], 'valueB': np.random.randn(4)})
C = pd.DataFrame({'key': ['D', 'E', 'J', 'C'], 'valueC': np.ones(4)})
dfs = [A, B, C]
# Note: the "key" column values are unique, so the index is unique.
A2 = A.set_index('key')
B2 = B.set_index('key')
C2 = C.set_index('key')
dfs2 = [A2, B2, C2]
Multiway merge on unique keys
If your keys (here, the key could either be a column or an index) are unique, then you can use pd.concat
. Note that pd.concat
joins DataFrames on the index.
# Merge on `key` column. You'll need to set the index before concatenating
pd.concat(
[df.set_index('key') for df in dfs], axis=1, join='inner'
).reset_index()
key valueA valueB valueC
0 D 2.240893 -0.977278 1.0
# Merge on `key` index.
pd.concat(dfs2, axis=1, sort=False, join='inner')
valueA valueB valueC
key
D 2.240893 -0.977278 1.0
Omit join='inner'
for a FULL OUTER JOIN. Note that you cannot specify LEFT or RIGHT OUTER joins (if you need these, use join
, described below).
Multiway merge on keys with duplicates
concat
is fast, but has its shortcomings. It cannot handle duplicates.
A3 = pd.DataFrame({'key': ['A', 'B', 'C', 'D', 'D'], 'valueA': np.random.randn(5)})
pd.concat([df.set_index('key') for df in [A3, B, C]], axis=1, join='inner')
ValueError: Shape of passed values is (3, 4), indices imply (3, 2)
In this situation, we can use join
since it can handle non-unique keys (note that join
joins DataFrames on their index; it calls merge
under the hood and does a LEFT OUTER JOIN unless otherwise specified).
# Join on `key` column. Set as the index first.
# For inner join. For left join, omit the "how" argument.
A.set_index('key').join([B2, C2], how='inner').reset_index()
key valueA valueB valueC
0 D 2.240893 -0.977278 1.0
# Join on `key` index.
A3.set_index('key').join([B2, C2], how='inner')
valueA valueB valueC
key
D 1.454274 -0.977278 1.0
D 0.761038 -0.977278 1.0
Continue Reading
Jump to other topics in Pandas Merging 101 to continue learning:
Merging basics - basic types of joins
Index-based joins
Generalizing to multiple DataFrames *
Cross join
* you are here
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