Styjun

Intelligent Ants in the Amazon

Why does wrapping Aluminium foil around my food help it keep warm, aluminium be good conductor should have no effect?

Why weren't bootable game disks ever a thing on the IBM PC?

Elf (adjective) vs. Elvish vs. Elven

Is it possible to split a vertex?

How quality assurance engineers test calculations?

How do native German speakers usually express skepticism (using even) about a premise?

Using Open with a filename that contains :

Could you brine steak?

A horrible Stockfish chess engine evaluation

Data Encryption by Application vs Data Encryption in Database

How to drill holes in 3/8" steel plates?

Why archangel Michael didn't save Jesus when he was crucified?

Misrepresented my work history

Is there any reason why MCU changed the Snap to Blip

What is the minimum time required for final wash in film development?

Through: how to use it with subtraction of functions?

What attributes and how big would a sea creature(s) need to be able to tow a ship?

LED glows slightly during soldering

Party going through airport security at separate times?

Should I include code in my research paper?

Is a request to book a business flight ticket for a graduate student an unreasonable one?

What's it called when the bad guy gets eaten?

What happens when adult Billy Batson says "Shazam"?

Joining dataframes by the columns with same names

How to sort a dataframe by multiple column(s)Selecting multiple columns in a pandas dataframeRenaming columns in pandasAdding new column to existing DataFrame in Python pandasDelete column from pandas DataFrameChange data type of columns in PandasHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headersWhy is “1000000000000000 in range(1000000000000001)” so fast in Python 3?

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;

1

I have the following dataframes:

df:

A B C
1 x 1
2 y 2

and

df2:

A C D E
3 3 x l
4 4 z k

I want the following:

df_r:

A C
1 1
2 2
3 3
4 4

Note: This is just and example, the answer should be capable of not knowing first hand what are the same columns. i.e. Imagine you have a thousand columns.

python pandas dataframe

share|improve this question

edited Apr 2 at 4:44

Antonio López Ruiz

asked Mar 26 at 0:54

Antonio López RuizAntonio López Ruiz

47111 gold badge55 silver badges2525 bronze badges

add a comment | 

1

I have the following dataframes:

df:

A B C
1 x 1
2 y 2

and

df2:

A C D E
3 3 x l
4 4 z k

I want the following:

df_r:

A C
1 1
2 2
3 3
4 4

Note: This is just and example, the answer should be capable of not knowing first hand what are the same columns. i.e. Imagine you have a thousand columns.

python pandas dataframe

share|improve this question

edited Apr 2 at 4:44

Antonio López Ruiz

asked Mar 26 at 0:54

Antonio López RuizAntonio López Ruiz

47111 gold badge55 silver badges2525 bronze badges

add a comment | 

I have the following dataframes:

df:

A B C
1 x 1
2 y 2

and

df2:

A C D E
3 3 x l
4 4 z k

I want the following:

df_r:

A C
1 1
2 2
3 3
4 4

Note: This is just and example, the answer should be capable of not knowing first hand what are the same columns. i.e. Imagine you have a thousand columns.

python pandas dataframe

share|improve this question

edited Apr 2 at 4:44

Antonio López Ruiz

asked Mar 26 at 0:54

Antonio López RuizAntonio López Ruiz

47111 gold badge55 silver badges2525 bronze badges

A B C
1 x 1
2 y 2

A C D E
3 3 x l
4 4 z k

A C
1 1
2 2
3 3
4 4

share|improve this question

edited Apr 2 at 4:44

Antonio López Ruiz

asked Mar 26 at 0:54

Antonio López RuizAntonio López Ruiz

47111 gold badge55 silver badges2525 bronze badges

share|improve this question

edited Apr 2 at 4:44

Antonio López Ruiz

asked Mar 26 at 0:54

Antonio López RuizAntonio López Ruiz

47111 gold badge55 silver badges2525 bronze badges

asked Mar 26 at 0:54

Antonio López RuizAntonio López Ruiz

47111 gold badge55 silver badges2525 bronze badges

asked Mar 26 at 0:54

Antonio López RuizAntonio López Ruiz

47111 gold badge55 silver badges2525 bronze badges

2 Answers
2

active

oldest

votes

3

It is time to introduce concat with join

pd.concat([df1,df2],join='inner',ignore_index =True)
Out[30]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

Another way using align

pd.concat(df1.align(df2,join='inner',axis=1),ignore_index =True)
Out[37]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

---

Both of the methods working for outer and inner join for merge index or columns

share|improve this answer

edited Mar 26 at 1:04

answered Mar 26 at 1:00

WeNYoBenWeNYoBen

145k88 gold badges5151 silver badges8282 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Great answer, I like that it is so clean.
  
  – Antonio López Ruiz
  Mar 26 at 1:03
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz ha happy coding :-)
  
  – WeNYoBen
  Mar 26 at 1:03
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz I have updated another method , if you would like to check :-), the only weakness of this , is hard to manage more than 2 dataframe combine together .
  
  – WeNYoBen
  Mar 26 at 1:05
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I checked it, I believe that the first method is the best one, since you can have theoretically an unlimited amount of dataframes and it should still work. That is also why I am choosing it as the correct answer, since it works for a lot of scenarios and it is really clean.
  
  – Antonio López Ruiz
  Mar 26 at 1:33
  
  

  
  
  
  

add a comment | 

2

Simple with pd.concat

cols = set(df.columns).intersection(df2.columns)
pd.concat([df[cols], df2[cols]])

---

Also simple with df.append

df[cols].append(df2[cols])

share|improve this answer

edited Mar 26 at 1:00

answered Mar 26 at 0:56

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I mush be more specific. I am talking about thousands of columns, so I do not know the ones that are the same.
  
  – Antonio López Ruiz
  Mar 26 at 0:59
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz updated
  
  – rafaelc
  Mar 26 at 1:00
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  intersection is good fix :-)
  
  – WeNYoBen
  Mar 26 at 1:01
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Wen-Ben thanks! Nice concat+join btw ;)
  
  – rafaelc
  Mar 26 at 1:01
  

  
  
  
  

add a comment | 

Your Answer

StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");

StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);

else
createEditor();

);

function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);



);

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);

Sign up using Google

Sign up using Facebook

Sign up using Email and Password

Post as a guest

Name

Email

Required, but never shown

StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f55348394%2fjoining-dataframes-by-the-columns-with-same-names%23new-answer', 'question_page');

);

Post as a guest

Name

Email

Required, but never shown

3

It is time to introduce concat with join

pd.concat([df1,df2],join='inner',ignore_index =True)
Out[30]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

Another way using align

pd.concat(df1.align(df2,join='inner',axis=1),ignore_index =True)
Out[37]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

---

Both of the methods working for outer and inner join for merge index or columns

share|improve this answer

edited Mar 26 at 1:04

answered Mar 26 at 1:00

WeNYoBenWeNYoBen

145k88 gold badges5151 silver badges8282 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Great answer, I like that it is so clean.
  
  – Antonio López Ruiz
  Mar 26 at 1:03
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz ha happy coding :-)
  
  – WeNYoBen
  Mar 26 at 1:03
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz I have updated another method , if you would like to check :-), the only weakness of this , is hard to manage more than 2 dataframe combine together .
  
  – WeNYoBen
  Mar 26 at 1:05
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I checked it, I believe that the first method is the best one, since you can have theoretically an unlimited amount of dataframes and it should still work. That is also why I am choosing it as the correct answer, since it works for a lot of scenarios and it is really clean.
  
  – Antonio López Ruiz
  Mar 26 at 1:33
  
  

  
  
  
  

add a comment | 

3

It is time to introduce concat with join

pd.concat([df1,df2],join='inner',ignore_index =True)
Out[30]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

Another way using align

pd.concat(df1.align(df2,join='inner',axis=1),ignore_index =True)
Out[37]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

---

Both of the methods working for outer and inner join for merge index or columns

share|improve this answer

edited Mar 26 at 1:04

answered Mar 26 at 1:00

WeNYoBenWeNYoBen

145k88 gold badges5151 silver badges8282 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Great answer, I like that it is so clean.
  
  – Antonio López Ruiz
  Mar 26 at 1:03
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz ha happy coding :-)
  
  – WeNYoBen
  Mar 26 at 1:03
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz I have updated another method , if you would like to check :-), the only weakness of this , is hard to manage more than 2 dataframe combine together .
  
  – WeNYoBen
  Mar 26 at 1:05
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I checked it, I believe that the first method is the best one, since you can have theoretically an unlimited amount of dataframes and it should still work. That is also why I am choosing it as the correct answer, since it works for a lot of scenarios and it is really clean.
  
  – Antonio López Ruiz
  Mar 26 at 1:33
  
  

  
  
  
  

add a comment | 

It is time to introduce concat with join

pd.concat([df1,df2],join='inner',ignore_index =True)
Out[30]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

Another way using align

pd.concat(df1.align(df2,join='inner',axis=1),ignore_index =True)
Out[37]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

---

Both of the methods working for outer and inner join for merge index or columns

share|improve this answer

edited Mar 26 at 1:04

answered Mar 26 at 1:00

WeNYoBenWeNYoBen

145k88 gold badges5151 silver badges8282 bronze badges

pd.concat([df1,df2],join='inner',ignore_index =True)
Out[30]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

pd.concat(df1.align(df2,join='inner',axis=1),ignore_index =True)
Out[37]: 
 A C
0 1 1
1 2 2
2 3 3
3 4 4

share|improve this answer

edited Mar 26 at 1:04

answered Mar 26 at 1:00

WeNYoBenWeNYoBen

145k88 gold badges5151 silver badges8282 bronze badges

answered Mar 26 at 1:00

WeNYoBenWeNYoBen

145k88 gold badges5151 silver badges8282 bronze badges

answered Mar 26 at 1:00

WeNYoBenWeNYoBen

145k88 gold badges5151 silver badges8282 bronze badges

2

Simple with pd.concat

cols = set(df.columns).intersection(df2.columns)
pd.concat([df[cols], df2[cols]])

---

Also simple with df.append

df[cols].append(df2[cols])

share|improve this answer

edited Mar 26 at 1:00

answered Mar 26 at 0:56

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I mush be more specific. I am talking about thousands of columns, so I do not know the ones that are the same.
  
  – Antonio López Ruiz
  Mar 26 at 0:59
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz updated
  
  – rafaelc
  Mar 26 at 1:00
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  intersection is good fix :-)
  
  – WeNYoBen
  Mar 26 at 1:01
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Wen-Ben thanks! Nice concat+join btw ;)
  
  – rafaelc
  Mar 26 at 1:01
  

  
  
  
  

add a comment | 

2

Simple with pd.concat

cols = set(df.columns).intersection(df2.columns)
pd.concat([df[cols], df2[cols]])

---

Also simple with df.append

df[cols].append(df2[cols])

share|improve this answer

edited Mar 26 at 1:00

answered Mar 26 at 0:56

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I mush be more specific. I am talking about thousands of columns, so I do not know the ones that are the same.
  
  – Antonio López Ruiz
  Mar 26 at 0:59
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @AntonioLópezRuiz updated
  
  – rafaelc
  Mar 26 at 1:00
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  intersection is good fix :-)
  
  – WeNYoBen
  Mar 26 at 1:01
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  @Wen-Ben thanks! Nice concat+join btw ;)
  
  – rafaelc
  Mar 26 at 1:01
  

  
  
  
  

add a comment | 

Simple with pd.concat

cols = set(df.columns).intersection(df2.columns)
pd.concat([df[cols], df2[cols]])

---

Also simple with df.append

df[cols].append(df2[cols])

share|improve this answer

edited Mar 26 at 1:00

answered Mar 26 at 0:56

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

cols = set(df.columns).intersection(df2.columns)
pd.concat([df[cols], df2[cols]])

df[cols].append(df2[cols])

share|improve this answer

edited Mar 26 at 1:00

answered Mar 26 at 0:56

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

answered Mar 26 at 0:56

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges

answered Mar 26 at 0:56

rafaelcrafaelc

31.2k88 gold badges3232 silver badges5555 bronze badges