Pandas Inner Join with axis is one

I was working with inner join using concat in pandas. Using two DataFrames as below:-

df1 = pd.DataFrame([['a',1],['b',2]], columns=['letter','number'])

df3 = pd.DataFrame([['c',3,'cat'],['d',4,'dog']],
                    columns=['letter','number','animal'])

pd.concat([df1,df3], join='inner')

The out is below

letter  number
0   a   1
1   b   2
0   c   3
1   d   4

But after using axis=1 the output is as below

pd.concat([df1,df3], join='inner', axis=1)

letter  number  letter  number  animal
0   a   1   c   3   cat
1   b   2   d   4   dog

Why it is showing animal column while doing inner join when axis=1?

In Pandas.concat()

axis argument defines whether to concat the dataframes based on index or columns.

axis=0 // based on index (default value)
axis=1 // based on columns

when you Concatenated df1 and df3, it uses index to combine dataframes and thus output is

letter  number
0   a   1
1   b   2
0   c   3
1   d   4

But when you used axis=1, pandas combined the data based on columns. thats why the output is

letter  number  letter  number  animal
0   a   1   c   3   cat
1   b   2   d   4   dog

EDIT:

you asked But inner join only join same column right? Then why it is showing 'animal' column?

So, Because right now you have 2 rows in both the dataframes and join only works in indexes.

For explaining to you, I have added another row in df3 Let's suppose df3 is

   0  1     2
0  c  3   cat
1  d  4   dog
2  e  5  bird

Now, If you concat the df1 and df3

pd.concat([df1,df3], join='inner', axis=1)

  letter  number  0  1    2
0      a       1  c  3  cat
1      b       2  d  4  dog

pd.concat([df1,df3], join='outer', axis=1)

  letter  number  0  1     2
0      a     1.0  c  3   cat
1      b     2.0  d  4   dog
2    NaN     NaN  e  5  bird

As you can see, in inner join only 0 and 1 indexes are in output but in outer join, all the indexes are in output with NAN values.