You can use concat([df1, df2, …], axis=1) in order to concatenate two or more DFs aligned by indexes:

pd.concat([df1, df2, df3, ...], axis=1)

Or merge for concatenating by custom fields / indexes:

# join by _common_ columns: `col1`, `col3`
pd.merge(df1, df2, on=['col1','col3'])

# join by: `df1.col1 == df2.index`
pd.merge(df1, df2, left_on='col1' right_index=True)

or join for joining by index:

 df1.join(df2)

If you want to join two dataframes in Pandas, you can simply use available attributes like merge or concatenate.

For example, if I have two dataframes df1 and df2, I can join them by:

newdataframe = merge(df1, df2, left_index=True, right_index=True)

A silly bug that got me: the joins failed because index dtypes differed. This was not obvious as both tables were pivot tables of the same original table. After reset_index, the indices looked identical in Jupyter. It only came to light when saving to Excel…

I fixed it with: df1[['key']] = df1[['key']].apply(pd.to_numeric)

Hopefully this saves somebody an hour!

By default:
join is a column-wise left join
pd.merge is a column-wise inner join
pd.concat is a row-wise outer join

pd.concat:
takes Iterable arguments. Thus, it cannot take DataFrames directly (use [df,df2])
Dimensions of DataFrame should match along axis

Join and pd.merge:
can take DataFrame arguments

This answer has been resolved for a while and all the available
options are already out there. However in this answer I’ll attempt to
shed a bit more light on these options to help you understand when to
use what.

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
• 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.

1. DataFrame.merge with left_index and right_index (or left_on and right_on using named indexes)
2. DataFrame.join (joins on index)
3. pd.concat (joins on index)

---

Index to index joins

Typically, an inner join on index would look like this:

left.merge(right, left_index=True, right_index=True)

Other types of joins (left, right, outer) follow similar syntax (and can be controlled using how=...).

Notable Alternatives

1. DataFrame.join defaults to a left outer join on the index.

    left.join(right, how='inner',)
   

   If you happen to get ValueError: columns overlap but no suffix specified, you will need to specify lsuffix and rsuffix= arguments to resolve this. Since the column names are same, a differentiating suffix is required.

2. pd.concat joins on the index and can join two or more DataFrames at once. It does a full outer join by default.

    pd.concat([left, right], axis=1, sort=False)
   

   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=....

left.merge(right, left_index=True, right_on='key')

Other joins follow a similar structure. Note that only merge can perform index to column joins. You can join on multiple levels/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.

---

^{This post is an abridged version of my work in Pandas Merging 101. Please follow this link for more examples and other topics on merging.}

You can try these few ways to merge/join your dataframe.

1. merge (inner join by default)

   df = pd.merge(df1, df2, left_index=True, right_index=True)

2. join (left join by default)

   df = df1.join(df2)

3. concat (outer join by default)

   df = pd.concat([df1, df2], axis=1)