Combine two pandas Data Frames (join on a common column)

I have 2 dataframes:

restaurant_ids_dataframe

Data columns (total 13 columns):
business_id      4503  non-null values
categories       4503  non-null values
city             4503  non-null values
full_address     4503  non-null values
latitude         4503  non-null values
longitude        4503  non-null values
name             4503  non-null values
neighborhoods    4503  non-null values
open             4503  non-null values
review_count     4503  non-null values
stars            4503  non-null values
state            4503  non-null values
type             4503  non-null values
dtypes: bool(1), float64(3), int64(1), object(8)`

and

restaurant_review_frame

Int64Index: 158430 entries, 0 to 229905
Data columns (total 8 columns):
business_id    158430  non-null values
date           158430  non-null values
review_id      158430  non-null values
stars          158430  non-null values
text           158430  non-null values
type           158430  non-null values
user_id        158430  non-null values
votes          158430  non-null values
dtypes: int64(1), object(7)

I would like to join these two DataFrames to make them into a single dataframe using the DataFrame.join() command in pandas.

I have tried the following line of code:

#the following line of code creates a left join of restaurant_ids_frame and   restaurant_review_frame on the column 'business_id'
restaurant_review_frame.join(other=restaurant_ids_dataframe,on='business_id',how='left')

But when I try this I get the following error:

Exception: columns overlap: Index([business_id, stars, type], dtype=object)

I am very new to pandas and have no clue what I am doing wrong as far as executing the join statement is concerned.

any help would be much appreciated.

You can use merge to combine two dataframes into one:

import pandas as pd
pd.merge(restaurant_ids_dataframe, restaurant_review_frame, on='business_id', how='outer')

where on specifies field name that exists in both dataframes to join on, and how defines whether its inner/outer/left/right join, with outer using 'union of keys from both frames (SQL: full outer join).' Since you have 'star' column in both dataframes, this by default will create two columns star_x and star_y in the combined dataframe. As @DanAllan mentioned for the join method, you can modify the suffixes for merge by passing it as a kwarg. Default is suffixes=('_x', '_y'). if you wanted to do something like star_restaurant_id and star_restaurant_review, you can do:

 pd.merge(restaurant_ids_dataframe, restaurant_review_frame, on='business_id', how='outer', suffixes=('_restaurant_id', '_restaurant_review'))

The parameters are explained in detail in this link.

Joining fails if the DataFrames have some column names in common. The simplest way around it is to include an lsuffix or rsuffix keyword like so:

restaurant_review_frame.join(restaurant_ids_dataframe, on='business_id', how='left', lsuffix="_review")

This way, the columns have distinct names. The documentation addresses this very problem.

Or, you could get around this by simply deleting the offending columns before you join. If, for example, the stars in restaurant_ids_dataframe are redundant to the stars in restaurant_review_frame, you could del restaurant_ids_dataframe['stars'].

In case anyone needs to try and merge two dataframes together on the index (instead of another column), this also works!

T1 and T2 are dataframes that have the same indices

import pandas as pd
T1 = pd.merge(T1, T2, on=T1.index, how='outer')

P.S. I had to use merge because append would fill NaNs in unnecessarily.

Combine two pandas Data Frames (join on a common column)

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