Joining Pandas Dataframes

Last updated on 2023-05-04 | Edit this page

Overview

Questions

  • How can I join two Dataframes with a common key?

Objectives

  • Understand why we would want to join Dataframes
  • Know what is needed for a join to be possible
  • Understand the different types of joins
  • Understand what the joined results tell us about our data

Joining Dataframes

Why do we want to do this

There are many occasions when we have related data spread across multiple files.

The data can be related to each other in different ways. How they are related and how completely we can join the data from the datasets will vary.

In this episode we will consider different scenarios and show we might join the data. We will use csv files and in all cases the first step will be to read the datasets into a pandas Dataframe from where we will do the joining. The csv files we are using are cut down versions of the SN7577 dataset to make the displays more manageable.

First, let’s download the datafiles. They are listed in the setup page for the lesson. Alternatively, you can download the GitHub repository for this lesson. The data files are in the data directory. If you’re using Jupyter, make sure to place these files in the same directory where your notebook file is.

Scenario 1 - Two data sets containing the same columns but different rows of data

Here we want to add the rows from one Dataframe to the rows of the other Dataframe. In order to do this we can use the pd.concat() function.

PYTHON 

import pandas as pd

df_SN7577i_a = pd.read_csv("SN7577i_a.csv")
df_SN7577i_b = pd.read_csv("SN7577i_b.csv")

Have a quick look at what these Dataframes look like with

PYTHON 

print(df_SN7577i_a)
print(df_SN7577i_b)
  Id  Q1  Q2  Q3  Q4
0   1   1  -1   1   8
1   2   3  -1   1   4
2   3  10   3   2   6
3   4   9  -1  10  10
...

  Id  Q1  Q2  Q3  Q4
0  1277  10  10   4   6
1  1278   2  -1   5   4
2  1279   2  -1   4   5
3  1280   1  -1   2   3
...

{: output}

The concat() function appends the rows from the two Dataframes to create the df_all_rows Dataframe. When you list this out you can see that all of the data rows are there, however, there is a problem with the index.

PYTHON 

df_all_rows = pd.concat([df_SN7577i_a, df_SN7577i_b])
df_all_rows

We didn’t explicitly set an index for any of the Dataframes we have used. For df_SN7577i_a and df_SN7577i_b default indexes would have been created by pandas. When we concatenated the Dataframes the indexes were also concatenated resulting in duplicate entries.

This is really only a problem if you need to access a row by its index. We can fix the problem with the following code.

PYTHON 

df_all_rows=df_all_rows.reset_index(drop=True)

# or, alternatively, there's the `ignore_index` option in the `pd.concat()` function:
df_all_rows = pd.concat([df_SN7577i_a, df_SN7577i_b], ignore_index=True)

df_all_rows

What if the columns in the Dataframes are not the same?

PYTHON 

df_SN7577i_aa = pd.read_csv("SN7577i_aa.csv")
df_SN7577i_bb = pd.read_csv("SN7577i_bb.csv")
df_all_rows = pd.concat([df_SN7577i_aa, df_SN7577i_bb])
df_all_rows

In this case df_SN7577i_aa has no Q4 column and df_SN7577i_bb has no Q3 column. When they are concatenated, the resulting Dataframe has a column for Q3 and Q4. For the rows corresponding to df_SN7577i_aa the values in the Q4 column are missing and denoted by NaN. The same applies to Q3 for the df_SN7577i_bb rows.

Scenario 2 - Adding the columns from one Dataframe to those of another Dataframe

PYTHON 

df_SN7577i_c = pd.read_csv("SN7577i_c.csv")
df_SN7577i_d = pd.read_csv("SN7577i_d.csv")
df_all_cols = pd.concat([df_SN7577i_c, df_SN7577i_d], axis = 1)
df_all_cols

We use the axis=1 parameter to indicate that it is the columns that need to be joined together. Notice that the Id column appears twice, because it was a column in each dataset. This is not particularly desirable, but also not necessarily a problem. However, there are better ways of combining columns from two Dataframes which avoid this problem.

Scenario 3 - Using merge to join columns

We can join columns from two Dataframes using the merge() function. This is similar to the SQL ‘join’ functionality.

A detailed discussion of different join types is given in the SQL lesson.

You specify the type of join you want using the how parameter. The default is the inner join which returns the columns from both tables where the key or common column values match in both Dataframes.

The possible values of the how parameter are shown in the picture below (taken from the Pandas documentation)

pandas_join_types

The different join types behave in the same way as they do in SQL. In Python/pandas, any missing values are shown as NaN

In order to merge the Dataframes we need to identify a column common to both of them.

PYTHON 

df_cd = pd.merge(df_SN7577i_c, df_SN7577i_d, how='inner')
df_cd

In fact, if there is only one column with the same name in each Dataframe, it will be assumed to be the one you want to join on. In this example the Id column

Leaving the join column to default in this way is not best practice. It is better to explicitly name the column using the on parameter.

PYTHON 

df_cd = pd.merge(df_SN7577i_c, df_SN7577i_d, how='inner', on = 'Id')

In many circumstances, the column names that you wish to join on are not the same in both Dataframes, in which case you can use the left_on and right_on parameters to specify them separately.

PYTHON 

df_cd = pd.merge(df_SN7577i_c, df_SN7577i_d, how='inner', left_on = 'Id', right_on = 'Id')

Practice with data

  1. Examine the contents of the SN7577i_aa and SN7577i_bb csv files using Excel or equivalent.
  2. Using the SN7577i_aa and SN7577i_bb csv files, create a Dataframe which is the result of an outer join using the Id column to join on.
  3. What do you notice about the column names in the new Dataframe?
  4. Using shift+tab in Jupyter examine the possible parameters for the merge() function.
  5. re-write the code so that the columns names which are common to both files have suffixes indicating the filename from which they come
  6. If you add the parameter indicator=True, what additional information is provided in the resulting Dataframe?

PYTHON 

df_SN7577i_aa = pd.read_csv("SN7577i_aa.csv")
df_SN7577i_bb = pd.read_csv("SN7577i_bb.csv")
df_aabb = pd.merge(df_SN7577i_aa, df_SN7577i_bb, how='outer', on = 'Id')
df_aabb

PYTHON 

df_SN7577i_aa = pd.read_csv("SN7577i_aa.csv")
df_SN7577i_bb = pd.read_csv("SN7577i_bb.csv")
df_aabb = pd.merge(df_SN7577i_aa, df_SN7577i_bb, how='outer', on = 'Id',suffixes=('_aa', '_bb'), indicator = True)
df_aabb

Key Points

  • You can join pandas Dataframes in much the same way as you join tables in SQL
  • The concat() function can be used to concatenate two Dataframes by adding the rows of one to the other.
  • concat() can also combine Dataframes by columns but the merge() function is the preferred way
  • The merge() function is equivalent to the SQL JOIN clause. ‘left’, ‘right’ and ‘inner’ joins are all possible.