pandas merge but keep certain columns with code examples

When working with data in pandas, it's common to need to merge two or more dataframes together. Merging is when you combine data from two or more dataframes into a single dataframe. However, sometimes you may not want to include all columns from both dataframes. In this case, you can use pandas merge and keep certain columns.

In this article, we'll discuss how to merge dataframes in pandas while keeping certain columns. We'll cover different types of merges, what happens when there are duplicate columns, and provide code examples for each.

Types of Merge

Before we dive into how to merge dataframes and keep certain columns, let's first go over the different types of merges.

  1. Inner Join – An inner join combines rows from both dataframes where the join condition is true. This means that only rows where the join condition exists in both dataframes will be included in the resulting dataframe.

  2. Left Join – A left join includes all rows from the left dataframe and only rows from the right dataframe where the join condition is true.

  3. Right Join – A right join includes all rows from the right dataframe and only rows from the left dataframe where the join condition is true.

  4. Outer Join – An outer join includes all rows from both dataframes. If the join condition does not exist in both dataframes, then the missing values will be filled with NaN.

Handling Duplicate Columns

When merging dataframes, it's possible that both dataframes contain the same column names. In this case, pandas will automatically append suffixes to the column names to differentiate between them.

For example, let's say we have two dataframes – df1 and df2 – with columns 'id' and 'name'.

df1 = pd.DataFrame({'id': [1,2,3], 'name': ['John', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1,2,4], 'name': ['Mary', 'Mike', 'Alan']})

When we merge these two dataframes using the 'id' column as the join condition, we end up with:

df_merged = pd.merge(df1, df2, on='id')
   id name_x name_y
0   1   John   Mary
1   2   Jane   Mike

Notice that pandas has appended '_x' and '_y' to the column names to differentiate between them.

To keep certain columns when merging dataframes, you can use the 'merge' function's 'suffixes' parameter. This parameter allows you to specify suffixes for ambiguous column names.

Here's an example of how to merge two dataframes while keeping certain columns:

df_merged = pd.merge(df1, df2[['id', 'name']], on='id', suffixes=('_left', '_right'))

Here, we're merging df1 and a subset of columns from df2 based on the 'id' column. We're also using the 'suffixes' parameter to add '_left' and '_right' to the column names to differentiate between them.

   id name_left name_right
0   1      John       Mary
1   2      Jane       Mike

As you can see, we've successfully merged the dataframes while keeping certain columns.

Conclusion

Merging dataframes in pandas is a common task when working with data. However, it's not always necessary to include all columns from both dataframes. In this article, we've discussed how to merge dataframes and keep certain columns by using the 'suffixes' parameter. We've also covered different types of merges and how to handle duplicate columns. Hopefully, this article has helped you with your data manipulation tasks in python.

let's discuss the different types of merges and how to handle duplicate columns in more detail.

Types of Merge

  1. Inner Join – An inner join combines rows from both dataframes where the join condition is true. This means that only rows where the join condition exists in both dataframes will be included in the resulting dataframe.

For example, let's say we have two dataframes – df1 and df2 – with columns 'id' and 'name'.

df1 = pd.DataFrame({'id': [1,2,3], 'name': ['John', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1,2,4], 'age': [30, 25, 40]})

When we merge these two dataframes using the 'id' column as the join condition with an inner join, we end up with:

df_merged = pd.merge(df1, df2, on='id', how='inner')
   id name  age
0   1 John  30
1   2 Jane  25

Notice that only rows with 'id' 1 and 2 are included in the resulting dataframe, as those are the only ones that exist in both dataframes.

  1. Left Join – A left join includes all rows from the left dataframe and only rows from the right dataframe where the join condition is true.

Continuing with our previous example, let's now merge the dataframes with a left join:

df_merged = pd.merge(df1, df2, on='id', how='left')
   id name   age
0   1 John  30.0
1   2 Jane  25.0
2   3  Bob   NaN

Notice that all rows from the left dataframe (df1) are included in the resulting dataframe. Rows with 'id' 1 and 2 also include data from the right dataframe (df2), but row 3 has NaN values for the 'age' column since there's no matching 'id' value in df2.

  1. Right Join – A right join includes all rows from the right dataframe and only rows from the left dataframe where the join condition is true.

Let's merge the dataframes with a right join:

df_merged = pd.merge(df1, df2, on='id', how='right')
   id name  age
0   1 John   30
1   2 Jane   25
2   4  NaN   40

Notice that all rows from the right dataframe (df2) are included in the resulting dataframe. Rows with 'id' 1 and 2 also include data from the left dataframe (df1), but row 4 has NaN values for the 'name' column since there's no matching 'id' value in df1.

  1. Outer Join – An outer join includes all rows from both dataframes. If the join condition does not exist in both dataframes, then the missing values will be filled with NaN.

Let's merge the dataframes with an outer join:

df_merged = pd.merge(df1, df2, on='id', how='outer')
   id name   age
0   1 John  30.0
1   2 Jane  25.0
2   3  Bob   NaN
3   4  NaN  40.0

Notice that all rows from both dataframes are included in the resulting dataframe. Rows with 'id' 1 and 2 include data from both dataframes, row 3 has NaN values for the 'age' column since there's no matching 'id' value in df2, and row 4 has NaN values for the 'name' column since there's no matching 'id' value in df1.

Handling Duplicate Columns

When merging dataframes, it's common to have duplicate column names. In this case, pandas will automatically append suffixes to the column names to differentiate between them.

df1 = pd.DataFrame({'id': [1,2,3], 'name': ['John', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1,2,4], 'name': ['Mary', 'Mike', 'Alan']})

When we merge these two dataframes based on the 'id' column, we end up with duplicate column names:

df_merged = pd.merge(df1, df2, on='id')
   id name_x name_y
0   1   John   Mary
1   2   Jane   Mike

Notice that pandas has appended '_x' and '_y' to the column names to differentiate between them.

To keep certain columns while merging dataframes with duplicate column names, you can use the 'suffixes' parameter to specify suffixes for ambiguous column names:

df_merged = pd.merge(df1, df2[['id', 'name']], on='id', suffixes=('_left', '_right'))
   id name_left name_right
0   1      John       Mary
1   2      Jane       Mike

Notice that we're only including the 'id' and 'name' columns from df2 using a subset. We're also using the 'suffixes' parameter to add '_left' and '_right' to the column names to differentiate between them.

Conclusion

Merging dataframes in pandas is a powerful way to combine data. However, sometimes you only need to include certain columns in the merged dataframe. By using the 'merge' function's different parameters, you can control which data is included in the resulting dataframe. Additionally, pandas automatically handles duplicate column names, but you can use the 'suffixes' parameter to specify suffixes for ambiguous column names.

Popular questions

Sure, I'd be happy to answer some questions for you. Here are five potential questions you might have about pandas merge with code examples and their answers:

  1. What happens when both dataframes have duplicate column names after merging? How can you handle this?

When both dataframes have duplicate column names after merging, pandas automatically appends suffixes to the column names to differentiate between them. However, if you want to specify your own suffixes, you can use the 'suffixes' parameter. Here's an example:

df1 = pd.DataFrame({'id': [1,2,3], 'name': ['John', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1,2,4], 'name': ['Mary', 'Mike', 'Alan']})
df_merged = pd.merge(df1, df2[['id', 'name']], on='id', suffixes=('_left', '_right'))

In this example, we're merging df1 with a subset of columns from df2 based on the 'id' column, and using '_left' and '_right' to differentiate between ambiguous column names.

  1. What types of merge can you use when merging dataframes in pandas?

There are four types of merge you can use in pandas: inner join, left join, right join, and outer join. An inner join combines rows from both dataframes where the join condition is true. A left join includes all rows from the left dataframe and only rows from the right dataframe where the join condition is true. A right join includes all rows from the right dataframe and only rows from the left dataframe where the join condition is true. An outer join includes all rows from both dataframes.

Here's an example of an inner join:

df1 = pd.DataFrame({'id': [1,2,3], 'name': ['John', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1,2,4], 'age': [30, 25, 40]})
df_merged = pd.merge(df1, df2, on='id', how='inner')
  1. How can you merge dataframes in pandas while keeping certain columns?

To merge dataframes in pandas while keeping certain columns, you can use a subset of columns from one of the dataframes. Here's an example:

df1 = pd.DataFrame({'id': [1,2,3], 'name': ['John', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1,2,4], 'age': [30, 25, 40]})
df_merged = pd.merge(df1, df2[['id', 'age']], on='id')

In this example, we're only including the 'id' and 'age' columns from df2.

  1. What happens when there are missing values in one of the dataframes when merging?

When there are missing values in one of the dataframes when merging, pandas will fill those values with NaN in the resulting dataframe. Here's an example:

df1 = pd.DataFrame({'id': [1,2,3], 'name': ['John', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1,2,4], 'age': [30, 25, 40]})
df_merged = pd.merge(df1, df2, on='id', how='left')

In this example, there's only one row in df2 with 'id' 4, so when we perform a left join with df1, the resulting dataframe has a NaN value for the 'age' column in the row with 'id' 3.

  1. Is it possible to merge dataframes in pandas based on more than one column?

Yes, it's possible to merge dataframes in pandas based on more than one column. You can pass a list of column names to the 'on' parameter to specify multiple join conditions. Here's an example:

df1 = pd.DataFrame({'id': [1, 1, 2, 3], 'name': ['John', 'Jack', 'Jane', 'Bob']})
df2 = pd.DataFrame({'id': [1, 2, 3], 'age': [30, 25, 40], 'gender': ['M', 'F', 'M']})
df_merged = pd.merge(df1, df2, on=['id', 'gender'])

In this example, we're merging df1 and df2 based on both the 'id' and 'gender' columns. The resulting dataframe includes only rows where both conditions are true.

Tag

"SelectiveMerge"

Example:

import pandas as pd

# Creating sample dataframes
df1 = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['London', 'New York', 'Paris']
})
df2 = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'David'],
    'Salary': [50000, 70000, 60000],
    'Company': ['ABC', 'XYZ', 'PQR']
})

# Merging dataframes and keeping only certain columns
df_merge = pd.merge(df1[['Name', 'Age']], df2[['Name', 'Salary']], on='Name')

print(df_merge)

Output:

    Name  Age  Salary
0  Alice   25   50000
1    Bob   30   70000
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