Pandas Drop Level From Multi-Level Column Index

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  • Post last modified:March 27, 2024
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By using DataFrame.droplevel() or DataFrame.columns.droplevel() you can drop a level from multi-level column index from pandas DataFrame. Note that when you have multi-index columns, DataFrame.columns return pandas.Multiindex.

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A multi-level index DataFrame is a type of DataFrame that contains multiple level or hierarchical indexing. Dropping a level of a multi-level column index in a pandas DataFrame removes the entire column level.

Key Points –

  • Dropping levels from a multi-level column index in Pandas facilitates data manipulation and analysis by simplifying the DataFrame structure.
  • The droplevel() function provides a straightforward method to reduce the complexity of multi-level column indices in Pandas DataFrames.
  • It is particularly useful when dealing with complex multi-level DataFrame structures.
  • Dropping a level simplifies the DataFrame, making it easier to work with.
  • This operation does not modify the original DataFrame but returns a new DataFrame with the specified level(s) removed.
  • The droplevel() function is used to remove one or more levels from the column index.

1. Quick Examples of Drop Level From Columns

Below are some most used examples of how to drop level from multi-level column index from pandas DataFrame.


# Quick examples of drop level from columns

# Using droplevel() 
# To drop level from multi level column labels
df = df.droplevel(0, axis=1) 

# Drop first level of column label
df.columns = df.columns.droplevel(0)

# Alternate way
df.columns = [col[1] for col in df.columns]

# By assigning new columns
df.columns = ['Toyoto', 'Ford','Tesla','Nio']

# Drops second level
df.columns = df.columns.droplevel(1)

# Merge on level name with another level
df.columns = ['_'.join(col) for col in df.columns]

Now let’s see the syntax of these methods, execute with some test data and validate results.


import pandas as pd
new_index = pd.MultiIndex.from_tuples([("r0", "rA"),
                                       ("r1", "rB")],
                                       names=['Courses','Fee'])

cols = pd.MultiIndex.from_tuples([("Gasoline", "Toyoto"), 
                                  ("Gasoline", "Ford"), 
                                  ("Electric", "Tesla"),
                                  ("Electric", "Nio")])
data=[[100,300, 900,400 ], [200,500, 300,600]]

df = pd.DataFrame(data, columns=cols,index=new_index)
print(df)

# Output:
#            Gasoline      Electric     
#              Toyoto Ford    Tesla  Nio
# Courses Fee                            
# r0      rA       100  300      900  400
# r1      rB       200  500      300  600

2. Pandas DataFrame.droplevel() Syntax & Examples

Below is the syntax of the DataFrame.droplevel() method.


# DataFrame.droplevel() Syntax
DataFrame.droplevel(level, axis=0)
  • level – Used with MultiIndex. Takes Integer value, str, or list-like. Default set to None.
  • axis – Value can be either 0 or index | 1 or columns. Default set to ‘0’.

3. Drop a Level From Multi-Level Column Index in Pandas

Use DataFrame.droplevel() to drop single or more levels from multi-level column labels. Multi-level columns are used when you wanted to group columns together. This droplevel() method can be used to drop levels from columns and rows. Use axis=1 param to drop columns. To drop row-level use axis=0


# Using droplevel() 
# To drop level from multi level column labels
df = pd.DataFrame(data, columns=cols,index=new_index)
df = df.droplevel(0, axis=1) 
print(df)

Yields below output.


# Output:
             Toyoto  Ford  Tesla  Nio
Courses Fee                          
r0      rA      100   300    900  400
r1      rB      200   500    300  600

4. Drop Level Using MultiIndex.droplevel()

You can also use pandas.MultiIndex.droplevel() to drop columns level. When you have Multi-level columns DataFrame.columns return MultiIndex object and use droplevel() on this object to drop level.


# Drop first level of column label
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = df.columns.droplevel(0)
print(df)

Yields the same output as above.

5. Alternate Ways to Drop Level From Multi Level Columns

Below are some other alternative ways to drop levels from Multi-Level Columns of pandas DataFrame.


# Alternate ways to drop level from
# multilevel columns
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = [col[1] for col in df.columns]
print(df)

By assigning new columns. In order to use this, you need to know all column names from DataFrame.


# By assigning new columns
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = ['Toyoto', 'Ford','Tesla','Nio']
print(df)

In case if you wanted to prefix the level you are dropping to the next level, use the below approach.


# Drop first level and add level label as prefix
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = ['_'.join(col) for col in df.columns]
print(df)

Yields below output


# Output:
             Gasoline_Toyoto  Gasoline_Ford  Electric_Tesla  Electric_Nio
Courses Fee                                                              
r0      rA               100            300             900           400
r1      rB               200            500             300           600

5. Drop Two Levels Using Multi-Level Column Index

If you have multiple levels (3 or more), you may need to drop multiple levels, you can do this by chaining droplevel().


# Drop two levels from multi-level column index
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = df.columns.droplevel(0)
df.columns = df.columns.droplevel(1)
print(df)

6. Drop level from multi-index Row


To drop a level from a multi-index row in a Pandas DataFrame, you can use the droplevel() method. If you have multiple indexes, you can use the below examples to drop one index.


# Drop level from index
df = pd.DataFrame(data, columns=cols,index=new_index)
df.index = df.index.droplevel(1) 
print(df)

# Using droplevel() for row index
df = pd.DataFrame(data, columns=cols,index=new_index)
df=df.droplevel(0, axis=0) 
print(df)

Here,

  • We create a DataFrame with multi-index rows and columns.
  • To drop a level from the row index, we use the droplevel() method with level=1, indicating we want to drop the second level from the row index.
  • The resulting DataFrame will have a simplified row index with only one level. This example yields the below output.

# Output:
    Gasoline      Electric     
      Toyoto Ford    Tesla  Nio
Fee                            
rA       100  300      900  400
rB       200  500      300  600

7. Complete Example For Drop Multi-Level Column Index


import pandas as pd
new_index = pd.MultiIndex.from_tuples([("r0", "rA"),
                                       ("r1", "rB")],
                                       names=['Courses','Fee'])

cols = pd.MultiIndex.from_tuples([("Gasoline", "Toyoto"), 
                                  ("Gasoline", "Ford"), 
                                  ("Electric", "Tesla"),
                                  ("Electric", "Nio")])
data=[[100,300, 900,400 ], [200,500, 300,600]]

df = pd.DataFrame(data, columns=cols,index=new_index)
print(df)

# Using droplevel() To drop level From Multi Level Column Labels
df = pd.DataFrame(data, columns=cols,index=new_index)
df=df.droplevel(0, axis=1) 
print(df)

# Drop First Level of Column Label
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = df.columns.droplevel(0)
print(df)

# Alternate ways
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = [col[1] for col in df.columns]
print(df)

# Another Alternate way
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = ['Toyoto', 'Ford','Tesla','Nio']
print(df)

# Drop second Level
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = df.columns.droplevel(1)
print(df)

# Drop First Level and add level label as prefix
df = pd.DataFrame(data, columns=cols,index=new_index)
df.columns = ['_'.join(col) for col in df.columns]
print(df)

# Drop Level From Index
df = pd.DataFrame(data, columns=cols,index=new_index)
df.index = df.index.droplevel(1) 
print(df)

# Using droplevel() For Row index
df = pd.DataFrame(data, columns=cols,index=new_index)
df=df.droplevel(0, axis=0) 
print(df)

Frequently Asked Questions on Drop Level From Multi-Level Column Index

What does it mean to drop a level from a multi-level column index in Pandas?

Dropping a level from a multi-level column index in Pandas refers to simplifying the structure of the DataFrame by removing one or more levels from the column index hierarchy.

How can I drop a level from a multi-level column index in Pandas?

You can drop a level from a multi-level column index using the droplevel() method in Pandas. This method allows you to specify the level or levels you want to remove from the column index.

Does dropping a level modify the original DataFrame in Pandas?

Dropping a level from a multi-level column index in Pandas does not modify the original DataFrame. Instead, it returns a new DataFrame with the specified level(s) removed. This ensures that the original DataFrame remains unchanged, maintaining data integrity and allowing for reproducibility in data processing pipelines.

Why would I want to drop levels from a multi-level column index?

Dropping levels from a multi-level column index can simplify data manipulation and analysis, making it easier to work with the DataFrame, especially when dealing with complex hierarchical data structures. It can also improve readability and reduce complexity in data processing tasks.

Can I drop multiple levels from a multi-level column index at once?

You can drop multiple levels from a multi-level column index at once in Pandas by passing a list of levels to the level parameter of the droplevel() method. For example, level=[0, 1] indicates that you want to drop both the first and second levels from the column index, resulting in a DataFrame with a simplified column index containing only one level.

Are there alternative methods to drop levels from a multi-level column index?

There are alternative methods such as directly assigning column labels or using other DataFrame manipulation techniques. However, using droplevel() is often the most straightforward and explicit way to drop levels from a multi-level column index.

Conclusion

In this article, you have learned how to drop level from multi-level column index in pandas DataFrame using DataFrame.columns.droplevel() and pandas.MultiIndex.droplevel() method.

Happy Learning !!

References

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