pandas: Mastering MultiIndex Level Reordering with reorder_levels

MultiIndex and Index Objects in pandas

• MultiIndex
  A MultiIndex is an extension of the standard Index object, allowing for hierarchical labeling with multiple levels. It essentially creates a layered structure for indexing your data. Each level acts like a sub-index within the main index.
• Index Objects
  In pandas, an Index object serves as a fundamental labeling structure for DataFrames and Series. It holds a collection of unique labels that correspond to the rows or columns of the data.

pandas.MultiIndex.reorder_levels Function

• Syntax
  

• Purpose
  The reorder_levels method is specifically designed for MultiIndex objects. It provides a way to rearrange the order of the levels within the MultiIndex. This is useful when you want to analyze or present your data from a different perspective by prioritizing certain levels in the hierarchy.

reordered_index = multi_index.reorder_levels(order)

• Parameters
  
  • multi_index: The MultiIndex object whose levels you want to reorder.
  • order: A list that specifies the new desired order of the levels. Each element in the list corresponds to the original level number (0-based indexing). For example, order=[1, 0] would swap the first and second levels.

Example

import pandas as pd

data = {'City': ['New York', 'Chicago', 'Los Angeles', 'Houston', 'Miami'],
        'Month': ['January', 'February', 'March', 'April', 'May'],
        'Sales': [1000, 800, 1200, 900, 1100]}

df = pd.DataFrame(data)
multi_index = pd.MultiIndex.from_tuples([('New York', 'January'), ('Chicago', 'February'),
                                        ('Los Angeles', 'March'), ('Houston', 'April'),
                                        ('Miami', 'May')],
                                       names=('City', 'Month'))
df.index = multi_index

# Original MultiIndex order (City, Month)
print(df.index)

# Reorder levels to Month, City
reordered_index = df.index.reorder_levels([1, 0])
df.index = reordered_index
print(df.index)

Output

MultiIndex([(  'New York',   'January'), (  'Chicago',   'February'),
               (  'Los Angeles',    'March'), (  'Houston',     'April'),
               (  'Miami',        'May')],
              names=('City', 'Month'))
MultiIndex([(  'January',  'New York'), (  'February',  'Chicago'),
               (    'March', 'Los Angeles'), (    'April',  'Houston'),
               (        'May',    'Miami')],
              names=('Month', 'City'))

As you can see, the reorder_levels function effectively changed the order of levels in the MultiIndex, resulting in a different hierarchical view of your data.

Key Points

• reorder_levels doesn't add or remove levels; it only changes their order within the hierarchy.
• The order list must have the same length as the number of levels in the original MultiIndex.
• reorder_levels creates a new MultiIndex object with the reordered levels. It doesn't modify the original MultiIndex in place.

---

Reordering by Level Name

Sometimes, you might know the names of the levels instead of their positions (0-based indexing). reorder_levels allows you to specify the desired order using the level names.

import pandas import Series as pd.Series

# Create a MultiIndex with named levels
multi_index = pd.MultiIndex.from_tuples([('Product A', 'East', 20),
                                        ('Product B', 'West', 10),
                                        ('Product C', 'East', 30)],
                                       names=('Product', 'Region', 'Year'))
data = {'Sales': [100, 150, 200]}
series = pd.Series(data, index=multi_index)

# Reorder levels by name (Year, Product, Region)
reordered_index = series.index.reorder_levels(['Year', 'Product', 'Region'])
series.index = reordered_index
print(series)

Reordering with Level Selection

If you want to focus on specific levels and maintain the original order for others, you can use slicing with reorder_levels.

import pandas as pd

# Create a MultiIndex with multiple levels
multi_index = pd.MultiIndex.from_tuples([('Dept A', 'Project X', 'Manager 1'),
                                        ('Dept B', 'Project Y', 'Manager 2'),
                                        ('Dept A', 'Project Z', 'Manager 3')],
                                       names=('Department', 'Project', 'Manager'))
data = {'Performance': [85, 92, 78]}
df = pd.DataFrame(data, index=multi_index)

# Reorder Department level, keep others as-is
reordered_index = df.index.reorder_levels([0, 2, 1])  # Move Department to level 0
df.index = reordered_index
print(df.index)

Rearranging for Specific Analysis

You can leverage reorder_levels to prepare your data for a particular analysis by prioritizing relevant levels. For example, if you're interested in monthly sales trends across different product categories, you might reorder a MultiIndex with (Product Category, Month, Year) to (Month, Product Category, Year).

---

Concatenation and Resetting Index

• This approach involves creating a new DataFrame by concatenating multiple DataFrames with the desired order of levels. You can achieve this by:
  • Splitting the DataFrame based on the level you want to prioritize.
  • Setting the desired level as the index in each split DataFrame.
  • Concatenating the resulting DataFrames.
  • Resetting the index if necessary.

import pandas as pd

# Create a MultiIndex with multiple levels
multi_index = pd.MultiIndex.from_tuples([('Dept A', 'Project X', 'Manager 1'),
                                        ('Dept B', 'Project Y', 'Manager 2'),
                                        ('Dept A', 'Project Z', 'Manager 3')],
                                       names=('Department', 'Project', 'Manager'))
data = {'Performance': [85, 92, 78]}
df = pd.DataFrame(data, index=multi_index)

# Desired order: Project, Department, Manager
df_by_project = df.groupby('Project')
reordered_df = df_by_project.apply(lambda x: x.set_index(['Department', 'Manager'])).reset_index()
print(reordered_df)

• This method is less efficient for large DataFrames and can be more cumbersome than reorder_levels.

Creating a New MultiIndex

• You can construct a new MultiIndex with the desired level order and assign it to your DataFrame. This involves defining the levels in the correct sequence and assigning them to the index.

import pandas as pd

# Create a MultiIndex with multiple levels
multi_index = pd.MultiIndex.from_tuples([('Dept A', 'Project X', 'Manager 1'),
                                        ('Dept B', 'Project Y', 'Manager 2'),
                                        ('Dept A', 'Project Z', 'Manager 3')],
                                       names=('Department', 'Project', 'Manager'))
data = {'Performance': [85, 92, 78]}
df = pd.DataFrame(data, index=multi_index)

# Desired order: Project, Department, Manager
new_levels = [('Project', df['Project']), ('Department', df['Department']), 
              ('Manager', df['Manager'])]
reordered_index = pd.MultiIndex.from_tuples(new_levels)
df.index = reordered_index
print(df.index)

• This method can be verbose and less efficient than reorder_levels for complex MultiIndex structures.

• In specific situations with a small number of levels, you might loop through the DataFrame and create a new index based on the desired order. This is generally not recommended for large DataFrames due to performance reasons.

import pandas as pd

# Create a MultiIndex with multiple levels (limited example)
multi_index = pd.MultiIndex.from_tuples([('A', 1, 'X'), ('B', 2, 'Y')],
                                       names=('Level1', 'Level2', 'Level3'))
data = {'Value': [10, 20]}
df = pd.DataFrame(data, index=multi_index)

# Desired order: Level3, Level1, Level2 (simple example)
reordered_index = []
for row in df.itertuples():
    reordered_index.append((row.Level3, row.Level1, row.Level2))
df.index = pd.MultiIndex.from_tuples(reordered_index)
print(df.index)