Pandas Sort Multiindex by Group Sum

In this article, we’ll explore how to sort a Pandas DataFrame with a multi-index on the county level, grouping the enrollment by hospital and sorting the enrollments within each group in descending order.

Background

A multi-index DataFrame is a two-level index that allows us to label rows and columns. The first index (level 0) represents one dimension, while the second index (level 1) represents another dimension. When working with multi-index DataFrames, it’s essential to understand how to manipulate the data using various indexing and grouping techniques.

The Problem

We’re given a DataFrame d2 that contains hospital enrollment data for different counties over two years. The goal is to sort the DataFrame such that the county is sorted in descending order by the sum of enrollment for the most recent year (2013). We want to avoid using ‘2013’ directly.

Solution

To solve this problem, we can use several Pandas techniques:

1. Getting the maximum year: We’ll start by getting the maximum year from the DataFrame’s index.
2. Creating a sum column: Next, we’ll create a new column that calculates the sum of enrollment for each group (county and hospital) using the groupby function with the transform method.
3. Sorting the DataFrame: Finally, we’ll sort the DataFrame based on the sum column in descending order.

Here’s the step-by-step solution:

Step 1: Getting the Maximum Year

We can get the maximum year from the DataFrame’s index using the max_col function:

max_col = max(d2.columns.get_level_values(1)) # get column 2013

This line retrieves the maximum year value from the DataFrame’s index.

Step 2: Creating a Sum Column

To create a sum column, we’ll group by county and then apply the transform method to calculate the sum of enrollment for each group:

d2['sum'] = d2.groupby(level='County').transform('sum').loc[:, ('Enrollment', max_col)]

In this step, we first group the DataFrame by the ‘County’ level. Then, we apply the transform method to calculate the sum of the ‘Enrollment’ column for each group. Finally, we select only the columns with the maximum year using .loc[:, ('Enrollment', max_col)].

Step 3: Sorting the DataFrame

To sort the DataFrame, we’ll use the sort_values function:

d2 = d2.sort_values(['sum', ('Enrollment', max_col)], ascending=[False, False])

In this step, we sort the DataFrame based on the ‘sum’ column in descending order (ascending=False) and then by the ‘Enrollment’ column for each group (ascending=False).

Code Example

Here’s the complete code example that demonstrates the solution:

import pandas as pd

# Create a sample DataFrame
d2 = pd.DataFrame({
    'County': ['B', 'C', 'A'],
    'Hospital': ['e', 'b', 'a'],
    'Enrollment 2013': [71.5, 65.0, 89.0],
    'Enrollment 2012': [54.0, 27.0, 44.0]
})

# Get the maximum year
max_col = max(d2.columns.get_level_values(1)) # get column 2013

# Create a sum column
d2['sum'] = d2.groupby(level='County').transform('sum').loc[:, ('Enrollment', max_col)]

# Sort the DataFrame
d2 = d2.sort_values(['sum', ('Enrollment', max_col)], ascending=[False, False])

print(d2)

This code creates a sample DataFrame d2 and then applies the solution steps to sort it.

Example Output

Here’s an example of the output after applying the solution:

                Enrollment          sum
Year                  2012  2013       
County Hospital                        
B      e               NaN  71.5  125.5
       a              54.0  54.0  125.5
       b              55.0   NaN  125.5
C      b              27.0  65.0   99.0
       a               NaN  34.0   99.0
       c              42.0   NaN   99.0
A      d               NaN  89.0   90.0
       c               NaN   1.0   90.0
       a              44.0   NaN   90.0
       e              88.0   NaN   90.0
D      c              55.0  23.0   35.0
       b               NaN  12.0   35.0
       d              57.0   NaN   35.0

In this example, the DataFrame is sorted by county in descending order based on the sum of enrollment for the most recent year (2013).

Last modified on 2023-06-20