Sort pandas dataframe both on values of a column and index?

Sorting Pandas DataFrames by Column Values and Index

Learn how to effectively sort a Pandas DataFrame, combining sorting by one or more column values with a secondary sort by its index, ensuring precise data ordering.

Pandas DataFrames are powerful tools for data manipulation in Python. A common task is sorting data to make it more readable or to prepare it for further analysis. While sorting by column values is straightforward using df.sort_values(), scenarios often arise where a secondary sort by the DataFrame's index is required, especially when column values might be identical. This article will guide you through the process of achieving this combined sorting, providing clear examples and explanations.

Understanding the Need for Combined Sorting

Consider a DataFrame where you want to sort by a specific column, say 'Score'. If multiple rows have the same 'Score', their relative order after sorting by 'Score' alone might not be deterministic or might not meet specific requirements. By adding the index as a secondary sort key, you can ensure a consistent and predictable order for rows with identical column values. This is particularly useful when maintaining the original entry order (if the index represents that) or when you need a stable sort across multiple operations.

flowchart TD
    A[Start with Unsorted DataFrame] --> B{Sort by Column 'Score'}
    B --> C{Are 'Score' values unique?}
    C -->|Yes| D[Result is Sorted by 'Score']
    C -->|No| E[Rows with same 'Score' have arbitrary order]
    E --> F{Add Index as Secondary Sort Key}
    F --> G[Result is Sorted by 'Score', then by Index]

Decision flow for combined sorting by column and index.

Sorting by Column and Then by Index

The sort_values() method in Pandas is versatile and can accept a list of column names for sorting. To sort by a column and then by the index, you need to include the index in this list. However, the index is not a regular column. Pandas provides a special way to refer to the index within sort_values() by using df.index.name or by resetting the index temporarily. The most direct approach is to make the index a column for sorting purposes.

import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve', 'Frank'],
        'Score': [85, 92, 85, 78, 92, 85],
        'City': ['NY', 'LA', 'NY', 'SF', 'LA', 'CHI']}
df = pd.DataFrame(data, index=[3, 1, 5, 2, 0, 4])

print("Original DataFrame:")
print(df)

# Sort by 'Score' and then by index
# First, make the index a temporary column
df_sorted = df.reset_index().sort_values(by=['Score', 'index']).set_index('index')

print("\nSorted by 'Score' then by index:")
print(df_sorted)

Example of sorting a DataFrame by a column ('Score') and then by its index.

Handling MultiIndex DataFrames

If your DataFrame has a MultiIndex, the principle remains the same. You would reset the index, which would convert all levels of the MultiIndex into regular columns. Then, you can sort by your desired column(s) followed by the index level columns. Remember to set_index() back to the original MultiIndex if needed.

import pandas as pd

# Create a sample DataFrame with MultiIndex
index_tuples = [('A', 1), ('B', 2), ('A', 3), ('B', 1), ('A', 2)]
multi_index = pd.MultiIndex.from_tuples(index_tuples, names=['Group', 'ID'])
data_multi = {'Value': [10, 20, 10, 30, 20]}
df_multi = pd.DataFrame(data_multi, index=multi_index)

print("Original MultiIndex DataFrame:")
print(df_multi)

# Sort by 'Value' and then by MultiIndex levels 'Group' and 'ID'
df_multi_sorted = df_multi.reset_index().sort_values(by=['Value', 'Group', 'ID']).set_index(['Group', 'ID'])

print("\nSorted by 'Value' then by MultiIndex levels:")
print(df_multi_sorted)

Sorting a MultiIndex DataFrame by a column and then by its index levels.