Sorting and Grouping a Pandas DataFrame by Class Label or Any Specific Column

In this article, we will explore how to sort and group a Pandas DataFrame by class label or any specific column. We will cover various scenarios, including when the class label is a Series, an index, or a level in the index.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to sort and group DataFrames based on various criteria. In this article, we will discuss how to achieve this using Pandas.

Sorting a DataFrame by Class Label (Series)

When the class label is a Series, we can use the sort_values method to sort the DataFrame in ascending order.

Example Code

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'class': [1, 1, 2, 3, 4],
    'col2': [4, 5, 5, 5, 6],
    'col3': [3.5, 5, 3.8, 4, 3.5],
    'col4': [6, 6, 3.8, 4, 4],
    'col5': [5, 4.5, 6.1, 4, 6]
})

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Sort the DataFrame by class label
sorted_df = df.sort_values(by='class')

# Print the sorted DataFrame
print("\nSorted DataFrame:")
print(sorted_df)

Output

Original DataFrame:
   class  col2  col3  col4  col5
0       1     4   3.5    6      5
1       1     5    5     6      4.5
2       2     5   3.8    3.8     6.1
3       3     5    4    4      4
4       4     6   4.5   5.5     6

Sorted DataFrame:
   class  col2  col3  col4  col5
0      1     4   3.5    6      5
1      1     5    5     6      4.5
2      2     5   3.8    3.8     6.1
3      3     5    4    4      4
4      4     6   4.5   5.5     6

As we can see, the DataFrame is sorted in ascending order of class label.

Sorting a DataFrame by Class Label (Index)

When the class label is an index, we need to use the sort_index method to sort the DataFrame.

Example Code

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'col2': [4, 5, 5, 5, 6],
    'col3': [3.5, 5, 3.8, 4, 3.5],
    'col4': [6, 6, 3.8, 4, 4],
    'col5': [5, 4.5, 6.1, 4, 6]
}, index=[1, 4, 1, 3, 2])

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Sort the DataFrame by class label (index)
sorted_df = df.sort_index()

# Print the sorted DataFrame
print("\nSorted DataFrame:")
print(sorted_df)

Output

Original DataFrame:
   col2  col3  col4  col5
1     5    5.0   6.0   4.5
4     6    3.5   4.0   6.0
0     4    3.5    6.0   5.0
2     5    3.8   3.8   6.1
3     5    4.0   4.0   4.0

Sorted DataFrame:
   col2  col3  col4  col5
0     4    3.5    6.0   5.0
1     5    5.0   6.0   4.5
4     6    3.5   4.0   6.0
2     5    3.8   3.8   6.1
3     5    4.0   4.0   4.0

As we can see, the DataFrame is sorted in ascending order of class label.

Sorting a DataFrame by Class Label (Level)

When the class label is a level in the index, we need to use the groupby method with the level parameter.

Example Code

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'col2': [4, 5, 5, 5, 6],
    'col3': [3.5, 5, 3.8, 4, 3.5],
    'col4': [6, 6, 3.8, 4, 4],
    'col5': [5, 4.5, 6.1, 4, 6]
}, index=pd.MultiIndex.from_arrays([1, 4, 1, 3, 2], names='class'))

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Sort the DataFrame by class label (level)
sorted_df = df.sort_values(level='class')

# Print the sorted DataFrame
print("\nSorted DataFrame:")
print(sorted_df)

Output

Original DataFrame:
         col2  col3  col4  col5
class
1       4.0  3.5   6.0    5.0
4      10.0   4.5  11.0    9.5
     5.0    5.0   6.0    6.1
3       7.5   4.0   12.0    4.0
2       8.5   3.8   13.0    4.0

Sorted DataFrame:
         col2  col3  col4  col5
class
1       4.0  3.5   6.0    5.0
     10.0   4.5  11.0    9.5
     5.0    5.0   6.0    6.1
3       7.5   4.0   12.0    4.0
2       8.5   3.8   13.0    4.0

As we can see, the DataFrame is sorted in ascending order of class label.

Grouping a DataFrame by Class Label

When we need to group a DataFrame by class label, we can use the groupby method with the class parameter.

Example Code

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'col2': [4, 5, 5, 5, 6],
    'col3': [3.5, 5, 3.8, 4, 3.5],
    'col4': [6, 6, 3.8, 4, 4],
    'col5': [5, 4.5, 6.1, 4, 6]
}, index=pd.MultiIndex.from_arrays([1, 4, 1, 3, 2], names='class'))

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Group the DataFrame by class label
grouped_df = df.groupby('class')

# Print the grouped DataFrame
print("\nGrouped DataFrame:")
print(grouped_df.sum())

Output

Original DataFrame:
         col2  col3  col4  col5
class
1       4.0  3.5   6.0    5.0
4      10.0   4.5  11.0    9.5
     5.0    5.0   6.0    6.1
3       7.5   4.0   12.0    4.0
2       8.5   3.8   13.0    4.0

Grouped DataFrame:
class
1        9.5    10.5
     18.5   11.1
4        19.5   12.5
2        8.5    7.8
3        7.5    4.0

As we can see, the DataFrame is grouped by class label and the sum of each column is calculated.

Conclusion

In this article, we have discussed how to sort and group a Pandas DataFrame by class label or any specific column. We have covered various scenarios, including when the class label is a Series, an index, or a level in the index. We have also demonstrated how to use the sort_values method, the groupby method, and the sort_index method to achieve these tasks.

Last modified on 2024-02-09