Sort Rows or Columns in Pandas Dataframe Based on Values

Sorting is a fundamental operation when working with data in Pandas. Whether you need to arrange rows in ascending or descending order or reorder columns based on values, Pandas provides powerful functions to make sorting easy and efficient. In this article, we'll explore different ways to sort rows and columns in a DataFrame, helping you organize and analyze your data effectively.

Sorting Rows in a DataFrame

sort_values() function sorts rows based on one or multiple columns in ascending or descending order. Functions like nsmallest() and nlargest() quickly fetch the lowest or highest values.

Example 1. Sorting Rows by a Specific Column

import pandas as pd

# Creating a sample DataFrame
data = {'Name': ['Ravi', 'Anushka', 'Prajjwal', 'Kareena'],
        'Age': [24, 19, 22, 25],
        'Score': [85, 92, 78, 88]}
df1 = pd.DataFrame(data)

# Sorting rows based on the 'Age' column
df2 = df1.sort_values(by='Age')
print(df2)

       Name  Age  Score
1   Anushka   19     92
2  Prajjwal   22     78
0      Ravi   24     85
3   Kareena   25     88

Explanation: The sort_values(by='Age') function sorts the DataFrame in ascending order based on the Age column.

Example 2. Sorting Rows in Descending Order

import pandas as pd

# Creating a sample DataFrame
data = {'Name': ['Ujjwal', 'Brij', 'Kareena', 'Prajjwal'],
        'Age': [24, 19, 22, 25],
        'Score': [85, 92, 78, 88]}
df1 = pd.DataFrame(data)

df2 = df1.sort_values(by='Score', ascending=False)
print(df2)

       Name  Age  Score
1      Brij   19     92
3  Prajjwal   25     88
0    Ujjwal   24     85
2   Kareena   22     78

Explanation: The ascending=False argument sorts the DataFrame in descending order based on the Score column.

Example 3. Sorting Rows Based on Multiple Columns

import pandas as pd

# Creating a sample DataFrame
data = {'Name': ['Vaibhav', 'Brij', 'Sachin', 'Prajjwal'],
        'Age': [24, 19, 22, 25],
        'Score': [85, 92, 78, 88]}
df1 = pd.DataFrame(data)

df2 = df1.sort_values(by=['Age', 'Score'], ascending=[True, False])
print(df2)

       Name  Age  Score
1      Brij   19     92
2    Sachin   22     78
0   Vaibhav   24     85
3  Prajjwal   25     88

Explanation: The DataFrame is first sorted by Age in ascending order. If two rows have the same Age, they are further sorted by Score in descending order.

Example 4. Sorting Rows Using nsmallest() and nlargest()

import pandas as pd

# Creating a sample DataFrame
data = {'Name': ['Ujjwal', 'Brij', 'Kareena', 'Prajjwal'],
        'Age': [24, 19, 22, 25],
        'Score': [85, 92, 78, 88]}
df = pd.DataFrame(data)

# Getting the 2 rows with the lowest scores
res1 = df.nsmallest(2, 'Score')
print(res1)

# Getting the 2 rows with the highest scores
res2 = df.nlargest(2, 'Score')
print(res2)

      Name  Age  Score
2  Kareena   22     78
0   Ujjwal   24     85
       Name  Age  Score
1      Brij   19     92
3  Prajjwal   25     88

Explanation:

• nsmallest(n, column) returns the n rows with the smallest values in the specified column.
• nlargest(n, column) returns the n rows with the largest values in the specified column.

Sorting columns in a Dataframe

Sorting columns in Pandas helps in organizing data for better readability and analysis. The sort_index() function sorts columns alphabetically, while custom sorting can be done based on column values, such as their sum.

Example 1. Sorting Columns Alphabetically

import pandas as pd

# Creating a sample DataFrame
data = {'Name': ['Anurag', 'Brij', 'Kareena', 'Prajjwal'],
        'Age': [24, 19, 22, 25],
        'Score': [85, 92, 78, 88]}
df1 = pd.DataFrame(data)

df2 = df1.sort_index(axis=1)
print(df2)

   Age      Name  Score
0   24    Anurag     85
1   19      Brij     92
2   22   Kareena     78
3   25  Prajjwal     88

Explanation: The axis=1 argument tells Pandas to sort the column labels in alphabetical order.

Example 2: Sorting Columns by the sum of values

import pandas as pd

# Creating a sample DataFrame
data = {'Name': ['Anurag', 'Aryan', 'Ravi', 'Anushka'],
        'Age': [24, 19, 22, 25],
        'Score': [85, 92, 78, 88]}
df1 = pd.DataFrame(data)

df2 = df.sort_index(axis=1, ascending=False)
print(df2)

   Score     Name  Age
0     85   Anurag   24
1     92    Aryan   19
2     78     Ravi   22
3     88  Anushka   25

Explanation: The ascending=False argument sorts the column names in descending order.

Example 3: Sorting columns by sum of values

import pandas as pd

# Sample DataFrame
data = {'A': [3, 2, 5], 'B': [1, 4, 2], 'C': [7, 6, 8]}
df1 = pd.DataFrame(data)

# Sorting columns based on sum of values
df2 = df1[sorted(df1.columns, key=lambda col: df1[col].sum())]
print(df2)

   B  A  C
0  1  3  7
1  4  2  6
2  2  5  8

Explanation: This code sorts DataFrame columns in ascending order based on their sum. It retrieves column names, sorts them using sorted() with a lambda function that calculates column sums and then reorders the DataFrame accordingly.