Pandas Sorting and Ranking Practice Questions with Solutions

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Sorting and ranking are essential data analysis techniques in Pandas. The sort_values(), sort_index(), and rank() functions help organize data, identify top-performing records, and prepare datasets for reporting and analysis. These operations are widely used in sales dashboards, employee performance reports, financial analysis, and business intelligence. Pandas Sorting and Ranking Practice questions with solutions help to understand the concepts

1. Python Program to Sort a DataFrame by a Single Column

Problem Statement

Write a Python program to sort employees based on their salary in ascending order.

Python Solution

import pandas as pd

data = {
    "Employee": ["Rahul", "Aman", "Priya"],
    "Salary": [50000, 45000, 60000]
}

df = pd.DataFrame(data)

result = df.sort_values("Salary")

print(result)

Sample Output

  Employee  Salary
1     Aman   45000
0    Rahul   50000
2    Priya   60000

Explanation

The sort_values() function sorts rows based on the values in the Salary column.

Concepts Covered

  • sort_values()
  • Ascending Sort
  • Data Sorting

2. Python Program to Sort a DataFrame in Descending Order

Problem Statement

Write a Python program to sort products based on price in descending order.

Python Solution

import pandas as pd

data = {
    "Product": ["Laptop", "Mouse", "Keyboard"],
    "Price": [65000, 800, 1500]
}

df = pd.DataFrame(data)

result = df.sort_values(
    "Price",
    ascending=False
)

print(result)

Sample Output

    Product  Price
0    Laptop  65000
2  Keyboard   1500
1     Mouse    800

Explanation

Setting ascending=False sorts the DataFrame from highest to lowest values.

Concepts Covered

  • sort_values()
  • Descending Sort
  • Data Ordering

3. Python Program to Sort Using Multiple Columns

Problem Statement

Write a Python program to sort employees by Department and Salary.

Python Solution

import pandas as pd

data = {
    "Department": [
        "IT",
        "HR",
        "IT",
        "HR"
    ],
    "Salary": [50000, 45000, 60000, 47000]
}

df = pd.DataFrame(data)

result = df.sort_values(
    ["Department", "Salary"]
)

print(result)

Sample Output

  Department  Salary
1         HR   45000
3         HR   47000
0         IT   50000
2         IT   60000

Explanation

Passing multiple column names sorts the DataFrame first by department and then by salary.

Concepts Covered

  • Multiple Column Sorting
  • sort_values()
  • Hierarchical Sorting

4. Python Program to Sort a DataFrame by Index

Problem Statement

Write a Python program to sort a DataFrame based on its index.

Python Solution

import pandas as pd

data = {
    "Marks": [90, 80, 85]
}

df = pd.DataFrame(
    data,
    index=[2, 0, 1]
)

result = df.sort_index()

print(result)

Sample Output

   Marks
0     80
1     85
2     90

Explanation

The sort_index() function sorts rows according to their index values.

Concepts Covered

  • sort_index()
  • Index Sorting
  • DataFrame Index

5. Python Program to Assign Ranks to Students

Problem Statement

Write a Python program to assign ranks to students based on their marks.

Python Solution

import pandas as pd

data = {
    "Student": [
        "Rahul",
        "Aman",
        "Priya"
    ],
    "Marks": [85, 92, 78]
}

df = pd.DataFrame(data)

df["Rank"] = df["Marks"].rank(
    ascending=False
)

print(df)

Sample Output

  Student  Marks  Rank
0   Rahul     85   2.0
1    Aman     92   1.0
2   Priya     78   3.0

Explanation

The rank() function assigns ranking based on the values in the selected column.

Concepts Covered

  • rank()
  • Student Ranking
  • Data Analysis

6. Python Program to Assign Dense Ranks

Problem Statement

Write a Python program to assign dense ranks to employees based on their salary.

Python Solution

import pandas as pd

data = {
    "Employee": ["Rahul", "Aman", "Priya", "Rohit"],
    "Salary": [50000, 60000, 50000, 70000]
}

df = pd.DataFrame(data)

df["Rank"] = df["Salary"].rank(
    method="dense",
    ascending=False
)

print(df)

Sample Output

  Employee  Salary  Rank
0    Rahul   50000   3.0
1     Aman   60000   2.0
2    Priya   50000   3.0
3    Rohit   70000   1.0

Explanation

The dense ranking method assigns the same rank to duplicate values without skipping the next rank.

Concepts Covered

  • rank()
  • method="dense"
  • Dense Ranking

7. Python Program to Assign Average Ranks

Problem Statement

Write a Python program to assign average ranks to duplicate values.

Python Solution

import pandas as pd

data = {
    "Marks": [90, 85, 90, 80]
}

df = pd.DataFrame(data)

df["Rank"] = df["Marks"].rank(
    ascending=False
)

print(df)

Sample Output

   Marks  Rank
0     90   1.5
1     85   3.0
2     90   1.5
3     80   4.0

Explanation

By default, rank() assigns the average rank to duplicate values.

Concepts Covered

  • rank()
  • Average Ranking
  • Duplicate Values

8. Python Program to Sort Strings Alphabetically

Problem Statement

Write a Python program to sort employee names alphabetically.

Python Solution

import pandas as pd

data = {
    "Employee": [
        "Rahul",
        "Aman",
        "Priya",
        "Karan"
    ]
}

df = pd.DataFrame(data)

result = df.sort_values("Employee")

print(result)

Sample Output

  Employee
1     Aman
3    Karan
2   Priya
0   Rahul

Explanation

The sort_values() function sorts string values alphabetically.

Concepts Covered

  • sort_values()
  • String Sorting
  • Alphabetical Order

9. Python Program to Sort Dates

Problem Statement

Write a Python program to sort records by joining date.

Python Solution

import pandas as pd

data = {
    "Employee": ["Rahul", "Aman", "Priya"],
    "Joining_Date": [
        "2024-03-10",
        "2023-08-15",
        "2024-01-20"
    ]
}

df = pd.DataFrame(data)

df["Joining_Date"] = pd.to_datetime(
    df["Joining_Date"]
)

result = df.sort_values("Joining_Date")

print(result)

Sample Output

  Employee Joining_Date
1     Aman   2023-08-15
2    Priya   2024-01-20
0    Rahul   2024-03-10

Explanation

The to_datetime() function converts text into datetime format before sorting.

Concepts Covered

  • to_datetime()
  • Date Sorting
  • sort_values()

10. Python Program to Display Top 3 Highest Salaries

Problem Statement

Write a Python program to display the top three highest salaries.

Python Solution

import pandas as pd

data = {
    "Employee": [
        "Rahul",
        "Aman",
        "Priya",
        "Rohit",
        "Sneha"
    ],
    "Salary": [
        50000,
        60000,
        70000,
        55000,
        65000
    ]
}

df = pd.DataFrame(data)

result = df.sort_values(
    "Salary",
    ascending=False
).head(3)

print(result)

Sample Output

  Employee  Salary
2   Priya   70000
4   Sneha   65000
1    Aman   60000

Explanation

The DataFrame is sorted in descending order, and head(3) returns the first three rows.

Concepts Covered

  • sort_values()
  • head()
  • Top Records

11. Python Program to Display Bottom 3 Lowest Salaries

Problem Statement

Write a Python program to display the bottom three lowest salaries.

Python Solution

import pandas as pd

data = {
    "Employee": [
        "Rahul",
        "Aman",
        "Priya",
        "Rohit",
        "Sneha"
    ],
    "Salary": [
        50000,
        60000,
        70000,
        55000,
        65000
    ]
}

df = pd.DataFrame(data)

result = df.sort_values(
    "Salary"
).head(3)

print(result)

Sample Output

  Employee  Salary
0    Rahul   50000
3    Rohit   55000
1     Aman   60000

Explanation

The DataFrame is sorted in ascending order, and head(3) returns the three lowest salary records.

Concepts Covered

  • sort_values()
  • head()
  • Lowest Records

12. Python Program to Sort Values While Ignoring the Original Index

Problem Statement

Write a Python program to sort a DataFrame and reset the index.

Python Solution

import pandas as pd

data = {
    "Employee": ["Rahul", "Aman", "Priya"],
    "Salary": [50000, 45000, 60000]
}

df = pd.DataFrame(data)

result = df.sort_values(
    "Salary",
    ignore_index=True
)

print(result)

Sample Output

  Employee  Salary
0     Aman   45000
1    Rahul   50000
2    Priya   60000

Explanation

Setting ignore_index=True resets the index after sorting.

Concepts Covered

  • sort_values()
  • ignore_index
  • Index Reset

13. Python Program to Rank Employees Within Each Department

Problem Statement

Write a Python program to assign salary ranks within each department.

Python Solution

import pandas as pd

data = {
    "Department": ["IT", "IT", "HR", "HR"],
    "Employee": ["Rahul", "Priya", "Aman", "Sneha"],
    "Salary": [50000, 60000, 45000, 47000]
}

df = pd.DataFrame(data)

df["Rank"] = df.groupby(
    "Department"
)["Salary"].rank(
    ascending=False
)

print(df)

Sample Output

  Department Employee  Salary  Rank
0         IT    Rahul   50000   2.0
1         IT    Priya   60000   1.0
2         HR     Aman   45000   2.0
3         HR   Sneha   47000   1.0

Explanation

The groupby() function creates separate groups, and rank() assigns rankings within each department.

Concepts Covered

  • groupby()
  • rank()
  • Department-wise Ranking

14. Python Program to Sort Columns Alphabetically

Problem Statement

Write a Python program to sort DataFrame columns alphabetically.

Python Solution

import pandas as pd

data = {
    "Salary": [50000, 60000],
    "Employee": ["Rahul", "Aman"],
    "Department": ["IT", "HR"]
}

df = pd.DataFrame(data)

result = df.sort_index(axis=1)

print(result)

Sample Output

  Department Employee  Salary
0         IT    Rahul   50000
1         HR     Aman   60000

Explanation

Using sort_index(axis=1) sorts the column names alphabetically.

Concepts Covered

  • sort_index()
  • axis=1
  • Column Sorting

15. Python Program to Rank Products Based on Sales

Problem Statement

Write a Python program to rank products according to their sales.

Python Solution

import pandas as pd

data = {
    "Product": [
        "Laptop",
        "Mouse",
        "Keyboard",
        "Monitor"
    ],
    "Sales": [
        250,
        520,
        310,
        180
    ]
}

df = pd.DataFrame(data)

df["Rank"] = df["Sales"].rank(
    ascending=False,
    method="dense"
)

print(df)

Sample Output

    Product  Sales  Rank
0    Laptop    250   3.0
1     Mouse    520   1.0
2  Keyboard    310   2.0
3   Monitor    180   4.0

Explanation

The rank() function assigns rankings based on sales values, with the highest sales receiving Rank 1.

Concepts Covered

  • rank()
  • method="dense"
  • Product Ranking

Chapter Summary

In this chapter, you learned how to organize and analyze data using Pandas sorting and ranking functions. You practiced sorting data by values, indexes, strings, dates, and multiple columns. You also learned how to assign ranks, use dense ranking, rank data within groups, reset indexes after sorting, and identify the highest and lowest records. These techniques are commonly used in reporting, dashboards, business intelligence, HR analytics, finance, and sales analysis.

Key Takeaways

  • sort_values() sorts rows based on column values.
  • sort_index() sorts rows or columns by index.
  • ascending=False performs descending sorting.
  • ignore_index=True resets the index after sorting.
  • rank() assigns rankings to numerical values.
  • method="dense" creates continuous rankings.
  • Rankings can be calculated within groups using groupby().
  • Sorting can be performed on numbers, strings, dates, and multiple columns.
  • head() is useful for displaying top records.
  • Sorting and ranking are essential for data analysis and reporting.

Frequently Asked Questions (FAQs)

1. Which function is used to sort rows in Pandas?

df.sort_values("Salary")

2. How do you sort data in descending order?

df.sort_values("Salary", ascending=False)

3. Which function sorts a DataFrame by index?

df.sort_index()

4. How do you assign rankings in Pandas?

df["Rank"] = df["Marks"].rank()

5. What is the purpose of method="dense" in rank()?

It assigns consecutive ranks without skipping numbers when duplicate values exist.

6. How do you rank data within each group?

df.groupby("Department")["Salary"].rank()

7. How do you reset the index after sorting?

df.sort_values(
    "Salary",
    ignore_index=True
)

8. Why are sorting and ranking important in Pandas?

Sorting and ranking help organize datasets, identify top and bottom performers, generate reports, prepare dashboards, analyze trends, and simplify decision-making in data analysis and business intelligence.

Written by Shubhranshu Shekhar, who has trained 20000+ students in coding.

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