PYnative

Python Programming

Home » Python Exercises » Python Pandas Exercise

Python Pandas Exercise

Updated on:  | 56 Comments

Python Pandas Exercise

This Pandas exercise project will help Python developers to learn and practice pandas. Pandas is an open-source, BSD-licensed Python library. Pandas is a handy and useful data-structure tool for analyzing large and complex data.

Practice DataFrame, Data Selection, Group-By, Series, Sorting, Searching, statistics. Practice Data analysis using Pandas.

In this exercise, we are using Automobile Dataset for data analysis. This Dataset has different characteristics of an auto such as body-style, wheel-base, engine-type, price, mileage, horsepower, etc.

Automobile_data setDownload Automobile Dataset

Also Read:

What included in this Pandas exercise?

  • It contains 10 questions. The solution is provided for each question.
  • Each question includes a specific Pandas topic you need to learn.

When you complete each question, you get more familiar with data analysis using pandas.

Exercise 1: From the given dataset print the first and last five rows

Expected Output:

Python Pandas printing first 5 rows
Python Pandas printing first 5 rows
Python Pandas printing last 5 rows
Python Pandas printing last 5 rows
Show Solution

Print first five rows

import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
df.head(5)Code language: Python (python)

Print last five rows

import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
df.tail(5)Code language: Python (python)

Exercise 2: Clean the dataset and update the CSV file

Replace all column values which contain ?, n.a, or NaN.

Show Solution
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv", na_values={
'price':["?","n.a"],
'stroke':["?","n.a"],
'horsepower':["?","n.a"],
'peak-rpm':["?","n.a"],
'average-mileage':["?","n.a"]})
print (df)

df.to_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")Code language: Python (python)

Exercise 3: Find the most expensive car company name

Print most expensive car’s company name and price.

Expected Output:

Python Pandas printing most costly car name
Python Pandas printing most costly car name
Show Solution
import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
df = df [['company','price']][df.price==df['price'].max()]
dfCode language: Python (python)

Exercise 4: Print All Toyota Cars details

Expected Output:

Python Pandas printing all Toyota cars data
Python Pandas printing all Toyota cars data
Show Solution
import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
car_Manufacturers = df.groupby('company')
toyotaDf = car_Manufacturers.get_group('toyota')
toyotaDfCode language: Python (python)

Exercise 5: Count total cars per company

Expected Outcome:

Python Pandas count total cars per company
Python Pandas count total cars per company
Show Solution
import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
df['company'].value_counts()Code language: Python (python)

Exercise 6: Find each company’s Higesht price car

Expected Outcome:

Python Pandas printing each company's highest price car
Show Solution
import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
car_Manufacturers = df.groupby('company')
priceDf = car_Manufacturers['company','price'].max()
priceDfCode language: Python (python)

Exercise 7: Find the average mileage of each car making company

Expected Output:

Python Pandas printing average mileage of each car making company
Python Pandas printing average mileage of each car making company
Show Solution
import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
car_Manufacturers = df.groupby('company')
mileageDf = car_Manufacturers['company','average-mileage'].mean()
mileageDfCode language: Python (python)

Exercise 8: Sort all cars by Price column

Expected Output:

Python Pandas sort all cars by price column
Python Pandas sort all cars by price column
Show Solution
import pandas as pd
carsDf = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
carsDf = carsDf.sort_values(by=['price', 'horsepower'], ascending=False)
carsDf.head(5)Code language: Python (python)

Exercise 9: Concatenate two data frames using the following conditions

Create two data frames using the following two dictionaries.

GermanCars = {'Company': ['Ford', 'Mercedes', 'BMV', 'Audi'], 'Price': [23845, 171995, 135925 , 71400]}
japaneseCars = {'Company': ['Toyota', 'Honda', 'Nissan', 'Mitsubishi '], 'Price': [29995, 23600, 61500 , 58900]}Code language: Python (python)

Expected Output:

Python Pandas concatenate two data frames and create key for each data frame
Python Pandas concatenate two data frames and create a key for each data frame
Show Solution
import pandas as pd

GermanCars = {'Company': ['Ford', 'Mercedes', 'BMV', 'Audi'], 'Price': [23845, 171995, 135925 , 71400]}
carsDf1 = pd.DataFrame.from_dict(GermanCars)

japaneseCars = {'Company': ['Toyota', 'Honda', 'Nissan', 'Mitsubishi '], 'Price': [29995, 23600, 61500 , 58900]}
carsDf2 = pd.DataFrame.from_dict(japaneseCars)

carsDf = pd.concat([carsDf1, carsDf2], keys=["Germany", "Japan"])
carsDfCode language: Python (python)

Exercise 10: Merge two data frames using the following condition

Create two data frames using the following two Dicts, Merge two data frames, and append the second data frame as a new column to the first data frame.

Car_Price = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'Price': [23845, 17995, 135925 , 71400]}
car_Horsepower = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'horsepower': [141, 80, 182 , 160]}Code language: Python (python)

Expected Output:

Python Pandas merge two data frames and append new data frame as new column
Python Pandas merge two data frames and append new data frame as a new column
Show Solution
import pandas as pd

Car_Price = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'Price': [23845, 17995, 135925 , 71400]}
carPriceDf = pd.DataFrame.from_dict(Car_Price)

car_Horsepower = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'horsepower': [141, 80, 182 , 160]}
carsHorsepowerDf = pd.DataFrame.from_dict(car_Horsepower)

carsDf = pd.merge(carPriceDf, carsHorsepowerDf, on="Company")
carsDfCode language: Python (python)
TweetF  sharein  shareP  Pin

About Vishal

I’m Vishal Hule, the Founder of PYnative.com. As a Python developer, I enjoy assisting students, developers, and learners. Follow me on Twitter.

Related Tutorial Topics:

Pandas Python Python Exercises

All Coding Exercises:

C Exercises
C++ Exercises
Python Exercises

Python Exercises and Quizzes

Free coding exercises and quizzes cover Python basics, data structure, data analytics, and more.

  • 15+ Topic-specific Exercises and Quizzes
  • Each Exercise contains 25+ questions
  • Each Quiz contains 25 MCQ
Exercises
Quizzes

Comments

  1. Alessandro says

    For Exercise 6 in Pandas Exercise, I think the right code is

    priceDf = car_Manufacturers[[‘company’,’price’]].max()

    instead of

    priceDf = car_Manufacturers[‘company’,’price’].max()

    Regards

    Reply

  4. Fainill Hall says

    Hi. Please, in which article can I learn indications about dataframe subsetting, in order to solve exercise n.3?

    Reply

  9. Kaaren says

    when I try to use the mean() function, it gives the following error, what do i do ?
    Could not convert alfa-romeroalfa-romeroalfa-romero to numeric

    Reply

  10. Alex says

    I have a question could anyone please help me:
    How to do this Generate descriptive statistics for the budget of all the movies.
    I dont know what code to use

    Reply

  12. Aditi says

    Your Python Basics is really amazing. It helped me to learn Python in a well structured way. Hatts off to you bro. Keep up your of upskilling people.
    I just had a suggestion whether is it possible for you to write articles on Statistics and Advance Statistics.

    Reply

  17. Menderes Karakaş says

    these exercises are very helpful thanks for your work. I will definitely look for other exercises too. I you are healthy. Lots of love.

    Reply

  19. Sandeep Mallela says

    Hi Vishal, this is a good exercise. Thanks for providing this. However, I couldn’t execute exercise 4 with the given code.

    Reply

  25. Meet says

    About Question 6: Find each company’s Higesht price car

    This solution code will return max value of all the columns that we include to be printed. So, for example, if I add wheel-base column, then the wheel-base value corresponding to the mercedes-benz company will be 120.9 instead of the 112 which corresponds to the row with max price 45400.

    Any way to do this?

    Reply

  28. Janesh says

    Wow.
    This exercise helped me a lot in reinforcing the concepts.
    Found no issues with this exercise.It is great.
    Just need more such exercises to practice.

    Reply

    • vini1955 says

      suggestions to fix Dingus Bingus’s issue:

      i) change Exercise 2 wording from:
      Replace all column values which contain ?, n.a, or NaN.
      to read:
      Replace all column values which contain ? and n.a, with NaN.

      ii) In MS-Excel:
      a) open the csv file
      b) insert “?” into cells M24, M25 and M49; then
      c) save the changed csv file

      Reference:
      https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html

      (Students may also consider modifying the csv file to test their understanding about how various read_csv parameters work in practice)

      Reply

  32. Noel danton says

    could you provide the link to the data please, preferably in CSV format it would be a real help so that we work with the real data, otherwise great job overall thank you.

    Reply

  33. vinay says

    This exercise is really helpful it helps in reminding and and working with all the functions at
    once. i hope you will add more no of exercise in the practice series.

    Reply

  35. Gaurav Sharma says

    for question 4:

    import pandas as pd
df=pd.DataFrame(pd.read_csv(r'path.csv'))
df[(df.company == 'toyota')]

    Great content 🙂

    Reply

  38. Manuel Juarez says

    Hi Vishal, is there any possibility we can practice online with this data. Thank you very much

    Reply

    • Vishal says

      Hey Manuel Juarez, unfortunately, we don’t have this feature right now. we are planning to add the interactive exercises in coming months

      Reply

  39. Girish says

    Very Very useful excercises sir. Please add few more questions around the same lines of pandas and datafram. You can raise the complexity level as well. please provide similar excercises on EDA(Binning, Missing Values, Outlier treatment etc.)

    May God Bless you.

    Reply

    • Vishal says

      Hey Girish, I appreciate your kind words, thank you. And, Yes I will add more such exercises with complex problems.

      Reply

    • Sahil Jain says

      Hii, this exercise is really very helpful while learning pandas.
      I don’t understand solution of question 3.
      Can u please explain the solution of question no. 3 ?

      Reply

  40. Vishnu Konjeti says

    This exercise is very helpful in practising Pandas.
    Please add some more datasets and exercises.

    Reply

Leave a Reply

your email address will NOT be published. all comments are moderated according to our comment policy.

Use <pre> tag for posting code. E.g. <pre> Your entire code </pre>

About PYnative

PYnative.com is for Python lovers. Here, You can get Tutorials, Exercises, and Quizzes to practice and improve your Python skills.

Follow Us

To get New Python Tutorials, Exercises, and Quizzes

Explore Python

Coding Exercises

Legal Stuff

We use cookies to improve your experience. While using PYnative, you agree to have read and accepted our:

Copyright © 2018–2026 pynative.com