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 set Download Automobile Dataset

Also Read:

Pandas DataFrame

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:

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

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

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

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

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

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)

Python Pandas Exercise

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

Exercise 2: Clean the dataset and update the CSV file

Exercise 3: Find the most expensive car company name

Exercise 4: Print All Toyota Cars details

Exercise 5: Count total cars per company

Exercise 6: Find each company’s Higesht price car

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

Exercise 8: Sort all cars by Price column

Exercise 9: Concatenate two data frames using the following conditions

Exercise 10: Merge two data frames using the following condition

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