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Python Pandas Exercise

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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. For this exercise, we are using Automobile Dataset.  This Automobile Dataset has a different characteristic of an auto such as body-style, wheel-base, engine-type, price, mileage, horsepower, and many more.

Download Automobile data Set

Further Reading: Explore All Python Exercises and Python Quizzes to practice Python.

What included in this Pandas exercise?

This exercise contains 10 questions. The solution 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.

Question 1: From given data set print 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

Solution:

Printing first five rows: –

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

Printing last five rows:

import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
df.tail(5)
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Question 2: Clean data and update the CSV file

Replace all column values which contain ‘?’ and n.a with NaN.

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")
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Question 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

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()]
df
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Question 4: Print All Toyota Cars details

Expected Output:

Python Pandas printing all Toyota cars data
Python Pandas printing all Toyota cars data

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')
toyotaDf
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Question 5: Count total cars per company

Expected Outcome:

Python Pandas count total cars per company
Python Pandas count total cars per company

Solution:

import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
df['company'].value_counts()
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Question 6: Find each company’s Higesht price car

Expected Outcome:

Python Pandas printing each company's highest price car

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()
priceDf
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Question 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

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()
mileageDf
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Question 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

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)
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Question 9: Concatenate two data frames using the following conditions

Create two data frames using the following two Dicts, Concatenate those two data frames and create a key for each data frame.

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

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

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"])
carsDf
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Question 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]}

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

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")
carsDf
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Comments

  2. Noel danton

    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

  3. vinay

    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

  5. Gaurav Sharma

    for question 4:

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

    Great content 🙂

    Reply

    • Vishal

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

      Reply

  9. Girish

    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

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

      Reply

    • Sahil Jain

      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

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