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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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