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Python Pandas Exercise

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Python Pandas Exercise

Pandas is an open-source, BSD-licensed Python library. Pandas is a handy and useful data-structure tool for analyzing large and complex data.

This Pandas exercise project is to help Python developer to learn and practice pandas by solving the questions and problems from the real world. 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

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.

Let’s start the exercise.

Question 1: From given data set print first and last five rows

Printing first five rows: –

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

Output:

Python Pandas printing first 5 rows
Python Pandas printing first 5 rows

Printing last five rows: –

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

Output:

Python Pandas printing last 5 rows
Python Pandas printing last 5 rows

Question 2: Clean data and update the CSV file

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

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")

Question 3: Find the most expensive car company name

Print most expensive car’s company name and price.

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

Output:

Python Pandas printing most costly car name
Python Pandas printing most costly car name

Question 4: Print All Toyota Cars details

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

Output:

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

Question 5: Count total cars per company

import pandas as pd
df = pd.read_csv("D:\\Python\\Articles\\pandas\\automobile-dataset\\Automobile_data.csv")
df['company'].value_counts()

Output:

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

Question 6: Find each company’s Higesht price car

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

Output:

Python Pandas printing each company's highest price car

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

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

Output:

Python Pandas printing average mileage of each car making company
Python Pandas printing average mileage of each car making company

Question 8: Sort all cars by Price column

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)

Output:

Python Pandas sort all cars by price column
Python Pandas sort all cars by price column

Question 9: Concatenate two data frames using following conditions

Create two data frames using following two Dicts, Concatenate those two data frame 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]}
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

Output:

Python Pandas concatenate two data frames and create key for each data frame
Python Pandas concatenate two data frames and create key for each data frame

Question 10: Merge two data frames using following condition

Create two data frames using following two Dicts, Merge two data frames, and append second data frame as a new column to 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]}
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

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 new column

Keep Reading
Python Basic (10),  Python MySQL (14),  Python PostgreSQL (8),  Python Exercises (11),  Python Generate Random Data (13),  Python Quizzes (3)

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