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Intro to Pandas for Data Analysis

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Practice DataFrame Mutations using Good Reads Books and Reviews Data

Finished

September 24, 2024 12:00 AM

Elapsed time (min)

107

Completed activities

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Activities

Import the libraries and load the dataset¶

In [1]:

import warnings 
# Ignore FutureWarning 
warnings.simplefilter(action='ignore', category=FutureWarning)

In [2]:

import pandas as pd

df = pd.read_csv('Best_Books_Ever.csv')

In [3]:

df.head()

Out[3]:

	bookId	title	series	author	rating	description	language	isbn	genres	characters	...	firstPublishDate	awards	numRatings	ratingsByStars	likedPercent	setting	coverImg	bbeScore	bbeVotes	price
0	2.Harry_Potter_and_the_Order_of_the_Phoenix	Harry Potter and the Order of the Phoenix	Harry Potter #5	J.K. Rowling, Mary GrandPré (Illustrator)	4.50	There is a door at the end of a silent corrido...	English	9780439358071	['Fantasy', 'Young Adult', 'Fiction', 'Magic',...	['Sirius Black', 'Draco Malfoy', 'Ron Weasley'...	...	2003-06-21	['Bram Stoker Award for Works for Young Reader...	2507623	['1593642', '637516', '222366', '39573', '14526']	98.0	['Hogwarts School of Witchcraft and Wizardry (...	https://i.gr-assets.com/images/S/compressed.ph...	2632233	26923	7.38
1	30.J_R_R_Tolkien_4_Book_Boxed_Set	J.R.R. Tolkien 4-Book Boxed Set: The Hobbit an...	The Lord of the Rings #0-3	J.R.R. Tolkien	4.60	This four-volume, boxed set contains J.R.R. To...	English	9780345538376	['Fantasy', 'Fiction', 'Classics', 'Adventure'...	['Frodo Baggins', 'Gandalf', 'Bilbo Baggins', ...	...	2055-10-20	[]	110146	['78217', '22857', '6628', '1477', '967']	98.0	['Middle-earth']	https://i.gr-assets.com/images/S/compressed.ph...	1159802	12111	21.15
2	375802.Ender_s_Game	Ender's Game	Ender's Saga #1	Orson Scott Card, Stefan Rudnicki (Narrator), ...	4.30	Andrew "Ender" Wiggin thinks he is playing com...	English	9780812550702	['Science Fiction', 'Fiction', 'Young Adult', ...	['Dink', 'Bernard', 'Valentine Wiggin', 'Peter...	...	1985-10-28	['Hugo Award for Best Novel (1986)', 'Nebula A...	1131303	['603209', '339819', '132305', '35667', '20303']	95.0	[]	https://i.gr-assets.com/images/S/compressed.ph...	720651	7515	4.60
3	17245.Dracula	Dracula	Dracula #1	Bram Stoker, Nina Auerbach (Editor), David J. ...	4.00	You can find an alternative cover edition for ...	English	9780393970128	['Classics', 'Horror', 'Fiction', 'Fantasy', '...	['Jonathan Harker', 'Lucy Westenra', 'Abraham ...	...	1997-05-26	[]	938325	['345260', '329217', '197206', '48642', '18000']	93.0	['Transylvania (Romania)', 'Budapest (Hungary)...	https://i.gr-assets.com/images/S/compressed.ph...	646782	6988	4.55
4	28187.The_Lightning_Thief	The Lightning Thief	Percy Jackson and the Olympians #1	Rick Riordan (Goodreads Author)	4.26	Alternate cover for this ISBN can be found her...	English	9780786838653	['Fantasy', 'Young Adult', 'Mythology', 'Ficti...	['Annabeth Chase', 'Grover Underwood', 'Luke C...	...	2005-06-28	["Young Readers' Choice Award (2008)", 'Books ...	1992300	['1006885', '604999', '289310', '64014', '27092']	95.0	['New York City, New York (United States)', 'M...	https://i.gr-assets.com/images/S/compressed.ph...	597132	6370	1.79

5 rows × 25 columns

In [4]:

df.shape

Out[4]:

(794, 25)

In [5]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 794 entries, 0 to 793
Data columns (total 25 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   bookId            794 non-null    object 
 1   title             794 non-null    object 
 2   series            794 non-null    object 
 3   author            794 non-null    object 
 4   rating            794 non-null    float64
 5   description       794 non-null    object 
 6   language          794 non-null    object 
 7   isbn              794 non-null    int64  
 8   genres            794 non-null    object 
 9   characters        794 non-null    object 
 10  bookFormat        794 non-null    object 
 11  edition           794 non-null    object 
 12  pages             794 non-null    int64  
 13  publisher         794 non-null    object 
 14  publishDate       794 non-null    object 
 15  firstPublishDate  794 non-null    object 
 16  awards            794 non-null    object 
 17  numRatings        794 non-null    int64  
 18  ratingsByStars    794 non-null    object 
 19  likedPercent      794 non-null    float64
 20  setting           794 non-null    object 
 21  coverImg          794 non-null    object 
 22  bbeScore          794 non-null    int64  
 23  bbeVotes          794 non-null    int64  
 24  price             794 non-null    float64
dtypes: float64(3), int64(5), object(17)
memory usage: 155.2+ KB

Activities¶

Activity 1. Calculating the Price-to-Rating Ratio¶

In [6]:

df['price_to_rating'] = df['price'] / df['rating']

Activity 2. Remove the "isbn" Column¶

In [7]:

del df['isbn']

Activity 3. Extract and Add the "Year Published" Column¶

In [8]:

df['YearPublished'] = df['publishDate'].map(lambda x:x[:x.find('-')]).astype('int32')

Activity 4. Filter Books with Ratings Above 4.5¶

In [9]:

best_books = df[df['rating'] >= 4.5]

Activity 5. Count and Add the Number of Genres¶

In [10]:

df['GenreCount'] = df['genres'].map(lambda x: x.count("'")//2)

Activity 6. Split Author Names into First and Last Name Columns¶

In [11]:

df['FirstName'] = df['author'].map(lambda x:" ".join(x.split()[:-1]) if len(x.split())>1 else x)
df['LastName'] = df['author'].map(lambda x:"".join(x.split()[-1]) if len(x.split())>1 else None)
df['LastName'][380]

Activity 7. Drop Books with Fewer than 100 Pages¶

In [12]:

df.drop(df[df['pages']<100].index,inplace=True)

Activity 8. Extract the Primary Genre¶

In [13]:

df['PrimaryGenre'] = df['genres'].map(lambda x:eval(x)[0] if len(eval(x)) > 0 else None)

Activity 9. Flag Books with multiple Awards¶

In [14]:

df['MultipleAwards'] = df['awards'].map(lambda x: True if len(eval(x)) > 1 else False)

Activity 10. Estimate Reading Time Based on Page Count¶

In [15]:

df['ReadingTimeHours'] = df['pages']*300/250/60

Activity 11. Flag books published in year 2000 onwards¶

In [16]:

df['Published21stCentury'] = df['YearPublished'] >= 2000

Activity 12. Simplifying the DataFrame by Dropping Columns¶

In [17]:

df.drop(['coverImg','description','ratingsByStars'],axis=1,inplace=True)

Activity 13. Adding a New Book Entry¶

In [18]:

new_book = {
    "bookId": '10000',
    "title": "The Great Gatsby",
    "author": "F. Scott Fitzgerald",
    "rating": 3.9,
    "pages": 180,
    "publishDate": '1925-04-10',
    "publisher": "Scribner",
    "price": 7.99,
    "genres": "['Fiction', 'Classics']",
    "GenreCount": 2,
    "FirstName": "F.",
    "LastName": "Fitzgerald",
    "PrimaryGenre": "Fiction",
    "MultipleAwards": False,
    "ReadingTimeHours": 9.0,
    "Published21stCentury": True
}
new_book = pd.DataFrame(new_book,index=[len(df)])
df = pd.concat([df,new_book])

Activity 14. Transforming Publish Dates into Datetime Format¶

In [19]:

df['publishDate'] = pd.to_datetime(df['publishDate'])
df['firstPublishDate'] = pd.to_datetime(df['firstPublishDate'])

Activity 15. Bulk Adding New Book Entries to the DataFrame¶

In [20]:

new_books = [
    {
        "bookId": '10001',
        "title": "To Kill a Mockingbird",
        "author": "Harper Lee",
        "rating": 4.3,
        "pages": 281,
        "publishDate": pd.to_datetime('1960-07-11'),
        "firstPublishDate": pd.to_datetime('1960-07-11'),
        "publisher": "J.B. Lippincott & Co.",
        "price": 9.99,
        "genres": "['Fiction', 'Classics']",
        "GenreCount": 2,
        "FirstName": "Harper",
        "LastName": "Lee",
        "PrimaryGenre": "Fiction",
        "MultipleAwards": False,
        "ReadingTimeHours": 11.24,
        "Published21stCentury": False
    },
    {
        "bookId": '10002',
        "title": "1984",
        "author": "George Orwell",
        "rating": 4.2,
        "pages": 328,
        "publishDate": pd.to_datetime('1949-06-08'),
        "firstPublishDate": pd.to_datetime('1949-06-08'),
        "publisher": "Secker & Warburg",
        "price": 12.99,
        "genres": "['Fiction', 'Classics']",
        "GenreCount": 2,
        "FirstName": "George",
        "LastName": "Orwell",
        "PrimaryGenre": "Fiction",
        "MultipleAwards": False,
        "ReadingTimeHours": 13.12,
        "Published21stCentury": False
    }
]

index = len(df)

for i in range(len(new_books)):
    new_books[i] = pd.DataFrame(new_books[i],index=[index])
    index+=1
df = pd.concat([df,new_books[0],new_books[1]],ignore_index=True)

Statement of Completion#6c131a5a