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Data Science Project on Amazon Best Selling Books Analysis with the Python Programming Language¶
About the dataset: This dataset contains information about top selling books of Amazon(550 books) either belonging to fiction or non-fiction.
The data I’ll be using in this data science project is a dataset of Amazon’s 50 Best Books between 2009 and 2019.
Importing Libraries¶
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Loading the Dataset¶
In [3]:
data = pd.read_csv("bestsellers with categories.csv")
data.head()
Out[3]:
| Name | Author | User Rating | Reviews | Price | Year | Genre | |
|---|---|---|---|---|---|---|---|
| 0 | 10-Day Green Smoothie Cleanse | JJ Smith | 4.7 | 17350 | 8 | 2016 | Non Fiction |
| 1 | 11/22/63: A Novel | Stephen King | 4.6 | 2052 | 22 | 2011 | Fiction |
| 2 | 12 Rules for Life: An Antidote to Chaos | Jordan B. Peterson | 4.7 | 18979 | 15 | 2018 | Non Fiction |
| 3 | 1984 (Signet Classics) | George Orwell | 4.7 | 21424 | 6 | 2017 | Fiction |
| 4 | 5,000 Awesome Facts (About Everything!) (Natio… | National Geographic Kids | 4.8 | 7665 | 12 | 2019 | Non Fiction |
Subtask 1.2: Inspect the dataframe¶
Inspect the dataframe’s columns, shapes, variable types, unique values etc.
Check Shape¶
In [4]:
r,c = data.shape
print(f"The dataset has {r} rows and {c} columns.")
The dataset has 550 rows and 7 columns.
Check Datatype of Each Column¶
In [5]:
data.dtypes
Out[5]:
Name object Author object User Rating float64 Reviews int64 Price int64 Year int64 Genre object dtype: object
Detail Information about Dataset¶
In [6]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 550 entries, 0 to 549 Data columns (total 7 columns): Name 550 non-null object Author 550 non-null object User Rating 550 non-null float64 Reviews 550 non-null int64 Price 550 non-null int64 Year 550 non-null int64 Genre 550 non-null object dtypes: float64(1), int64(3), object(3) memory usage: 30.2+ KB
Check The Total Unique Values In Each Column¶
In [7]:
data.nunique()
Out[7]:
Name 351 Author 248 User Rating 14 Reviews 346 Price 40 Year 11 Genre 2 dtype: int64
Get An Overview Of The Dataframe¶
In [8]:
data.describe()
Out[8]:
| User Rating | Reviews | Price | Year | |
|---|---|---|---|---|
| count | 550.000000 | 550.000000 | 550.000000 | 550.000000 |
| mean | 4.618364 | 11953.281818 | 13.100000 | 2014.000000 |
| std | 0.226980 | 11731.132017 | 10.842262 | 3.165156 |
| min | 3.300000 | 37.000000 | 0.000000 | 2009.000000 |
| 25% | 4.500000 | 4058.000000 | 7.000000 | 2011.000000 |
| 50% | 4.700000 | 8580.000000 | 11.000000 | 2014.000000 |
| 75% | 4.800000 | 17253.250000 | 16.000000 | 2017.000000 |
| max | 4.900000 | 87841.000000 | 105.000000 | 2019.000000 |
Note: It is clear from the above table that maximum price of a book is 105$ and maximum rating is 4.9.
Column-Wise Null Count¶
In [9]:
data.isnull().sum()
Out[9]:
Name 0 Author 0 User Rating 0 Reviews 0 Price 0 Year 0 Genre 0 dtype: int64
Row-Wise Null Count¶
In [10]:
data.isnull().sum(axis=1).sort_values(ascending=False)
Out[10]:
549 0
180 0
186 0
185 0
184 0
..
372 0
373 0
374 0
375 0
0 0
Length: 550, dtype: int64
Subtask 2.2: Check if the dataset has some duplicate values?¶
In [11]:
data[data.duplicated()]
Out[11]:
| Name | Author | User Rating | Reviews | Price | Year | Genre |
|---|
In [12]:
len(data[data.duplicated()])
Out[12]:
0
Note: There is no duplicate row found in the dataset
Check Total number of different books¶
In [13]:
len(data.Name.unique())
Out[13]:
351
Note: Total number of unique books are 351 but total rows are 550.
This is clear if we see the dataframe that some books are repeatedly among the top sellers in different years.
Check The Duplicate Books¶
In [14]:
data[['Name','Author']].duplicated().sum()
Out[14]:
199
Task 3: Data Analysis¶
Subtask 3.1: Find the Book with highest profit¶
- Create a new column called
Profitwhich contains the product of the two columns:ReviewsandPrice. - Sort the dataframe using the
profitcolumn as reference. - Extract the top ten profiting Books in descending order and store them in a new dataframe –
top10
- Create a new column called
In [15]:
data['Profit'] = data['Reviews'] * data['Price']
data
Out[15]:
| Name | Author | User Rating | Reviews | Price | Year | Genre | Profit | |
|---|---|---|---|---|---|---|---|---|
| 0 | 10-Day Green Smoothie Cleanse | JJ Smith | 4.7 | 17350 | 8 | 2016 | Non Fiction | 138800 |
| 1 | 11/22/63: A Novel | Stephen King | 4.6 | 2052 | 22 | 2011 | Fiction | 45144 |
| 2 | 12 Rules for Life: An Antidote to Chaos | Jordan B. Peterson | 4.7 | 18979 | 15 | 2018 | Non Fiction | 284685 |
| 3 | 1984 (Signet Classics) | George Orwell | 4.7 | 21424 | 6 | 2017 | Fiction | 128544 |
| 4 | 5,000 Awesome Facts (About Everything!) (Natio… | National Geographic Kids | 4.8 | 7665 | 12 | 2019 | Non Fiction | 91980 |
| … | … | … | … | … | … | … | … | … |
| 545 | Wrecking Ball (Diary of a Wimpy Kid Book 14) | Jeff Kinney | 4.9 | 9413 | 8 | 2019 | Fiction | 75304 |
| 546 | You Are a Badass: How to Stop Doubting Your Gr… | Jen Sincero | 4.7 | 14331 | 8 | 2016 | Non Fiction | 114648 |
| 547 | You Are a Badass: How to Stop Doubting Your Gr… | Jen Sincero | 4.7 | 14331 | 8 | 2017 | Non Fiction | 114648 |
| 548 | You Are a Badass: How to Stop Doubting Your Gr… | Jen Sincero | 4.7 | 14331 | 8 | 2018 | Non Fiction | 114648 |
| 549 | You Are a Badass: How to Stop Doubting Your Gr… | Jen Sincero | 4.7 | 14331 | 8 | 2019 | Non Fiction | 114648 |
550 rows × 8 columns
In [16]:
Top10 = data.sort_values(by='Profit',ascending=False).head(10)
Top10
Out[16]:
| Name | Author | User Rating | Reviews | Price | Year | Genre | Profit | |
|---|---|---|---|---|---|---|---|---|
| 382 | The Girl on the Train | Paula Hawkins | 4.1 | 79446 | 18 | 2015 | Fiction | 1430028 |
| 338 | The Alchemist | Paulo Coelho | 4.7 | 35799 | 39 | 2014 | Fiction | 1396161 |
| 534 | Where the Crawdads Sing | Delia Owens | 4.8 | 87841 | 15 | 2019 | Fiction | 1317615 |
| 70 | Diagnostic and Statistical Manual of Mental Di… | American Psychiatric Association | 4.5 | 6679 | 105 | 2014 | Non Fiction | 701295 |
| 69 | Diagnostic and Statistical Manual of Mental Di… | American Psychiatric Association | 4.5 | 6679 | 105 | 2013 | Non Fiction | 701295 |
| 159 | Harry Potter Paperback Box Set (Books 1-7) | J. K. Rowling | 4.8 | 13471 | 52 | 2016 | Fiction | 700492 |
| 393 | The Goldfinch: A Novel (Pulitzer Prize for Fic… | Donna Tartt | 3.9 | 33844 | 20 | 2014 | Fiction | 676880 |
| 392 | The Goldfinch: A Novel (Pulitzer Prize for Fic… | Donna Tartt | 3.9 | 33844 | 20 | 2013 | Fiction | 676880 |
| 33 | Becoming | Michelle Obama | 4.8 | 61133 | 11 | 2019 | Non Fiction | 672463 |
| 32 | Becoming | Michelle Obama | 4.8 | 61133 | 11 | 2018 | Non Fiction | 672463 |
Subtask 3.2: Drop duplicate values¶
After you found out the top 10 profiting books, you might have notice a duplicate value. So, it seems like the dataframe has duplicate values as well. Group the dataframe by name and repeat Subtask 3.1.
In [17]:
Top10 = data.groupby('Name')[['Profit']].max().sort_values(by='Profit',ascending=False).head(10)
Top10
Out[17]:
| Profit | |
|---|---|
| Name | |
| The Girl on the Train | 1430028 |
| The Alchemist | 1396161 |
| Where the Crawdads Sing | 1317615 |
| Diagnostic and Statistical Manual of Mental Disorders, 5th Edition: DSM-5 | 701295 |
| Harry Potter Paperback Box Set (Books 1-7) | 700492 |
| The Goldfinch: A Novel (Pulitzer Prize for Fiction) | 676880 |
| Becoming | 672463 |
| Fifty Shades of Grey: Book One of the Fifty Shades Trilogy (Fifty Shades of Grey Series) | 661710 |
| The Fault in Our Stars | 656266 |
| A Game of Thrones / A Clash of Kings / A Storm of Swords / A Feast of Crows / A Dance with Dragons | 592050 |
In [18]:
sns.barplot(x=Top10.Profit,y=Top10.index.values)
plt.xlabel("Earned(millions)")
plt.ylabel("Books")
plt.title('Earning by Books')
Out[18]:
Text(0.5, 1.0, 'Earning by Books')
Subtask 3.3: Which Genre Has The Most Books In This Category and Their Distribution?¶
In [19]:
data.Genre.unique()
Out[19]:
array(['Non Fiction', 'Fiction'], dtype=object)
In [20]:
data.Genre.value_counts()
Out[20]:
Non Fiction 310 Fiction 240 Name: Genre, dtype: int64
In [21]:
sns.countplot(x="Genre", data=data)
plt.show()
In [22]:
plt.pie(data.Genre.value_counts(),labels=['Non Fiction','Fiction'],autopct='%.0f%%');
Non Fiction books are in majority in top selling category.
Subtask 3.4: What is The Average Rating of Each Genre and Plot Histogtam of each Rating¶
In [23]:
data.groupby('Genre')['User Rating'].mean()
Out[23]:
Genre Fiction 4.648333 Non Fiction 4.595161 Name: User Rating, dtype: float64
In [24]:
sns.histplot(data=data['User Rating'],bins=10)
plt.xlabel("Ratings")
Out[24]:
Text(0.5, 0, 'Ratings')
Subtask 3.5: Find the Relationship of Ratings with Time.¶
In [25]:
sns.lineplot(y=data['User Rating'],x=data['Year'],hue=data['Genre']);
plt.ylabel("Ratings")
plt.xlabel("Years");
It is clear from the above graph that most of the books received ratings between 4.5 to 4.9.
In [26]:
sns.lmplot(y='User Rating',x='Price',data=data)
plt.ylabel('Ratings')
plt.xlabel('Price');
Note: As we seen price increases, rating goes down.
In [27]:
data.groupby('Genre')['User Rating'].mean()
Out[27]:
Genre Fiction 4.648333 Non Fiction 4.595161 Name: User Rating, dtype: float64
This graph shows that there is not a significant relationship between price and ratings but with increasing price , the ratings are falling for both Fiction and Non fiction.
Subtask 3.6: Find How Much Books Have Earned Yearly.¶
In [28]:
data.groupby('Year').Profit.sum().sort_values(ascending=False)
Out[28]:
Year 2014 10625500 2012 8929419 2019 8336955 2013 8321579 2016 7951530 2015 7745165 2018 7183575 2017 6669195 2011 5548689 2010 3620509 2009 3567282 Name: Profit, dtype: int64
In [29]:
data.groupby('Year').Profit.sum().plot(kind='bar')
plt.show()
In [30]:
data.groupby(['Year','Genre']).Profit.sum().sort_values(ascending=False)
Out[30]:
Year Genre
2014 Fiction 6858148
2013 Fiction 5444489
2012 Fiction 5098394
2018 Non Fiction 4713219
2019 Non Fiction 4500705
2015 Fiction 4364798
2016 Fiction 4064705
Non Fiction 3886825
2019 Fiction 3836250
2012 Non Fiction 3831025
2014 Non Fiction 3767352
2017 Non Fiction 3568679
2015 Non Fiction 3380367
2017 Fiction 3100516
2011 Non Fiction 3082561
2013 Non Fiction 2877090
2018 Fiction 2470356
2011 Fiction 2466128
2009 Fiction 2058643
2010 Non Fiction 1930069
Fiction 1690440
2009 Non Fiction 1508639
Name: Profit, dtype: int64
Plot using Seaborn¶
In [31]:
sns.barplot(x=data['Year'],y=data['Profit'],hue=data['Genre'])
plt.xlabel('Years')
plt.ylabel("Eearned(millions)")
plt.title('Money earned each year');
Above graph above shows the earning of these top books ,in most cases, are below 0.8 million.
Subtask 3.7: Average Profit Earned by Each Book Depending on its Genre.¶
In [32]:
genre_average=data.groupby(['Genre'])['Profit'].mean()
genre_average
Out[32]:
Genre Fiction 172720.279167 Non Fiction 119504.938710 Name: Profit, dtype: float64
In [33]:
sns.barplot(x=genre_average.index,y=genre_average);
Subtask 3.8:. Find Books Which Earned The Most Per Year (2009-19)¶
In [34]:
data.groupby('Year')['Profit'].max()
Out[34]:
Year 2009 394680 2010 474768 2011 508470 2012 661710 2013 701295 2014 1396161 2015 1430028 2016 700492 2017 458730 2018 672463 2019 1317615 Name: Profit, dtype: int64
In [35]:
data.groupby('Year')['Profit'].transform(max)
Out[35]:
0 700492
1 508470
2 672463
3 458730
4 1317615
...
545 1317615
546 700492
547 458730
548 672463
549 1317615
Name: Profit, Length: 550, dtype: int64
In [36]:
data[data.groupby('Year')['Profit'].transform(max) == data['Profit']]
Out[36]:
| Name | Author | User Rating | Reviews | Price | Year | Genre | Profit | |
|---|---|---|---|---|---|---|---|---|
| 32 | Becoming | Michelle Obama | 4.8 | 61133 | 11 | 2018 | Non Fiction | 672463 |
| 69 | Diagnostic and Statistical Manual of Mental Di… | American Psychiatric Association | 4.5 | 6679 | 105 | 2013 | Non Fiction | 701295 |
| 106 | Fifty Shades of Grey: Book One of the Fifty Sh… | E L James | 3.8 | 47265 | 14 | 2012 | Fiction | 661710 |
| 159 | Harry Potter Paperback Box Set (Books 1-7) | J. K. Rowling | 4.8 | 13471 | 52 | 2016 | Fiction | 700492 |
| 265 | Player’s Handbook (Dungeons & Dragons) | Wizards RPG Team | 4.8 | 16990 | 27 | 2017 | Fiction | 458730 |
| 271 | Publication Manual of the American Psychologic… | American Psychological Association | 4.5 | 8580 | 46 | 2009 | Non Fiction | 394680 |
| 338 | The Alchemist | Paulo Coelho | 4.7 | 35799 | 39 | 2014 | Fiction | 1396161 |
| 382 | The Girl on the Train | Paula Hawkins | 4.1 | 79446 | 18 | 2015 | Fiction | 1430028 |
| 410 | The Hunger Games Trilogy Boxed Set (1) | Suzanne Collins | 4.8 | 16949 | 30 | 2011 | Fiction | 508470 |
| 515 | Unbroken: A World War II Story of Survival, Re… | Laura Hillenbrand | 4.8 | 29673 | 16 | 2010 | Non Fiction | 474768 |
| 534 | Where the Crawdads Sing | Delia Owens | 4.8 | 87841 | 15 | 2019 | Fiction | 1317615 |
In [37]:
most_earning_book_per_year=data[data.groupby('Year')['Profit'].transform(max) == data['Profit']]
most_earning_book_per_year
Out[37]:
| Name | Author | User Rating | Reviews | Price | Year | Genre | Profit | |
|---|---|---|---|---|---|---|---|---|
| 32 | Becoming | Michelle Obama | 4.8 | 61133 | 11 | 2018 | Non Fiction | 672463 |
| 69 | Diagnostic and Statistical Manual of Mental Di… | American Psychiatric Association | 4.5 | 6679 | 105 | 2013 | Non Fiction | 701295 |
| 106 | Fifty Shades of Grey: Book One of the Fifty Sh… | E L James | 3.8 | 47265 | 14 | 2012 | Fiction | 661710 |
| 159 | Harry Potter Paperback Box Set (Books 1-7) | J. K. Rowling | 4.8 | 13471 | 52 | 2016 | Fiction | 700492 |
| 265 | Player’s Handbook (Dungeons & Dragons) | Wizards RPG Team | 4.8 | 16990 | 27 | 2017 | Fiction | 458730 |
| 271 | Publication Manual of the American Psychologic… | American Psychological Association | 4.5 | 8580 | 46 | 2009 | Non Fiction | 394680 |
| 338 | The Alchemist | Paulo Coelho | 4.7 | 35799 | 39 | 2014 | Fiction | 1396161 |
| 382 | The Girl on the Train | Paula Hawkins | 4.1 | 79446 | 18 | 2015 | Fiction | 1430028 |
| 410 | The Hunger Games Trilogy Boxed Set (1) | Suzanne Collins | 4.8 | 16949 | 30 | 2011 | Fiction | 508470 |
| 515 | Unbroken: A World War II Story of Survival, Re… | Laura Hillenbrand | 4.8 | 29673 | 16 | 2010 | Non Fiction | 474768 |
| 534 | Where the Crawdads Sing | Delia Owens | 4.8 | 87841 | 15 | 2019 | Fiction | 1317615 |
In [38]:
most_earning_book_per_year=most_earning_book_per_year.sort_values('Year').set_index('Year')
most_earning_book_per_year
Out[38]:
| Name | Author | User Rating | Reviews | Price | Genre | Profit | |
|---|---|---|---|---|---|---|---|
| Year | |||||||
| 2009 | Publication Manual of the American Psychologic… | American Psychological Association | 4.5 | 8580 | 46 | Non Fiction | 394680 |
| 2010 | Unbroken: A World War II Story of Survival, Re… | Laura Hillenbrand | 4.8 | 29673 | 16 | Non Fiction | 474768 |
| 2011 | The Hunger Games Trilogy Boxed Set (1) | Suzanne Collins | 4.8 | 16949 | 30 | Fiction | 508470 |
| 2012 | Fifty Shades of Grey: Book One of the Fifty Sh… | E L James | 3.8 | 47265 | 14 | Fiction | 661710 |
| 2013 | Diagnostic and Statistical Manual of Mental Di… | American Psychiatric Association | 4.5 | 6679 | 105 | Non Fiction | 701295 |
| 2014 | The Alchemist | Paulo Coelho | 4.7 | 35799 | 39 | Fiction | 1396161 |
| 2015 | The Girl on the Train | Paula Hawkins | 4.1 | 79446 | 18 | Fiction | 1430028 |
| 2016 | Harry Potter Paperback Box Set (Books 1-7) | J. K. Rowling | 4.8 | 13471 | 52 | Fiction | 700492 |
| 2017 | Player’s Handbook (Dungeons & Dragons) | Wizards RPG Team | 4.8 | 16990 | 27 | Fiction | 458730 |
| 2018 | Becoming | Michelle Obama | 4.8 | 61133 | 11 | Non Fiction | 672463 |
| 2019 | Where the Crawdads Sing | Delia Owens | 4.8 | 87841 | 15 | Fiction | 1317615 |
Subtask 3.9: What is the Average Earning of Each Genre on per Year Basis¶
In [39]:
genres_per_year_mean=data.groupby(['Year','Genre'])[['Profit']].mean().round(2)
genres_per_year_mean
Out[39]:
| Profit | ||
|---|---|---|
| Year | Genre | |
| 2009 | Fiction | 85776.79 |
| Non Fiction | 58024.58 | |
| 2010 | Fiction | 84522.00 |
| Non Fiction | 64335.63 | |
| 2011 | Fiction | 117434.67 |
| Non Fiction | 106295.21 | |
| 2012 | Fiction | 242780.67 |
| Non Fiction | 132104.31 | |
| 2013 | Fiction | 226853.71 |
| Non Fiction | 110657.31 | |
| 2014 | Fiction | 236487.86 |
| Non Fiction | 179397.71 | |
| 2015 | Fiction | 256752.82 |
| Non Fiction | 102435.36 | |
| 2016 | Fiction | 213931.84 |
| Non Fiction | 125381.45 | |
| 2017 | Fiction | 129188.17 |
| Non Fiction | 137256.88 | |
| 2018 | Fiction | 117636.00 |
| Non Fiction | 162524.79 | |
| 2019 | Fiction | 191812.50 |
| Non Fiction | 150023.50 |
Subtask 3.10: Trend of books selling (Top rated) over the years¶
In [40]:
Earning_Graph=data.groupby('Year')['Profit'].sum()
Earning_Graph
Out[40]:
Year 2009 3567282 2010 3620509 2011 5548689 2012 8929419 2013 8321579 2014 10625500 2015 7745165 2016 7951530 2017 6669195 2018 7183575 2019 8336955 Name: Profit, dtype: int64
In [41]:
sns.lineplot(data=Earning_Graph)
plt.xlabel('Year')
plt.ylabel("Earned")
plt.title("EARNING PER YEAR")
plt.figure(figsize=(12,12));
<Figure size 864x864 with 0 Axes>
Subtask 3.11: Top 10 authors which earned the most¶
In [42]:
authors=data.groupby('Author')['Profit'].sum().sort_values(ascending=False).head(10)
authors
Out[42]:
Author American Psychological Association 3946800 Suzanne Collins 3368646 E L James 2517303 John Green 2381609 Laura Hillenbrand 2284821 Paula Hawkins 1986150 Gillian Flynn 1660859 Gary Chapman 1516167 Dr. Seuss 1423598 American Psychiatric Association 1402590 Name: Profit, dtype: int64
In [43]:
sns.barplot(y=authors.index,x=authors)
plt.title('The Money Makers ')
Out[43]:
Text(0.5, 1.0, 'The Money Makers ')
