ML/Data/Dev Books
Contents
ML/Data/Dev Books#
import pandas as pd
df = pd.read_json('../data/books.json')
print(df.columns.to_list())
['title', 'sub_title', 'ed.', 'series', 'authors', 'foreword', 'publisher', 'copyright', 'pub_date', 'site', 'amazon', 'repo']
Preprocessing#
df['pub_date'] = pd.to_datetime(df['pub_date'])
Filtering#
def make_clickable(val):
return f'<a href="{val}" target="_blank">link</a>' if val else '-'
def df_style(styler):
styler.format({
'ed.': lambda x: str(x) if x != 1 else '',
'pub_date': lambda x: x.strftime("%b %Y"),
'site': make_clickable,
'amazon': make_clickable,
'repo': make_clickable})
return styler
cols = ['title', 'ed.', 'publisher', 'pub_date', 'series', 'site','amazon', 'repo']
df2 = df.sort_values(by=['pub_date'], ascending=False).reset_index(drop=True)[cols]
df2.style.pipe(df_style)
| title | ed. | publisher | pub_date | series | site | amazon | repo | |
|---|---|---|---|---|---|---|---|---|
| 0 | Developing Apps with GPT-4 and ChatGPT | O'Reilly | Jan 2024 | link | - | - | ||
| 1 | Architecting Data and Machine Learning Platforms | O'Reilly | Dec 2023 | link | link | - | ||
| 2 | Python for Data Science For Dummies | 3 | Wiley | Nov 2023 | link | link | - | |
| 3 | 50 Algorithms Every Programmer Should Know | 2 | Packt | Sep 2023 | Expert Insight | link | link | - |
| 4 | Machine Learning for Time Series | 2 | Packt | Aug 2023 | Expert Insight | link | link | - |
| 5 | Dive Into Data Science | No Starch | Apr 2023 | link | link | - | ||
| 6 | Effective XGBoost | MetaSnake | Mar 2023 | Treading on Python | link | link | - | |
| 7 | Software Requirements Essentials | Addison-Wesley | Mar 2023 | link | link | - | ||
| 8 | Mastering Blockchain | 4 | Packt | Mar 2023 | Expert Insight | link | link | link |
| 9 | The Kaggle Workbook | Packt | Feb 2023 | Expert Insight | link | link | link | |
| 10 | Creating Software with Modern Diagramming Techniques | Pragmatic Bookshelf | Feb 2023 | link | link | - | ||
| 11 | Python in a Nutshell | 4 | O'Reilly | Jan 2023 | link | link | - | |
| 12 | AI at the Edge | O'Reilly | Jan 2023 | link | link | link | ||
| 13 | Pandas for Everyone | 2 | Addison-Wesley | Dec 2022 | Data & Analytics | link | link | - |
| 14 | Python Data Science Handbook | 2 | O'Reilly | Dec 2022 | link | link | link | |
| 15 | Functional Python Programming | 3 | Packt | Dec 2022 | Expert Insight | link | link | link |
| 16 | Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow | 3 | O'Reilly | Oct 2022 | link | link | link | |
| 17 | Deep Learning with TensorFlow and Keras | 3 | Packt | Oct 2022 | Expert Insight | link | link | link |
| 18 | Journey to Become a Google Cloud Machine Learning Engineer | Packt | Sep 2022 | link | link | link | ||
| 19 | Python for Data Analysis | 3 | O'Reilly | Aug 2022 | link | link | link | |
| 20 | TensorFlow in Action | Manning | Aug 2022 | link | link | link | ||
| 21 | Natural Language Processing with TensorFlow | 2 | Packt | Jul 2022 | Expert Insight | link | link | link |
| 22 | Hands-On Data Structures and Algorithms with Python | 3 | Packt | Jul 2022 | Expert Insight | link | link | link |
| 23 | Fundamentals of Data Engineering | O'Reilly | Jun 2022 | link | link | - | ||
| 24 | Designing Machine Learning Systems | O'Reilly | May 2022 | link | link | link | ||
| 25 | Mastering Python | 2 | Packt | May 2022 | Expert Insight | link | link | link |
| 26 | Interpretable AI | Manning | May 2022 | link | link | link | ||
| 27 | Data Science on the Google Cloud Platform | 2 | O'Reilly | Apr 2022 | link | link | link | |
| 28 | The Kaggle Book | Packt | Apr 2022 | Expert Insight | link | link | link | |
| 29 | TinyML Cookbook | Packt | Apr 2022 | link | link | link | ||
| 30 | Transformers for Natural Language Processing | 2 | Packt | Mar 2022 | Expert Insight | link | link | link |
| 31 | Algorithms For Dummies | 2 | Wiley | Mar 2022 | link | link | - | |
| 32 | Machine Learning with PyTorch and Scikit-Learn | Packt | Feb 2022 | Expert Insight | link | link | link | |
| 33 | Modern Software Engineering | Addison-Wesley | Dec 2021 | link | link | - | ||
| 34 | Effective Pandas | MetaSnake | Dec 2021 | Treading on Python | link | link | link | |
| 35 | The Self-Taught Computer Scientist | Wiley | Dec 2021 | link | link | - | ||
| 36 | Job Ready Python | Wiley | Nov 2021 | Mthree Tech Skills | link | link | - | |
| 37 | Deep Learning with Python | 2 | Manning | Oct 2021 | link | link | link | |
| 38 | Python Distilled | Addison-Wesley | Sep 2021 | link | link | - | ||
| 39 | Learning Deep Learning | Addison-Wesley | Aug 2021 | link | link | link | ||
| 40 | Python for Geeks | Packt | Aug 2021 | link | link | link | ||
| 41 | The Official Guide to Mermaid.js | Packt | Aug 2021 | link | link | link | ||
| 42 | Python Object-Oriented Programming | 4 | Packt | Jul 2021 | Expert Insight | link | link | link |
| 43 | Becoming a Data Head | Wiley | May 2021 | link | link | - |
Data Analysis#
def get_authors(df):
authors_set = set()
for authors in df['authors']:
for author in authors:
authors_set.add(author)
return sorted(list(authors_set))
def get_books_with_keyword(keyword):
title_has_keyword = df['title'].str.lower().str.contains(keyword)
sub_title_has_keyword = df['sub_title'].str.lower().str.contains(keyword)
is_keyword = title_has_keyword | sub_title_has_keyword
return df[is_keyword].sort_values(
by=['pub_date'], ascending=False).reset_index(drop=True)[cols].style.pipe(df_style)
get_books_with_keyword('business')
get_books_with_keyword('algorithm')
| title | ed. | publisher | pub_date | series | site | amazon | repo | |
|---|---|---|---|---|---|---|---|---|
| 0 | 50 Algorithms Every Programmer Should Know | 2 | Packt | Sep 2023 | Expert Insight | link | link | - |
| 1 | Hands-On Data Structures and Algorithms with Python | 3 | Packt | Jul 2022 | Expert Insight | link | link | link |
| 2 | Algorithms For Dummies | 2 | Wiley | Mar 2022 | link | link | - | |
| 3 | The Self-Taught Computer Scientist | Wiley | Dec 2021 | link | link | - |
get_books_with_keyword('data scien')
| title | ed. | publisher | pub_date | series | site | amazon | repo | |
|---|---|---|---|---|---|---|---|---|
| 0 | Python for Data Science For Dummies | 3 | Wiley | Nov 2023 | link | link | - | |
| 1 | Dive Into Data Science | No Starch | Apr 2023 | link | link | - | ||
| 2 | The Kaggle Workbook | Packt | Feb 2023 | Expert Insight | link | link | link | |
| 3 | Python Data Science Handbook | 2 | O'Reilly | Dec 2022 | link | link | link | |
| 4 | Data Science on the Google Cloud Platform | 2 | O'Reilly | Apr 2022 | link | link | link | |
| 5 | The Kaggle Book | Packt | Apr 2022 | Expert Insight | link | link | link | |
| 6 | Becoming a Data Head | Wiley | May 2021 | link | link | - |
get_books_with_keyword('machine learning')
| title | ed. | publisher | pub_date | series | site | amazon | repo | |
|---|---|---|---|---|---|---|---|---|
| 0 | Architecting Data and Machine Learning Platforms | O'Reilly | Dec 2023 | link | link | - | ||
| 1 | Machine Learning for Time Series | 2 | Packt | Aug 2023 | Expert Insight | link | link | - |
| 2 | AI at the Edge | O'Reilly | Jan 2023 | link | link | link | ||
| 3 | Hands-On Machine Learning with Scikit-Learn, Keras, & TensorFlow | 3 | O'Reilly | Oct 2022 | link | link | link | |
| 4 | Journey to Become a Google Cloud Machine Learning Engineer | Packt | Sep 2022 | link | link | link | ||
| 5 | Natural Language Processing with TensorFlow | 2 | Packt | Jul 2022 | Expert Insight | link | link | link |
| 6 | Designing Machine Learning Systems | O'Reilly | May 2022 | link | link | link | ||
| 7 | Interpretable AI | Manning | May 2022 | link | link | link | ||
| 8 | Data Science on the Google Cloud Platform | 2 | O'Reilly | Apr 2022 | link | link | link | |
| 9 | The Kaggle Book | Packt | Apr 2022 | Expert Insight | link | link | link | |
| 10 | Machine Learning with PyTorch and Scikit-Learn | Packt | Feb 2022 | Expert Insight | link | link | link | |
| 11 | Becoming a Data Head | Wiley | May 2021 | link | link | - |