Python π Work With Data
ΠΡΠΊΡΡΡΡ Π² Telegram
A collection of books and articles on Python and various data manipulation tools. Overview of architecture of business intelligence systems, design and development of BI Reports, data processing in Python Pandas.
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ΠΈΠ² ΠΏΠΎΡΡΠΎΠ²
Π§Π΅ΠΊΠ»ΠΈΡΡ: Π½Π° ΡΡΠΎ ΡΠΌΠΎΡΡΠ΅ΡΡ, ΠΊΠΎΠ³Π΄Π° Π·Π°ΡΡΠ³ΠΈΠ²Π°Π΅ΡΡ Π² ΠΏΡΠΎΠ΅ΠΊΡ Π½ΠΎΠ²ΡΡ Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΡ
ΠΠ°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ β ΠΊΠΎΡΠΌΠ°Ρ Π»ΡΠ±ΠΎΠ³ΠΎ Π±ΠΎΠ»ΡΡΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠ°: ΠΎΠ½ΠΈ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡ ΠΊ ΡΡΠ·Π²ΠΈΠΌΠΎΡΡΡΠΌ, ΠΊΠΎΠ½ΡΠ»ΠΈΠΊΡΡΡΡ Π΄ΡΡΠ³ Ρ Π΄ΡΡΠ³ΠΎΠΌ, ΠΏΡΠΎΡΡΡ
Π°ΡΡ ΠΈ Π±Π»ΠΎΠΊΠΈΡΡΡΡ ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΠ΅ ΡΡΠ΅ΠΉΠΌΠ²ΠΎΡΠΊΠ°. Π’Π°ΠΊ ΠΏΠΎΠ»ΡΡΠ°Π΅ΡΡΡ ΠΏΠΎΡΠΎΠΌΡ, ΡΡΠΎ Π΄ΠΎΠ±Π°Π²ΠΈΡΡ Π² ΠΏΡΠΎΠ΅ΠΊΡ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΡ Π½Π΅ ΡΡΠΎΠΈΡ Π½ΠΈΡΠ΅Π³ΠΎ, Π° Π²ΠΎΡ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΈΠ²Π°ΡΡ Π΅Ρ (ΠΈΠ»ΠΈ ΠΏΡΠΎΡΡΠΎ Π²ΡΠΏΠΈΠ»ΠΈΡΡ) β ΠΎΠ³ΡΠΎΠΌΠ½ΡΠΉ ΡΡΡΠ΄.
ΠΡΠΎΠΌΠ΅ ΠΎΡΠ΅Π²ΠΈΠ΄Π½ΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π° ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΎΡ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠ΅ΠΉ (ΠΏΠΎΠΌΠ΅Π½ΡΡΠ΅ ΠΈΡ
ΠΏΡΠΈΡΠ°ΡΠΊΠΈΠ²Π°ΡΡ, ΠΊΠ΅ΠΊ), Π΅ΡΡΡ Π΅ΡΡ ΠΏΡΠΎΡΡΠ°Ρ Π³ΠΈΠ³ΠΈΠ΅Π½Π°, ΠΊΠΎΡΠΎΡΠ°Ρ ΠΏΠΎΠΌΠΎΠ³Π°Π΅Ρ ΡΠΏΡΠΎΡΡΠΈΡΡ ΠΆΠΈΠ·Π½Ρ. ΠΡΠ΅ΠΆΠ΄Π΅ ΡΠ΅ΠΌ Π½Π°Π±ΡΠ°ΡΡ npm install ΠΈΠ»ΠΈ ΡΡΠΎ ΡΠ°ΠΌ Ρ Π²Π°Ρ, Π½Π°ΠΉΠ΄ΠΈΡΠ΅ ΡΠ΅ΠΏΠΎΠ·ΠΈΡΠΎΡΠΈΠΉ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ Π² ΠΠΈΡΡ
Π°Π±Π΅ ΠΈ ΠΏΡΠΎΠ²Π΅ΡΡΡΠ΅ Π΅Π³ΠΎ:
β ΠΠ΅ ΡΠΌΠΎΡΡΠΈΡΠ΅ Π½Π° ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π»Π°ΠΉΠΊΠΎΠ².
β ΠΡΡΡ Π»ΠΈ ΡΠ΅ΡΡΡ? ΠΠΎΠ½ΡΡΠ½ΠΎ Π»ΠΈ Π½Π°ΠΏΠΈΡΠ°Π½Ρ?
β ΠΠΎΡΠΌΠΎΡΡΠΈΡΠ΅ 5 ΠΌΠΈΠ½ΡΡ Π½Π° ΠΊΠΎΠ΄. Π£Π΄Π°ΡΡΡΡ Π»ΠΈ ΠΏΠΎΠ½ΡΡΡ, ΠΊΠ°ΠΊ ΠΎΠ½ ΡΠ°Π±ΠΎΡΠ°Π΅Ρ?
β ΠΡΠ»ΠΈ Π»ΠΈ Π·Π½Π°ΡΠΈΠΌΡΠ΅ (Π½Π΅ Β«version bumpΒ») ΠΊΠΎΠΌΠΌΠΈΡΡ Π² ΠΏΠΎΡΠ»Π΅Π΄Π½ΠΈΠ΅ ΠΏΠΎΠ»Π³ΠΎΠ΄Π°?
β ΠΠ΅ ΡΠΌΠΎΡΡΠΈΡΠ΅ Π½Π° ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π»Π°ΠΉΠΊΠΎΠ².
β Π Π°ΡΡΡΡ ΠΈΠ»ΠΈ ΠΏΠ°Π΄Π°Π΅Ρ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠΊΠ°ΡΠΈΠ²Π°Π½ΠΈΠΉ (ΠΌΠΎΠΆΠ½ΠΎ Π½Π°ΠΉΡΠΈ Π² npm/pypi).
β Π‘ΠΊΠΎΠ»ΡΠΊΠΎ Π²ΠΈΡΠΈΡ Π½Π΅ΠΎΡΠ²Π΅ΡΠ΅Π½Π½ΡΡ
ΠΏΡΠ»Π»-ΡΠ΅ΠΊΠ²Π΅ΡΡΠΎΠ²?
β ΠΠ°ΠΊΠΈΠ΅ issues ΠΎΠ±ΡΡΠΆΠ΄Π°ΡΡ?
β ΠΠΎΠ½ΡΡΠ½ΠΎ Π»ΠΈ Π½Π°ΠΏΠΈΡΠ°Π½ΠΎ ΡΠΈΠ΄ΠΌΠΈ, ΠΌΠ½ΠΎΠ³ΠΎ Π»ΠΈ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ?
ΠΡ ΠΈ ΠΊΠΎΠ½Π΅ΡΠ½ΠΎ, Π½Π΅ ΡΠΌΠΎΡΡΠΈΡΠ΅ Π½Π° ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π»Π°ΠΉΠΊΠΎΠ² β Π»ΡΠ΄ΠΈ ΡΡΠ°Π²ΡΡ ΠΈΡ
Π·Π° Π³ΡΠΎΠΌΠΊΠΈΠ΅ Π½Π°Π·Π²Π°Π½ΠΈΡ ΠΈ ΠΊΡΠ°ΡΠΈΠ²ΡΠ΅ ΡΠΈΠ΄ΠΌΠΈ, Π° Π½Π΅ Π·Π° ΠΊΠΎΠ΄, ΠΊΠΎΡΠΎΡΡΠΉ ΡΠ΅ΡΠ°Π΅Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ Π±Π΅Π· Π³Π΅ΠΌΠΎΡΡΠΎΡ.
ΠΡΡΡ ΡΡΠΎ Π΄ΠΎΠ±Π°Π²ΠΈΡΡ Π² ΡΠ΅ΠΊ-Π»ΠΈΡΡ? ΠΠ°ΠΏΠΈΡΠΈΡΠ΅ Π½Π° fedor@borshev.com
Data analysis with Python a modern approach by Taieb, David.epub
Key Features
+ Bridge your data analysis with the power of programming, complex algorithms, and AI
+ Use Python and its extensive libraries to power your way to new levels of data insight
+ Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series
+ Explore this modern approach across with key industry case studies and hands-on projects
What you will learn
+ A new toolset that has been carefully crafted to meet for your data analysis challenges
+ Full and detailed case studies of the toolset across several of today's key industry contexts
+ Become super productive with a new toolset across Python and Jupyter Notebook
+ Look into the future of data science and which directions to develop your skills next
Beginning Data Science with Python and Jupyter by Alex Galea.epub
+ Identify potential areas of investigation and perform exploratory data analysis
+ Plan a machine learning classification strategy and train classification models
+ Use validation curves and dimensionality reduction to tune and enhance your models
+ Scrape tabular data from web pages and transform it into Pandas DataFrames
+ Create interactive, web-friendly visualizations to clearly communicate your findings
Learn Data Analysis with Python Lessons in Coding by A.J. Henley, Dave Wolf.pdf
Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.
If you arenβt using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.
What You Will Learn
+ Get data into and out of Python code
+ Prepare the data and its format
+ Find the meaning of the data
+ Visualize the data using iPython
Data Wrangling with Python Tips and Tools to Make Your Life Easier by Jacqueline Kazil, Katharine Jarmul.pdf
Through various step-by-step exercises, youβll learn how to acquire, clean, analyze, and present data efficiently. Youβll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain.
+ Quickly learn basic Python syntax, data types, and language concepts
+ Work with both machine-readable and human-consumable data
+ Scrape websites and APIs to find a bounty of useful information
+ Clean and format data to eliminate duplicates and errors in your datasets
+ Learn when to standardize data and when to test and script data cleanup
+ Explore and analyze your datasets with new Python libraries and techniques
+ Use Python solutions to automate your entire data-wrangling process
Data Visualization with Python and JavaScript Scrape, Clean, Explore Transform Your Data.epub
+ Learn how to manipulate data with Python
+ Understand the commonalities between Python and JavaScript
+ Extract information from websites by using Pythonβs web-scraping tools, BeautifulSoup and Scrapy
+ Clean and explore data with Pythonβs Pandas, Matplotlib, and Numpy libraries
+ Serve data and create RESTful web APIs with Pythonβs Flask framework
+ Create engaging, interactive web visualizations with JavaScriptβs D3 library
Data Science with Python and Dask by Jesse C. Daniel.epub
About the book
Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, youβll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, youβll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker.
What's inside
+ Working with large, structured and unstructured datasets
+ Visualization with Seaborn and Datashader
+ Implementing your own algorithms
+ Building distributed apps with Dask Distributed
+ Packaging and deploying Dask apps
Data Analysis With Python A Modern ApproachSource Code by David Taieb.zip
Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis.
Key Features
+ Bridge your data analysis with the power of programming, complex algorithms, and AI
+ Use Python and its extensive libraries to power your way to new levels of data insight
+ Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series
+ Explore this modern approach across with key industry case studies and hands-on projects
Foundations for Analytics with Python From Non-Programmer to Hacker.pdf
# Create and run your own Python scripts by learning basic syntax
# Use Pythonβs csv module to read and parse CSV files
# Read multiple Excel worksheets and workbooks with the xlrd module
# Perform database operations in MySQL or with the mysqlclient module
# Create Python applications to find specific records, group data, and parse text files
# Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn
# Produce summary statistics, and estimate regression and classification models
# Schedule your scripts to run automatically in both Windows and Mac environments
Principles of Data Wrangling. Practical Techniques for data preparation. 2017
+ Understand what kind of data is available
+ Choose which data to use and at what level of detail
+ Meaningfully combine multiple sources of data
+ Decide how to distill the results to a size and shape that can drive downstream analysis
Π£ΡΠΎΠΊ #2 ΠΏΠΎ Microsoft Power BI. ΠΡΠΈΠΌΠ΅ΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠ·ΡΠΊΠ° DAX Π΄Π»Ρ Power BI
DAX β ΡΡΠΎ Π½Π°Π±ΠΎΡ ΡΡΠ½ΠΊΡΠΈΠΉ, ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡΠΎΠ² ΠΈ ΠΊΠΎΠ½ΡΡΠ°Π½Ρ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π² ΡΠΎΡΠΌΡΠ»Π΅ ΠΈΠ»ΠΈ Π²ΡΡΠ°ΠΆΠ΅Π½ΠΈΠΈ, ΡΡΠΎΠ±Ρ ΠΏΠΎΠ΄ΡΡΠΈΡΡΠ²Π°ΡΡ ΠΈ Π²ΠΎΠ·Π²ΡΠ°ΡΠ°ΡΡ ΠΎΠ΄Π½ΠΎ ΠΈΠ»ΠΈ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ. ΠΠΎΠ²ΠΎΡΡ ΠΏΡΠΎΡΠ΅, DAX ΠΏΠΎΠΌΠΎΠ³Π°Π΅Ρ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ Π½ΠΎΠ²ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΈΠ· Π΄Π°Π½Π½ΡΡ
, ΡΠΆΠ΅ ΠΈΠΌΠ΅ΡΡΠΈΡ
ΡΡ Π² ΠΌΠΎΠ΄Π΅Π»ΠΈ.
https://youtu.be/82e5FAn_s0s
Information Quality: The Potential of Data and Analytics to Generate Knowledge
Ron S. Kenett, Galit Shmueli, Ron Kenett
This book:
π Explains how to integrate the notions of goal, data, analysis and utility that are the main building blocks of data analysis within any domain.
π Presents a framework for integrating domain knowledge with data analysis.
π Provides a combination of both methodological and practical aspects of data analysis.
π Discusses issues surrounding the implementation and integration of InfoQ in both academic programmes and business / industrial projects.
π Showcases numerous case studies in a variety of application areas such as education, healthcare, official statistics, risk management and marketing surveys.
π Presents a review of software tools from the InfoQ perspective along with example datasets on an accompanying website.
Π Π°ΡΡΠ΅Π» ΠΡΡΡΡ, ΠΠ»Π°ΡΡΠ΅Π½ ΠΠΈΡ
Π°ΠΈΠ». Data Mining. ΠΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· Facebook, Twitter, LinkedIn, Instagram, GitHub
+ Twitter: ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠ΅ΠΌ, ΠΎ ΡΠ΅ΠΌ Π³ΠΎΠ²ΠΎΡΡΡ Π»ΡΠ΄ΠΈ ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠ΅ Π΄ΡΡΠ³ΠΎΠ΅
+ Facebook: Π°Π½Π°Π»ΠΈΠ· ΡΠ°Π½-ΡΠ³ΡΠ°Π½ΠΈΡ, ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄ΡΡΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠ²ΡΠ·Π΅ΠΉ ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠ΅ Π΄ΡΡΠ³ΠΎΠ΅
+ Instagram: ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ΅ Π·ΡΠ΅Π½ΠΈΠ΅, Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ, ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈ Π»ΠΈΡ
+ LinkedIn: ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΏΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠΈΡΠΌ, Π³ΡΡΠΏΠΏΠΈΡΠΎΠ²ΠΊΠ° ΠΊΠΎΠ»Π»Π΅Π³ ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠ΅ Π΄ΡΡΠ³ΠΎΠ΅
+ ΠΠ½Π°Π»ΠΈΠ· ΡΠ΅ΠΊΡΡΠΎΠ²ΡΡ
ΡΠ°ΠΉΠ»ΠΎΠ²: ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΡ
ΠΎΠ΄ΡΡΠ²Π° Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ², ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ ΡΠ»ΠΎΠ²ΠΎΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠΉ ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠ΅ Π΄ΡΡΠ³ΠΎΠ΅
+ ΠΠ½Π°Π»ΠΈΠ· Π²Π΅Π±-ΡΡΡΠ°Π½ΠΈΡ: ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ·ΡΠΊΠ°, ΠΎΠ±ΠΎΠ±ΡΠ΅Π½ΠΈΠ΅ ΡΡΠ°ΡΠ΅ΠΉ ΠΈΠ· Π±Π»ΠΎΠ³ΠΎΠ² ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠ΅ Π΄ΡΡΠ³ΠΎΠ΅
+ ΠΠ½Π°Π»ΠΈΠ· ΡΠ»Π΅ΠΊΡΡΠΎΠ½Π½ΠΎΠΉ ΠΏΠΎΡΡΡ: ΠΊΡΠΎ ΠΊΠΎΠΌΡ ΠΏΠΈΡΠ΅Ρ, ΠΎ ΡΠ΅ΠΌ, ΠΊΠ°ΠΊ ΡΠ°ΡΡΠΎ ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠ΅ Π΄ΡΡΠ³ΠΎΠ΅
+ ΠΠ½Π°Π»ΠΈΠ· GitHub: ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΎΡΡΡΠ΄Π½ΠΈΡΠ΅ΡΡΠ²Π° ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΠ, Π³ΡΠ°ΡΡ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ² ΠΈ ΠΌΠ½ΠΎΠ³ΠΎΠ΅ Π΄ΡΡΠ³ΠΎΠ΅
A General Introduction to Data Analytics by JoΓ£o Moreira, Andre Carvalho, TomΓ‘s Horvath.pdf
A guide to the principles and methods of data analysis that does not require knowledge of statistics or programming.
Thought to be easily accessible to non-experts, the book provides motivation to the necessity of analyzing data. It explains how to visualize and summarize data, and how to find natural groups and frequent patterns in a dataset. The book also explores predictive tasks, be them classification or regression. Finally, the book discusses popular data analytic applications, like mining the web, information retrieval, social network analysis, working with text, and recommender systems.
Learn Algorithmic Trading Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis.pdf
What you will learn
π· Understand the components of modern algorithmic trading systems and strategies
π· Apply machine learning in algorithmic trading signals and strategies using Python
π· Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more
π· Quantify and build a risk management system for Python trading strategies
π· Build a backtester to run simulated trading strategies for improving the performance of your trading bot
π· Deploy and incorporate trading strategies in the live market to maintain and improve profitability
Learning the Pandas Library Python Tools for Data Munging, Analysis, and Visual.pdf
