Foundations for Analytics with Python. From Non-Programmer to Hacker - Helion

ebook

Autor: Clinton W. Brownley
ISBN: 978-14-919-2248-4
stron: 352, Format: ebook
Data wydania: 2016-08-16
Księgarnia: Helion

Cena książki: 143,65 zł (poprzednio: 167,03 zł)
Oszczędzasz: 14% (-23,38 zł)

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If you’re like many of Excel’s 750 million users, you want to do more with your data—like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats—by using Python.

After author Clinton Brownley takes you through Python basics, you’ll be able to write simple scripts for processing data in spreadsheets as well as databases. You’ll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.

Create and run your own Python scripts by learning basic syntax
Use Python’s 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

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Spis treści

Foundations for Analytics with Python. From Non-Programmer to Hacker eBook -- spis treści

Preface
- Why Read This Book? Why Learn These Skills?
- Who Is This Book For?
- Why Windows?
- Why Python?
- Base Python and pandas
- Anaconda Python
  - Installing Anaconda Python (Windows or Mac)
- Text Editors
- Download Book Materials
- Overview of Chapters
- Conventions Used in This Book
- Using Code Examples
- Safari Books Online
- How to Contact Us
- Acknowledgments
1. Python Basics
- How to Create a Python Script
- How to Run a Python Script
- Useful Tips for Interacting with the Command Line
- Pythons Basic Building Blocks
  - Numbers
    - Integers
    - Floating-point numbers
  - Strings
    - split
    - join
    - strip
    - replace
    - lower, upper, capitalize
  - Regular Expressions and Pattern Matching
  - Dates
  - Lists
    - Create a list
    - Index values
    - List slices
    - Copy a list
    - Using in and not in
    - append, remove, pop
    - reverse
    - sort
  - Tuples
    - Create a tuple
    - Unpack tuples
    - Convert tuples to lists (and vice versa)
  - Dictionaries
    - Create a dictionary
    - Access a value in a dictionary
    - copy
    - keys, values, and items
    - Using in, not in, and get
    - Sorting
  - Control Flow
    - if-else
    - if-elif-else
    - for loops
    - Compact for loops: list, set, and dictionary comprehensions
      - List comprehension
      - Set comprehension
      - Dictionary comprehension
    - while loops
    - Functions
    - Exceptions
    - try-except
    - try-except-else-finally
- Reading a Text File
  - Create a Text File
  - Script and Input File in Same Location
  - Modern File-Reading Syntax
- Reading Multiple Text Files with glob
  - Create Another Text File
- Writing to a Text File
  - Add Code to first_script.py
  - Writing to a Comma-Separated Values (CSV) File
- print Statements
- Chapter Exercises
2. Comma-Separated Values (CSV) Files
- Base Python Versus pandas
  - Read and Write a CSV File (Part 1)
    - Base Python, without csv module
    - Pandas
  - How Basic String Parsing Can Fail
  - Read and Write a CSV File (Part 2)
    - Base Python, with csv module
- Filter for Specific Rows
  - Value in Row Meets a Condition
    - Base Python
    - Pandas
  - Value in Row Is in a Set of Interest
    - Base Python
    - Pandas
  - Value in Row Matches a Pattern/Regular Expression
    - Base Python
    - Pandas
- Select Specific Columns
  - Column Index Values
    - Base Python
    - Pandas
  - Column Headings
    - Base Python
    - Pandas
- Select Contiguous Rows
  - Base Python
  - Pandas
- Add a Header Row
  - Base Python
  - Pandas
- Reading Multiple CSV Files
  - Count Number of Files and Number of Rows and Columns in Each File
- Concatenate Data from Multiple Files
  - Base Python
  - Pandas
- Sum and Average a Set of Values per File
  - Base Python
  - Pandas
- Chapter Exercises
3. Excel Files
- Introspecting an Excel Workbook
- Processing a Single Worksheet
  - Read and Write an Excel File
    - Base Python with xlrd and xlwt modules
      - Format dates
    - Pandas
  - Filter for Specific Rows
    - Value in Row Meets a Condition
      - Base Python
      - Pandas
    - Value in Row Is in a Set of Interest
      - Base Python
      - Pandas
    - Value in Row Matches a Specific Pattern
      - Base Python
      - Pandas
  - Select Specific Columns
    - Column Index Values
      - Base Python
      - Pandas
    - Column Headings
      - Base Python
      - Pandas
- Reading All Worksheets in a Workbook
  - Filter for Specific Rows Across All Worksheets
    - Base Python
    - Pandas
  - Select Specific Columns Across All Worksheets
    - Base Python
    - Pandas
- Reading a Set of Worksheets in an Excel Workbook
  - Filter for Specific Rows Across a Set of Worksheets
    - Base Python
    - Pandas
- Processing Multiple Workbooks
  - Count Number of Workbooks and Rows and Columns in Each Workbook
  - Concatenate Data from Multiple Workbooks
    - Base Python
    - Pandas
  - Sum and Average Values per Workbook and Worksheet
    - Base Python
    - Pandas
- Chapter Exercises
4. Databases
- Pythons Built-in sqlite3 Module
  - Insert New Records into a Table
  - Update Records in a Table
- MySQL Database
  - Insert New Records into a Table
  - Query a Table and Write Output to a CSV File
  - Update Records in a Table
- Chapter Exercises
5. Applications
- Find a Set of Items in a Large Collection of Files
- Calculate a Statistic for Any Number of Categories from Data in a CSV File
- Calculate Statistics for Any Number of Categories from Data in a Text File
- Chapter Exercises
6. Figures and Plots
- matplotlib
  - Bar Plot
  - Histogram
  - Line Plot
  - Scatter Plot
  - Box Plot
- pandas
- ggplot
- seaborn
7. Descriptive Statistics and Modeling
- Datasets
  - Wine Quality
  - Customer Churn
- Wine Quality
  - Descriptive Statistics
  - Grouping, Histograms, and t-tests
  - Pairwise Relationships and Correlation
  - Linear Regression with Least-Squares Estimation
  - Interpreting Coefficients
  - Standardizing Independent Variables
  - Making Predictions
- Customer Churn
  - Logistic Regression
  - Interpreting Coefficients
  - Making Predictions
8. Scheduling Scripts to Run Automatically
- Task Scheduler (Windows)
- The cron Utility (macOS and Unix)
  - Crontab File: One-Time Set-up
  - Adding Cron Jobs to the Crontab File
9. Where to Go from Here
- Additional Standard Library Modules and Built-in Functions
  - Python Standard Library (PSL): A Few More Standard Modules
  - Built-in Functions
- Python Package Index (PyPI): Additional Add-in Modules
  - NumPy
    - Reading and writing CSV and Excel files
    - loadtxt
    - genfromtxt
    - Convert to a NumPy array
    - CSV files
    - Excel files
    - savetxt
    - Filter rows
    - Select specific columns
    - Concatenate data
    - Additional features
  - SciPy
    - linalg
      - Linear systems of equations
      - Least-squares regression
    - interpolate
    - stats
      - Descriptive statistics
      - Linear regression
  - Scikit-Learn
  - A Few Additional Add-in Packages
- Additional Data Structures
  - Stacks
  - Queues
  - Graphs
  - Trees
- Where to Go from Here
A. Download Instructions
- Download Python 3
  - Windows
  - macOS
- Download the xlrd Package
  - Windows
    - Option 1
    - Option 2
  - macOS
    - Option 1
    - Option 2
- Download the MySQL Database Server
  - Windows
  - macOS
  - Setting Up MySQL
- Download mysqlclient (Python 3.x)/MySQL-python (Python 2.x)
  - Windows
    - Option 1
    - Option 2
  - macOS
    - Option 1
    - Option 2
B. Answers to Exercises
- Chapter 1
Bibliography
Index