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Python Data Analysis : Master Python Analytics with Machine Learning, Deep Learning, GenAI, LLMs, and Data Engineering.
- Format:
- Book
- Author/Creator:
- Navlani, Avinash.
- Language:
- English
- Subjects (All):
- Python (Computer program language).
- Physical Description:
- 1 online resource (767 p.)
- Edition:
- Fourth Edition.
- Place of Publication:
- Birmingham : Packt Publishing, Limited, 2026.
- Summary:
- Understand data analysis pipelines using Python Data Analysis, machine learning, pandas, scikit-learn, and data visualization techniques.Build scalable workflows for time series, NLP, image analytics, and big data processing.
- Contents:
- Cover
- Copyright Page
- Contributors
- Table of Contents
- Preface
- Free benefits with your book
- Part 1: Foundations for Data Analysis
- Chapter 01: Getting Started with Python Libraries
- Navigating the landscape of data analysis
- Exploring libraries for data analysis
- Data analysis process methodology
- Knowledge discovery from data
- Sample, explore, modify, model, and assess
- CRoss-InduStry Process for Data Mining
- Standard process of data analysis
- Comparing data analysis, data science and data engineering
- Data analysis
- Data science
- Data engineering
- Job roles in the data science domain
- Roles of data analyst, data scientist and data engineer
- Roles of ML engineer and NLP engineer
- A quick look at MLOps
- Installing Python 3
- Python installation and setup on Windows
- Python installation and setup on Linux
- Python installation and setup on mac OS X with a GUI installer
- Python installation and setup on Mac OS X with brew
- Software tools used in this book
- Using IPython as a shell
- Hands-on with IPython
- Reading manual pages
- Using JupyterLab
- Using Jupyter notebooks
- Keyboard shortcuts
- Installing other kernels
- Running shell commands
- Extensions for notebooks
- Using PyCharm and visual studio code
- PyCharm
- Visual studio code
- Using Databricks for PySpark
- Summary
- Further reading
- Chapter 02: NumPy and Pandas
- Technical requirements
- Grasping the essence of NumPy arrays
- Array properties and attributes
- Selecting array elements
- NumPy array numeric data types
- Data type objects
- Data type character codes
- Data type constructors
- Data type attributes
- Converting arrays
- Manipulating array shapes
- Stacking arrays
- Splitting arrays
- Creating views and copies
- Slicing NumPy arrays
- Boolean and fancy indexing
- Broadcasting arrays
- More on NumPy methods
- Creating pandas DataFrames and Series
- Describing pandas DataFrames
- Understanding pandas Series
- Reading and querying the Quandl and Nasdaq Data Link data
- Grouping and joining pandas DataFrames
- Concatenating DataFrames
- Working with missing values
- Creating pivot tables
- Dealing with dates
- Date Features
- Date Methods
- References
- Chapter 03: Statistics for Data Insights
- Understanding attributes of data and their types
- Nominal attributes
- Ordinal attributes
- Numeric attributes
- Discrete and continuous attributes
- Measuring central tendency
- Mean
- Mode
- Median
- Measuring dispersion
- Range
- Interquartile range (IQR)
- Variance
- Standard deviation
- Skewness and kurtosis
- Understanding relationships using covariance and correlation coefficients
- Covariance
- Correlation
- Pearson correlation coefficient
- Spearman's rank correlation coefficient
- Kendall's rank correlation coefficient
- Collecting samples
- Probability sampling
- Non-probability sampling
- Notes:
- Description based upon print version of record.
- Performing parametric tests
- OCLC-licensed vendor bibliographic record.
- ISBN:
- 9781806022878
- OCLC:
- 1597390741
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