更新时间：2021-07-02 20:38:38
coverpage
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
pandas and Data Analysis
Introducing pandas
Data manipulation analysis science and pandas
Data manipulation
Data analysis
Data science
Where does pandas fit?
The process of data analysis
The process
Ideation
Retrieval
Preparation
Exploration
Modeling
Presentation
Reproduction
A note on being iterative and agile
Relating the book to the process
Concepts of data and analysis in our tour of pandas
Types of data
Structured
Unstructured
Semi-structured
Variables
Categorical
Continuous
Discrete
Time series data
General concepts of analysis and statistics
Quantitative versus qualitative data/analysis
Single and multivariate analysis
Descriptive statistics
Inferential statistics
Stochastic models
Probability and Bayesian statistics
Correlation
Regression
Other Python libraries of value with pandas
Numeric and scientific computing - NumPy and SciPy
Statistical analysis – StatsModels
Machine learning – scikit-learn
PyMC - stochastic Bayesian modeling
Data visualization - matplotlib and seaborn
Matplotlib
Seaborn
Summary
Up and Running with pandas
Installation of Anaconda
IPython and Jupyter Notebook
IPython
Jupyter Notebook
Introducing the pandas Series and DataFrame
Importing pandas
The pandas Series
The pandas DataFrame
Loading data from files into a DataFrame
Visualization
Representing Univariate Data with the Series
Configuring pandas
Creating a Series
Creating a Series using Python lists and dictionaries
Creation using NumPy functions
Creation using a scalar value
The .index and .values properties
The size and shape of a Series
Specifying an index at creation
Heads tails and takes
Retrieving values in a Series by label or position
Lookup by label using the [] operator and the .ix[] property
Explicit lookup by position with .iloc[]
Explicit lookup by labels with .loc[]
Slicing a Series into subsets
Alignment via index labels
Performing Boolean selection
Re-indexing a Series
Modifying a Series in-place
Representing Tabular and Multivariate Data with the DataFrame
Creating DataFrame objects
