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Molin, Stefanie. Hands-On Data Analysis with Pandas: A Python Data Science Handbook for Data Collection, Wrangling, Analysis, and Visualization / Stefanie Molin. — Second edition. — 1 online resource — <URL:http://elib.fa.ru/ebsco/2922165.pdf>.

Дата создания записи: 05.05.2021

Тематика: Python (Computer program language); Data mining.; Data mining.; Python (Computer program language)

Коллекции: EBSCO

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Оглавление

  • Cover
  • Title Page
  • Copyright and Credits
  • Dedicated
  • Foreword to the Second Edition
  • Foreword to the First Edition
  • Contributors
  • Table of Contents
  • Preface
  • Section 1: Getting Started with Pandas
  • Chapter 1: Introduction to Data Analysis
    • Chapter materials
    • The fundamentals of data analysis
      • Data collection
      • Data wrangling
      • Exploratory data analysis
      • Drawing conclusions
    • Statistical foundations
      • Sampling
      • Descriptive statistics
      • Prediction and forecasting
      • Inferential statistics
    • Setting up a virtual environment
      • Virtual environments
      • Installing the required Python packages
      • Why pandas?
      • Jupyter Notebooks
    • Summary
    • Exercises
    • Further reading
  • Chapter 2: Working with Pandas DataFrames
    • Chapter materials
    • Pandas data structures
      • Series
      • Index
      • DataFrame
    • Creating a pandas DataFrame
      • From a Python object
      • From a file
      • From a database
      • From an API
    • Inspecting a DataFrame object
      • Examining the data
      • Describing and summarizing the data
    • Grabbing subsets of the data
      • Selecting columns
      • Slicing
      • Indexing
      • Filtering
    • Adding and removing data
      • Creating new data
      • Deleting unwanted data
    • Summary
    • Exercises
    • Further reading
  • Section 2: Using Pandas for Data Analysis
  • Chapter 3: Data Wrangling with Pandas
    • Chapter materials
    • Understanding data wrangling
      • Data cleaning
      • Data transformation
      • Data enrichment
    • Exploring an API to find and collect temperature data
    • Cleaning data
      • Renaming columns
      • Type conversion
      • Reordering, reindexing, and sorting data
    • Reshaping data
      • Transposing DataFrames
      • Pivoting DataFrames
      • Melting DataFrames
    • Handling duplicate, missing, or invalid data
      • Finding the problematic data
      • Mitigating the issues
    • Summary
    • Exercises
    • Further reading
  • Chapter 4: Aggregating Pandas DataFrames
    • Chapter materials
    • Performing database-style operations on DataFrames
      • Querying DataFrames
      • Merging DataFrames
    • Using DataFrame operations to enrich data
      • Arithmetic and statistics
      • Binning
      • Applying functions
      • Window calculations
      • Pipes
    • Aggregating data
      • Summarizing DataFrames
      • Aggregating by group
      • Pivot tables and crosstabs
    • Working with time series data
      • Time-based selection and filtering
      • Shifting for lagged data
      • Differenced data
      • Resampling
      • Merging time series
    • Summary
    • Exercises
    • Further reading
  • Chapter 5: Visualizing Data with Pandas and Matplotlib
    • Chapter materials
    • An introduction to matplotlib
      • The basics
      • Plot components
      • Additional options
    • Plotting with pandas
      • Evolution over time
      • Relationships between variables
      • Distributions
      • Counts and frequencies
    • The pandas.plotting module
      • Scatter matrices
      • Lag plots
      • Autocorrelation plots
      • Bootstrap plots
    • Summary
    • Exercises
    • Further reading
  • Chapter 6: Plotting with Seaborn and Customization Techniques
    • Chapter materials
    • Utilizing seaborn for advanced plotting
      • Categorical data
      • Correlations and heatmaps
      • Regression plots
      • Faceting
    • Formatting plots with matplotlib
      • Titles and labels
      • Legends
      • Formatting axes
    • Customizing visualizations
      • Adding reference lines
      • Shading regions
      • Annotations
      • Colors
      • Textures
    • Summary
    • Exercises
    • Further reading
  • Section 3: Applications – Real-World Analyses Using Pandas
  • Chapter 7: Financial Analysis – Bitcoin and the Stock Market
    • Chapter materials
    • Building a Python package
      • Package structure
      • Overview of the stock_analysis package
      • UML diagrams
    • Collecting financial data
      • The StockReader class
      • Collecting historical data from Yahoo! Finance
    • Exploratory data analysis
      • The Visualizer class family
      • Visualizing a stock
      • Visualizing multiple assets
    • Technical analysis of financial instruments
      • The StockAnalyzer class
      • The AssetGroupAnalyzer class
      • Comparing assets
    • Modeling performance using historical data
      • The StockModeler class
      • Time series decomposition
      • ARIMA
      • Linear regression with statsmodels
      • Comparing models
    • Summary
    • Exercises
    • Further reading
  • Chapter 8: Rule-Based Anomaly Detection
    • Chapter materials
    • Simulating login attempts
      • Assumptions
      • The login_attempt_simulator package
      • Simulating from the command line
    • Exploratory data analysis
    • Implementing rule-based anomaly detection
      • Percent difference
      • Tukey fence
      • Z-score
      • Evaluating performance
    • Summary
    • Exercises
    • Further reading
  • Section 4: Introduction to Machine Learning with Scikit-Learn
  • Chapter 9: Getting Started with Machine Learning in Python
    • Chapter materials
    • Overview of the machine learning landscape
      • Types of machine learning
      • Common tasks
      • Machine learning in Python
    • Exploratory data analysis
      • Red wine quality data
      • White and red wine chemical properties data
      • Planets and exoplanets data
    • Preprocessing data
      • Training and testing sets
      • Scaling and centering data
      • Encoding data
      • Imputing
      • Additional transformers
      • Building data pipelines
    • Clustering
      • k-means
      • Evaluating clustering results
    • Regression
      • Linear regression
      • Evaluating regression results
    • Classification
      • Logistic regression
      • Evaluating classification results
    • Summary
    • Exercises
    • Further reading
  • Chapter 10: Making Better Predictions – Optimizing Models
    • Chapter materials
    • Hyperparameter tuning with grid search
    • Feature engineering
      • Interaction terms and polynomial features
      • Dimensionality reduction
      • Feature unions
      • Feature importances
    • Ensemble methods
      • Random forest
      • Gradient boosting
      • Voting
    • Inspecting classification prediction confidence
    • Addressing class imbalance
      • Under-sampling
      • Over-sampling
    • Regularization
    • Summary
    • Exercises
    • Further reading
  • Chapter 11: Machine Learning Anomaly Detection
    • Chapter materials
    • Exploring the simulated login attempts data
    • Utilizing unsupervised methods of anomaly detection
      • Isolation forest
      • Local outlier factor
      • Comparing models
    • Implementing supervised anomaly detection
      • Baselining
      • Logistic regression
    • Incorporating a feedback loop with online learning
      • Creating the PartialFitPipeline subclass
      • Stochastic gradient descent classifier
    • Summary
    • Exercises
    • Further reading
  • Section 5: Additional Resources
  • Chapter 12: The Road Ahead
    • Data resources
      • Python packages
      • Searching for data
      • APIs
      • Websites
    • Practicing working with data
    • Python practice
    • Summary
    • Exercises
    • Further reading
  • Solutions
  • Appendix
  • About Packt
  • Other Books You May Enjoy
  • Index

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