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Python：Data Analytics and Visualization
Phuong Vo.T.H Martin Czygan Ashish Kumar Kirthi Raman 著
更新時(shí)間：2021-07-09 18:52:29
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ThisbookisforPythonDeveloperswhoarekeentogetintodataanalysisandwishtovisualizetheiranalyzeddatainamoreefficientandinsightfulmanner.
最新章節(jié)
- Index
- Bibliography
- About matplotlib
- Packages websites
- Packages installed with Anaconda
- An overview of conda
品牌：中圖公司
上架時(shí)間：2021-07-09 18:08:42
出版社：Packt Publishing
本書數(shù)字版權(quán)由中圖公司提供，并由其授權(quán)上海閱文信息技術(shù)有限公司制作發(fā)行
- Index 更新時(shí)間：2021-07-09 18:52:29
- Bibliography
- About matplotlib
- Packages websites
- Packages installed with Anaconda
- An overview of conda
- Appendix B. Go Forth and Explore Visualization
- Summary
- Computer simulation
- Chapter 8. Advanced Visualization
- Summary
- Stochastic block models
- A genetic programming example
- Maximum flow and minimum cut
- The directed acyclic graph test
- The planar graph test
- Analysis of social networks
- The clustering coefficient of graphs
- Directed graphs and multigraphs
- Chapter 7. Bioinformatics Genetics and Network Models
- Summary
- k-means clustering
- Principal component analysis
- Support vector machines
- Logistic regression
- k-nearest neighbors
- Viewing positive sentiments using word clouds
- The Na?ˉve Bayes classifier using TextBlob
- The Na?ˉve Bayes classifier
- The Bayes theorem
- Decision tree
- Linear regression
- Understanding linear regression
- Classification methods
- Chapter 6. Statistical and Machine Learning
- Summary
- Creating animated and interactive plots
- An overview of statistical and machine learning
- The threshold model
- The stochastic model
- The deterministic model
- Chapter 5. Financial and Statistical Models
- Summary
- The visualization example in sports
- Visualization using matplotlib
- Other data structures
- Array indexing
- Slicing
- Scalar selection
- NumPy SciPy and MKL functions
- Chapter 4. Numerical Computing and Interactive Plotting
- Summary
- Interactive visualization packages
- Visualization plots with Anaconda
- The IDE tools in Python
- Chapter 3. Getting Started with the Python IDE
- Summary
- Interactive visualization
- Visualization tools in Python
- Some best practices for visualization
- Perception and presentation methods
- Creating interesting stories with data
- A sports example
- The Ebola example
- Why does visualization require planning?
- Chapter 2. Data Analysis and Visualization
- Summary
- Visualization plots
- How does visualization help decision-making?
- Data visualization history
- The transformation of data
- Data information knowledge and insight
- Chapter 1. A Conceptual Framework for Data Visualization
- Part 3. Module 3
- Appendix A. A List of Links
- Summary
- Best practices for business contexts
- Best practices for statistics
- Best practices for algorithms
- Best practices for data handling
- Best practices for coding
- Chapter 9. Best Practices for Predictive Modelling
- Summary
- Understanding and implementing random forests
- Understanding and implementing regression trees
- Implementing a decision tree with scikit-learn
- Understanding the mathematics behind decision trees
- Introducing decision trees
- Chapter 8. Trees and Random Forests with Python
- Summary
- Fine-tuning the clustering
- Implementing clustering using Python
- Mathematics behind clustering
- Introduction to clustering – what why and how?
- Chapter 7. Clustering with Python
- Summary
- Model validation
- Model validation and evaluation
- Implementing logistic regression with Python
- Understanding the math behind logistic regression
- Linear regression versus logistic regression
- Chapter 6. Logistic Regression with Python
- Summary
- Handling other issues in linear regression
- Model validation
- Implementing linear regression with Python
- Making sense of result parameters
- Understanding the maths behind linear regression
- Chapter 5. Linear Regression with Python
- Summary
- Correlation
- Chi-square tests
- Hypothesis testing
- Random sampling and the central limit theorem
- Chapter 4. Statistical Concepts for Predictive Modelling
- Summary
- Merging/joining datasets
- Concatenating and appending data
- Random sampling – splitting a dataset in training and testing datasets
- Grouping the data – aggregation filtering and transformation
- Generating random numbers and their usage
- Subsetting a dataset
- Chapter 3. Data Wrangling
- Summary
- Visualizing a dataset by basic plotting
- Creating dummy variables
- Handling missing values
- Basics – summary dimensions and structure
- Case 4 – miscellaneous cases
- Case 3 – reading data from a URL
- Case 2 – reading a dataset using the open method of Python
- Use cases of the read_csv method
- The read_csv method
- Various methods of importing data in Python
- Reading the data – variations and examples
- Chapter 2. Data Cleaning
- Summary
- IDEs for Python
- Python and its packages for predictive modelling
- Python and its packages – download and installation
- Applications and examples of predictive modelling
- Introducing predictive modelling
- Chapter 1. Getting Started with Predictive Modelling
- Part 2. Module 2
- Summary
- Measuring prediction performance
- Unsupervised learning – clustering and dimensionality reduction
- Supervised learning – classification and regression
- Data representation in scikit-learn
- The scikit-learn modules for different models
- An overview of machine learning models
- Chapter 8. Machine Learning Models with scikit-learn
- Summary
- Grouping data
- Data aggregation
- Data munging
- Chapter 7. Data Analysis Application Examples
- Summary
- Interacting with data in Redis
- Interacting with data in MongoDB
- Interacting with data in binary format
- Interacting with data in text format
- Chapter 6. Interacting with Databases
- Summary
- Time series plotting
- Timedeltas
- Time zone handling
- Upsampling time series data
- Downsampling time series data
- Resampling time series
- Working with date and time objects
- Time series primer
- Chapter 5. Time Series
- Summary
- Additional Python data visualization tools
- Plotting functions with Pandas
- Legends and annotations
- Exploring plot types
- The matplotlib API primer
- Chapter 4. Data Visualization
- Summary
- Advanced uses of Pandas for data analysis
- Working with missing data
- Computational tools
- Indexing and selecting data
- The essential basic functionality
- The Pandas data structure
- An overview of the Pandas package
- Chapter 3. Data Analysis with Pandas
- Summary
- NumPy random numbers
- Linear algebra with NumPy
- Data processing using arrays
- Array functions
- NumPy arrays
- Chapter 2. NumPy Arrays and Vectorized Computation
- Summary
- Python libraries in data analysis
- An overview of the libraries in data analysis
- Data analysis and processing
- Chapter 1. Introducing Data Analysis and Libraries
- Part 1. Module 1
- Preface
- Credits
- 版權(quán)信息
- 封面
- 封面
- 版權(quán)信息
- Credits
- Preface
- Part 1. Module 1
- Chapter 1. Introducing Data Analysis and Libraries
- Data analysis and processing
- An overview of the libraries in data analysis
- Python libraries in data analysis
- Summary
- Chapter 2. NumPy Arrays and Vectorized Computation
- NumPy arrays
- Array functions
- Data processing using arrays
- Linear algebra with NumPy
- NumPy random numbers
- Summary
- Chapter 3. Data Analysis with Pandas
- An overview of the Pandas package
- The Pandas data structure
- The essential basic functionality
- Indexing and selecting data
- Computational tools
- Working with missing data
- Advanced uses of Pandas for data analysis
- Summary
- Chapter 4. Data Visualization
- The matplotlib API primer
- Exploring plot types
- Legends and annotations
- Plotting functions with Pandas
- Additional Python data visualization tools
- Summary
- Chapter 5. Time Series
- Time series primer
- Working with date and time objects
- Resampling time series
- Downsampling time series data
- Upsampling time series data
- Time zone handling
- Timedeltas
- Time series plotting
- Summary
- Chapter 6. Interacting with Databases
- Interacting with data in text format
- Interacting with data in binary format
- Interacting with data in MongoDB
- Interacting with data in Redis
- Summary
- Chapter 7. Data Analysis Application Examples
- Data munging
- Data aggregation
- Grouping data
- Summary
- Chapter 8. Machine Learning Models with scikit-learn
- An overview of machine learning models
- The scikit-learn modules for different models
- Data representation in scikit-learn
- Supervised learning – classification and regression
- Unsupervised learning – clustering and dimensionality reduction
- Measuring prediction performance
- Summary
- Part 2. Module 2
- Chapter 1. Getting Started with Predictive Modelling
- Introducing predictive modelling
- Applications and examples of predictive modelling
- Python and its packages – download and installation
- Python and its packages for predictive modelling
- IDEs for Python
- Summary
- Chapter 2. Data Cleaning
- Reading the data – variations and examples
- Various methods of importing data in Python
- The read_csv method
- Use cases of the read_csv method
- Case 2 – reading a dataset using the open method of Python
- Case 3 – reading data from a URL
- Case 4 – miscellaneous cases
- Basics – summary dimensions and structure
- Handling missing values
- Creating dummy variables
- Visualizing a dataset by basic plotting
- Summary
- Chapter 3. Data Wrangling
- Subsetting a dataset
- Generating random numbers and their usage
- Grouping the data – aggregation filtering and transformation
- Random sampling – splitting a dataset in training and testing datasets
- Concatenating and appending data
- Merging/joining datasets
- Summary
- Chapter 4. Statistical Concepts for Predictive Modelling
- Random sampling and the central limit theorem
- Hypothesis testing
- Chi-square tests
- Correlation
- Summary
- Chapter 5. Linear Regression with Python
- Understanding the maths behind linear regression
- Making sense of result parameters
- Implementing linear regression with Python
- Model validation
- Handling other issues in linear regression
- Summary
- Chapter 6. Logistic Regression with Python
- Linear regression versus logistic regression
- Understanding the math behind logistic regression
- Implementing logistic regression with Python
- Model validation and evaluation
- Model validation
- Summary
- Chapter 7. Clustering with Python
- Introduction to clustering – what why and how?
- Mathematics behind clustering
- Implementing clustering using Python
- Fine-tuning the clustering
- Summary
- Chapter 8. Trees and Random Forests with Python
- Introducing decision trees
- Understanding the mathematics behind decision trees
- Implementing a decision tree with scikit-learn
- Understanding and implementing regression trees
- Understanding and implementing random forests
- Summary
- Chapter 9. Best Practices for Predictive Modelling
- Best practices for coding
- Best practices for data handling
- Best practices for algorithms
- Best practices for statistics
- Best practices for business contexts
- Summary
- Appendix A. A List of Links
- Part 3. Module 3
- Chapter 1. A Conceptual Framework for Data Visualization
- Data information knowledge and insight
- The transformation of data
- Data visualization history
- How does visualization help decision-making?
- Visualization plots
- Summary
- Chapter 2. Data Analysis and Visualization
- Why does visualization require planning?
- The Ebola example
- A sports example
- Creating interesting stories with data
- Perception and presentation methods
- Some best practices for visualization
- Visualization tools in Python
- Interactive visualization
- Summary
- Chapter 3. Getting Started with the Python IDE
- The IDE tools in Python
- Visualization plots with Anaconda
- Interactive visualization packages
- Summary
- Chapter 4. Numerical Computing and Interactive Plotting
- NumPy SciPy and MKL functions
- Scalar selection
- Slicing
- Array indexing
- Other data structures
- Visualization using matplotlib
- The visualization example in sports
- Summary
- Chapter 5. Financial and Statistical Models
- The deterministic model
- The stochastic model
- The threshold model
- An overview of statistical and machine learning
- Creating animated and interactive plots
- Summary
- Chapter 6. Statistical and Machine Learning
- Classification methods
- Understanding linear regression
- Linear regression
- Decision tree
- The Bayes theorem
- The Na?ˉve Bayes classifier
- The Na?ˉve Bayes classifier using TextBlob
- Viewing positive sentiments using word clouds
- k-nearest neighbors
- Logistic regression
- Support vector machines
- Principal component analysis
- k-means clustering
- Summary
- Chapter 7. Bioinformatics Genetics and Network Models
- Directed graphs and multigraphs
- The clustering coefficient of graphs
- Analysis of social networks
- The planar graph test
- The directed acyclic graph test
- Maximum flow and minimum cut
- A genetic programming example
- Stochastic block models
- Summary
- Chapter 8. Advanced Visualization
- Computer simulation
- Summary
- Appendix B. Go Forth and Explore Visualization
- An overview of conda
- Packages installed with Anaconda
- Packages websites
- About matplotlib
- Bibliography
- Index 更新時(shí)間：2021-07-09 18:52:29
