Osoby które kupowały "Python Data Science Handbook. Essential Tools for Working with Data", wybierały także:

• Receptura na Python. Kurs Video. 54 praktyczne porady dla programist 199,00 zÅ‚, (59,70 zÅ‚ -70%)
• ZÅ‚am ten kod z Pythonem. Jak tworzyć, testować i Å‚amać szyfry 86,77 zÅ‚, (26,90 zÅ‚ -69%)
• NLP. Kurs video. Analiza danych tekstowych w j 149,00 zÅ‚, (59,60 zÅ‚ -60%)
• Web scraping w Data Science. Kurs video. Uczenie maszynowe i architektura splotowych sieci neuronowych 179,00 zÅ‚, (71,60 zÅ‚ -60%)
• Python dla administrator 179,00 zÅ‚, (71,60 zÅ‚ -60%)

Spis treści

Python Data Science Handbook. Essential Tools for Working with Data eBook -- spis treści

• Preface
  • What Is Data Science?
  • Who Is This Book For?
  • Why Python?
    • Python 2 Versus Python 3
  • Outline of This Book
  • Using Code Examples
  • Installation Considerations
  • Conventions Used in This Book
  • OReilly Safari
  • How to Contact Us
• 1. IPython: Beyond Normal Python
  • Shell or Notebook?
    • Launching the IPython Shell
    • Launching the Jupyter Notebook
  • Help and Documentation in IPython
    • Accessing Documentation with ?
    • Accessing Source Code with ??
    • Exploring Modules with Tab Completion
      • Tab completion of object contents
      • Tab completion when importing
      • Beyond tab completion: Wildcard matching
  • Keyboard Shortcuts in the IPython Shell
    • Navigation Shortcuts
    • Text Entry Shortcuts
    • Command History Shortcuts
    • Miscellaneous Shortcuts
  • IPython Magic Commands
    • Pasting Code Blocks: %paste and %cpaste
    • Running External Code: %run
    • Timing Code Execution: %timeit
    • Help on Magic Functions: ?, %magic, and %lsmagic
  • Input and Output History
    • IPythons In and Out Objects
    • Underscore Shortcuts and Previous Outputs
    • Suppressing Output
    • Related Magic Commands
  • IPython and Shell Commands
    • Quick Introduction to the Shell
    • Shell Commands in IPython
    • Passing Values to and from the Shell
  • Shell-Related Magic Commands
  • Errors and Debugging
    • Controlling Exceptions: %xmode
    • Debugging: When Reading Tracebacks Is Not Enough
      • Partial list of debugging commands
  • Profiling and Timing Code
    • Timing Code Snippets: %timeit and %time
    • Profiling Full Scripts: %prun
    • Line-by-Line Profiling with %lprun
    • Profiling Memory Use: %memit and %mprun
  • More IPython Resources
    • Web Resources
    • Books
• 2. Introduction to NumPy
  • Understanding Data Types in Python
    • A Python Integer Is More Than Just an Integer
    • A Python List Is More Than Just a List
    • Fixed-Type Arrays in Python
    • Creating Arrays from Python Lists
    • Creating Arrays from Scratch
    • NumPy Standard Data Types
  • The Basics of NumPy Arrays
    • NumPy Array Attributes
    • Array Indexing: Accessing Single Elements
    • Array Slicing: Accessing Subarrays
      • One-dimensional subarrays
      • Multidimensional subarrays
        • Accessing array rows and columns
      • Subarrays as no-copy views
      • Creating copies of arrays
    • Reshaping of Arrays
    • Array Concatenation and Splitting
      • Concatenation of arrays
      • Splitting of arrays
  • Computation on NumPy Arrays: Universal Functions
    • The Slowness of Loops
    • Introducing UFuncs
    • Exploring NumPys UFuncs
      • Array arithmetic
      • Absolute value
      • Trigonometric functions
      • Exponents and logarithms
      • Specialized ufuncs
    • Advanced Ufunc Features
      • Specifying output
      • Aggregates
      • Outer products
    • Ufuncs: Learning More
  • Aggregations: Min, Max, and Everything in Between
    • Summing the Values in an Array
    • Minimum and Maximum
      • Multidimensional aggregates
      • Other aggregation functions
    • Example: What Is the Average Height of US Presidents?
  • Computation on Arrays: Broadcasting
    • Introducing Broadcasting
    • Rules of Broadcasting
      • Broadcasting example 1
      • Broadcasting example 2
      • Broadcasting example 3
    • Broadcasting in Practice
      • Centering an array
      • Plotting a two-dimensional function
  • Comparisons, Masks, and Boolean Logic
    • Example: Counting Rainy Days
      • Digging into the data
    • Comparison Operators as ufuncs
    • Working with Boolean Arrays
      • Counting entries
      • Boolean operators
    • Boolean Arrays as Masks
  • Fancy Indexing
    • Exploring Fancy Indexing
    • Combined Indexing
    • Example: Selecting Random Points
    • Modifying Values with Fancy Indexing
    • Example: Binning Data
  • Sorting Arrays
    • Fast Sorting in NumPy: np.sort and np.argsort
      • Sorting along rows or columns
    • Partial Sorts: Partitioning
    • Example: k-Nearest Neighbors
  • Structured Data: NumPys Structured Arrays
    • Creating Structured Arrays
    • More Advanced Compound Types
    • RecordArrays: Structured Arrays with a Twist
    • On to Pandas
• 3. Data Manipulation with Pandas
  • Installing and Using Pandas
  • Introducing Pandas Objects
    • The Pandas Series Object
      • Series as generalized NumPy array
      • Series as specialized dictionary
      • Constructing Series objects
    • The Pandas DataFrame Object
      • DataFrame as a generalized NumPy array
      • DataFrame as specialized dictionary
      • Constructing DataFrame objects
        • From a single Series object
        • From a list of dicts
        • From a dictionary of Series objects
        • From a two-dimensional NumPy array
        • From a NumPy structured array
    • The Pandas Index Object
      • Index as immutable array
      • Index as ordered set
  • Data Indexing and Selection
    • Data Selection in Series
      • Series as dictionary
      • Series as one-dimensional array
      • Indexers: loc, iloc, and ix
    • Data Selection in DataFrame
      • DataFrame as a dictionary
      • DataFrame as two-dimensional array
      • Additional indexing conventions
  • Operating on Data in Pandas
    • Ufuncs: Index Preservation
    • UFuncs: Index Alignment
      • Index alignment in Series
      • Index alignment in DataFrame
    • Ufuncs: Operations Between DataFrame and Series
  • Handling Missing Data
    • Trade-Offs in Missing Data Conventions
    • Missing Data in Pandas
      • None: Pythonic missing data
      • NaN: Missing numerical data
      • NaN and None in Pandas
    • Operating on Null Values
      • Detecting null values
      • Dropping null values
      • Filling null values
  • Hierarchical Indexing
    • A Multiply Indexed Series
      • The bad way
      • The better way: Pandas MultiIndex
      • MultiIndex as extra dimension
    • Methods of MultiIndex Creation
      • Explicit MultiIndex constructors
      • MultiIndex level names
      • MultiIndex for columns
    • Indexing and Slicing a MultiIndex
      • Multiply indexed Series
      • Multiply indexed DataFrames
    • Rearranging Multi-Indices
      • Sorted and unsorted indices
      • Stacking and unstacking indices
      • Index setting and resetting
    • Data Aggregations on Multi-Indices
  • Combining Datasets: Concat and Append
    • Recall: Concatenation of NumPy Arrays
    • Simple Concatenation with pd.concat
      • Duplicate indices
        • Catching the repeats as an error
        • Ignoring the index
        • Adding MultiIndex keys
      • Concatenation with joins
      • The append() method
  • Combining Datasets: Merge and Join
    • Relational Algebra
    • Categories of Joins
      • One-to-one joins
      • Many-to-one joins
      • Many-to-many joins
    • Specification of the Merge Key
      • The on keyword
      • The left_on and right_on keywords
      • The left_index and right_index keywords
    • Specifying Set Arithmetic for Joins
    • Overlapping Column Names: The suffixes Keyword
    • Example: US States Data
  • Aggregation and Grouping
    • Planets Data
    • Simple Aggregation in Pandas
    • GroupBy: Split, Apply, Combine
      • Split, apply, combine
      • The GroupBy object
        • Column indexing
        • Iteration over groups
        • Dispatch methods
      • Aggregate, filter, transform, apply
        • Aggregation
        • Filtering
        • Transformation
        • The apply() method
      • Specifying the split key
        • A list, array, series, or index providing the grouping keys
        • A dictionary or series mapping index to group
        • Any Python function
        • A list of valid keys
      • Grouping example
  • Pivot Tables
    • Motivating Pivot Tables
    • Pivot Tables by Hand
    • Pivot Table Syntax
      • Multilevel pivot tables
      • Additional pivot table options
    • Example: Birthrate Data
      • Further data exploration
  • Vectorized String Operations
    • Introducing Pandas String Operations
    • Tables of Pandas String Methods
      • Methods similar to Python string methods
      • Methods using regular expressions
      • Miscellaneous methods
        • Vectorized item access and slicing
        • Indicator variables
    • Example: Recipe Database
      • A simple recipe recommender
      • Going further with recipes
  • Working with Time Series
    • Dates and Times in Python
      • Native Python dates and times: datetime and dateutil
      • Typed arrays of times: NumPys datetime64
      • Dates and times in Pandas: Best of both worlds
    • Pandas Time Series: Indexing by Time
    • Pandas Time Series Data Structures
      • Regular sequences: pd.date_range()
    • Frequencies and Offsets
    • Resampling, Shifting, and Windowing
      • Resampling and converting frequencies
      • Time-shifts
      • Rolling windows
    • Where to Learn More
    • Example: Visualizing Seattle Bicycle Counts
      • Visualizing the data
      • Digging into the data
  • High-Performance Pandas: eval() and query()
    • Motivating query() and eval(): Compound Expressions
    • pandas.eval() for Efficient Operations
      • Operations supported by pd.eval()
        • Arithmetic operators
        • Comparison operators
        • Bitwise operators
        • Object attributes and indices
        • Other operations
    • DataFrame.eval() for Column-Wise Operations
      • Assignment in DataFrame.eval()
      • Local variables in DataFrame.eval()
    • DataFrame.query() Method
    • Performance: When to Use These Functions
  • Further Resources
• 4. Visualization with Matplotlib
  • General Matplotlib Tips
    • Importing matplotlib
    • Setting Styles
    • show() or No show()? How to Display Your Plots
      • Plotting from a script
      • Plotting from an IPython shell
      • Plotting from an IPython notebook
    • Saving Figures to File
  • Two Interfaces for the Price of One
    • MATLAB-style interface
    • Object-oriented interface
  • Simple Line Plots
    • Adjusting the Plot: Line Colors and Styles
    • Adjusting the Plot: Axes Limits
    • Labeling Plots
  • Simple Scatter Plots
    • Scatter Plots with plt.plot
    • Scatter Plots with plt.scatter
    • plot Versus scatter: A Note on Efficiency
  • Visualizing Errors
    • Basic Errorbars
    • Continuous Errors
  • Density and Contour Plots
    • Visualizing a Three-Dimensional Function
  • Histograms, Binnings, and Density
    • Two-Dimensional Histograms and Binnings
      • plt.hist2d: Two-dimensional histogram
      • plt.hexbin: Hexagonal binnings
      • Kernel density estimation
  • Customizing Plot Legends
    • Choosing Elements for the Legend
    • Legend for Size of Points
    • Multiple Legends
  • Customizing Colorbars
    • Customizing Colorbars
      • Choosing the colormap
      • Color limits and extensions
      • Discrete colorbars
    • Example: Handwritten Digits
  • Multiple Subplots
    • plt.axes: Subplots by Hand
    • plt.subplot: Simple Grids of Subplots
    • plt.subplots: The Whole Grid in One Go
    • plt.GridSpec: More Complicated Arrangements
  • Text and Annotation
    • Example: Effect of Holidays on US Births
    • Transforms and Text Position
    • Arrows and Annotation
  • Customizing Ticks
    • Major and Minor Ticks
    • Hiding Ticks or Labels
    • Reducing or Increasing the Number of Ticks
    • Fancy Tick Formats
    • Summary of Formatters and Locators
  • Customizing Matplotlib: Configurations and Stylesheets
    • Plot Customization by Hand
    • Changing the Defaults: rcParams
    • Stylesheets
      • Default style
      • FiveThirtyEight style
      • ggplot
      • Bayesian Methods for Hackers style
      • Dark background
      • Grayscale
      • Seaborn style
  • Three-Dimensional Plotting in Matplotlib
    • Three-Dimensional Points and Lines
    • Three-Dimensional Contour Plots
    • Wireframes and Surface Plots
    • Surface Triangulations
      • Example: Visualizing a Möbius strip
  • Geographic Data with Basemap
    • Map Projections
      • Cylindrical projections
      • Pseudo-cylindrical projections
      • Perspective projections
      • Conic projections
      • Other projections
    • Drawing a Map Background
    • Plotting Data on Maps
    • Example: California Cities
    • Example: Surface Temperature Data
  • Visualization with Seaborn
    • Seaborn Versus Matplotlib
    • Exploring Seaborn Plots
      • Histograms, KDE, and densities
      • Pair plots
      • Faceted histograms
      • Factor plots
      • Joint distributions
      • Bar plots
    • Example: Exploring Marathon Finishing Times
  • Further Resources
    • Matplotlib Resources
    • Other Python Graphics Libraries
• 5. Machine Learning
  • What Is Machine Learning?
    • Categories of Machine Learning
    • Qualitative Examples of Machine Learning Applications
      • Classification: Predicting discrete labels
      • Regression: Predicting continuous labels
      • Clustering: Inferring labels on unlabeled data
      • Dimensionality reduction: Inferring structure of unlabeled data
    • Summary
  • Introducing Scikit-Learn
    • Data Representation in Scikit-Learn
      • Data as table
      • Features matrix
      • Target array
    • Scikit-Learns Estimator API
      • Basics of the API
      • Supervised learning example: Simple linear regression
      • Supervised learning example: Iris classification
      • Unsupervised learning example: Iris dimensionality
      • Unsupervised learning: Iris clustering
    • Application: Exploring Handwritten Digits
      • Loading and visualizing the digits data
      • Unsupervised learning: Dimensionality reduction
      • Classification on digits
    • Summary
  • Hyperparameters and Model Validation
    • Thinking About Model Validation
      • Model validation the wrong way
      • Model validation the right way: Holdout sets
      • Model validation via cross-validation
    • Selecting the Best Model
      • The biasvariance trade-off
      • Validation curves in Scikit-Learn
    • Learning Curves
      • Learning curves in Scikit-Learn
    • Validation in Practice: Grid Search
    • Summary
  • Feature Engineering
    • Categorical Features
    • Text Features
    • Image Features
    • Derived Features
    • Imputation of Missing Data
    • Feature Pipelines
  • In Depth: Naive Bayes Classification
    • Bayesian Classification
    • Gaussian Naive Bayes
    • Multinomial Naive Bayes
      • Example: Classifying text
    • When to Use Naive Bayes
  • In Depth: Linear Regression
    • Simple Linear Regression
    • Basis Function Regression
      • Polynomial basis functions
      • Gaussian basis functions
    • Regularization
      • Ridge regression ( regularization)
      • Lasso regularization ()
    • Example: Predicting Bicycle Traffic
  • In-Depth: Support Vector Machines
    • Motivating Support Vector Machines
    • Support Vector Machines: Maximizing the Margin
      • Fitting a support vector machine
      • Beyond linear boundaries: Kernel SVM
      • Tuning the SVM: Softening margins
    • Example: Face Recognition
    • Support Vector Machine Summary
  • In-Depth: Decision Trees and Random Forests
    • Motivating Random Forests: Decision Trees
      • Creating a decision tree
      • Decision trees and overfitting
    • Ensembles of Estimators: Random Forests
    • Random Forest Regression
    • Example: Random Forest for Classifying Digits
    • Summary of Random Forests
  • In Depth: Principal Component Analysis
    • Introducing Principal Component Analysis
      • PCA as dimensionality reduction
      • PCA for visualization: Handwritten digits
      • What do the components mean?
      • Choosing the number of components
    • PCA as Noise Filtering
    • Example: Eigenfaces
    • Principal Component Analysis Summary
  • In-Depth: Manifold Learning
    • Manifold Learning: HELLO
    • Multidimensional Scaling (MDS)
    • MDS as Manifold Learning
    • Nonlinear Embeddings: Where MDS Fails
    • Nonlinear Manifolds: Locally Linear Embedding
    • Some Thoughts on Manifold Methods
    • Example: Isomap on Faces
    • Example: Visualizing Structure in Digits
  • In Depth: k-Means Clustering
    • Introducing k-Means
    • k-Means Algorithm: ExpectationMaximization
      • Caveats of expectationmaximization
    • Examples
      • Example 1: k-Means on digits
      • Example 2: k-means for color compression
  • In Depth: Gaussian Mixture Models
    • Motivating GMM: Weaknesses of k-Means
    • Generalizing EM: Gaussian Mixture Models
      • Choosing the covariance type
    • GMM as Density Estimation
      • How many components?
    • Example: GMM for Generating New Data
  • In-Depth: Kernel Density Estimation
    • Motivating KDE: Histograms
    • Kernel Density Estimation in Practice
      • Selecting the bandwidth via cross-validation
    • Example: KDE on a Sphere
    • Example: Not-So-Naive Bayes
      • The anatomy of a custom estimator
      • Using our custom estimator
  • Application: A Face Detection Pipeline
    • HOG Features
    • HOG in Action: A Simple Face Detector
    • Caveats and Improvements
  • Further Machine Learning Resources
    • Machine Learning in Python
    • General Machine Learning
• Index