Python for Data Science For Dummies, 3rd Edition

Title Page
Copyright Page
Table of Contents
Introduction
	About This Book
	Foolish Assumptions
	Icons Used in This Book
	Beyond the Book
	Where to Go from Here
Part 1 Getting Started with Data Science and Python
	Chapter 1 Discovering the Match between Data Science and Python
		Understanding Python as a Language
			Viewing Python’s various uses as a general-purpose language
			Interpreting Python
			Compiling Python
		Defining Data Science
			Considering the emergence of data science
			Outlining the core competencies of a data scientist
			Linking data science, big data, and AI
		Creating the Data Science Pipeline
		Understanding Python’s Role in Data Science
			Considering the shifting profile of data scientists
			Working with a multipurpose, simple, and efficient language
		Learning to Use Python Fast
			Loading data
			Training a model
			Viewing a result
	Chapter 2 Introducing Python’s Capabilities and Wonders
		Working with Python
			Contributing to data science
			Getting a taste of the language
			Understanding the need for indentation
			Working with Jupyter Notebook and Google Colab
		Performing Rapid Prototyping and Experimentation
		Considering Speed of Execution
		Visualizing Power
		Using the Python Ecosystem for Data Science
			Accessing scientific tools using SciPy
			Performing fundamental scientific computing using NumPy
			Performing data analysis using pandas
			Implementing machine learning using Scikit-learn
			Going for deep learning with Keras and TensorFlow
			Performing analysis efficiently using XGBoost
			Plotting the data using Matplotlib
			Creating graphs with NetworkX
	Chapter 3 Setting Up Python for Data Science
		Working with Anaconda
			Using Jupyter Notebook
			Accessing the Anaconda Prompt
		Installing Anaconda on Windows
		Installing Anaconda on Linux
		Installing Anaconda on Mac OS X
		Downloading the Datasets and Example Code
			Using Jupyter Notebook
				Starting Jupyter Notebook
				Stopping the Jupyter Notebook server
			Defining the code repository
				Defining a new folder
				Creating a new notebook
				Adding notebook content
				Exporting a notebook
				Removing a notebook
				Importing a notebook
			Understanding the datasets used in this book
	Chapter 4 Working with Google Colab
		Defining Google Colab
			Understanding what Google Colab does
			Considering the online coding difference
			Using local runtime support
		Working with Notebooks
			Creating a new notebook
			Opening existing notebooks
				Using Google Drive for existing notebooks
				Using GitHub for existing notebooks
				Using local storage for existing notebooks
			Saving notebooks
				Using Drive to save notebooks
				Using GitHub to save notebooks
				Using GitHub gists to save notebooks
			Downloading notebooks
		Performing Common Tasks
			Creating code cells
			Creating text cells
			Creating special cells
			Editing cells
			Moving cells
		Using Hardware Acceleration
		Executing the Code
		Viewing Your Notebook
			Displaying the table of contents
			Getting notebook information
			Checking code execution
		Sharing Your Notebook
		Getting Help
Part 2 Getting Your Hands Dirty with Data
	Chapter 5 Working with Jupyter Notebook
		Using Jupyter Notebook
			Working with styles
			Getting Python help
			Using magic functions
				Obtaining the magic functions list
				Working with magic functions
			Discovering objects
				Getting object help
				Obtaining object specifics
				Using extended Python object help
			Restarting the kernel
			Restoring a checkpoint
		Performing Multimedia and Graphic Integration
			Embedding plots and other images
			Loading examples from online sites
			Obtaining online graphics and multimedia
	Chapter 6 Working with Real Data
		Uploading, Streaming, and Sampling Data
			Uploading small amounts of data into memory
			Streaming large amounts of data into memory
			Generating variations on image data
			Sampling data in different ways
		Accessing Data in Structured Flat-File Form
			Reading from a text file
			Reading CSV delimited format
			Reading Excel and other Microsoft Office files
		Sending Data in Unstructured File Form
		Managing Data from Relational Databases
		Interacting with Data from NoSQL Databases
		Accessing Data from the Web
	Chapter 7 Processing Your Data
		Juggling between NumPy and pandas
			Knowing when to use NumPy
			Knowing when to use pandas
		Validating Your Data
			Figuring out what’s in your data
			Removing duplicates
			Creating a data map and data plan
		Manipulating Categorical Variables
			Creating categorical variables
			Renaming levels
			Combining levels
		Dealing with Dates in Your Data
			Formatting date and time values
			Using the right time transformation
		Dealing with Missing Data
			Finding the missing data
			Encoding missingness
			Imputing missing data
		Slicing and Dicing: Filtering and Selecting Data
			Slicing rows
			Slicing columns
			Dicing
		Concatenating and Transforming
			Adding new cases and variables
			Removing data
			Sorting and shuffling
		Aggregating Data at Any Level
	Chapter 8 Reshaping Data
		Using the Bag of Words Model to Tokenize Data
			Understanding the bag of words model
			Sequencing text items with n-grams
			Implementing TF-IDF transformations
		Working with Graph Data
			Understanding the adjacency matrix
			Using NetworkX basics
	Chapter 9 Putting What You Know into Action
		Contextualizing Problems and Data
			Evaluating a data science problem
			Researching solutions
			Formulating a hypothesis
			Preparing your data
		Considering the Art of Feature Creation
			Defining feature creation
			Combining variables
			Understanding binning and discretization
			Using indicator variables
			Transforming distributions
		Performing Operations on Arrays
			Using vectorization
			Performing simple arithmetic on vectors and matrices
			Performing matrix vector multiplication
			Performing matrix multiplication
Part 3 Visualizing Information
	Chapter 10 Getting a Crash Course in Matplotlib
		Starting with a Graph
			Defining the plot
			Drawing multiple lines and plots
			Saving your work to disk
		Setting the Axis, Ticks, and Grids
			Getting the axes
			Formatting the axes
			Adding grids
		Defining the Line Appearance
			Working with line styles
			Using colors
			Adding markers
		Using Labels, Annotations, and Legends
			Adding labels
			Annotating the chart
			Creating a legend
	Chapter 11 Visualizing the Data
		Choosing the Right Graph
			Creating comparisons with bar charts
			Showing distributions using histograms
			Depicting groups using boxplots
			Seeing data patterns using scatterplots
		Creating Advanced Scatterplots
			Depicting groups
			Showing correlations
		Plotting Time Series
			Representing time on axes
			Plotting trends over time
		Plotting Geographical Data
			Using an environment in Notebook
			Using Cartopy to plot geographic data
			Avoiding outdated libraries: The Basemap Toolkit
		Visualizing Graphs
			Developing undirected graphs
			Developing directed graphs
Part 4 Wrangling Data
	Chapter 12 Stretching Python’s Capabilities
		Playing with Scikit-learn
			Understanding classes in Scikit-learn
			Defining applications for data science
		Using Transformative Functions
			Chaining estimators
			Transforming targets
			Composing features
			Handling heterogeneous data
		Considering Timing and Performance
			Benchmarking with timeit
			Working with the memory profiler
		Running in Parallel on Multiple Cores
			Performing multicore parallelism
			Demonstrating multiprocessing
	Chapter 13 Exploring Data Analysis
		The EDA Approach
		Defining Descriptive Statistics for Numeric Data
			Measuring central tendency
			Measuring variance and range
			Working with percentiles
			Defining measures of normality
		Counting for Categorical Data
			Understanding frequencies
			Creating contingency tables
		Creating Applied Visualization for EDA
			Inspecting boxplots
			Performing t-tests after boxplots
			Observing parallel coordinates
			Graphing distributions
			Plotting scatterplots
		Understanding Correlation
			Using covariance and correlation
			Using nonparametric correlation
			Considering chi-square for tables
		Working with Cramér’s V
		Modifying Data Distributions
			Using different statistical distributions
			Creating a Z-score standardization
			Transforming other notable distributions
	Chapter 14 Reducing Dimensionality
		Understanding SVD
			Looking for dimensionality reduction
			Using SVD to measure the invisible
		Performing Factor Analysis and PCA
			Considering the psychometric model
			Looking for hidden factors
			Using components, not factors
			Achieving dimensionality reduction
			Squeezing information with t-SNE
		Understanding Some Applications
			Recognizing faces with PCA
			Extracting topics with NMF
			Recommending movies
	Chapter 15 Clustering
		Clustering with K-means
			Understanding centroid-based algorithms
			Creating an example with image data
			Looking for optimal solutions
			Clustering big data
		Performing Hierarchical Clustering
			Using a hierarchical cluster solution
			Visualizing aggregative clustering solutions
		Discovering New Groups with DBScan
	Chapter 16 Detecting Outliers in Data
		Considering Outlier Detection
			Finding more things that can go wrong
			Understanding anomalies and novel data
		Examining a Simple Univariate Method
			Leveraging on the Gaussian distribution
			Remediating outliers
		Developing a Multivariate Approach
			Using principal component analysis
			Using cluster analysis for spotting outliers
			Automating detection with Isolation Forests
Part 5 Learning from Data
	Chapter 17 Exploring Four Simple and Effective Algorithms
		Guessing the Number: Linear Regression
			Defining the family of linear models
			Using more variables
			Understanding limitations and problems
		Moving to Logistic Regression
			Applying logistic regression
			Considering the case when there are more classes
		Making Things as Simple as Naïve Bayes
			Finding out that Naïve Bayes isn’t so naïve
			Predicting text classifications
		Learning Lazily with Nearest Neighbors
			Predicting after observing neighbors
			Choosing your k parameter wisely
	Chapter 18 Performing Cross-Validation, Selection, and Optimization
		Pondering the Problem of Fitting a Model
			Understanding bias and variance
			Defining a strategy for picking models
			Dividing between training and test sets
		Cross-Validating
			Using cross-validation on k folds
			Sampling stratifications for complex data
		Selecting Variables Like a Pro
			Selecting by univariate measures
			Employing forward and backward selection
		Pumping Up Your Hyperparameters
			Implementing a grid search
			Trying a randomized search
	Chapter 19 Increasing Complexity with Linear and Nonlinear Tricks
		Using Nonlinear Transformations
			Doing variable transformations
			Creating interactions between variables
		Regularizing Linear Models
			Relying on Ridge regression (L2)
			Using the Lasso (L1)
			Leveraging regularization
			Combining L1 & L2: Elasticnet
		Fighting with Big Data Chunk by Chunk
			Determining when there is too much data
			Implementing Stochastic Gradient Descent
		Understanding Support Vector Machines
			Relying on a computational method
			Fixing many new parameters
			Classifying with SVC
			Going nonlinear is easy
			Performing regression with SVR
			Creating a stochastic solution with SVM
		Playing with Neural Networks
			Understanding neural networks
			Classifying and regressing with neurons
	Chapter 20 Understanding the Power of the Many
		Starting with a Plain Decision Tree
			Understanding a decision tree
			Creating classification trees
			Creating regression trees
		Getting Lost in a Random Forest
			Making machine learning accessible
			Working with a Random Forest classifier
			Working with a Random Forest regressor
			Optimizing a Random Forest
		Boosting Predictions
			Knowing that many weak predictors win
			Setting a gradient boosting classifier
			Running a gradient boosting regressor
			Using GBM hyperparameters
			Using XGBoost
Part 6 The Part of Tens
	Chapter 21 Ten Essential Data Resources
		Discovering the News with Reddit
		Getting a Good Start with KDnuggets
		Locating Free Learning Resources with Quora
		Gaining Insights with Oracle’s AI & Data Science Blog
		Accessing the Huge List of Resources on Data Science Central
		Discovering New Beginner Data Science Methodologies at Data Science 101
		Obtaining the Most Authoritative Sources at Udacity
		Receiving Help with Advanced Topics at Conductrics
		Obtaining the Facts of Open Source Data Science from Springboard
		Zeroing In on Developer Resources with Jonathan Bower
	Chapter 22 Ten Data Challenges You Should Take
		Removing Personally Identifiable Information
		Creating a Secure Data Environment
		Working with a Multiple-Data- Source Problem
		Honing Your Overfit Strategies
		Trudging Through the MovieLens Dataset
		Locating the Correct Data Source
		Working with Handwritten Information
		Working with Pictures
		Indentifying Data Lineage
		Interacting with a Huge Graph
Index
EULA