Hands-On Exploratory Data Analysis with Python: Perform EDA techniques to understand, summarize, and investigate your data

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Section 1: The Fundamentals of EDA
Chapter 01: Exploratory Data Analysis Fundamentals
	Understanding data science
	The significance of EDA
		Steps in EDA
	Making sense of data
		Numerical data
			Discrete data
			Continuous data
		Categorical data
		Measurement scales
			Nominal
			Ordinal 
			Interval
			Ratio
	Comparing EDA with classical and Bayesian analysis
	Software tools available for EDA
	Getting started with EDA
		NumPy
		Pandas
		SciPy
		Matplotlib
	Summary
	Further reading
Chapter 02: Visual Aids for EDA
	Technical requirements
	Line chart
		Steps involved
	Bar charts
	Scatter plot
		Bubble chart
		Scatter plot using seaborn
	Area plot and stacked plot
	Pie chart
	Table chart
	Polar chart
	Histogram
	Lollipop chart
	Choosing the best chart
	Other libraries to explore
	Summary
	Further reading
Chapter 03: EDA with Personal Email
	Technical requirements
	Loading the dataset
	Data transformation
		Data cleansing
		Loading the CSV file
		Converting the date
		Removing NaN values
		Applying descriptive statistics
		Data refactoring
		Dropping columns
		Refactoring timezones
	Data analysis
		Number of emails
		Time of day
		Average emails per day and hour
		Number of emails per day
		Most frequently used words
	Summary
	Further reading
Chapter 04: Data Transformation
	Technical requirements
	Background
	Merging database-style dataframes
		Concatenating along with an axis
		Using df.merge with an inner join
		Using the pd.merge() method with a left join
		Using the pd.merge() method with a right join
		Using pd.merge() methods with outer join
		Merging on index
		Reshaping and pivoting
	Transformation techniques
		Performing data deduplication
		Replacing values
		Handling missing data
			NaN values in pandas objects
			Dropping missing values
				Dropping by rows
				Dropping by columns
			Mathematical operations with NaN
			Filling missing values
			Backward and forward filling
			Interpolating missing values
		Renaming axis indexes
		Discretization and binning
		Outlier detection and filtering
		Permutation and random sampling
			Random sampling without replacement
			Random sampling with replacement
		Computing indicators/dummy variables
		String manipulation
	Benefits of data transformation
		Challenges
	Summary
	Further reading
Section 2: Descriptive Statistics
Chapter 05: Descriptive Statistics
	Technical requirements
	Understanding statistics
		Distribution function
			Uniform distribution
			Normal distribution
			Exponential distribution
			Binomial distribution
		Cumulative distribution function
		Descriptive statistics
	Measures of central tendency
		Mean/average
		Median
		Mode
	Measures of dispersion
		Standard deviation
		Variance
		Skewness
		Kurtosis
			Types of kurtosis
		Calculating percentiles
		Quartiles
			Visualizing quartiles
	Summary
	Further reading
Chapter 06: Grouping Datasets
	Technical requirements
	Understanding groupby() 
	Groupby mechanics
		Selecting a subset of columns
		Max and min
		Mean
	Data aggregation
		Group-wise operations
			Renaming grouped aggregation columns
		Group-wise transformations
	Pivot tables and cross-tabulations
		Pivot tables
		Cross-tabulations
	Summary
	Further reading
Chapter 07: Correlation
	Technical requirements
	Introducing correlation
	Types of analysis
		Understanding univariate analysis
		Understanding bivariate analysis
		Understanding multivariate analysis
	Discussing multivariate analysis using the Titanic dataset
	Outlining Simpson\'s paradox
	Correlation does not imply causation
	Summary
	Further reading
Chapter 08: Time Series Analysis
	Technical requirements
	Understanding the time series dataset
		Fundamentals of TSA
			Univariate time series
		Characteristics of time series data
	TSA with Open Power System Data
		Data cleaning
		Time-based indexing
		Visualizing time series
		Grouping time series data
		Resampling time series data
	Summary
	Further reading
Section 3: Model Development and Evaluation
Chapter 09: Hypothesis Testing and Regression
	Technical requirements
	Hypothesis testing
		Hypothesis testing principle
		statsmodels library
		Average reading time 
		Types of hypothesis testing
		T-test
	p-hacking
	Understanding regression
		Types of regression
			Simple linear regression
			Multiple linear regression
			Nonlinear regression
	Model development and evaluation
		Constructing a linear regression model
			Model evaluation
			Computing accuracy
			Understanding accuracy
		Implementing a multiple linear regression model
	Summary
	Further reading
Chapter 10: Model Development and Evaluation
	Technical requirements
	Types of machine learning
	Understanding supervised learning
		Regression
		Classification
	Understanding unsupervised learning
		Applications of unsupervised learning 
		Clustering using MiniBatch K-means clustering 
			Extracting keywords
			Plotting clusters
			Word cloud
	Understanding reinforcement learning
		Difference between supervised and reinforcement learning
		Applications of reinforcement learning
	Unified machine learning workflow 
		Data preprocessing
			Data collection
			Data analysis
			Data cleaning, normalization, and transformation
		Data preparation
		Training sets and corpus creation
		Model creation and training
		Model evaluation
		Best model selection and evaluation
		Model deployment
	Summary
	Further reading
Chapter 11: EDA on Wine Quality Data Analysis
	Technical requirements
	Disclosing the wine quality dataset
		Loading the dataset
		Descriptive statistics
		Data wrangling
	Analyzing red wine
		Finding correlated columns
		Alcohol versus quality
		Alcohol versus pH
	Analyzing white wine
		Red wine versus white wine 
		Adding a new attribute
		Converting into a categorical column
		Concatenating dataframes
		Grouping columns
		Univariate analysis
		Multivariate analysis on the combined dataframe
		Discrete categorical attributes
		3-D visualization
	Model development and evaluation
	Summary
	Further reading
Appendix
	String manipulation
		Creating strings
		Accessing characters in Python 
		String slicing
		Deleting/updating from a string
		Escape sequencing in Python
		Formatting strings
	Using pandas vectorized string functions
		Using string functions with a pandas DataFrame
	Using regular expressions
	Further reading
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