Numerical Computing with Python电子书

Title Page

Copyright

Numerical Computing with Python

Contributors

About the authors

About the reviewers

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Conventions used

Get in touch

Reviews

Journey from Statistics to Machine Learning

Statistical terminology for model building and validation

Machine learning

Statistical fundamentals and terminology for model building and validation

Bias versus variance trade-off

Train and test data

Summary

Tree-Based Machine Learning Models

Introducing decision tree classifiers

Terminology used in decision trees

Decision tree working methodology from first principles

Comparison between logistic regression and decision trees

Comparison of error components across various styles of models

Remedial actions to push the model towards the ideal region

HR attrition data example

Decision tree classifier

Tuning class weights in decision tree classifier

Bagging classifier

Random forest classifier

Random forest classifier - grid search

AdaBoost classifier

Gradient boosting classifier

Comparison between AdaBoosting versus gradient boosting

Extreme gradient boosting - XGBoost classifier

Ensemble of ensembles - model stacking

Ensemble of ensembles with different types of classifiers

Ensemble of ensembles with bootstrap samples using a single type of classifier

Summary

K-Nearest Neighbors and Naive Bayes

K-nearest neighbors

KNN voter example

Curse of dimensionality

Curse of dimensionality with 1D, 2D, and 3D example

KNN classifier with breast cancer Wisconsin data example

Tuning of k-value in KNN classifier

Naive Bayes

Probability fundamentals

Joint probability

Understanding Bayes theorem with conditional probability

Naive Bayes classification

Laplace estimator

Naive Bayes SMS spam classification example

Summary

Unsupervised Learning

K-means clustering

K-means working methodology from first principles

Optimal number of clusters and cluster evaluation

The elbow method

K-means clustering with the iris data example

Principal Component Analysis - PCA

PCA working methodology from first principles

PCA applied on handwritten digits using scikit-learn

Singular value decomposition - SVD

SVD applied on handwritten digits using scikit-learn

Deep auto encoders

Model building technique using encoder-decoder architecture

Deep auto encoders applied on handwritten digits using Keras

Summary

Reinforcement Learning

Reinforcement learning basics

Category 1 - value based

Category 2 - policy based

Category 3 - actor-critic

Category 4 - model-free

Category 5 - model-based

Fundamental categories in sequential decision making

Markov decision processes and Bellman equations

Dynamic programming

Algorithms to compute optimal policy using dynamic programming

Grid world example using value and policy iteration algorithms with basic Python

Monte Carlo methods

Monte Carlo prediction

The suitability of Monte Carlo prediction on grid-world problems

Modeling Blackjack example of Monte Carlo methods using Python

Temporal difference learning

TD prediction

Driving office example for TD learning

SARSA on-policy TD control

Q-learning - off-policy TD control

Cliff walking example of on-policy and off-policy of TD control

Further reading

Summary

Hello Plotting World!

Hello Matplotlib!

What is Matplotlib?

What's new in Matplotlib 2.0?

Changes to the default style

Color cycle

Colormap

Scatter plot

Legend

Line style

Patch edges and color

Fonts

Improved functionality or performance

Improved color conversion API and RGBA support

Improved image support

Faster text rendering

Change in the default animation codec

Changes in settings

New configuration parameters (rcParams)

Style parameter blacklist

Change in Axes property keywords

Plotting our first graph

Loading data for plotting

Data structures

List

Numpy array

pandas dataframe

Loading data from files

The basic Python way

The Numpy way

The pandas way

Importing the Matplotlib pyplot module

Plotting a curve

Viewing the figure

Saving the figure

Setting the output format

PNG (Portable Network Graphics)

PDF (Portable Document Format)

SVG (Scalable Vector Graphics)

Post (Postscript)

Adjusting the resolution

Summary

Visualizing Online Data

Typical API data formats

CSV

JSON

XML

Introducing pandas

Importing online population data in the CSV format

Importing online financial data in the JSON format

Visualizing the trend of data

Area chart and stacked area chart

Introducing Seaborn

Visualizing univariate distribution

Bar chart in Seaborn

Histogram and distribution fitting in Seaborn

Visualizing a bivariate distribution

Scatter plot in Seaborn

Visualizing categorical data

Categorical scatter plot

Strip plot and swarm plot

Box plot and violin plot

Controlling Seaborn figure aesthetics

Preset themes

Removing spines from the figure

Changing the size of the figure

Fine-tuning the style of the figure

More about colors

Color scheme and color palettes

Summary

Visualizing Multivariate Data

Getting End-of-Day (EOD) stock data from Quandl

Grouping the companies by industry

Converting the date to a supported format

Getting the percentage change of the closing price

Two-dimensional faceted plots

Factor plot in Seaborn

Faceted grid in Seaborn

Pair plot in Seaborn

Other two-dimensional multivariate plots

Heatmap in Seaborn

Candlestick plot in matplotlib.finance

Visualizing various stock market indicators

Building a comprehensive stock chart

Three-dimensional (3D) plots

3D scatter plot

3D bar chart

Caveats of Matplotlib 3D

Summary

Adding Interactivity and Animating Plots

Scraping information from websites

Non-interactive backends

Interactive backends

Tkinter-based backend

Interactive backend for Jupyter Notebook

Plot.ly-based backend

Creating animated plots

Installation of FFmpeg

Creating animations

Summary

Selecting Subsets of Data

Selecting Series data

Getting ready

How to do it...

How it works...

There's more...

See also

Selecting DataFrame rows

Getting ready

How to do it...

How it works...

There's more...

See also

Selecting DataFrame rows and columns simultaneously

Getting ready

How to do it...

How it works...

There's more...

Selecting data with both integers and labels

Getting ready

How to do it...

How it works...

There's more...

Speeding up scalar selection

Getting ready

How to do it...

How it works...

There's more...

Slicing rows lazily

Getting ready

How to do it...

How it works...

There's more...

Slicing lexicographically

Getting ready

How to do it...

How it works...

There's more...

Boolean Indexing

Calculating boolean statistics

Getting ready

How to do it...

How it works...

There's more...

Constructing multiple boolean conditions

Getting ready

How to do it...

How it works...

There's more...

See also

Filtering with boolean indexing

Getting ready

How to do it...

How it works...

There's more...

See also

Replicating boolean indexing with index selection

Getting ready

How to do it...

How it works...

There's more...

Selecting with unique and sorted indexes

Getting ready

How to do it...

How it works...

There's more...

See also

Gaining perspective on stock prices

Getting ready

How to do it...

How it works...

There's more...

Translating SQL WHERE clauses

Getting ready

How to do it...

How it works...

There's more...

See also

Determining the normality of stock market returns

Getting ready

How to do it...

How it works...

There's more...

See also

Improving readability of boolean indexing with the query method

Getting ready

How to do it...

How it works...

There's more...

See also

Preserving Series with the where method

Getting ready

How to do it...

How it works...

There's more...

See also

Masking DataFrame rows

Getting ready

How to do it...

How it works...

There's more...

See also

Selecting with booleans, integer location, and labels

Getting ready

How to do it...

How it works...

There's more...

See also

Index Alignment

Examining the Index object

Getting ready

How to do it...

How it works...

There's more...

See also

Producing Cartesian products

Getting ready

How to do it...

How it works...

There's more...

Exploding indexes

Getting ready

How to do it...

How it works...

There's more...

Filling values with unequal indexes

Getting ready

How to do it...

How it works...

There's more...

Appending columns from different DataFrames

Getting ready

How to do it...

How it works...

There's more...

Highlighting the maximum value from each column

Getting ready

How to do it...

How it works...

There's more...

See also

Replicating idxmax with method chaining

Getting ready

How to do it...

How it works...

There's more...

Finding the most common maximum

Getting ready

How to do it...

How it works...

There's more...

Grouping for Aggregation, Filtration, and Transformation

Defining an aggregation

Getting ready

How to do it...

How it works...

There's more...

See also

Grouping and aggregating with multiple columns and functions

Getting ready

How to do it...

How it works...

There's more...

Removing the MultiIndex after grouping

Getting ready

How to do it...

How it works...

There's more...

Customizing an aggregation function

Getting ready

How to do it...

How it works...

There's more...

Customizing aggregating functions with *args and **kwargs

Getting ready

How to do it...

How it works...

There's more...

See also

Examining the groupby object

Getting ready

How to do it...

How it works...

There's more...

See also

Filtering for states with a minority majority

Getting ready

How to do it...

How it works...

There's more...

See also

Transforming through a weight loss bet

Getting ready

How to do it...

How it works...

There's more...

See also

Calculating weighted mean SAT scores per state with apply

Getting ready

How to do it...

How it works...

There's more...

See also

Grouping by continuous variables

Getting ready

How to do it...

How it works...

There's more...

See also

Counting the total number of flights between cities

Getting ready

How to do it...

How it works...

There's more...

See also

Finding the longest streak of on-time flights

Getting ready

How to do it...

How it works...

There's more...

See also

Restructuring Data into a Tidy Form

Tidying variable values as column names with stack

Getting ready

How to do it...

How it works...

There's more...

See also

Tidying variable values as column names with melt

Getting ready

How to do it...

How it works...

There's more...

See also

Stacking multiple groups of variables simultaneously

Getting ready

How to do it...

How it works...

There's more...

See also

Inverting stacked data

Getting ready

How to do it...

How it works...

There's more...

See also

Unstacking after a groupby aggregation

Getting ready

How to do it...

How it works...

There's more...

See also

Replicating pivot_table with a groupby aggregation

Getting ready

How to do it...

How it works...

There's more...

Renaming axis levels for easy reshaping

Getting ready

How to do it...

How it works...

There's more...

Tidying when multiple variables are stored as column names

Getting ready...

How to do it...

How it works...

There's more...

See also

Tidying when multiple variables are stored as column values

Getting ready

How to do it...

How it works...

There's more...

See also

Tidying when two or more values are stored in the same cell

Getting ready...

How to do it...

How it works...

There's more...

Tidying when variables are stored in column names and values

Getting ready

How to do it...

How it works...

There's more...

Tidying when multiple observational units are stored in the same table

Getting ready

How to do it...

How it works...

There's more...

See also

Combining Pandas Objects

Appending new rows to DataFrames

Getting ready

How to do it...

How it works...

There's more...

Concatenating multiple DataFrames together

Getting ready

How to do it...

How it works...

There's more...

Comparing President Trump's and Obama's approval ratings

Getting ready

How to do it...

How it works...

There's more...

See also

Understanding the differences between concat, join, and merge

Getting ready

How to do it...

How it works...

There's more...

See also

Connecting to SQL databases

Getting ready

How to do it...

How it works...

There's more...

See also

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