![home, leaving everything behind him but his spear and a little food,
and walked from forty to forty-five miles in two days. No European
that I have ever seen would have had a chance with him in his own
forests.
Six miles a day is quite enough for any man who wishes to take
his followers long journeys, unless specially favoured by the ground
and the paths; Galton, in speaking of African travelling, says three
miles a day with waggons, horses, and cattle, and he is of some
authority. I have often thought that we must have walked twenty
miles, but the bearings have always proved to me that we have
seldom done half that distance. It requires great experience not to
judge distance by the fatigue we feel.
Whilst referring to the mistakes in the estimates of distance, I may
notice the very remarkable errors into which two visitors to the
Limbang have fallen. Mr. Motley[9] mentions exploring that stream to
an estimated distance of one hundred and fifty miles, by the
windings of the river, and about fifty in a general south-west
direction. He reached the Limbuak village, which by my
measurement is under twenty miles in a straight line from the mouth:
fifty miles in a direct line to the south-west, would have nearly
brought him to the Baram, across numerous ranges of hills, and
several navigable streams, and a hundred and fifty miles up the river
would have brought him nearly to the farthest point I reached, long
past the limestone districts. It proves how impossible it is to trust to
estimates.
The next curious mistake I may notice, was made by Mr. de
Crespigny; he ascended the river Limbang as far as the river Damit. I
have seen a sketch map of his, and he places the mouth of that
stream in north latitude, 3° 48’, and the mountain of Molu to the
north-east of it, in latitude 4° 3’, whereas Molu Peak is a little to the
westward of south from the Damit, and nearly twenty-five miles
distant in a direct line.
In drawing attention to these errors, I by no means claim immunity
from them in the map of the Limbang and Baram rivers which
accompanies this volume; but I think they will be found free from](https://image.slidesharecdn.com/29339-240802202412-4bc955a8/85/Get-Data-Science-with-Python-1st-Edition-Coll-free-all-chapters-42-320.jpg)
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- 6. Table of Contents Python: Real-World Data Science Meet Your Course Guide What's so cool about Data Science? Course Structure Course Journey The Course Roadmap and Timeline 1. Course Module 1: Python Fundamentals 1. Introduction and First Steps – Take a Deep Breath A proper introduction Enter the Python About Python Portability Coherence Developer productivity An extensive library Software quality Software integration Satisfaction and enjoyment What are the drawbacks? Who is using Python today? Setting up the environment Python 2 versus Python 3 – the great debate What you need for this course Installing Python Installing IPython Installing additional packages How you can run a Python program Running Python scripts Running the Python interactive shell Running Python as a service Running Python as a GUI application How is Python code organized
- 7. How do we use modules and packages Python's execution model Names and namespaces Scopes Guidelines on how to write good code The Python culture A note on the IDEs 2. Object-oriented Design Introducing object-oriented Objects and classes Specifying attributes and behaviors Data describes objects Behaviors are actions Hiding details and creating the public interface Composition Inheritance Inheritance provides abstraction Multiple inheritance Case study 3. Objects in Python Creating Python classes Adding attributes Making it do something Talking to yourself More arguments Initializing the object Explaining yourself Modules and packages Organizing the modules Absolute imports Relative imports Organizing module contents Who can access my data? Third-party libraries Case study 4. When Objects Are Alike
- 8. Basic inheritance Extending built-ins Overriding and super Multiple inheritance The diamond problem Different sets of arguments Polymorphism Abstract base classes Using an abstract base class Creating an abstract base class Demystifying the magic Case study 5. Expecting the Unexpected Raising exceptions Raising an exception The effects of an exception Handling exceptions The exception hierarchy Defining our own exceptions Case study 6. When to Use Object-oriented Programming Treat objects as objects Adding behavior to class data with properties Properties in detail Decorators – another way to create properties Deciding when to use properties Manager objects Removing duplicate code In practice Case study 7. Python Data Structures Empty objects Tuples and named tuples Named tuples Dictionaries Dictionary use cases
- 9. Using defaultdict Counter Lists Sorting lists Sets Extending built-ins Queues FIFO queues LIFO queues Priority queues Case study 8. Python Object-oriented Shortcuts Python built-in functions The len() function Reversed Enumerate File I/O Placing it in context An alternative to method overloading Default arguments Variable argument lists Unpacking arguments Functions are objects too Using functions as attributes Callable objects Case study 9. Strings and Serialization Strings String manipulation String formatting Escaping braces Keyword arguments Container lookups Object lookups Making it look right Strings are Unicode
- 10. Converting bytes to text Converting text to bytes Mutable byte strings Regular expressions Matching patterns Matching a selection of characters Escaping characters Matching multiple characters Grouping patterns together Getting information from regular expressions Making repeated regular expressions efficient Serializing objects Customizing pickles Serializing web objects Case study 10. The Iterator Pattern Design patterns in brief Iterators The iterator protocol Comprehensions List comprehensions Set and dictionary comprehensions Generator expressions Generators Yield items from another iterable Coroutines Back to log parsing Closing coroutines and throwing exceptions The relationship between coroutines, generators, and functions Case study 11. Python Design Patterns I The decorator pattern A decorator example Decorators in Python The observer pattern
- 11. An observer example The strategy pattern A strategy example Strategy in Python The state pattern A state example State versus strategy State transition as coroutines The singleton pattern Singleton implementation The template pattern A template example 12. Python Design Patterns II The adapter pattern The facade pattern The flyweight pattern The command pattern The abstract factory pattern The composite pattern 13. Testing Object-oriented Programs Why test? Test-driven development Unit testing Assertion methods Reducing boilerplate and cleaning up Organizing and running tests Ignoring broken tests Testing with py.test One way to do setup and cleanup A completely different way to set up variables Skipping tests with py.test Imitating expensive objects How much testing is enough? Case study Implementing it 14. Concurrency
- 12. Threads The many problems with threads Shared memory The global interpreter lock Thread overhead Multiprocessing Multiprocessing pools Queues The problems with multiprocessing Futures AsyncIO AsyncIO in action Reading an AsyncIO future AsyncIO for networking Using executors to wrap blocking code Streams Executors Case study 2. Course Module 2: Data Analysis 1. Introducing Data Analysis and Libraries Data analysis and processing An overview of the libraries in data analysis Python libraries in data analysis NumPy pandas Matplotlib PyMongo The scikit-learn library 2. NumPy Arrays and Vectorized Computation NumPy arrays Data types Array creation Indexing and slicing Fancy indexing Numerical operations on arrays Array functions
- 13. Data processing using arrays Loading and saving data Saving an array Loading an array Linear algebra with NumPy NumPy random numbers 3. Data Analysis with pandas An overview of the pandas package The pandas data structure Series The DataFrame The essential basic functionality Reindexing and altering labels Head and tail Binary operations Functional statistics Function application Sorting Indexing and selecting data Computational tools Working with missing data Advanced uses of pandas for data analysis Hierarchical indexing The Panel data 4. Data Visualization The matplotlib API primer Line properties Figures and subplots Exploring plot types Scatter plots Bar plots Contour plots Histogram plots Legends and annotations Plotting functions with pandas Additional Python data visualization tools
- 14. Bokeh MayaVi 5. Time Series Time series primer Working with date and time objects Resampling time series Downsampling time series data Upsampling time series data Timedeltas Time series plotting 6. Interacting with Databases Interacting with data in text format Reading data from text format Writing data to text format Interacting with data in binary format HDF5 Interacting with data in MongoDB Interacting with data in Redis The simple value List Set Ordered set 7. Data Analysis Application Examples Data munging Cleaning data Filtering Merging data Reshaping data Data aggregation Grouping data 3. Course Module 3: Data Mining 1. Getting Started with Data Mining Introducing data mining A simple affinity analysis example What is affinity analysis? Product recommendations
- 15. Loading the dataset with NumPy Implementing a simple ranking of rules Ranking to find the best rules A simple classification example What is classification? Loading and preparing the dataset Implementing the OneR algorithm Testing the algorithm 2. Classifying with scikit-learn Estimators scikit-learn estimators Nearest neighbors Distance metrics Loading the dataset Moving towards a standard workflow Running the algorithm Setting parameters Preprocessing using pipelines An example Standard preprocessing Putting it all together Pipelines 3. Predicting Sports Winners with Decision Trees Loading the dataset Collecting the data Using pandas to load the dataset Cleaning up the dataset Extracting new features Decision trees Parameters in decision trees Using decision trees Sports outcome prediction Putting it all together Random forests How do ensembles work? Parameters in Random forests Applying Random forests
- 16. Engineering new features 4. Recommending Movies Using Affinity Analysis Affinity analysis Algorithms for affinity analysis Choosing parameters The movie recommendation problem Obtaining the dataset Loading with pandas Sparse data formats The Apriori implementation The Apriori algorithm Implementation Extracting association rules Evaluation 5. Extracting Features with Transformers Feature extraction Representing reality in models Common feature patterns Creating good features Feature selection Selecting the best individual features Feature creation Creating your own transformer The transformer API Implementation details Unit testing Putting it all together 6. Social Media Insight Using Naive Bayes Disambiguation Downloading data from a social network Loading and classifying the dataset Creating a replicable dataset from Twitter Text transformers Bag-of-words N-grams Other features
- 17. Naive Bayes Bayes' theorem Naive Bayes algorithm How it works Application Extracting word counts Converting dictionaries to a matrix Training the Naive Bayes classifier Putting it all together Evaluation using the F1-score Getting useful features from models 7. Discovering Accounts to Follow Using Graph Mining Loading the dataset Classifying with an existing model Getting follower information from Twitter Building the network Creating a graph Creating a similarity graph Finding subgraphs Connected components Optimizing criteria 8. Beating CAPTCHAs with Neural Networks Artificial neural networks An introduction to neural networks Creating the dataset Drawing basic CAPTCHAs Splitting the image into individual letters Creating a training dataset Adjusting our training dataset to our methodology Training and classifying Back propagation Predicting words Improving accuracy using a dictionary Ranking mechanisms for words Putting it all together 9. Authorship Attribution
- 18. Attributing documents to authors Applications and use cases Attributing authorship Getting the data Function words Counting function words Classifying with function words Support vector machines Classifying with SVMs Kernels Character n-grams Extracting character n-grams Using the Enron dataset Accessing the Enron dataset Creating a dataset loader Putting it all together Evaluation 10. Clustering News Articles Obtaining news articles Using a Web API to get data Reddit as a data source Getting the data Extracting text from arbitrary websites Finding the stories in arbitrary websites Putting it all together Grouping news articles The k-means algorithm Evaluating the results Extracting topic information from clusters Using clustering algorithms as transformers Clustering ensembles Evidence accumulation How it works Implementation Online learning An introduction to online learning
- 19. Implementation 11. Classifying Objects in Images Using Deep Learning Object classification Application scenario and goals Use cases Deep neural networks Intuition Implementation An introduction to Theano An introduction to Lasagne Implementing neural networks with nolearn GPU optimization When to use GPUs for computation Running our code on a GPU Setting up the environment Application Getting the data Creating the neural network Putting it all together 12. Working with Big Data Big data Application scenario and goals MapReduce Intuition A word count example Hadoop MapReduce Application Getting the data Naive Bayes prediction The mrjob package Extracting the blog posts Training Naive Bayes Putting it all together Training on Amazon's EMR infrastructure 13. Next Steps… Chapter 1 – Getting Started with Data Mining
- 20. Scikit-learn tutorials Extending the IPython Notebook Chapter 2 – Classifying with scikit-learn Estimators More complex pipelines Comparing classifiers Chapter 3: Predicting Sports Winners with Decision Trees More on pandas Chapter 4 – Recommending Movies Using Affinity Analysis The Eclat algorithm Chapter 5 – Extracting Features with Transformers Vowpal Wabbit Chapter 6 – Social Media Insight Using Naive Bayes Natural language processing and part-of-speech tagging Chapter 7 – Discovering Accounts to Follow Using Graph Mining More complex algorithms Chapter 8 – Beating CAPTCHAs with Neural Networks Deeper networks Reinforcement learning Chapter 9 – Authorship Attribution Local n-grams Chapter 10 – Clustering News Articles Real-time clusterings Chapter 11 – Classifying Objects in Images Using Deep Learning Keras and Pylearn2 Mahotas Chapter 12 – Working with Big Data Courses on Hadoop Pydoop Recommendation engine More resources 4. Course Module 4: Machine Learning 1. Giving Computers the Ability to Learn from Data How to transform data into knowledge The three different types of machine learning Making predictions about the future with supervised learning
- 21. Classification for predicting class labels Regression for predicting continuous outcomes Solving interactive problems with reinforcement learning Discovering hidden structures with unsupervised learning Finding subgroups with clustering Dimensionality reduction for data compression An introduction to the basic terminology and notations A roadmap for building machine learning systems Preprocessing – getting data into shape Training and selecting a predictive model Evaluating models and predicting unseen data instances Using Python for machine learning 2. Training Machine Learning Algorithms for Classification Artificial neurons – a brief glimpse into the early history of machine learning Implementing a perceptron learning algorithm in Python Training a perceptron model on the Iris dataset Adaptive linear neurons and the convergence of learning Minimizing cost functions with gradient descent Implementing an Adaptive Linear Neuron in Python Large scale machine learning and stochastic gradient descent 3. A Tour of Machine Learning Classifiers Using scikit-learn Choosing a classification algorithm First steps with scikit-learn Training a perceptron via scikit-learn Modeling class probabilities via logistic regression Logistic regression intuition and conditional probabilities Learning the weights of the logistic cost function Training a logistic regression model with scikit-learn Tackling overfitting via regularization Maximum margin classification with support vector machines Maximum margin intuition Dealing with the nonlinearly separable case using slack variables Alternative implementations in scikit-learn
- 22. Solving nonlinear problems using a kernel SVM Using the kernel trick to find separating hyperplanes in higher dimensional space Decision tree learning Maximizing information gain – getting the most bang for the buck Building a decision tree Combining weak to strong learners via random forests K-nearest neighbors – a lazy learning algorithm 4. Building Good Training Sets – Data Preprocessing Dealing with missing data Eliminating samples or features with missing values Imputing missing values Understanding the scikit-learn estimator API Handling categorical data Mapping ordinal features Encoding class labels Performing one-hot encoding on nominal features Partitioning a dataset in training and test sets Bringing features onto the same scale Selecting meaningful features Sparse solutions with L1 regularization Sequential feature selection algorithms Assessing feature importance with random forests 5. Compressing Data via Dimensionality Reduction Unsupervised dimensionality reduction via principal component analysis Total and explained variance Feature transformation Principal component analysis in scikit-learn Supervised data compression via linear discriminant analysis Computing the scatter matrices Selecting linear discriminants for the new feature subspace Projecting samples onto the new feature space LDA via scikit-learn
- 23. Using kernel principal component analysis for nonlinear mappings Kernel functions and the kernel trick Implementing a kernel principal component analysis in Python Example 1 – separating half-moon shapes Example 2 – separating concentric circles Projecting new data points Kernel principal component analysis in scikit-learn 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning Streamlining workflows with pipelines Loading the Breast Cancer Wisconsin dataset Combining transformers and estimators in a pipeline Using k-fold cross-validation to assess model performance The holdout method K-fold cross-validation Debugging algorithms with learning and validation curves Diagnosing bias and variance problems with learning curves Addressing overfitting and underfitting with validation curves Fine-tuning machine learning models via grid search Tuning hyperparameters via grid search Algorithm selection with nested cross-validation Looking at different performance evaluation metrics Reading a confusion matrix Optimizing the precision and recall of a classification model Plotting a receiver operating characteristic The scoring metrics for multiclass classification 7. Combining Different Models for Ensemble Learning Learning with ensembles Implementing a simple majority vote classifier Combining different algorithms for classification with majority vote Evaluating and tuning the ensemble classifier Bagging – building an ensemble of classifiers from bootstrap samples
- 24. Leveraging weak learners via adaptive boosting 8. Predicting Continuous Target Variables with Regression Analysis Introducing a simple linear regression model Exploring the Housing Dataset Visualizing the important characteristics of a dataset Implementing an ordinary least squares linear regression model Solving regression for regression parameters with gradient descent Estimating the coefficient of a regression model via scikit- learn Fitting a robust regression model using RANSAC Evaluating the performance of linear regression models Using regularized methods for regression Turning a linear regression model into a curve – polynomial regression Modeling nonlinear relationships in the Housing Dataset Dealing with nonlinear relationships using random forests Decision tree regression Random forest regression A. Reflect and Test Yourself! Answers Module 2: Data Analysis Chapter 1: Introducing Data Analysis and Libraries Chapter 2: Object-oriented Design Chapter 3: Data Analysis with pandas Chapter 4: Data Visualization Chapter 5: Time Series Chapter 6: Interacting with Databases Chapter 7: Data Analysis Application Examples Module 3: Data Mining Chapter 1: Getting Started with Data Mining Chapter 2: Classifying with scikit-learn Estimators Chapter 3: Predicting Sports Winners with Decision Trees Chapter 4: Recommending Movies Using Affinity Analysis Chapter 5: Extracting Features with Transformers Chapter 6: Social Media Insight Using Naive Bayes
- 25. Chapter 7: Discovering Accounts to Follow Using Graph Mining Chapter 8: Beating CAPTCHAs with Neural Networks Chapter 9: Authorship Attribution Chapter 10: Clustering News Articles Chapter 11: Classifying Objects in Images Using Deep Learning Chapter 12: Working with Big Data Module 4: Machine Learning Chapter 1: Giving Computers the Ability to Learn from Data Chapter 2: Training Machine Learning Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn Chapter 4: Building Good Training Sets – Data Preprocessing Chapter 5: Compressing Data via Dimensionality Reduction Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning Chapter 7: Combining Different Models for Ensemble Learning Chapter 8: Predicting Continuous Target Variables with Regression Analysis B. Bibliography Index
- 27. Python: Real-World Data Science A course in four modules Unleash the power of Python and its robust data science capabilities with your Course Guide Ankita Thakur Learn to use powerful Python libraries for effective data processing and analysis To contact your Course Guide Email: <[email protected]>
- 28. Meet Your Course Guide Hello and welcome to this Data Science with Python course. You now have a clear pathway from learning Python core features right through to getting acquainted with the concepts and techniques of the data science field—all using Python! This course has been planned and created for you by me Ankita Thakur – I am your Course Guide, and I am here to help you have a great journey along the pathways of learning that I have planned for you. I've developed and created this course for you and you'll be seeing me through the whole journey, offering you my thoughts and ideas behind what you're going to learn next and why I recommend each step. I'll provide tests and quizzes to help you reflect on your learning, and code challenges that will be pitched just right for you through the course. If you have any questions along the way, you can reach out to me over e-mail or telephone and I'll make sure you get everything from the course that we've planned – for you to start your career in the field of data science. Details of how to contact me are included on the first page of this course.
- 29. What's so cool about Data Science? What is Data Science and why is there so much of buzz about this in the world? Is it of great importance? Well, the following sentence will answer all such questions: "This hot new field promises to revolutionize industries from business to government, health care to academia." --The New York Times The world is generating data at an increasing pace. Consumers, sensors, or scientific experiments emit data points every day. In finance, business, administration, and the natural or social sciences, working with data can make up a significant part of the job. Being able to efficiently work with small or large datasets has become a valuable skill. Also, we live in a world of connected things where tons of data is generated and it is humanly impossible to analyze all the incoming data and make decisions. Human decisions are increasingly replaced by decisions made by computers. Thanks to the field of Data Science! Data science has penetrated deeply in our connected world and there is a growing demand in the market for people who not only understand data science algorithms thoroughly, but are also capable of programming these algorithms. A field that is at the intersection of many fields, including data mining, machine learning, and statistics, to name a few. This puts an immense burden on all levels of data scientists; from the one who is aspiring to become a data scientist and those who are currently practitioners in this field. Treating these algorithms as a black box and using them in decision- making systems will lead to counterproductive results. With tons of algorithms and innumerable problems out there, it requires a good
- 30. grasp of the underlying algorithms in order to choose the best one for any given problem. Python as a programming language has evolved over the years and today, it is the number one choice for a data scientist. Python has become the most popular programming language for data science because it allows us to forget about the tedious parts of programming and offers us an environment where we can quickly jot down our ideas and put concepts directly into action. It has been used in industry for a long time, but it has been popular among researchers as well. In contrast to more specialized applications and environments, Python is not only about data analysis. The list of industrial-strength libraries for many general computing tasks is long, which makes working with data in Python even more compelling. Whether your data lives inside SQL or NoSQL databases or is out there on the Web and must be crawled or scraped first, the Python community has already developed packages for many of those tasks.
- 31. Course Structure Frankly speaking, it's a wise decision to know the nitty-gritty of Python as it's a trending language. I'm sure you'll gain lot of knowledge through this course and be able to implement all those in practice. However, I want to highlight that the road ahead may be bumpy on occasions, and some topics may be more challenging than others, but I hope that you will embrace this opportunity and focus on the reward. Remember that we are on this journey together, and throughout this course, we will add many powerful techniques to your arsenal that will help us solve even the toughest problems the data-driven way. I've created this learning path for you that consist of four models. Each of these modules are a mini-course in their own way, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. So let's now look at the pathway these modules create—basically all the topics that will be exploring in this learning journey.
- 32. Course Journey We start the course with our very first module, Python Fundamentals, to help you get familiar with Python. Installing Python correctly is equal to half job done. This module starts with the installation of Python, IPython, and all the necessary packages. Then, we'll see the fundamentals of object-oriented programming because Python itself is an object-oriented programming language. Finally, we'll make friends with some of the core concepts of Python—how to get Python programming basics nailed down. Then we'll move towards the analysis part. The second module, Data Analysis, will get you started with Python data analysis in a practical and example-driven way. You'll see how we can use Python libraries for effective data processing and analysis. So, if you want to to get started with basic data processing tasks or time series, then you can find lot of hands-on knowledge in the examples of this module.
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- 34. 9th.—This morning Musa was very feverish, so the men asked me to stop a day to collect provisions. To this I unwillingly assented, but they did little else than lie down. About nine a.m. five of the men proposed that I should allow them to leave their muskets and all their baggage, and push on as fast as possible to the boats, while I moved on quietly with the sick and sore-footed. I consented, on the condition that they would stay at the boats no longer than would be necessary to cook a meal; that then they were to return laden with provisions. To this they agreed, and then left us. The two who stayed behind the day before yesterday came up with us; it was for them I was anxious. During the ascent of the steep mountain on the 8th the last remnant of my shoes was carried away, and yesterday I attempted to protect my feet by fastening some goats’ skin over them in the form of moccassins. To-day I am trying to improve them, but with very little success. 10th.—Musa having shaken off his fever, was enabled to start; but all the men are weak, and many are ailing. Though perfectly well in health, I find that, having only had for yesterday’s dinner a glass of rice-water with the palm-cabbage, I am not fit for very long walks, particularly as my moccassins are cut to pieces by the rough stones and thorns, and I am compelled to walk bare-footed. My greatest torment are the leeches getting between my toes and crawling up my trousers, reaching even to my waist, where the tight belt prevented their farther progress. Squeezing a little tobacco-juice made them drop off, but I could not be continually stopping to do this. 11th.—We stopped all day. The industrious cut down some Loba- palms, and made a little sago; the lazy collected a few cabbages. I tried a little of the former; it was indeed delicious. Some of the men, who wandered farther than the others in search of wild fruits, reported seeing some very large monkeys, which they said might be orang-utans, and whilst speaking of them I was reminded of the various stories told of people being carried off by them. I have referred to this subject in my account of my journeys among the Sea Dayaks, but although many stories are related of the male orang-utan carrying off young Dayak maidens into the jungle, yet it is seldom that we hear of the female orang-utan running off
- 35. with a man. But the Muruts of Padas tell the following narrative, which, they say, may be believed. Some years ago, one of their young men was wandering in the jungle, armed with a sumpitan, or blow-pipe, and a sword. He came to the banks of a pebbly stream, and being a hot day, he thought he would have a bathe. He placed his arms and clothes at the foot of a tree, and then went into the water. After a time, being sufficiently refreshed, he was returning to dress, when he perceived an enormous female orang-utan standing between him and the tree. She advanced towards him, as he stood paralyzed by surprise, and seizing him by the arm, compelled him to follow her to a branching tree and climb up it. When he reached her resting-place, consisting of boughs and branches woven into a comfortable nest, she made him enter. There he remained some months jealously watched by his strange companion, fed by her on fruits and the cabbage of the palm, and rarely permitted to touch the earth with his feet, but compelled to move from tree to tree. This life continued some time, till the female orang-utan becoming less watchful permitted the Murut more liberty. He availed himself of it to slip down the trunk of the tree and run to the place where he had formerly left his weapons. She, seeing his attempted escape, followed, to be pierced, as she approached him, by a poisoned arrow. I was told if I would ascend the Padas river as far as the man’s village, I might hear the story from his own lips, as he was still alive. 12th.—Walked on a mile, the men excessively lazy. Finding the river smooth, they proposed trying rafts; so we stopped to construct them. One of the men, observing that I was dining on a cabbage- palm boiled in a little rice-water, presented me with a cup full of uncooked rice. I was very grateful to him for it; but we put it by, in case the palms should fail us, as they do in some districts. 13th.—About nine, we pushed off, and got on very well for two hours. Found one of the rafts smashed up against a rock, and the men away walking. Continued till about one o’clock, when ours also became fixed on a rock, and our men were too dispirited to get it off; and saying that the rapids ahead of us were dangerous, they proposed walking to-morrow. We should have thought nothing of
- 36. such paltry difficulties a week ago, but the men were losing their courage with their strength. I refused, however, to stop till to-morrow, and walked on for a couple of hours. In crossing a ravine to-day, we disturbed a female bear, which, however, dashed with her cubs into thick brushwood, so without dogs it was useless following her. She roared in a manner worthy of an animal double her size. 14th.—The river still full of rapids; but the hills are gradually receding from the banks, giving it more space, and it sometimes spreads out into extensive sheets of water, with immense pebbly flats. Islands are also beginning to appear. It was again proposed to build rafts, but I steadily refused, and kept walking till nearly five. After sunset, the last stragglers overtook us. We continually came upon the traces of the advance-party. At one of their resting-places, we found the bones of a fine fish, which by some means they had secured. Our old Pakatan declared they had either found it stranded, or else had startled a kite from his prey. It proved to be the former, though the latter had happened to us once. 15th.—Yesterday and to-day the character of the forest has altered. We are now marching through the old farming grounds of the Muruts; found some of their fruit trees; among others, one covered with fine-looking oranges, but intolerably sour. I secured the opium bottle to-day, intending to take a dose to deaden the pangs of hunger, but I put it off till the evening, thinking it might interfere with my walking. I noticed near the orange-tree above mentioned that the whole ground was a mass of water-worn pebbles, evidently the ancient bed of the stream; it was now at least a hundred feet above the water’s edge. At half-past four p.m. I brought up for the night, and after bathing stretched myself on my back, munching a great lump of cabbage, when my eyes, wandering over the scene, fell on a hill about three miles ahead of us. I sat up and looked at it again; and, turning to my companions, said, “Why, that reminds me of the high land near the mouth of the Madihit;” but we agreed that it was impossible, as our five men had been gone six days, and we felt assured that we should have met them ere this if we were so near our boats as that, particularly as we, both yesterday and to-day, had made very long
- 37. walks. Since we have had a bearing of Molu, we have been keeping generally in a west course, but the river has taken some very extraordinary windings. Having secured some fruit of the Jintawan, or Indian-rubber plant, and some cabbages, I was enabled to satisfy my hunger before going to sleep, so put off taking any laudanum, to which I had a very great dislike. The Jintawan fruit is very pleasantly acid, about the size of a very large pear, and of a deep orange colour. It consists of a thick rind full of Indian-rubber, surrounding some pulp-covered seeds. One of the plants we came across was very handsome, growing in the most luxuriant manner over a lofty tree with few branches. The Jintawan is a creeper, and this one had extended itself at least forty feet up the trunk and had covered the outspreading boughs. It was loaded with fruit, but my men had so lost heart that not one would climb the tree, but contented themselves with picking up the over-ripe produce which had fallen on the ground below. We had another very happy find to-day, for while passing under a fine tapang-tree, we noticed the remains of a bees’ nest scattered about, and every particle was eagerly appropriated. From the marks around it appeared as if a bear had climbed this lofty tree and torn down the nest to be devoured by its young below, as there were numerous tracks of the smaller animals around, but whether the comb had been sucked by the bears or not was very immaterial to our men, who rejoiced in securing the little honey still clinging to it. 16th.—Started early. About half my followers had so delayed us by their constantly lagging behind, that I determined to wait for them no longer, but to push on with such men as would follow me with all their strength. We felt that it would be impossible to walk many days farther on our scanty fare. The lazy ones having heard of our arrangement, tried to keep up with us, and did do so till eight, when I heard a shout from the foremost man, “Bandera! bandera!—the flag! the flag!” We rushed down the side of the hill like madmen, the fellows shouting for joy. Sure enough, there was the British flag hoisted, and our small boats at the mouth of the Madihit, with our five men looking fat and well beside my pale and famished followers. The
- 38. rascals having left my guns and all the baggage in the jungle, and all being in good health, had managed to reach the Madihit in three days, and then set to work to eat and drink as much as possible. We arrived to find the provisions nearly gone; they said the bears had found out our cache and destroyed everything, and the only provisions left were those we put into the garei. I could only divide a cupful of beans to each man, as the five had managed to consume thirty pounds of sago and forty-two pints of beans in the course of four days, and they confessed to have daily caught very fine fish. But what angered the men most was the signs of waste around, where, having only half finished a plate of sago, they had thrown the rest away. I saw some picking up the burnt pieces that had not been washed away by the rain. I asked why, according to their agreement, they had not come back to meet us, knowing that we had several sick men. They put the blame on each other: one man, a Javanese, had left his sick son with us, but he unfeelingly observed that he was old enough to look after himself; that son had given us more trouble than any one else, both in going and returning. I searched their baskets, and found that they had not only hidden some more beans, but had stolen some of my cloth, though I could not fix on the man. I determined to punish them, so told them to go back and fetch the things they had left in the forest; or, on my return, I would submit the case to the Sultan, whether they had not forfeited wages by their unfair abandonment of their sickly companions. They started off, but their cowardly hearts failed them, and before night they came back. The ravages of the bears were distressing. They destroyed a Deane’s pistol-case, tore open my box of books, and ruined them; reduced the cloth to shreds, and tumbled it into the mud, where the white ants afterwards finished the spoiling; opened the tin boxes containing the sugar and biscuits, and of course devoured them; so that I have nothing left but coffee and arrack. After Musa had cooked a meal, a very frugal one, he went off with a party to fetch the garei, hoping to find a little sago left, but was disappointed.
- 39. In the evening caught a few fish, but they were not much among so many. About seven, a most satisfactory savour rose to my nostrils. I found that Ahtan, having discovered a jar of pork fat, was preparing some cakes. I divided them, but he said, “No; you, sir, have the larger body, therefore should have the larger share.” I am not much given to emotions, but I never felt so thankful as when, stretched in the old Kayan hut, I watched them preparing an evening meal, and thought of all the dangers we had gone through without a single accident. True, we had lost guns, and goods, and ruined instruments of some value; but what of that?—there was no one the worse for his exertions. What was hunger now we were so near home? 17th.—Started early; and, as we have had no rain for two days, the river was quiet, and we only reached an island about fifteen miles from the Madalam. It shows the difference, however, between ascending and descending a river. About two a.m., our garei being well ahead, we saw before us a herd of wild cattle, quietly picking at a few blades of grass on a broad pebbly flat. I landed with a couple of men, to get between them and the jungle. I was within twenty yards of the nearest, a piebald, and was crawling through the tangled bushes to get a sight of him, whom I could hear browsing near me, when there arose a shout, then a rush, and the cattle were off dashing close to me, but perfectly concealed by the matted brushwood. It was the crew of one of the newly-arrived boats that, regardless of the warnings of their companions, had thus lost us a chance of a good dinner. I felt that, if my gun had been charged with shot, I could almost have peppered them. Shortly after I shot a pig through the back as he was crossing the river; but as all my men were Mahomedans, it was not worth while tracing him in the jungle. He bled so profusely in the water that he could not have run far. About five, we were passing down a rapid at a great pace, when one of the men touched me and pointed. I looked up, and there was a magnificent bull, three parts grown, standing within fifteen yards of me. To put up my gun and let fly was the work of a moment; but, before we had dashed on many yards, the beast, which had fallen on his forehead, was up and away. After a little time, we managed to
- 40. stop the garei; and, landing, found traces of the beast’s blood. My feet were so painfully wounded that I could not manage to follow it, but left it to my men. A couple came up with him, as he stood with his legs well stretched out, bleeding profusely. He took no notice of them, even when they were within spearing distance; but all their nerve was gone, and they were afraid to thrust their weapons into him. They waited till the whole mob of hunters arrived, when the bull apparently recovering himself, dashed away into the jungle. Having secured the boats under the islands, I divided a tablespoonful of beans each, with a little pork fat to those who would take it. Musa told me that most of the men wanted to stay behind and follow the wounded Tambadau; but that, if I wished to go on, there were five volunteers who would pull straight to Brunei, now about a hundred miles off by river. To this I agreed. 18th.—I get away at daylight in a sampir with five men. Ahtan with an attack of fever and ague. The reaction was too much for him, so I stopped at an island about five miles from the Madalam to cook. I now produced my secret store of beans, and the cupful of rice that I had treasured up since it was given us on the 12th. The beans I gave to the men, and the rice I had boiled into a thin sort of gruel for Ahtan. I thought his feverish symptoms arose principally from over fatigue and hunger. In fact, after he had swallowed a strong dose of quinine, and taken half the gruel, he felt much better; the rest of it I gave to the men, as I wanted to give them sufficient strength to pull to Pengkalan Jawa. I would not take anything myself, as I did nothing but sit all day. I reserved my powers for the food I knew the Chinese trader there would quickly prepare for us. As we approached the more frequented parts of the river, we met some Muruts, who told us that the report of my death had brought forty steamers to the capital to revenge it, and that if I did not turn up the place was to be burnt. I knew this was one of the usual stories that arise from very little, but still I was anxious to get home; but with all our exertions we did not reach the Chinese trader’s house till 7 p.m. He received us most hospitably, produced tea, sugar-candy, biscuit, and dried fish, to stay our appetites, while a proper meal was prepared. In about an hour this appeared, and we managed to
- 41. consume a very large fowl each, with an amount of rice that even startled the Baba. Before leaving at midnight, I made arrangements that a plentiful meal should be provided for the garei’s crew. 19th.—After pulling about fourteen hours, we reached Brunei by 2 p.m., to find the people beginning to wonder at our absence. The forty steamers proved to be Captain Cresswell, of the Surprise, who had visited the capital about ten days before with Mr. Low. The latter was beginning to be uneasy about my absence, and was preparing a party to come and search for me. 20th.—My boats now arrived, having failed to get the Tambadau. They said they followed him by the blood till mid-day, when they lost his traces among those of a herd which he had joined. I suspect they did not follow him very far. Thus ends my journal. As I have now made many journeys in Borneo, and seen much of forest walking, I think I can speak of it with something like certainty. I have ever found, in recording progress, that we can seldom allow more than a mile an hour under ordinary circumstances. Sometimes, when extremely difficult or winding, we do not make half a mile an hour. On certain occasions, when very hard pressed, I have seen the men manage a mile and a half; but, with all our exertions, I have never yet recorded more than ten miles progress in a day through thick pathless forests, and that was after ten hours of hard work. Of course we actually walk more than we record, as one cannot calculate the slight windings of the way; but allowing for all this, I have the strongest suspicion that Madame Pfeiffer measured her miles by her fatigue. She talks of twenty miles a day as a common performance of hers; and another visitor to this island beats her, recording walking thirty miles in one day through Bornean forests— an utter impossibility. There was an Adang man among the wax-hunters, the one who accompanied our guide for a short distance, who was pointed out to me as a model of activity, and he certainly appeared so; well built, strong, but light, he skimmed the ground; and the story is told of him, that on receiving information of the illness of his child, he started
- 42. home, leaving everything behind him but his spear and a little food, and walked from forty to forty-five miles in two days. No European that I have ever seen would have had a chance with him in his own forests. Six miles a day is quite enough for any man who wishes to take his followers long journeys, unless specially favoured by the ground and the paths; Galton, in speaking of African travelling, says three miles a day with waggons, horses, and cattle, and he is of some authority. I have often thought that we must have walked twenty miles, but the bearings have always proved to me that we have seldom done half that distance. It requires great experience not to judge distance by the fatigue we feel. Whilst referring to the mistakes in the estimates of distance, I may notice the very remarkable errors into which two visitors to the Limbang have fallen. Mr. Motley[9] mentions exploring that stream to an estimated distance of one hundred and fifty miles, by the windings of the river, and about fifty in a general south-west direction. He reached the Limbuak village, which by my measurement is under twenty miles in a straight line from the mouth: fifty miles in a direct line to the south-west, would have nearly brought him to the Baram, across numerous ranges of hills, and several navigable streams, and a hundred and fifty miles up the river would have brought him nearly to the farthest point I reached, long past the limestone districts. It proves how impossible it is to trust to estimates. The next curious mistake I may notice, was made by Mr. de Crespigny; he ascended the river Limbang as far as the river Damit. I have seen a sketch map of his, and he places the mouth of that stream in north latitude, 3° 48’, and the mountain of Molu to the north-east of it, in latitude 4° 3’, whereas Molu Peak is a little to the westward of south from the Damit, and nearly twenty-five miles distant in a direct line. In drawing attention to these errors, I by no means claim immunity from them in the map of the Limbang and Baram rivers which accompanies this volume; but I think they will be found free from
- 43. gross errors. The course of the latter river was taken down by Captain Brett of the Pluto, and observations of the latitude and longitude of the town of Lañgusan were made by many of the officers on board. In my land journeys, I had very inferior compasses, as I was unable to take with me the valuable levels and other instruments obligingly lent me by Dr. Coulthard, on account of their weight and size; but I used them as long as I was in my boats, to lay down the position of the mountains; and in order to enable me to correct my own errors, I put down the day’s observations on a rough map every evening during the journey, except after we had shot the cascade and wetted the paper too much to permit it being handled roughly. I may add, that of the whole party of nineteen, none after our return suffered severely from the exposure and privations we had undergone, and I believe the real reason was, that we always were dry at night. For many years we trusted during our expeditions to the leaf huts the natives are accustomed to construct for us and for themselves; but although with sufficient time, and when good materials are plentiful, they manage to make them tolerably watertight, yet they are never so good as the simple tents we always took with us during our later expeditions. With proper ropes and everything fitted to enable us to raise these tents on cross poles in ten minutes, the two did not weigh more than twenty pounds, and afforded comfortable accommodation for our whole party of nineteen people, with all our baggage, and on occasions our six guides took advantage of them also. I had suffered severely from exposure on former expeditions, particularly when we ascended the Sakarang, and were eight days sleeping in the leaf huts hastily erected by our followers. Of the seven Englishmen who slept on shore, I believe only one escaped without some severe attack of illness, and I remember the late Mr. Brereton mentioning that on his return from a visit to the Bugan country, where his men had been greatly exposed, a fourth of his party died of various diseases. Another precaution I took was to carry myself a few night things, as a light silk jacket, a pair of loose sleeping drawers of the same material, a jersey, and a dry towel, so
- 44. that if my men lagged far behind, I was not kept for hours in my wet clothes; and whilst travelling in these forests you are always wet, as if there be no rain there are sure to be many rivers to ford. On my return, I tried to remember the geographical information that was given me before starting. I was told it would take six days from Blimbing to Madihit: leaving out the two days’ detention from freshes, it took us about three hours over the six days. Even the walking distance was really correct; it was only two days from the Adang landing-place, and seven from Madihit; as, although we took ten, yet for the first five days we did not do a fair half-day’s work on any of them. We were warned that it would be impossible to use rafts, and that the banks were almost impassable, and we indeed found it so. Many months before starting, I was told that if I wished to penetrate far into the interior, to try the Trusan, and not the Limbang, as the former was inhabited, the latter not. I went up the Trusan a few miles, but found it so small, I had no idea it penetrated to so great a distance. The fact is, that the rains run off very fast, and that the ordinary states of the rivers give no idea of the amount of water they bring down, but had we taken that route, we should have reached our farthest point with comparatively little fatigue. The Orang Kaya Upit arrived at his house November 13th, twenty- five days after I reached Brunei; so that it is fortunate I did not wait for him. I may add, that on November 20th, some Bisaya chiefs set upon pañgeran Makota, the shabandar, and killed him. They were wearied with his exactions. The immediate cause of his death was seizing the daughters of seven Orang Kayas, one of whom he had in his curtains when attacked, and this caused his death. The girl pointed him out to her father, trying to escape in a small canoe. The alarm was given, and his boat tilting over while he tried to avoid the shower of spears and stones, he fell into the stream and was drowned; for he was the only Malay I ever heard of who could not swim. Such was the end of this clever bad man. The Sultan was furious, but his fury was not shared by his four viziers; so that the affair ended by a dozen lives being taken, instead of the hundreds the Sultan desired.
- 45. Brunei, February 8th, 1861.—Above two years have passed since I wrote this journal. The scheme of building a fort at the Madalam mouth did not succeed, as the Sultan, after the shabandar’s death, was very unwilling to assist any of the aborigines. I was away during the year 1860, and in the course of that time, a party of the Muruts, from the upper Trusan, came over and encamped at the mouth of the Salindong stream, and from thence sent on three men in a bark canoe to tell their friends of their presence. These men met the Orang Kaya Gomba, a Bisayan, at Batang Parak, and were treacherously slain by him. The Muruts waited a long time at Salindong, hoping to be fetched away by their friends. They could not walk the whole distance, as they had their women and children and all their worldly goods with them, intending to remove to the lower Limbang, and live with the Orang Kaya Upit. While thus detained, they were surprised by a large party of Kayans, and every one taken or slain. However, one of the prisoners afterwards managed to get away, and reached his friends, bringing this sad tale. The Orang Kaya Gomba declares that he mistook the three for Kayans, which is almost impossible, as no head-hunter would have been found paddling down a hostile stream in a bark canoe. Neither the Sultan nor any of the viziers will make the least inquiry into this affair, but the memory of it is treasured up in the hearts of the Muruts, and Orang Kaya Gomba may yet meet with a bloody death. I have remarked that during all our wanderings near Kina Balu we only at one place found the dried heads of enemies hung up in the villages there, and during my journeys up the Limbang, I do not remember noticing any, and yet Orang Kaya Gomba’s murderous action shows they do value them, which is confirmed by pañgeran mumin making a present of the head of the man he killed to the Gadang Muruts; and during my stay in Brunei, I have met small parties of head-hunters, but seeking only the heads of their real enemies. Yet I have always avoided spending a night in their
- 46. immediate neighbourhood, and have kept our arms ready for instant service. One evening, during a heavy squall, we took shelter in a little river to the south of Point Kitam, in the Limbang Bay, and to our exceeding discomfort found a Murut boat with eighteen armed men in ambush round a short turn of the stream; we knew they were not waiting for us, but having only four men, and a couple of fowling- pieces, we did not feel secure in their neighbourhood. As we rowed past them they took no notice of us, but no sooner had we anchored, than they pulled off towards our boat; but we should have felt little discomfort, had they not had their mat coverings stowed away, while all the Muruts had their arms ready for action. I told my men to show no sign of alarm, but keeping our guns within reach, waited their coming. It was a great relief to find that they only came to ask for a little tobacco, but some of us had been accustomed to the neighbourhood of the Seribas and Sakarang Dayaks, who on head-hunting expeditions spare none, if of a weaker party. We found they were on the look-out for some of the Tabuns, who, flying before the Kayans, had established themselves at Batu Miris, near the entrance of the Limbang river, and with whom they had an ancient feud. To show the apathy of the Bornean Government, I may mention that it permitted these skirmishes to take place close to the capital, and one day some of my men who were cutting wood near the Consulate, were startled by seeing two Tabuns rushing frantically past them; in a few minutes five Trusan Muruts appeared in full chase, and eagerly inquiring the direction taken by the fugitives, hurried at full speed on a false track purposely pointed out by my Manilla men. Another fact I may mention is that many Bisaya labourers who go over to our colony of Labuan to seek for work have actually attempted to disinter the bodies of those of our countrymen and women who have been buried there. They have tried this to the great grief and discomfort of their surviving friends, but the Bisayas have generally, if not always, been disappointed by the great depth of the graves, and their inefficient tools. It appears a disgusting thing that
- 47. there should be any necessity to watch over the graves of one’s friends to prevent them being desecrated. I shall have occasion hereafter to mention the Secret Societies established by the Chinese, but as an illustration of the influence the members exercise over each other, I will tell the following story:— Perhaps those who have read my journeys to Kina Balu, and this Limbang journal, may be interested in the fate of my boy Ahtan, and I am sorry to say his conduct ultimately made me lose all interest in him. In the year 1858 the Chinese in Brunei started a Secret Society, called there a Hué; they said they were a branch of the Tienti, or Heaven and Earth Society, that has ramifications in nearly all the countries in which the Chinese have spread. At first but few joined it, but by threats and cajolery they at last induced nearly all but the head traders to enter it, and on one of the great Chinese religious feasts, Ahtan asked my permission to go to it. When he returned, it appeared to me he had a very hang-dog look, and next day I noticed he was very busy about my medicine- chest, and I found my laudanum bottle on the table. Being very much engaged at the time preparing my letters for the mail, I took no particular notice of his movements, but immediately after dinner, having taken coffee, I felt drowsy, and had scarcely entered my mosquito room, when I fell on the sofa, and remained in a stupefied sleep for thirteen hours. On my recovery, Ahtan came with a scared look, and said somebody had stolen my heavy iron chest, and it proved to be the case, but as I had six dogs, one of whom was a savage mastiff, my suspicions instantly fell upon my own people, and passing over my household servants, I fixed on my boatmen as the culprits. The Sultan, however, sent and begged I would leave the matter in his hands, and on my expressing my willingness, instantly arrested two of my servants, Ahtan and a Manilla Christian named Peter. They were separated, and at dead of night the Sultan went himself with a drawn kris in his hand to the latter, and said if he would confess he would save his life, but if not——; he did not finish the sentence, as Peter instantly fell on his knees, and clinging to the Sultan’s feet, begged that his life might be spared and he would
- 48. explain all. It appeared that while he held and quieted the mastiff, Ahtan had taken a blacksmith and a carpenter to the chest, and they had carried it off. As these men were constantly employed by me, it explained the silence of the other dogs; but though he could tell how the chest was carried off, he knew nothing of what had since become of it. The Sultan then left him and went to Ahtan, but no threats or entreaties had the slightest effect on him, as he had sworn in the most solemn manner to be faithful to the members of the Tienti Hué, and would confess nothing. The two accused were seized, and as they also belonged to the Secret Society, suspicion was directed to it. I sent for the chief and the other officers of the Hué, and told them the whole story, and said, if the chest unbroken, with the 80l. in it, and all the papers, were not placed on the ground before my house within forty-eight hours, I would turn the Sultan’s attention on them. They protested their utter ignorance of the robbery, which was probably true, but they well knew how to influence all their members, and before the forty-eight hours were over, the chest, untouched, was thrown on the mud just above my house. Finding after a fortnight that the prisoners were receiving treatment totally at variance with English ideas of justice, I sent and begged the Sultan to pardon them, and turn his attention to getting rid of the Secret Society from his dominions. He complied, and it merely required a warm recommendation on his part to the chief officers to break up the society, to induce them to do it, as he declared that every robbery in future should be laid at their door, and every crime committed should be avenged on them. As the officers were men doing a good business at the time, they quickly got rid of their banners and meeting house, and I heard no more of the Hué during the rest of the time I remained in Borneo, but during my last visit I found the chief officers of the society reduced to comparative poverty, as their partners and agents in Singapore, happening to be real British subjects, had refused to have anything further to do with them when they knew of their conduct. I requested the Sultan to let the prisoners go, as all except Ahtan were kept in the stocks in an open verandah, exposed to sun and rain, and tormented the whole day by boat boys, who delighted in torturing those whom they considered as infidels: in Brunei they have
- 49. no prisons whatever. Ahtan was better treated, as he was known to have been a favourite servant, though his conduct was very bad, particularly in dosing me with opium, yet I could not forget his kindness to me during our wanderings in the interior, and asked for his liberty on that plea. The Sultan’s answer was,—“The plea is good, but the stubbornness of that boy in refusing to confess when all the others had acknowledged their crime, deserves death.” I heard a few months ago that he was keeping a small shop in Labuan. A man in whom I felt a very great interest, and was very sorry to part with, was Musa, my Manilla steersman and coxswain; as a boy he had been educated as a Christian, but having been captured very young and sold by the Balignini pirates to the Mahomedans, he had been circumcised, and joined their communion. He had a particular antipathy to Signor Cuarteron, who returned the dislike, and used gravely to assure me that my quiet, respectful follower had a design to massacre him. When the priests first came to Brunei, all my Manilla men attended mass, but were suddenly disgusted with something which took place; and on my inquiring the cause, one of them said, “We don’t like to be told that if we don’t again join the Padre’s religion, he will send for a Spanish man-of-war to take us all off prisoners to Manilla.” If he really did threaten them, he made a great mistake, as some of them never went near the church again. Musa, though modest and gentle in his manner, was as brave as a lion, and would have followed me anywhere. Though very short, he was squarely built, and exceedingly strong; a very powerful swimmer, and good boatman. Many of these men excite a personal regard, and I have always felt that for Inchi Mahomed, my Malay writer, who was entrusted with the charge of the Consulate during my lengthened absences, and he well deserved the trust.
- 51. CHAPTER VIII. THE SULU ARCHIPELAGO. FIRST VISIT. Picturesque Islands—Balambañgan—An old English Settlement— Large Monkeys—Optical Illusions—Flights of Birds—The Curlew —The way we shot them in Sarawak—Game—Banguey—Mali Wali—Cleared Hills—Fine Water—Sweet-smelling Jasmine— Cagayan Sulu—Intercourse with the Inhabitants—Appearance of the Country—Lovely Scenery—Market—Inhabitants—Insolent Traders—The Crater Harbour—Wall of Evergreen—Inner Lake— Climb the Cliff—Scenery—Alligators numerous—Sulu— Appearance from the Ship—Sugh, the Capital—Mr. Wyndham— The Watering-place—Suspicious Natives—Fugitives from Balignini—Reports—A Market—The Mountaineers civil—Walk to the Palace—The Stockades—Armed Crowd—Audience Hall— Absurd Reports—The Sultan and his Nobles—Dress—Politeness of the Sultan—Return to the Ship—Datu Daniel—The Racecourse—Effect of Dutch Shot—Tulyan Bay—Alarm of Villagers—Sulu Government—Laws—Feuds—The Mahomedan and the Pork—Population—Fighting-men—Slave Market— Dignified carriage of the Nobles—Dress—The Balignini—Dutch Attack—Appearance of the Country—Good Position of Island— Tulyan—Basilan—Numerous Islets—Samboañgan—Spanish Convict Settlement—Description of Country—Fort—Town— Shops—The Church—The Men—The Women—The Corner Shop —A Ball—Dancing difficult—Waltzes—Supplies at Samboañgan —A lonely Grave. During all the voyages I have made, I have never beheld islands so picturesque as those scattered over the surface of the Sulu Seas, or whose inhabitants more merited notice. I will therefore give a short account of the visits I made thither, before the attacks of the Spanish forces had altered the ancient state of affairs. I have already delineated the north-west coast, and will therefore commence my description from the island of Balambañgan, to the north of Maludu
- 52. Bay, which must always be of some interest to Englishmen from our two unsuccessful attempts to form a settlement there. We dropped anchor off Balambañgan, which at night appears a low wooded island. I visited it in the morning in a cutter, and vainly sought any fresh traces of human beings, though we found some old deserted huts of the fishermen who frequent this shore for tripang. Continuing our explorations, we noticed something moving along the beach, and there were various conjectures among our party, some affirming it to be a buffalo, others a man; I never was more deceived in my own estimate of the size of an animal, as it proved to be a large monkey, which with its companions was seeking shell-fish on the sands; it was certainly very large, but not of such dimensions as to warrant its being compared to a bull, but there appeared to be some optical illusion caused by its looming over the water. Pursuing our path along the beach, the seamen at last thought they saw houses among the trees, but on landing we found we were again deceived by a row of white rocks, prettily overhung with creepers. Though no traces of inhabitants were to be found, yet animal life was well represented, innumerable monkeys swarmed at the edge of the jungle, while flights of birds of every kind kept rising before us. I never saw more monstrous pelicans, but after having been so deceived by the monkeys, I must not attempt to estimate their height. The best birds, however, for culinary purposes, were the curlews, some of which are as large as small fowls: unfortunately we were not provided with shot, and ball fell harmlessly among them. They are wary birds, but may yet be readily circumvented. There is a spot at the entrance of one of the minor branches of the Sarawak river, where the curlews congregate in thousands, but only at the height of spring-tides can you get profitable shots at them. There are broad sands there, and the birds spread over them to feed; by degrees the rising waters gradually press them back towards the wooded shore; and as beach after beach is covered, they fly screaming above in wide circles, gradually narrowing till they all settle on the spot near which the sportsmen lie concealed, either behind scattered bushes, or in a prepared bower of leafy branches. The evening has well closed in before the tip-top of high-water; and
- 53. the loud scream of the myriads of birds deadens the detonation of the guns as they send their iron shower among them, enabling us to load and reload without completely scaring the birds. We once obtained ninety-five of the largest kind, and hundreds of smaller ones, to feast the crowd who were assembled near preparatory to a great tuba-fishing. Balambañgan is admirably situated to command the China seas; however, if its position be superior to Labuan, the latter has coal to counterbalance that advantage. While strolling along the beach we came upon the tracks of cattle, deer, and pigs, and another party from the ship had the good fortune to secure a large supply of excellent fish with the seine. This island, as well as the neighbouring coast of the mainland, appears to swarm with game; I landed on the latter, and found an extensive open plain, diversified with a few low eminences, backed by some cleared hills, and there the tracks of wild cattle, deer, and pigs were innumerable. The rhinoceros is also reported to be occasionally seen, but we came across no traces of it. Continuing our voyage, we passed the island of Banguey, in the northern part of which there are fine peaked hills, with inhabitants, and plenty of good water, therefore far preferable to Balambañgan for a settlement; as if the harbours be not good, there is sufficient shelter against both monsoons. The next island is Mali Wali, and here we anchored to examine it. We tried at many places, but found the little creeks shallow and lined with mangrove swamps; but by landing on the south side, at the east end of the stone beach, there is but a few minutes’ walk in dry forest between the shore and the cleared land. The appearance of the island is remarkable; for miles the hills apparently are clothed with grass, with only a narrow belt of jungle skirting the shingly beach; the reality, however, disappointed us, as we found that this verdant- looking land was but a heap of soft sandstone, with long coarse grass growing up between the crevices. However, there is a good supply of clear water from tiny streams and springs, and the tracks of deer were observed in many places, while on all sides a species of wild jasmine grew in luxuriance, bearing a very sweet-scented flower.