Python data analysis: perform data collection, data processing, wrangling, visualization, and model
Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python 3rd Edition Avinash Navlani
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Contributors
About the authors
Avinash Navlani has over 8 years of experience working in data science and AI. Currently, he is working as a senior data scientist, improving products and services for customers by using advanced analytics, deploying big data analytical tools, creating and maintaining models, and onboarding compelling new datasets. Previously, he was a university lecturer, where he trained and educated people in data science subjects such as Python for analytics, data mining, machine learning, database management, and NoSQL. Avinash has been involved in research activities in data science and has been a keynote speaker at many conferences in India.
Armando Fandango creates AI-empowered products by leveraging his expertise in deep learning, machine learning, distributed computing, and computational methods and has provided thought leadership roles as the chief data scientist and director at start-ups and large enterprises. He has advised high-tech AI-based start-ups. Armando has authored books such as Python Data Analysis - Second Edition and Mastering TensorFlow, Packt Publishing. He has also published research in international journals and conferences.
Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5 Beginner's Guide and NumPy Cookbook by Packt Publishing. You can find more information and a blog with a few NumPy examples at ivanidris.net.
About the reviewers
Greg Walters has been involved with computers and computer programming since 1972. He is well versed in Visual Basic, Visual Basic .NET, Python, and SQL and is an accomplished user of MySQL, SQLite, Microsoft SQL Server, Oracle, C++, Delphi, Modula-2, Pascal, C, 80x86 Assembler, COBOL, and Fortran. He is a programming trainer and has trained numerous people on many pieces of computer software, including MySQL, Open Database Connectivity, Quattro Pro, Corel Draw!, Paradox, Microsoft Word, Excel, DOS, Windows 3.11, Windows for Workgroups, Windows 95, Windows NT, Windows 2000, Windows XP, and Linux. He is semi-retired and has written over 100 articles for Full Circle Magazine. He is also a musician and loves to cook. He is open to working as a freelancer on various projects.
Alistair McMaster is currently employed as a Software Engineer and Quantitative Strategist at a major financial services firm. He graduated from the University of Cambridge in 2016 with a B.A. (Hons) in Natural Sciences specializing in Astrophysics. His broader career interests include applications of data science to relationship networks and supporting social causes.
Alistair is an active contributor to pandas and a strong advocate of open-source software. In his spare time, he enjoys distance running, cycling, rock climbing, and walks with his family and friends on weekends.
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Section 2: Exploratory Data Analysis and Data Cleaning
Section 3: Deep Dive into Machine Learning
Preface
Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you'll get up and running with using Python for data analysis by exploring the different phases and methodologies used in data analysis, and you'll learn how to use modern libraries from the Python ecosystem to create efficient data pipelines.
Starting with the essential statistical and data analysis fundamentals using Python, you'll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You'll then learn how to conduct time series analysis and signal processing using ARMA models. As you advance, you'll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering. In the concluding chapters, you'll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.
By the end of this data analysis book, you'll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations in order to forecast values from data.
Who this book is for
This book is for data analysts, business analysts, statisticians, and data scientists looking to learn how to use Python for data analysis. Students and academic faculties will also find this book useful for learning and teaching Python data analysis using a hands-on approach. A basic understanding of math and a working knowledge of Python will help you get started with this book.
What this book covers
Chapter 1, Getting Started with Python Libraries, explains the data analyst process and the successful installation of Python libraries and Anaconda. Also, we will discuss Jupyter Notebook and its advanced features.
Preface
Chapter 2, NumPy and Pandas, introduces NumPy and Pandas. This chapter provides a basic overview of NumPy arrays, Pandas DataFrames, and their associated functions.
Chapter 3, Statistics, gives a quick overview of descriptive and inferential statistics.
Chapter 4, Linear Algebra, gives a quick overview of linear algebra and its associated NumPy and SciPy functions.
Chapter 5, Data Visualization, introduces us to the matplotlib, seaborn, Pandas plotting, and bokeh visualization libraries.
Chapter 6, Retrieving, Processing, and Storing Data, explains how to read and write various data formats, such as CSV, Excel, JSON, HTML, and Parquet. Also, we will discuss how to acquire data from relational and NoSQL databases.
Chapter 7, Cleaning Messy Data, explains how to preprocess raw data and perform feature engineering.
Chapter 8, Signal Processing and Time Series, contains time series and signal processing examples using sales, beer production, and sunspot cycle dataset. In this chapter, we will mostly use NumPy, SciPy, and statsmodels.
Chapter 9, Supervised Learning – Regression Analysis, explains linear regression and logistic regression in detail with suitable examples using the scikit-learn library.
Chapter 10, Supervised Learning – Classification Techniques, explains various classification techniques, such as naive Bayes, decision tree, K-nearest neighbors, and SVM. Also, we will discuss model performance evaluation measures.
Chapter 11, Unsupervised Learning – PCA and Clustering, gives a detailed discussion on dimensionality reduction and clustering techniques. Also, we will evaluate the clustering performance.
Chapter 12, Analyzing Textual Data, gives a quick overview of text preprocessing, feature engineering, sentiment analysis, and text similarity. This chapter mostly uses the NLTK, SpaCy, and scikit-learn libraries.
Chapter 13, Analyzing Image Data, gives a quick overview of image processing operations using OpenCV. Also, we will discuss face detection.
Chapter 14, Parallel Computing Using Dask, explains how to perform data preprocessing and machine learning modeling in parallel using Dask.
To get the most out of this book
The execution of the code examples provided in this book requires the installation of Python 3.5 or newer on Mac OS X, Linux, or Microsoft Windows. In this book, we will frequently use SciPy, NumPy, Pandas, scikit-learn, statsmodels, matplotlib, and seaborn. Chapter 1, Getting Started with Python Libraries, provides instructions for the installation and advanced tips so that you can work smoothly. Also, the process of installing specific and additional libraries is explained in the respective chapters. Installation of Bokeh is explained in Chapter 5, Data Visualization. Similarly, the installation of NLTK and SpaCy is explained in Chapter 12, Analyzing Textual Data.
We can also install any library or package that you want to explore using the pip command. We need to run the following command with admin privileges:
$ pip install <library name>
We can also install it from our Jupyter Notebook with ! (exclamation mark) before the pip command:
!pip install <library name>
To uninstall a Python library or package installed with pip, use the following command:
$ pip uninstall <library name>
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Preface
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1
Section 1: Foundation for Data Analysis
The main objective of this section is to build fundamental data analysis skills for the learner. These skills involve the Jupyter Notebook, and basic Python libraries such as NumPy, Pandas, Scipy, and statsmodels. Also, this section focuses on subjective knowledge of statistics and linear algebra to build math capabilities.
This section includes the following chapters:
Chapter 1, Getting Started with Python Libraries
Chapter 2, NumPy and pandas
Chapter 3, Statistics
Chapter 4, Linear Algebra
1 Getting Started with Python Libraries
As you already know, Python has become one of the most popular, standard languages and is a complete package for data science-based operations. Python offers numerous libraries, such as NumPy, Pandas, SciPy, Scikit-Learn, Matplotlib, Seaborn, and Plotly. These libraries provide a complete ecosystem for data analysis that is used by data analysts, data scientists, and business analysts. Python also offers other features, such as flexibility, being easy to learn, faster development, a large active community, and the ability to work on complex numeric, scientific, and research applications. All these features make it the first choice for data analysis.
In this chapter, we will focus on various data analysis processes, such as KDD, SEMMA, and CRISP-DM. After this, we will provide a comparison between data analysis and data science, as well as the roles and different skillsets for data analysts and data scientists. Finally, we will shift our focus and start installing various Python libraries, IPython, Jupyter Lab, and Jupyter Notebook. We will also look at various advanced features of Jupyter Notebooks
In this introductory chapter, we will cover the following topics:
Understanding data analysis
The standard process of data analysis
The KDD process
SEMMA
CRISP-DM
Comparing data analysis and data science
The skillsets of data analysts and data scientists
Installing Python 3
Software used in this book
Using IPython as a shell
Using Jupyter Lab
Using Jupyter Notebooks
Advanced features of Jupyter Notebooks
Let's get started!
Understanding data analysis
The 21st century is the century of information. We are living in the age of information, which means that almost every aspect of our daily life is generating data. Not only this, but business operations, government operations, and social posts are also generating huge data. This data is accumulating day by day due to data being continually generated from business, government, scientific, engineering, health, social, climate, and environmental activities. In all these domains of decision-making, we need a systematic, generalized, effective, and flexible system for the analytical and scientific process so that we can gain insights into the data that is being generated.
In today's smart world, data analysis offers an effective decision-making process for business and government operations. Data analysis is the activity of inspecting, preprocessing, exploring, describing, and visualizing the given dataset. The main objective of the data analysis process is to discover the required information for decision-making. Data analysis offers multiple approaches, tools, and techniques, all of which can be applied to diverse domains such as business, social science, and fundamental science.
Let's look at some of the core fundamental data analysis libraries of the Python ecosystem:
NumPy: This is a short form of numerical Python. It is the most powerful scientific library available in Python for handling multidimensional arrays, matrices, and methods in order to compute mathematics efficiently.
SciPy: This is also a powerful scientific computing library for performing scientific, mathematical, and engineering operations.
Pandas: This is a data exploration and manipulation library that offers tabular data structures such as DataFrames and various methods for data analysis and manipulation.
Scikit-learn: This stands for "Scientific Toolkit for Machine learning". It is a machine learning library that offers a variety of supervised and unsupervised algorithms, such as regression, classification, dimensionality reduction, cluster analysis, and anomaly detection.
Matplotlib: This is a core data visualization library and is the base library for all other visualization libraries in Python. It offers 2D and 3D plots, graphs, charts, and figures for data exploration. It runs on top of NumPy and SciPy.
Seaborn: This is based on Matplotlib and offers easy to draw, high-level, interactive, and more organized plots.
Plotly: Plotly is a data visualization library. It offers high quality and interactive graphs, such as scatter charts, line charts, bar charts, histograms, boxplots, heatmaps, and subplots.
Installation instructions for the required libraries and software will be provided throughout this book when they're needed. In the meantime, let's discuss various data analysis processes, such as the standard process, KDD, SEMMA, and CRISP-DM.
The standard process of data analysis
Data analysis refers to investigating the data, finding meaningful insights from it, and drawing conclusions. The main goal of this process is to collect, filter, clean, transform, explore, describe, visualize, and communicate the insights from this data to discover decision-making information. Generally, the data analysis process is comprised of the following phases:
Collecting Data: Collect and gather data from several sources. 1.
Preprocessing Data: Filter, clean, and transform the data into the required 2. format.
Analyzing and Finding Insights: Explore, describe, and visualize the data and 3. find insights and conclusions.
Insights Interpretations: Understand the insights and find the impact each 4. variable has on the system.
Storytelling: Communicate your results in the form of a story so that a layman 5. can understand them.
We can summarize these steps of the data analysis process via the following process diagram:
In this section, we have covered the standard data analysis process, which emphasizes finding interpretable insights and converting them into a user story. In the next section, we will discuss the KDD process.
The KDD process
The KDD acronym stands for knowledge discovery from data or Knowledge Discovery in Databases. Many people treat KDD as one synonym for data mining. Data mining is referred to as the knowledge discovery process of interesting patterns. The main objective of KDD is to extract or discover hidden interesting patterns from large databases, data warehouses, and other web and information repositories. The KDD process has seven major phases:
Data Cleaning: In this first phase, data is preprocessed. Here, noise is removed, 1. missing values are handled, and outliers are detected.
Data Integration: In this phase, data from different sources is combined and 2. integrated together using data migration and ETL tools.
Data Selection: In this phase, relevant data for the analysis task is recollected. 3.
Data Transformation: In this phase, data is engineered in the required 4. appropriate form for analysis.
Data Mining: In this phase, data mining techniques are used to discover useful 5. and unknown patterns.
Pattern Evaluation: In this phase, the extracted patterns are evaluated. 6.
Knowledge Presentation: After pattern evaluation, the extracted knowledge 7. needs to be visualized and presented to business people for decision-making purposes.
The complete KDD process is shown in the following diagram:
KDD is an iterative process for enhancing data quality, integration, and transformation to get a more improved system. Now, let's discuss the SEMMA process.
SEMMA
The SEMMA acronym's full form is Sample, Explore, Modify, Model, and Assess. This sequential data mining process is developed by SAS. The SEMMA process has five major phases:
Sample: In this phase, we identify different databases and merge them. After 1. this, we select the data sample that's sufficient for the modeling process.
Explore: In this phase, we understand the data, discover the relationships among 2. variables, visualize the data, and get initial interpretations.
Modify: In this phase, data is prepared for modeling. This phase involves 3. dealing with missing values, detecting outliers, transforming features, and creating new additional features.
Model: In this phase, the main concern is selecting and applying different 4. modeling techniques, such as linear and logistic regression, backpropagation networks, KNN, support vector machines, decision trees, and Random Forest.
Assess: In this last phase, the predictive models that have been developed are 5. evaluated using performance evaluation measures.
The following diagram shows this process:
The preceding diagram shows the steps involved in the SEMMA process. SEMMA emphasizes model building and assessment. Now, let's discuss the CRISP-DM process.
CRISP-DM
CRISP-DM's full form is CRoss-InduStry Process for Data Mining. CRISP-DM is a welldefined, well-structured, and well-proven process for machine learning, data mining, and business intelligence projects. It is a robust, flexible, cyclic, useful, and practical approach to solving business problems. The process discovers hidden valuable information or patterns from several databases. The CRISP-DM process has six major phases:
Business Understanding: In this first phase, the main objective is to understand 1. the business scenario and requirements for designing an analytical goal and initial action plan.
Data Understanding: In this phase, the main objective is to understand the data 2. and its collection process, perform data quality checks, and gain initial insights.
Data Preparation: In this phase, the main objective is to prepare analytics-ready 3. data. This involves handling missing values, outlier detection and handling, normalizing data, and feature engineering. This phase is the most timeconsuming for data scientists/analysts.
Modeling: This is the most exciting phase of the whole process since this is 4. where you design the model for prediction purposes. First, the analyst needs to decide on the modeling technique and develop models based on data.
Evaluation: Once the model has been developed, it's time to assess and test the 5. model's performance on validation and test data using model evaluation measures such as MSE, RMSE, R-Square for regression and accuracy, precision, recall, and the F1-measure.
Deployment: In this final phase, the model that was chosen in the previous step 6. will be deployed to the production environment. This requires a team effort from data scientists, software developers, DevOps experts, and business professionals.
The following diagram shows the full cycle of the CRISP-DM process:
The standard process focuses on discovering insights and making interpretations in the form of a story, while KDD focuses on data-driven pattern discovery and visualizing this. SEMMA majorly focuses on model building tasks, while CRISP-DM focuses on business understanding and deployment. Now that we know about some of the processes surrounding data analysis, let's compare data analysis and data science to find out how they are related, as well as what makes them different from one other.
Comparing data analysis and data science
Data analysis is the process in which data is explored in order to discover patterns that help us make business decisions. It is one of the subdomains of data science. Data analysis methods and tools are widely utilized in several business domains by business analysts, data scientists, and researchers. Its main objective is to improve productivity and profits. Data analysis extracts and queries data from different sources, performs exploratory data analysis, visualizes data, prepares reports, and presents it to the business decisionmaking authorities.
On the other hand, data science is an interdisciplinary area that uses a scientific approach to extract insights from structured and unstructured data. Data science is a union of all terms, including data analytics, data mining, machine learning, and other related domains. Data science is not only limited to exploratory data analysis and is used for developing models and prediction algorithms such as stock price, weather, disease, fraud forecasts, and recommendations such as movie, book, and music recommendations.
The roles of data analysts and data scientists
A data analyst collects, filters, processes, and applies the required statistical concepts to capture patterns, trends, and insights from data and prepare reports for making decisions. The main objective of the data analyst is to help companies solve business problems using discovered patterns and trends. The data analyst also assesses the quality of the data and handles the issues concerning data acquisition. A data analyst should be proficient in writing SQL queries, finding patterns, using visualization tools, and using reporting tools Microsoft Power BI, IBM Cognos, Tableau, QlikView, Oracle BI, and more.
Data scientists are more technical and mathematical than data analysts. Data scientists are research- and academic-oriented, whereas data analysts are more application-oriented. Data scientists are expected to predict a future event, whereas data analysts extract significant insights out of data. Data scientists develop their own questions, while data analysts find answers to given questions. Finally, data scientists focus on what is going to happen, whereas data analysts focus on what has happened so far. We can summarize these two roles using the following table:
Features Data Scientist
Background Predict future events and scenarios based on data
Role Formulate questions that can profit the business
Knowledge of statistics, machine learning algorithms, NLP, and deep learning
R, Python, SAS, Hadoop, Spark, TensorFlow, and Keras
Knowledge of statistics, SQL, and data visualization
Excel, SQL, R, Tableau, and QlikView
Now that we know what defines a data analyst and data scientist, as well as how they are different from each other, let's have a look at the various skills that you would need to become one of them.
The skillsets of data analysts and data scientists
A data analyst is someone who discovers insights from data and creates value out of it. This helps decision-makers understand how the business is performing. Data analysts must acquire the following skills:
Exploratory Data Analysis (EDA): EDA is an essential skill for data analysts. It helps with inspecting data to discover patterns, test hypotheses, and assure assumptions.
Relational Database: Knowledge of at least one of the relational database tools, such as MySQL or Postgre, is mandatory. SQL is a must for working on relational databases.
Visualization and BI Tools: A picture speaks more than words. Visuals have more of an impact on humans and visuals are a clear and easy option for representing the insights. Visualization and BI tools such as Tableau, QlikView, MS Power BI, and IBM Cognos can help analysts visualize and prepare reports.
Spreadsheet: Knowledge of MS Excel, WPS, Libra, or Google Sheets is mandatory for storing and managing data in tabular form.
Storytelling and Presentation Skills: The art of storytelling is another necessary skill. A data analyst should be an expert in connecting data facts to an idea or an incident and turning it into a story.
On the other hand, the primary job of a data scientist is to solve problems using data. In order to do this, they need to understand the client's requirements, their domain, their problem space, and ensure that they get exactly what they really want. The tasks that data scientists undertake vary from company to company. Some companies use data analysts and offer the title of data scientist just to glorify the job designation. Some combine data analyst tasks with data engineers and offer data scientists designation; others assign them to machine learning-intensive tasks with data visualizations.
The task of the data scientist varies, depending on the company. Some employ data scientists as well-known data analysts and combine their responsibilities with data engineers. Others give them the task of performing intensive data visualization on machines.
A data scientist has to be a jack of all trades and wear multiple hats, including those of a data analyst, statistician, mathematician, programmer, ML, or NLP engineer. Most people are not skilled enough or experts in all these trades. Also, getting skilled enough requires lots of effort and patience. This is why data science cannot be learned in 3 or 6 months. Learning data science is a journey. A data scientist should have a wide variety of skills, such as the following:
Mathematics and Statistics: Most machine learning algorithms are based on mathematics and statistics. Knowledge of mathematics helps data scientists develop custom solutions.
Databases: Knowledge of SQL allows data scientists to interact with the database and collect the data for prediction and recommendation.
Machine Learning: Knowledge of supervised machine learning techniques such as regression analysis, classification techniques, and unsupervised machine learning techniques such as cluster analysis, outlier detection, and dimensionality reduction.
Programming Skills: Knowledge of programming helps data scientists automate their suggested solutions. Knowledge of Python and R is recommended.
Storytelling and Presentation skills: Communicating the results in the form of storytelling via PowerPoint presentations.
Big Data Technology: Knowledge of big data platforms such as Hadoop and Spark helps data scientists develop big data solutions for large-scale enterprises.
Deep Learning Tools: Deep learning tools such as Tensorflow and Keras are utilized in NLP and image analytics.
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escape, he took the pet from the basket, and placed him in Lady Jane’s arms.
“See here,” he said, “I’ve sewed this band of leather around his leg, and you can fasten a strong string to it. If your mama allows you to have him, you can always tie him to something when you go out, and leave him alone, and he will be there quite safe when you come back.”
“I should never leave him alone. I should keep him with me always,” said the child.
“But, if you should lose him,” continued the boy, spreading one of the pretty wings over Lady Jane’s plump little arm, “I’ll tell you how you can always know him. He’s marked. It’s as good as a brand. See those three black crosses on his wing feathers. As he grows larger they will grow too, and no matter how long a time should pass without your seeing him, you’d always know him by these three little crosses.”
“If mama says I can have him, I can take him with me, can’t I?”
“Certainly, this basket is very light. You can carry it yourself.”
“You know,” she whispered, glancing at her mother, who had leaned her head on the back of the seat in front of her, and appeared to be sleeping, “I want to see Carlo and kitty, and the ranch, and all the lambs; but I mustn’t let mama know, because it’ll make her cry.”
“You’re a good little girl to think of your mother,” said the boy, who was anxious to cultivate her confidence, but too well-bred to question her
“She has no one now but me to love her,” she continued, lowering her voice. “They took papa from us, and carried him away, and mama says he’ll never come back. He’s not gone to San Antonio, he’s gone to heaven; and we can’t go there now. We’re going to New York; but I’d rather go to heaven where papa is, only mama says there are no trains or ships to take us there, now, but by-and-by we’re going if we’re very good.”
The boy listened to her innocent prattle with a sad smile, glancing uneasily now and then at the mother, fearful lest the plaintive little voice might reach her ear; but she seemed to be sleeping, sleeping uneasily, and with that hot flush still burning on her cheeks.
“Have you ever been in New York?” he asked, looking tenderly at the little head nestled against his arm. She had taken off her hat, and was very comfortably curled up on the seat with Tony in her lap. The bird also seemed perfectly satisfied with his position.
“Oh, no; I’ve never been anywhere only on the ranch. That’s where Carlo, and kitty, and the lambs were, and my pony, Sunflower; he was named Sunflower, because he was yellow. I used to ride on him, and papa lifted me on, and took me off; and Sunflower was so gentle. Dear papa—I—loved him best of all and now he’s gone away, and I can’t see him again.”
Here the rosy little face was buried in Tony’s feathers, and something like a sob made the listener’s heart ache.
“Come, come,” he said softly, “you mustn’t cry, or I shall think you don’t care for the blue heron.”
In a moment, her little head was raised, and a smile shone through her tears. “Oh, I do, I do. And if I can have him I won’t cry for the others.”
“I’m quite sure your mama will consent. Now, let me tell you about my home. I live in New Orleans, and I have lots of pets,” and the boy went on to describe so many delightful things that the child forgot her grief in listening; and soon, very soon the weary little head drooped, and she was sleeping with her rosy cheek pressed against his shoulder, and Tony clasped close in her arms.
And so the long, hot afternoon passed away, and the train sped on toward its destination, while the mother and the child slept, happily unconscious of the strange fate that awaited them in that city, of which the spires and walls were even now visible, bathed in the red light of the evening sun.
CHAPTER II
TONY GOES WITH LADY JANE
A
ND now that the end of the journey was so near, the drowsy passengers began to bestir themselves. In order to look a little more presentable, dusty faces and hands were hastily wiped, frowsy heads were smoothed, tumbled hats and bonnets were arranged, and even the fretful babies, pulled and coaxed into shape, looked less miserable in their soiled garments, while their mothers wore an expression of mingled relief and expectation.
Lady Jane did not open her eyes until her companion gently tried to disengage Tony from her clasp in order to consign him to his basket; then she looked up with a smile of surprise at her mother, who was bending over her. “Why, mama,” she said brightly, “I’ve been asleep, and I had such a lovely dream; I thought I was at the ranch, and the blue heron was there too. Oh, I’m sorry it was only a dream!”
“My dear, you must thank this kind young gentleman for his care of you. We are near New Orleans now, and the bird must go to his basket. Come, let me smooth your hair and put on your hat.”
“But, mama, am I to have Tony?”
The boy was tying the cover over the basket, and, at the child’s question, he looked at the mother entreatingly. “It will amuse her,” he said, “and it’ll be no trouble. May she have it?”
“I suppose I must consent; she has set her heart on it.”
The boy held out the little basket, and Lady Jane grasped it rapturously.
“Oh, how good you are!” she cried. “I’ll never, never forget you, and I’ll love Tony always.”
At that moment the young fellow, although he was smiling brightly, was smothering a pang of regret, not at parting with the blue heron, which he really prized, but because his heart had gone out to the charming child, and she was about to leave him, without any certainty of their ever meeting again. While this thought was vaguely passing through his mind, the lady turned and said to him:
“I am going to Jackson Street, which I believe is uptown. Is there not a nearer station for that part of the city, than the lower one?”
“Certainly, you can stop at Gretna; the train will be there in a few minutes. You cross the river there, and the ferry-landing is at the foot of Jackson Street, where you will find carriages and horse-cars to take you where you wish to go, and you will save an hour.”
“I’m very glad of that; my friends are not expecting me, and I should like to reach them before dark. Is it far to the ferry?”
“Only a few blocks; you’ll have no trouble finding it,” and he was about to add, “Can’t I go with you and show you the way?” when the conductor flung open the door and bawled, “Grate-na! Grate-na! passengers for Grate-na!”
Before he could give expression to the request, the conductor had seized the lady’s satchel, and was hurrying them toward the door. When he reached the platform, the train had stopped, and they had already stepped off. For a moment, he saw them standing on the dusty road, the river and the setting sun behind them—the blackrobed, graceful figure of the woman, and the fair-haired child with her violet eyes raised to his, while she clasped the little basket and smiled.
He touched his hat and waved his hand in farewell; the mother lifted her veil and sent him a sad good-by smile, and the child pressed her rosy fingers to her lips, and gracefully and gravely threw him a kiss. Then the train moved on; and the last he saw of them, they were walking hand in hand toward the river.
As the boy went back to his seat, he was reproaching himself for his neglect and stupidity. “Why didn’t I find out her name?—or the name of the people to whom she was going?—or why didn’t I go with her? It was too bad to leave her to cross alone, and she a stranger and looking so ill. She seemed hardly able to walk and carry her bag. I don’t see how I could have been so stupid. It wouldn’t have been much out of my way, and, if I’d crossed with them, I should have found out who they were. I didn’t want to seem too presuming, and especially after I gave the child the heron; but I wish I’d gone with them. Oh, she’s left something,” and in an instant he was reaching under the seat lately occupied by the object of his solicitude.
“It’s a book, ‘Daily Devotions,’ bound in russia, silver clasp, monogram ‘J. C.,’” he said, as he opened it; “and here’s a name.”
On the fly-leaf was written J C .
From Papa, N Y , Christmas, 18—.
“‘Jane Chetwynd,’ that must be the mother. It can’t be the child, because the date is ten years ago. ‘New York.’ They’re from the North then; I thought they were. Hello! here’s a photograph.”
It was a group, a family group—the father, the mother, and the child; the father’s a bright, handsome, almost boyish face, the mother’s not pale and tear-stained, but fresh and winsome, with smiling lips and merry eyes, and the child, the little “Lady Jane,” clinging to her father’s neck, two years younger, perhaps, but the same lovely, golden-haired child.
The boy’s heart bounded with pleasure as he looked at the sweet little face that had such a fascination for him.
“I wish I could keep it,” he thought, “but it’s not mine, and I must try to return to it the owner. Poor woman! she will be miserable when she misses it. I’ll advertise it to-morrow, and through it I’m likely to find out all about them.”
Next morning some of the readers of the principal New Orleans journals noticed an odd little advertisement among the personals:
Found, “Daily Devotions”; bound in red russia-leather, silver clasp, with monogram, “J. C.” Address,
B H , P. O. Box 1121.
For more than a week this advertisement remained in the columns of the paper, but it was never answered, nor was the book ever claimed.
CHAPTER III
MADAME JOZAIN
MADAME JOZAIN was a creole of mixed French and Spanish ancestry. She was a tall, thin woman with great, soft black eyes, a nose of the hawk type, and lips that made a narrow line when closed. In spite of her forbidding features, the upper part of her face was rather pleasing, her mild eyes had a gently appealing expression when she lifted them upward, as she often did, and no one would have believed that the owner of those innocent, candid eyes could have a sordid, avaricious nature, unless he glanced at the lower part of her face, which was decidedly mean and disagreeable. Her nose and mouth had a wily and ensnaring expression, which was at the same time cruel and rapacious. Her friends, and she had but few, endowed her with many good qualities, while her enemies, and they were numerous, declared that she was but little better than a fiend incarnate; but Father Ducros, her confessor, knew that she was a combination of good and evil, the evil largely predominating.
With this strange and complex character, she had but two passions in life. One was for her worthless son, Adraste, and the other was a keen desire for the good opinion of those who knew her. She always wished to be considered something that she was not,— young, handsome, amiable, pious, and the best blanchisseuse de fin in whatever neighborhood she hung out her sign.
And perhaps it is not to be wondered at, that she felt a desire to compensate herself by duplicity for what fate had honestly deprived her of, for no one living had greater cause to complain of a cruel destiny than had Madame Jozain. Early in life she had great expectations. An only child of a well-to-do baker, she inherited quite a little fortune, and when she married the débonnair and handsome André Jozain, she intended, by virtue of his renown and her competency, to live like a lady. He was a politician, and a power in
his ward, which might eventually have led him to some prominence; but instead, this same agency had conducted him, by dark and devious ways, to life-long detention in the penitentiary of his State— not, however, until he had squandered her fortune, and lamed her for life by pushing her down-stairs in a quarrel. This accident, had it disabled her arms, might have incapacitated her from becoming a blanchisseuse de fin, which occupation she was obliged to adopt when she found herself deprived of her husband’s support by the too exacting laws of his country.
In her times of despondency it was not her husband’s disgrace, her poverty, her lameness, her undutiful son, her lost illusions, over which she mourned, as much as it was the utter futility of trying to make things seem better than they were. In spite of all her painting, and varnishing, and idealizing, the truth remained horribly apparent: She was the wife of a convict, she was plain, and old, and lame; she was poor, miserably poor, and she was but an indifferent blanchisseuse de fin, while Adraste, or Raste, as he was always called, was the worst boy in the State. If she had ever studied the interesting subject of heredity, she would have found in Raste the strongest confirmation in its favor, for he had inherited all his father’s bad qualities in a greater degree.
On account of Raste’s unsavory reputation and her own incompetency, she was constantly moving from one neighborhood to another, and, by a natural descent in the scale of misfortune, at last found herself in a narrow little street, in the little village of Gretna, one of the most unlovely suburbs of New Orleans.
The small one-story house she occupied contained but two rooms, and a shed, which served as a kitchen. It stood close to the narrow sidewalk, and its green door was reached by two small steps. Madame Jozain, dressed in a black skirt and a white sack, sat upon these steps in the evening and gossiped with her neighbor. The house was on the corner of the street that led to the ferry, and her greatest amusement (for, on account of her lameness, she could not run with the others to see the train arrive) was to sit on her doorstep and watch the passengers walking by on their way to the river.
On this particular hot July evening, she felt very tired, and very cross. Her affairs had gone badly all day. She had not succeeded with some lace she had been doing for Madame Joubert, the wife of the grocer, on the levee, and Madame Joubert had treated her crossly—in fact had condemned her work, and refused to take it until made up again; and Madame Jozain needed the money sorely. She had expected to be paid for the work, but instead of paying her that “little cat of a Madame Joubert” had fairly insulted her. She, Madame Jozain, née Bergeron. The Bergerons were better than the Jouberts. Her father had been one of the City Council, and had died rich, and her husband—well, her husband had been unfortunate, but he was a gentleman, while the Jouberts were common and always had been. She would get even with that proud little fool; she would punish her in some way. Yes, she would do her lace over, but she would soak it in soda, so that it would drop to pieces the first time it was worn.
Meantime she was tired and hungry, and she had nothing in the house but some coffee and cold rice. She had given Raste her last dime, and he had quarreled with her and gone off to play “craps” with his chums on the levee. Besides, she was very lonesome, for there was but one house on her left, and beyond it was a wide stretch of pasture, and opposite there was nothing but the blank walls of a row of warehouses belonging to the railroad, and her only neighbor, the occupant of the next cottage, had gone away to spend a month with a daughter who lived “down town,” on the other side of the river.
So, as she sat there alone, she looked around her with an expression of great dissatisfaction, yawning wearily, and wishing that she was not so lame, so that she could run out to the station, and see what was going on: and that boy, Raste, she wondered if he was throwing away her last dime. He often brought a little money home. If he did not bring some now, they would have no breakfast in the morning.
Then the arriving train whistled, and she straightened up and her face took on a look of expectancy.
“Not many passengers to-night,” she said to herself, as a few men hurried by with bags and bundles. “They nearly all go to the lower
ferry, now.”
In a moment they had all passed, and the event of the evening was over But no!—and she leaned forward and peered up the street with fresh curiosity. “Why, here come a lady and a little girl and they’re not hurrying at all. She’ll lose the ferry if she doesn’t mind. I wonder what ails her?—she walks as if she couldn’t see.”
Presently the two reached her corner, a lady in mourning, and a little yellow-haired girl carefully holding a small basket in one hand, while she clung to her mother’s gown with the other.
Madame Jozain noticed, before the lady reached her, that she tottered several times, as if about to fall, and put out her hand, as if seeking for some support. She seemed dizzy and confused, and was passing on by the corner, when the child said entreatingly, “Stop here a minute, mama, and rest.”
Then the woman lifted her veil and saw Madame Jozain looking up at her, her soft eyes full of compassion.
“Will you allow me to rest here a moment? I’m ill and a little faint,— perhaps you will give me a glass of water?”
“Why, certainly, my dear,” said madame, getting up alertly, in spite of her lameness. “Come in and sit down in my rocking-chair. You’re too late for the ferry. It’ll be gone before you get there, and you may as well be comfortable while you wait—come right in.”
The exhausted woman entered willingly. The room was neat and cool, and a large white bed, which was beautifully clean, for madame prided herself upon it, looked very inviting.
The mother sank into a chair, and dropped her head on the bed; the child set down the basket and clung to her mother caressingly, while she looked around with timid, anxious eyes.
Madame Jozain hobbled off for a glass of water and a bottle of ammonia, which she kept for her laces; then, with gentle, deft hands, she removed the bonnet and heavy veil, and bathed the poor woman’s hot forehead and burning hands, while the child clung to
her mother murmuring, “Mama, dear mama, does your head ache now?”
“I’m better now, darling,” the mother replied after a few moments; then turning to madame, she said in her sweet, soft tones, “Thank you so much. I feel quite refreshed. The heat and fatigue exhausted my strength. I should have fallen in the street had it not been for you.”
“Have you traveled far?” asked madame, gently sympathetic.
“From San Antonio, and I was ill when I started”; and again she closed her eyes and leaned her head against the back of the chair.
At the first glance, madame understood the situation. She saw from the appearance of mother and child, that they were not poor. In this accidental encounter was a possible opportunity, but how far she could use it she could not yet determine; so she said only, “That’s a long way to come alone”; then she added, in a casual tone, “especially when one’s ill.”
The lady did not reply, and madame went on tentatively, “Perhaps some one’s waiting for you on the other side, and’ll come back on the ferry to see what’s become of you.”
“No. No one expects me; I’m on my way to New York. I have a friend living on Jackson Street. I thought I would go there and rest a day or so; but I did wrong to get off the train here. I was not able to walk to the ferry. I should have gone on to the lower station, and saved myself the exertion of walking.”
“Well, don’t mind now, dear,” returned madame, soothingly “Just rest a little, and when it’s time for the boat to be back, I’ll go on down to the ferry with you. It’s only a few steps, and I can hobble that far. I’ll see you safe on board, and when you get across, you’ll find a carriage.”
“Thank you, you’re very good. I should like to get there as soon as possible, for I feel dreadfully ill,” and again the weary eyes closed, and the heavy head fell back against its resting-place.
Madame Jozain looked at her for a moment, seriously and silently; then she turned, smiling sweetly on the child. “Come here, my dear, and let me take off your hat and cool your head while you’re waiting.”
“No, thank you, I’m going with mama.”
“Oh, yes, certainly; but won’t you tell me your name?”
“My name is Lady Jane,” she replied gravely.
“Lady Jane? Well, I declare, that just suits you, for you are a little lady, and no mistake. Aren’t you tired, and warm?”
“I’m very hungry; I want my supper,” said the child frankly.
Madame winced, remembering her empty cupboard, but went on chatting cheerfully to pass away the time.
Presently the whistle of the approaching ferryboat sounded; the mother put on her bonnet, and the child took the bag in one hand, and the basket in the other. “Come, mama, let us go,” she cried eagerly.
“Dear, dear,” said madame, solicitously, “but you look so white and sick. I’m afraid you can’t get to the ferry even with me to help you. I wish my Raste was here; he’s so strong, he could carry you if you gave out.”
“I think I can walk; I’ll try,” and the poor woman staggered to her feet, only to fall back into Madame Jozain’s arms in a dead faint.
CHAPTER IV
AN INTERRUPTED JOURNEY
FOR a moment, madame debated on what was best to be done; then, finding herself equal to the emergency, she gently laid the unconscious woman on the bed, unfastened her dress, and slowly and softly removed her clothing. Although madame was lame, she was very strong, and in a few moments the sufferer was resting between the clean, cool sheets, while her child clung to her cold hands and sobbed piteously.
“Don’t cry, my little dear, don’t cry. Help me to bathe your mama’s face; help me like a good child, and she’ll be better soon, now she’s comfortable and can rest.”
With the thought that she could be of some assistance, Lady Jane struggled bravely to swallow her sobs, took off her hat with womanly gravity, and prepared herself to assist as nurse.
“Here’s smelling salts, and cologne-water,” she said, opening her mother’s bag. “Mama likes this; let me wet her handkerchief.”
Madame Jozain, watching the child’s movements, caught a glimpse of the silver fittings of the bag, and of a bulging pocket-book within it, and, while the little girl was hanging over her mother, she quietly removed the valuables to the drawer of her armoire, which she locked, and put the key in her bosom.
“I must keep these things away from Raste,” she said to herself; “he’s so thoughtless and impulsive, he might take them without considering the consequences.”
For some time madame bent over the stranger, using every remedy she knew to restore her to consciousness, while the child assisted her with thoughtfulness and self-control, really surprising in one of her age. Sometimes her hot tears fell on her mother’s white face, but no sob or cry escaped her little quivering lips, while she
bathed the pale forehead, smoothed the beautiful hair, and rubbed the soft, cold hands.
At length, with a shiver and a convulsive groan, the mother partly opened her eyes, but there was no recognition in their dull gaze.
“Mama, dear, dear mama, are you better?” implored the child, as she hung over her and kissed her passionately.
“You see she’s opened her eyes, so she must be better; but she’s sleepy,” said madame gently. “Now, my little dear, all she needs is rest, and you mustn’t disturb her. You must be very quiet, and let her sleep. Here’s some nice, fresh milk the milkman has just brought. Won’t you eat some rice and milk, and then let me take off your clothes, and bathe you, and you can slip on your little nightgown that’s in your mother’s bag; and then you can lie down beside her and sleep till morning, and in the morning you’ll both be well and nicely rested.”
Lady Jane agreed to madame’s arrangements with perfect docility, but she would not leave her mother, who had fallen into a heavy stupor, and appeared to be resting comfortably.
“If you’ll please to let me sit by the bed close to mama and eat the rice and milk, I’ll take it, for I’m very hungry.”
“Certainly, my dear; you can sit there and hold her hand all the time; I’ll put your supper on this little table close by you.”
And madame bustled about, apparently overflowing with kindly attentions. She watched the child eat the rice and milk, smiling benevolently the while; then she bathed her, and put on the fine little nightgown, braided the thick silken hair, and was about to lift her up beside her mother, when Lady Jane exclaimed in a shocked voice:
“You mustn’t put me to bed yet; I haven’t said my prayers.” Her large eyes were full of solemn reproach as she slipped from madame’s arms down to the side of the bed. “Mama can’t hear them, because she’s asleep, but God can, for he never sleeps.” Then she repeated the touching little formula that all pious mothers teach their children, adding fervently several times, “and please make dear
mama well, so that we can leave this place early to-morrow morning.”
Madame smiled grimly at the last clause of the petition, and a great many curious thoughts whirled through her brain.
As the child rose from her knees her eyes fell on the basket containing the blue heron, which stood quite neglected, just where she placed it when her mother fainted.
“Oh, oh!” she cried, springing toward it. “Why, I forgot it! My Tony, my dear Tony!”
“What is it?” asked madame, starting back in surprise at the rustling sound within the basket. “Why, it’s something alive!”
“Yes, it’s alive,” said Lady Jane, with a faint smile. “It’s a bird, a blue heron. Such a nice boy gave it to me on the cars.”
“Ah,” ejaculated madame, “a boy gave it to you; some one you knew?”
“No, I never saw him before.”
“Don’t you know his name?”
“That’s funny,” and the child laughed softly to herself. “No, I don’t know his name. I never thought to ask; besides he was a stranger, and it wouldn’t have been polite, you know.”
“No, it wouldn’t have been polite,” repeated madame. “But what are you going to do with this long-legged thing?”
“It’s not a thing. It’s a blue heron, and they’re very rare,” returned the child stoutly
She had untied the cover and taken the bird out of the basket, and now stood in her nightgown and little bare feet, holding it in her arms, and stroking the feathers softly, while she glanced every moment toward the bed.
“I’m sure I don’t know what to do with him to-night. I know he’s hungry and thirsty, and I’m afraid to let him out for fear he’ll get
away”; and she raised her little anxious face to madame inquiringly, for she felt overburdened with her numerous responsibilities.
“Oh, I know what we’ll do with him,” said madame, alertly—she was prepared for every emergency. “I’ve a fine large cage. It was my parrot’s cage; he was too clever to live, so he died a while ago, and his empty cage is hanging in the kitchen. I’ll get it, and you can put your bird in it for to-night, and we’ll feed him and give him water; he’ll be quite safe, so you needn’t worry about him.”
“Thank you very much,” said Lady Jane, with more politeness than warmth. “My mama will thank you, too, when she wakes.”
After seeing Tony safely put in the cage, with a saucer of rice for his supper, and a cup of water to wash it down, Lady Jane climbed up on the high bed, and not daring to kiss her mother good-night lest she might disturb her, she nestled close to her. Worn out with fatigue, she was soon sleeping soundly and peacefully.
For some time Madame Jozain sat by the bed, watching the sick stranger, and wondering who she was, and whether her sudden illness was likely to be long and serious. “If I could keep her here, and nurse her,” she thought, “no doubt she would pay me well. I’d rather nurse than do lace; and if she’s very bad she’d better not be moved. I’d take good care of her, and make her comfortable; and if she’s no friends about here to look after her, she’d be better off with me than in the hospital. Yes, it would be cruel to send her to the hospital. Ladies don’t like to go there. It looks to me as if she’s going to have a fever,” and madame laid her fingers on the burning hand and fluttering pulse of the sleeper. “This isn’t healthy, natural sleep. I’ve nursed too many with fever, not to know. I doubt if she’ll come to her senses again. If she doesn’t no one will ever know who she is, and I may as well have the benefit of nursing her as any one else; but I must be careful, I mustn’t let her lie here and die without a doctor. That would never do. If she’s not better in the morning I’ll send for Doctor Debrot; I know he’ll be glad to come, for he never has any practice to speak of now, he’s so old and stupid; he’s a good doctor, and I’d feel safe to have him.”
After a while she got up and went out on the doorstep to wait for Raste. The night was very quiet, a fresh breeze cooled the burning heat, the stars shone brightly and softly, and as she sat there alone and lifted her mild eyes toward the sky no one would have dreamed of the strange thoughts that were passing through her mind. Now she was neither hungry nor lonesome; a sudden excitement thrilled her through and through. She was about to engage in a project that might compensate her for all her misfortunes. The glimpse she had of money, of valuables, of possible gain, awakened all her cupidity. The only thing she cared for now was money She hated work, she hated to be at the beck and call of those she considered beneath her. What a gratification it would be to her to refuse to do Madame Joubert’s lace, to fling it at her, and tell her to take it elsewhere! With a little ready money, she could be so independent and so comfortable. Raste had a knack of getting together a great deal in one way and another. He was lucky; if he had a little to begin with he could, perhaps, make a fortune. Then she started, and looked around as one might who suddenly found himself on the brink of an awful chasm. From within she heard the sick stranger moan and toss restlessly; then, in a moment, all was quiet again. Presently, she began to debate in her mind how far she should admit Raste to her confidence. Should she let him know about the money and valuables she had hidden? The key in her bosom seemed to burn like a coal of fire. No, she would not tell him about the money. While taking the child’s nightgown from the bag, she had discovered the railroad tickets, two baggage checks, and a roll of notes and loose change in a little compartment of the bag. He would think that was all; and she would never tell him of the other.
At that moment, she heard him coming down the street, singing a rollicking song. So she got up, and hobbled toward him, for she feared he might waken the sleepers. He was a great overgrown, redfaced, black-eyed fellow, coarse and strong, with a loud, dashing kind of beauty, and he was very observing, and very shrewd. She often said he had all his father’s cunning and penetration, therefore she must disguise her plans carefully
“Hallo, mum,” he said, as he saw her limping toward him, her manner eager, her face rather pale and excited; “what’s up now?” It was unusual for her to meet him in that way.
“Hush, hush, Raste. Don’t make a noise. Such a strange thing has happened since you went out!” said madame, in a low voice. “Sit down here on the steps, and I’ll tell you.”
Then briefly, and without much show of interest, she told him of the arrival of the strangers, and of the young woman’s sudden illness.
“And they’re in there asleep,” he said, pointing with his thumb in the direction of the room.
“That’s a fine thing for you to do—to saddle yourself with a sick woman and a child.”
“What could I do?” asked madame indignantly. “You wouldn’t have me turn a fainting woman into the street? It won’t cost anything for her to sleep in my bed to-night.”
“What is she like? Is she one of the poor sort? Did you look over her traps? Has she got any money?” he asked eagerly
“Oh, Raste, Raste; as if I searched her pockets! She’s beautifully dressed, and so is the child. She’s got a fine watch and chain, and when I opened her bag to get the child’s nightgown, I saw that it was fitted up with silver.”
“What luck!” exclaimed Raste brightly. “Then she’s a swell, and tomorrow when she goes away she’ll give you as much as a ‘fiver.’”
“I don’t believe she’ll be able to go to-morrow. I think she’s down for a long sickness. If she’s no better in the morning, I want you to cross and find Dr. Debrot”
“Old Debrot? That’s fun! Why, he’s no good—he’ll kill her.”
“Nonsense; you know he’s one of the best doctors in the city.”
“Sometimes, yes. But you can’t keep the woman here, if she’s sick; you’ll have to send her to the hospital. And you didn’t find out
her name, nor where she belongs? Suppose she dies on your hands? What then?”
“If I take care of her and she dies, I can’t help it; and I may as well have her things as any one else.”
“But has she got anything worth having? Enough to pay you for trouble and expense?” he asked. Then he whistled softly, and added, “Oh, mum, you’re a deep one, but I see through you.”
“I don’t know what you mean, boy,” said madame, indignantly. “Of course, if I nurse the woman, and give up my bed to her, I expect to be paid. I hate to send her to the hospital, and I don’t know her name, nor the name of her friends. So what can I do?”
“Do just what you’ve planned to do, mum. Go right ahead, but be careful and cover up your tracks. Do you understand?”
Madame made no reply to this disinterested piece of advice, but sat silently thinking for some time. At last she said in a persuasive tone, “Didn’t you bring some money from the levee? I’ve had no supper, and I intend to sit up all night with that poor woman. Can’t you go to Joubert’s and get me some bread and cheese?”
“Money, money—look here!” and the young scapegrace pulled out a handful of silver. “That’s what I’ve brought.”
An hour later madame and Raste sat in the little kitchen, chatting over their supper in the most friendly way; while the sick woman and the child still slept profoundly in the small front room.
CHAPTER V
LAST DAYS AT GRETNA
THE next morning, Madame Jozain sent Raste across the river for Dr. Debrot, for the sick woman still lay in a heavy stupor, her dull eyes partly closed, her lips parched and dry, and the crimson flush of fever burning on cheek and brow.
Before Raste went, Madame Jozain took the traveling bag into the kitchen, and together they examined its contents. There were the two baggage-checks, the tickets and money, besides the usual articles of clothing, and odds and ends; but there was no letter, nor card, nor name, except the monogram, J. C., on the silver fittings, to assist in establishing the stranger’s identity.
“Hadn’t I better take these,” said Raste, slipping the baggagechecks into his pocket, “and have her baggage sent over? When she comes to, you can tell her that she and the young one needed clothes, and you thought it was best to get them. You can make that all right when she gets well,” and Raste smiled knowingly at madame, whose face wore an expression of grave solicitude as she said:
“Hurry, my son, and bring the doctor back with you. I’m so anxious about the poor thing, and I dread to have the child wake and find her mother no better.”
When Doctor Debrot entered Madame Jozain’s front room, his head was not as clear as it ought to have been, and he did not observe anything peculiar in the situation. He had known madame, more or less, for a number of years, and he might be considered one of the friends who thought well of her. Therefore, he never suspected that the young woman lying there in a stupor was any other than the relative from Texas madame represented her to be. And she was very ill, of that there could be no doubt; so ill as to awaken all the doctor’s long dormant professional ambition. There were new
