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Jupyter Notebook Features

1. Multiple Language Support: Jupyter Notebook supports various programming languages, including Python, R, Julia, and more. This versatility allows users to work with their preferred language in a single environment.
2. Interactive Code Execution: Jupyter Notebook enables users to execute code cells interactively. You can run individual code cells or the entire notebook, making it easy to experiment and see immediate results.
3. Rich Text and Markdown Support: You can incorporate rich text, equations, and formatted content using Markdown cells in Jupyter Notebook. This makes it ideal for creating data reports, documentation, and educational materials.
4. Data Visualization: Jupyter Notebook integrates well with popular data visualization libraries like Matplotlib, Seaborn, and Plotly. Users can create interactive and visually appealing plots and charts directly within the notebook.
5. Code Autocompletion: Jupyter Notebook provides code autocompletion, which suggests function and variable names as you type, speeding up coding and reducing errors.
6. Kernel Support: Jupyter Notebook operates using kernels, which are computational engines supporting specific programming languages. Users can switch between kernels without changing the notebook’s content, allowing for seamless integration of different languages.
7. Notebook Sharing and Collaboration: Jupyter Notebooks can be shared easily with others, allowing for collaborative work and sharing of insights and findings.
8. Version Control Integration: Jupyter Notebook works well with version control systems like Git, enabling users to track changes and collaborate effectively with team members.
9. Exporting and Saving Options: Notebooks can be saved in different formats, such as HTML, PDF, or plain text. This makes it convenient to share notebooks with individuals who may not have Jupyter installed.
10. Mathematical Equation Support: Jupyter Notebook supports mathematical equations using LaTeX notation, enabling the inclusion of complex mathematical expressions in the document.
11. Cell Organization: Notebooks are organized into cells, making it easy to divide code, text, and visualizations into separate sections, enhancing the overall structure and readability of the notebook.
12. Magic Commands: Jupyter Notebook provides “magic commands” that offer additional functionality and shortcuts, enhancing the user experience and providing access to system-level operations.

Assignment Coverege in Jupyter Notebook

1. Data Analysis and Visualization: Jupyter Notebook is widely used for data analysis and visualization tasks. It allows users to import, manipulate, and analyze data using various libraries like Pandas, NumPy, and Matplotlib, making it a preferred choice for data scientists and analysts.
2. Machine Learning and AI: Jupyter Notebook provides an interactive environment for developing and testing machine learning models and AI algorithms. Libraries like scikit-learn and TensorFlow are often used within Jupyter for machine learning projects.
3. Data Cleaning and Preprocessing: Data preprocessing tasks like cleaning, transforming, and feature engineering can be efficiently performed in Jupyter Notebook, ensuring data is ready for further analysis or modeling.
4. Statistical Analysis: Jupyter Notebook integrates well with statistical libraries like SciPy, Statsmodels, and R (via R magic), enabling statisticians and researchers to perform complex statistical analysis and hypothesis testing.
5. Educational Tool: Jupyter Notebook is an excellent educational tool for teaching programming, data science, and other scientific concepts. Its interactive nature allows students to experiment and see immediate results, enhancing their learning experience.
6. Documenting and Sharing: Jupyter Notebook allows users to combine code, visualizations, and explanatory text in a single document. This makes it ideal for creating interactive reports, presentations, and tutorials that can be easily shared with others.
7. Web Development: Jupyter Notebook can be used for prototyping and testing web applications and APIs, making it a valuable tool for web developers.
8. IoT and Embedded Systems: Jupyter Notebook can be used in IoT and embedded systems development for data visualization, analysis, and prototyping.
9. Scientific Research: Jupyter Notebook is popular among scientists and researchers for documenting and sharing their data analysis workflows and research findings.
10. Data Journalism: Data journalists often use Jupyter Notebook to analyze and visualize data, helping them create compelling and interactive data-driven stories.
11. Reproducible Research: Jupyter Notebook’s ability to combine code, text, and visualizations in an interactive manner promotes reproducible research, ensuring that others can replicate and validate research findings.

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2. Data Analysis and Visualization Challenges: Performing data analysis and creating visualizations using libraries like Pandas, Matplotlib, or Plotly can be daunting for students. They may need guidance in writing code for data manipulation and visualization tasks.
3. Machine Learning and Statistical Modeling: Implementing machine learning algorithms or statistical models in Jupyter Notebook may require advanced programming skills. Students may seek help in understanding the algorithms and applying them to their data.
4. Debugging and Error Handling: Students may encounter errors while executing code in Jupyter Notebook. They might need assistance in debugging code and handling common errors.
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7. Language Transition: Switching between programming languages within Jupyter Notebook can be confusing for students. They might require guidance in understanding language-specific syntax and functions.
8. Report and Documentation: Creating well-structured and visually appealing reports within Jupyter Notebook can be challenging. Students may need assistance in incorporating markdown, equations, and images effectively.
9. Domain-Specific Projects: Students studying specific domains like data science, biology, finance, or engineering may encounter Jupyter Notebook assignments that require expertise in both the domain and the tool. Assignment help can provide specialized support in these cases.
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Assignment Example:

Instructions
You may choose to work on this assignment on a hosted environment (e.g. Google Colab) or on your own local installation of Jupyter and Python. You should use Python 3.8 or higher for your work. If you choose to work locally, Anaconda is the easiest way to install and manage Python. If you work locally, you may launch Jupyter Lab either from the Navigator application or via the command-line as jupyter-lab.

In this assignment, we will analyze the Information Wanted dataset which tracks advertisements for those looking for lost friends and relatives. This dataset is provided by Boston College based on work supervised by Dr. Ruth-Ann Harris and is documented here. We will be working on a subset of this data, available here. You will do some analysis of this data to answer some questions about it. I have provided code to organize this data, but you may feel free to improve this rudimentary organization. I have also provided functions that allow you to check your work. Note that you may choose to organize the cells as you wish, but you must properly label each problem’s code and its solution. Use a Markdown cell to add a header denoting your work for a particular problem. Make sure to document what your answer is to the question, and make sure your code actually computes that. As the goal of this assignment is to become acquainted with core Python, do not use other libraries except for the csv and collections library.

You may start with the provided Jupyter Notebook, a1.ipynb. Download this notebook (right-click to save the link) and upload it to your Jupyter workspace (either locally or on a hosted environment). Make sure to execute the first two cells in the notebook (Shift+Enter). The second cell will download the data and define two variables field_names and records. The field_names variable is a string with the names of each data attribute, separated by commas. The records variable is a list of comma-delimited strings with the values of each field for each data item. The field names are

recid: record identifier
mm: the month
dd: the day
yy: the year, a two-digit number that represents the year between 1830 and 1920
firstname: the first name of the person being sought
surname: the surname of the person being sought
sex: the sex of the person being sought, if specified
age: the age of the person being sought, if specified
seek_surname: the surname of the person seeking information
seek_first: the first name of the person seeking information
To access the fourth entry’s year, you would access records[3][3]. Remember indexing is zero-based!

In the provided file, I provided examples of how to check your work. For example, for Problem 1, you would call the check1 function with the number of unique first names. After executing this function, you will see a message that indicates whether your answer is correct.

Due Date
The assignment is due at 11:59pm on Monday, February 1.

Submission
You should submit the completed notebook file required for this assignment on Blackboard. The filename of the notebook should be a1.ipynb.

Details

1. Number of Unique First Names (10 pts)
   Write code that computes the number of unique first names of people being sought in the dataset. Note that the empty string is not a valid first name.

Hints:
You will need to extract the first name from each record string
The split function for strings will be useful
The strip function will also be useful to trim whitespace
Consider using a set to keep track of all the names

2. Most Frequent First Name (10 pts)
   Write code that computes the most frequent first name among those being sought. Again, the empty string does not count!

Hints:
collections.Counter() is a good structure to help with counting.
Clean up the strings in the same manner as in Problem 1.

3. Age of the Oldest Person (10 pts)
   Write code that computes the age of the oldest person being sought. Note that you will need to ignore those age values that are not numbers. You may use a try-except block for this.

Hints:
You can convert a string to an integer by casting it. For example, int(“81”) returns an integer value of 81.
Try converting an invalid age string to an integer to see which error to catch.

4. Same Surname (10 pts)
   Write code that computes the number of entries where the person seeking information has the same surname as the person being sought. Assume capitalization does not matter; thus “Smith” is the same as “smith”. Also, a match of empty strings for surnames does not count.

Hints:
Look for a Python method that will convert all characters to the same case.
Remember to check that the surnames are not empty

5. Date & Surname of the Latest Entry (10 pts)
   Write code that determines the date and surname of the persons being sought in the most recent entries in the dataset. Be careful–the dataset uses two-digit years but encompasses data from 1831 to 1920.

Hints:
All of the persons being sought on the most recent date have the same surname
1920 – 1831 < 100
If you compare two lists or tuples, Python will do this entry by entry, but make sure to put the data in the correct order.