From d4cfa007fe4b709bbc4c20252c42a68e13c8a98b Mon Sep 17 00:00:00 2001 From: Tselot Tessema Date: Tue, 13 Oct 2026 22:10:29 -0400 Subject: [PATCH 1/2] https://github.com/tselot-t/python.git --- .../assignments/assignment_1 copy.ipynb | 258 ++++++++++++++++++ 1 file changed, 258 insertions(+) create mode 100644 02_activities/assignments/assignment_1 copy.ipynb diff --git a/02_activities/assignments/assignment_1 copy.ipynb b/02_activities/assignments/assignment_1 copy.ipynb new file mode 100644 index 000000000..d5b57f30c --- /dev/null +++ b/02_activities/assignments/assignment_1 copy.ipynb @@ -0,0 +1,258 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment #1: Anagram Checker\n", + "\n", + "**Background**: Anagram Checker is a program that takes two words and determines if an anagram can be made from it. If so, the program will return `true`, otherwise `false`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Submission Information\n", + "\n", + "🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.\n", + "\n", + "### Submission Parameters:\n", + "* Submission Due Date: `11:59 PM - October 13, 2026`\n", + "* The branch name for your repo should be: `assignment-1`\n", + "* What to submit for this assignment:\n", + " * This Jupyter Notebook (assignment_1.ipynb) should be populated and should be the only change in your pull request.\n", + "* What the pull request link should look like for this assignment: `https://github.com//python/pull/`\n", + " * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.\n", + "\n", + "Checklist:\n", + "- [ ] Created a branch with the correct naming convention.\n", + "- [ ] Ensured that the repository is public.\n", + "- [ ] Reviewed the PR description guidelines and adhered to them.\n", + "- [ ] Verify that the link is accessible in a private browser window.\n", + "\n", + "If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#dc4-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 1: Building the base Anagram Checker\n", + "\n", + "Given two valid strings, check to see if they are anagrams of each other. If it is, return `True`, else `False`. For this part, we can assume that uppercase letters are the same as if it was a lowercase character.\n", + "\n", + "Examples of anagrams:\n", + "* Silent and Listen\n", + "* Night and Thing\n", + "\n", + "Example outputs:\n", + "```python\n", + "anagram_checker(\"Silent\", \"listen\") # True\n", + "anagram_checker(\"Silent\", \"Night\") # False\n", + "anagram_checker(\"night\", \"Thing\") # True\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'True'" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For testing purposes, we will write our code in the function\n", + "def anagram_checker(word_a, word_b):\n", + " # criteria for angram: words must have same number of letters and must have the same letters\n", + " #check that length of words match\n", + " if len(word_a)==len(word_b):\n", + " #loop that checks whether each letter in word_a is in word_b\n", + " #not case sensitive to match above outputs for Silent and listen\n", + " #inverted logic using \"not in\" to avoid forcing the computer to check every letter\n", + " for i, letter in enumerate(word_a):\n", + " if letter.upper() not in word_b.upper():\n", + " #test print(True)\n", + " #test print(i,letter)\n", + " return('False') \n", + " return('True')\n", + " #complete top if statement for words that are not the same length\n", + " else:\n", + " return('False')\n", + " \n", + "# Run your code to check using the words below:\n", + "anagram_checker(\"Silent\", \"listen\")" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'False'" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anagram_checker(\"Silent\", \"Night\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'True'" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anagram_checker(\"night\", \"Thing\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 2: Expanding the functionality of the Anagram Checker\n", + "\n", + "Using your existing and functional anagram checker, let's add a boolean option called `is_case_sensitive`, which will return `True` or `False` based on if the two compared words are anagrams and if we are checking for case sensitivity." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'True'" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def anagram_checker(word_a, word_b, is_case_sensitive):\n", + " # Modify your existing code here\n", + " if len(word_a)==len(word_b):\n", + " #adding nested if statement for is_case_sensitive parameter\n", + " if is_case_sensitive==True:\n", + " #loop that checks whether each letter in word_a is in word_b\n", + " for i, letter in enumerate(word_a):\n", + " #remove .upper() to compare case of letters\n", + " if letter not in word_b:\n", + " return('False')\n", + " elif is_case_sensitive==False:\n", + " for i, letter in enumerate(word_a):\n", + " #add .upper() to stop comparing case of letters\n", + " if letter.upper() not in word_b.upper():\n", + " return('False') \n", + " return('True')\n", + " else:\n", + " return('False')\n", + " \n", + "\n", + "# Run your code to check using the words below:\n", + "anagram_checker(\"Silent\", \"listen\", False) # True" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'False'" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anagram_checker(\"Silent\", \"listen\", True) # False" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'False'" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "anagram_checker(\"Silent\", \"Listen\", True) # False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "|Criteria|Pass|Fail|\n", + "|---|---|---|\n", + "|Code Execution|All code cells execute without errors.|Any code cell produces an error upon execution.|\n", + "|Code Quality|Code is well-organized, concise, and includes necessary comments for clarity. E.g. Great use of variable names.|Code is unorganized, verbose, or lacks necessary comments. E.g. Single character variable names outside of loops.|" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "new-learner", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 68c6dc50d861cbe178117fd65d2257e2df023799 Mon Sep 17 00:00:00 2001 From: Tselot Tessema Date: Fri, 23 Oct 2026 21:26:04 -0400 Subject: [PATCH 2/2] Completed assignment 2. --- 02_activities/assignments/assignment_2.ipynb | 232 +++++++++++++++++-- 1 file changed, 208 insertions(+), 24 deletions(-) diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb index debc6c5e1..83ae7bce6 100644 --- a/02_activities/assignments/assignment_2.ipynb +++ 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"0,0,0,2,4,4,5,3,3,3,10,4,4,4,14,11,15,13,10,14,11,17,9,11,11,7,10,12,10,10,10,8,7,5,2,2,4,1,2,1\n", + "\n", + "0,0,2,1,1,4,4,7,2,9,4,10,12,7,6,6,11,12,9,15,15,6,6,13,5,12,9,6,4,7,7,6,5,4,1,4,2,2,2,1\n", + "\n", + "0,1,2,1,1,4,5,4,4,5,9,7,10,3,13,13,8,9,17,16,16,15,12,13,5,12,10,9,11,9,4,5,5,2,2,5,1,0,0,1\n", + "\n", + "0,0,1,3,2,3,6,4,5,7,2,4,11,11,3,8,8,16,5,13,16,5,8,8,6,9,10,10,9,3,3,5,3,5,4,5,3,3,0,1\n", + "\n", + "0,1,1,2,2,5,1,7,4,2,5,5,4,6,6,4,16,11,14,16,14,14,8,17,4,14,13,7,6,3,7,7,5,6,3,4,2,2,1,1\n", + "\n", + "0,1,1,1,4,1,6,4,6,3,6,5,6,4,14,13,13,9,12,19,9,10,15,10,9,10,10,7,5,6,8,6,6,4,3,5,2,1,1,1\n", + "\n", + "0,0,0,1,4,5,6,3,8,7,9,10,8,6,5,12,15,5,10,5,8,13,18,17,14,9,13,4,10,11,10,8,8,6,5,5,2,0,2,0\n", + "\n", + "0,0,1,0,3,2,5,4,8,2,9,3,3,10,12,9,14,11,13,8,6,18,11,9,13,11,8,5,5,2,8,5,3,5,4,1,3,1,1,0\n", + "\n" + ] + } + ], "source": [ "all_paths = [\n", " \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n", @@ -95,8 +222,20 @@ "\n", "with open(all_paths[0], 'r') as f:\n", " # YOUR CODE HERE: Use the readline() or readlines() method to read the .csv file into a variable\n", - " \n", - " # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection" + " contents = f.readlines()\n", + " # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection\n", + " for row in contents:\n", + " print(row)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extra testing\n", + "1. checking the type of all_paths\n", + "\n", + "type(all_paths) -> it is a list and must be converted to array for np functions to work" ] }, { @@ -130,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 146, "metadata": { "id": "82-bk4CBB1w4" }, @@ -141,35 +280,45 @@ "def patient_summary(file_path, operation):\n", " data = np.loadtxt(fname=file_path, delimiter=',') # Load the data from the file\n", " ax = 1 # This specifies that the operation should be done for each row (patient)\n", - "\n", + " data_array=np.array(data) #converting data to array because it is read as a list (see extra testing above)\n", " # Implement the specific operation based on the 'operation' argument\n", " if operation == 'mean':\n", " # YOUR CODE HERE: Calculate the mean (average) number of flare-ups for each patient\n", - "\n", + " summary_values=data_array.mean(axis=1)\n", " elif operation == 'max':\n", " # YOUR CODE HERE: Calculate the maximum number of flare-ups experienced by each patient\n", - "\n", + " summary_values=data_array.max(axis=1)\n", " elif operation == 'min':\n", " # YOUR CODE HERE: Calculate the minimum number of flare-ups experienced by each patient\n", - "\n", + " summary_values=data_array.min(axis=1)\n", " else:\n", " # If the operation is not one of the expected values, raise an error\n", " raise ValueError(\"Invalid operation. Please choose 'mean', 'max', or 'min'.\")\n", - "\n", - " return summary_values" + " \n", + " return summary_values\n", + " " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 147, "metadata": { "id": "3TYo0-1SDLrd" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "60\n" + ] + } + ], "source": [ "# Test it out on the data file we read in and make sure the size is what we expect i.e., 60\n", "# Your output for the first file should be 60\n", "data_min = patient_summary(all_paths[0], 'min')\n", + "#print(data_min)\n", "print(len(data_min))" ] }, @@ -228,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": { "id": "_svDiRkdIwiT" }, @@ -242,34 +391,69 @@ " Return True if any values found, else returns False.\n", " '''\n", " # np.where() checks every value in x against the condition (x == 0) and returns a tuple of indices where it was True (i.e. x was 0)\n", - " flag = np.where(x == 0)[0]\n", - "\n", + " flag = np.where(x == 0)[0] #[0] is used to identify the first line if present\n", + " \n", " # Checks if there are any objects in flag (i.e. not empty)\n", " # If not empty, it found at least one zero so flag is True, and vice-versa.\n", - " return len(flag) > 0" + " return len(flag) > 0 and tuple(flag) #returns boolean and tuple of the flag (index) which is an array type\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extra testing\n", + "1. check whether 'data_mean' (i.e. the output from patient_summary(operation=mean)) actually has zeros\n", + "\n", + "data_mean = patient_summary(all_paths[0], 'mean')\n", + "print(data_mean)\n", + "print(len(data_mean))\n", + "\n", + "2. create a copy 'data_meanv2' with a zero because there are no zeros in 'data_mean'\n", + "\n", + "data_meanv2=np.append(data_mean,0)\n", + "print(data_meanv2)\n", + "\n", + "3. use the check_zeros function to compare the output for 'data_mean' vs. 'data_meanv2', which contains a zero\n", + "\n", + "print(check_zeros(data_meanv2))\n", + "print(check_zeros(data_mean))\n", + "\n", + "4. check the type of 'flag' in check_zeros function using a substitute variable 'location'\n", + "location=np.where(check==0,np.where(check==0),'')\n", + "print(type(location)) -> it is an array type and must be converted to a tuple so the output formatting is correct" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 149, "metadata": { "id": "LEYPM5v4JT0i" }, "outputs": [], "source": [ "# Define your function `detect_problems` here\n", - "\n", "def detect_problems(file_path):\n", " #YOUR CODE HERE: Use patient_summary() to get the means and check_zeros() to check for zeros in the means\n", - "\n", - " return" + " x=patient_summary(file_path,operation='mean')\n", + " output=check_zeros(x)\n", + " return output" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], "source": [ "# Test out your code here\n", "# Your output for the first file should be False\n", @@ -327,7 +511,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.4" } }, "nbformat": 4,