diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index bee48d5a0..94a0de59b 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -26,10 +26,10 @@ " * 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", + "- [X] Created a branch with the correct naming convention.\n", + "- [X] Ensured that the repository is public.\n", + "- [X] Reviewed the PR description guidelines and adhered to them.\n", + "- [X] 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 `#dc-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." ] @@ -56,13 +56,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 38, + "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", - " # Your code here\n", + " ''' sorts word_a and word_b and checks if they are anagrams\n", + " word_a & word_b are strings \n", + " '''\n", + " \n", + " word_a = word_a.lower()\n", + " word_b = word_b.lower()\n", + "\n", + " return(sorted(word_a) == sorted(word_b))\n", + "\n", "\n", "# Run your code to check using the words below:\n", "anagram_checker(\"Silent\", \"listen\")" @@ -70,18 +89,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "anagram_checker(\"Silent\", \"Night\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "anagram_checker(\"night\", \"Thing\")" ] @@ -99,10 +140,37 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def anagram_checker(word_a, word_b, is_case_sensitive):\n", - " # Modify your existing code here\n", + " '''\n", + " Sorts word_a and word_b and checks if they are anagrams\n", + " word_a & word_b are strings\n", + " is_case_sensitive is a boolean (True = consider case, False = ignore case)\n", + " '''\n", + " \n", + " # Only convert to lowercase if not case-sensitive\n", + " if not is_case_sensitive:\n", + " word_a = word_a.lower()\n", + " word_b = word_b.lower()\n", + "\n", + " return(sorted(word_a) == sorted(word_b)) \n", + " \n", + " else:\n", + " \n", + " return(sorted(word_a) == sorted(word_b)) \n", + " \n", "\n", "# Run your code to check using the words below:\n", "anagram_checker(\"Silent\", \"listen\", False) # True" @@ -110,9 +178,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "anagram_checker(\"Silent\", \"Listen\", True) # False" ] @@ -130,7 +209,7 @@ ], "metadata": { "kernelspec": { - "display_name": "new-learner", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -144,7 +223,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb index 36a3e2bb7..e8764f5f3 100644 --- a/02_activities/assignments/assignment_2.ipynb +++ b/02_activities/assignments/assignment_2.ipynb @@ -39,10 +39,10 @@ " * 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", + "- [X] Created a branch with the correct naming convention.\n", + "- [X] Ensured that the repository is public.\n", + "- [X] Reviewed the PR description guidelines and adhered to them.\n", + "- [X] 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 `#dc-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges." ] @@ -76,7 +76,134 @@ "metadata": { "id": "n0m48JsS-nMC" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n", + "\n", + "0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n", + "\n", + "0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\n", + "\n", + "0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1\n", + "\n", + "0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1\n", + "\n", + "0,0,1,2,2,4,2,1,6,4,7,6,6,9,9,15,4,16,18,12,12,5,18,9,5,3,10,3,12,7,8,4,7,3,5,4,4,3,2,1\n", + "\n", + "0,0,2,2,4,2,2,5,5,8,6,5,11,9,4,13,5,12,10,6,9,17,15,8,9,3,13,7,8,2,8,8,4,2,3,5,4,1,1,1\n", + "\n", + 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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" + " # readlines() method reads the .csv file into a variable\n", + " inflammation = f.readlines() \n", + " # Iterate through the variable using a for loop and print each row for inspection\n", + " for row in inflammation:\n", + " print(row)" ] }, { @@ -139,19 +268,22 @@ "import numpy as np\n", "\n", "def patient_summary(file_path, operation):\n", + " '''\n", + " Given .csv file and operation 'mean', 'max', 'min', calculates value for each row.\n", + " Returns value for each row. \n", + " '''\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", " # 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", + " # Calculate the mean (average) number of flare-ups for each patient\n", + " summary_values = data.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.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.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", @@ -161,11 +293,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "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", @@ -228,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": { "id": "_svDiRkdIwiT" }, @@ -260,16 +400,31 @@ "# 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", + " Given .csv, calculates mean of each row then checks if any means are equal to 0.\n", + " Returns True if any means are 0, else returns False. \n", + " '''\n", + " #Use patient_summary() to get the means and check_zeros() to check for zeros in the means\n", + " patient_means = patient_summary(file_path, 'mean')\n", "\n", - " return" + " has_zeros = check_zeros(patient_means)\n", + "\n", + " return(has_zeros)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "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 +482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.9.6" } }, "nbformat": 4,