diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb index 1ae6fe242..1ebc55e47 100644 --- a/02_activities/assignments/assignment_2.ipynb +++ b/02_activities/assignments/assignment_2.ipynb @@ -50,24 +50,24 @@ { "cell_type": "markdown", "metadata": { - "id": "yMsC2qsiKNP8" + "id": "JhJAJb1m-nkn" }, "source": [ - "**The file is located under `python/05_src/data/assignment_2_data/`. The filtered list has been made for you `all_paths` below.**\n", - "\n" + "## 1. Reading and Displaying Data from the First File\n", + "\n", + "With the list of the relevant `inflammation_xx.csv` file paths above, write a program to read the `inflammation_xx.csv` files, and display the contents of the first file in this list.\n", + "\n", + "**Hint**: Remember to use appropriate Python file handling and data reading methods. If you need guidance on how to handle CSV files in Python, refer to the relevant sections in your Python learning resources." ] }, { "cell_type": "markdown", "metadata": { - "id": "JhJAJb1m-nkn" + "id": "yMsC2qsiKNP8" }, "source": [ - "## 1. Reading and Displaying Data from the First File\n", - "\n", - "With the list of the relevant `inflammation_xx.csv` file paths above, write a program to read the `inflammation_xx.csv` files, and display the contents of the first file in this list.\n", - "\n", - "**Hint**: Remember to use appropriate Python file handling and data reading methods. If you need guidance on how to handle CSV files in Python, refer to the relevant sections in your Python learning resources." + "**The file is located under `python/05_src/data/assignment_2_data/`. The filtered list has been made for you `all_paths` below.**\n", + "\n" ] }, { @@ -76,7 +76,74 @@ "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", + "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", + "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", + "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", + "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", + "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", + "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", + "0,0,1,2,3,1,2,3,5,3,7,8,8,5,10,9,15,11,18,19,20,8,5,13,15,10,6,10,6,7,4,9,3,5,2,5,3,2,2,1\n", + 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"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", + "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" + ] + } + ], "source": [ "all_paths = [\n", " \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n", @@ -95,8 +162,13 @@ "\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" + " # this code creates a variable called 'patient_1' that reads lines in the .csv file one at a time \n", + " patient_1 = f.readlines()\n", + " \n", + " # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection\n", + " # This code creates a loop and prints the rows in the correct order for the data in patient_1-the first .csv file \n", + " for patient_1 in lines:\n", + " print(patient_1.strip())" ] }, { @@ -131,6 +203,16 @@ { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#had to include this because it wasn't working below for some reason \n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": { "id": "82-bk4CBB1w4" }, @@ -142,15 +224,17 @@ " 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", + " # This code ialculates the mean flare-ups for each patient \n", " if operation == 'mean':\n", - " # YOUR CODE HERE: Calculate the mean (average) number of flare-ups for each patient\n", + " summary_values = np.mean(data,axis=ax)\n", "\n", + " # this code identifies the maximum number of flareups for each patient \n", " elif operation == 'max':\n", - " # YOUR CODE HERE: Calculate the maximum number of flare-ups experienced by each patient\n", - "\n", + " summary_values = np.max(data,axis=ax)\n", + " \n", + " #this code identifies the minimum number of flareups that for each patient\n", " elif operation == 'min':\n", - " # YOUR CODE HERE: Calculate the minimum number of flare-ups experienced by each patient\n", + " summary_values = np.min(data,axis=ax)\n", "\n", " else:\n", " # If the operation is not one of the expected values, raise an error\n", @@ -159,13 +243,26 @@ " return summary_values" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "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 +325,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "_svDiRkdIwiT" }, @@ -249,6 +346,11 @@ " return len(flag) > 0" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -259,17 +361,40 @@ "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", + "def detect_problems(file_path):\n", + " \"\"\"\n", + " Detects patients with a mean inflammation score of 0.\n", "\n", - " return" + " Parameters:\n", + " file_path (str): Path to the CSV file containing patient data.\n", + "\n", + " Returns:\n", + " bool: True if at least one patient has a mean inflammation of 0, False otherwise.\n", + " \"\"\"\n", + " # this calculates the mean inflammation for each patient \n", + " mean_scores = patient_summary(file_path, 'mean')\n", + "\n", + " # this checks for zeros using the helper function provided above \n", + " problem_found = check_zeros(mean_scores)\n", + "\n", + " # this is a boolean that says 'True' if any zeros were found, otherwise 'False'\n", + " return problem_found\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "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", @@ -314,7 +439,8 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": ".venv (3.11.14)", + "language": "python", "name": "python3" }, "language_info": { @@ -327,7 +453,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.14" } }, "nbformat": 4,