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UofT-DSI | Python - Assignment 2 #2

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abab074
adding the first version of assignment 2
greglitster Mar 11, 2026
a564851
adding in my code for DSI certificate Python module assignment 2
greglitster Mar 16, 2026
243420f
adding in another comment
greglitster Mar 16, 2026
0b4b7f1
adding in another comment
greglitster Mar 16, 2026
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133 changes: 113 additions & 20 deletions 02_activities/assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,12 +72,81 @@
},
{
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"execution_count": null,
"execution_count": 77,
"metadata": {
"id": "n0m48JsS-nMC"
},
"outputs": [],
"outputs": [
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"name": "stdout",
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]
}
],
"source": [
"import csv #Adding this to be able to read the csv file easier\n",
"\n",
"all_paths = [\n",
" \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_02.csv\",\n",
Expand All @@ -94,9 +163,10 @@
"]\n",
"\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 = csv.reader(f)\n",
" for row in contents:\n",
" print(row)\n",
" "
]
},
{
Expand Down Expand Up @@ -139,18 +209,20 @@
"import numpy as np\n",
"\n",
"def patient_summary(file_path, operation):\n",
" '''patient_summary(str, str) takes file path to a CSV file and an operation and return an array \n",
" with the appropriate operation applied for each row in the file'''\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",
" \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, ax) # Calculating the mean value for each patient\n",
"\n",
" elif operation == 'max':\n",
" # YOUR CODE HERE: Calculate the maximum number of flare-ups experienced by each patient\n",
" summary_values = np.max(data, ax) # Calculating the maximum number of flare-ups experienced by each patient\n",
"\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, ax) # Calculating the minimum number of flare-ups experienced by each patient\n",
"\n",
" else:\n",
" # If the operation is not one of the expected values, raise an error\n",
Expand All @@ -161,11 +233,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 83,
"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",
Expand Down Expand Up @@ -228,7 +308,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 60,
"metadata": {
"id": "_svDiRkdIwiT"
},
Expand Down Expand Up @@ -260,20 +340,32 @@
"# 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"
" '''detect problems determines if there are any patients with a mean inflammation score of 0 by using patient summary to find the mean values of each participant \n",
" and the check_zeros helper function to see if any of those values are 0. It returns True if there is a patient entry with a mean value of 0 and False otherwise'''\n",
" mean_list = patient_summary(file_path,'mean')\n",
" return check_zeros(mean_list)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"[False, False, True, False, False, False, False, True, False, False, True, False]\n"
]
}
],
"source": [
"# Test out your code here\n",
"# Your output for the first file should be False\n",
"print(detect_problems(all_paths[0]))"
"print(detect_problems(all_paths[0]))\n",
"\n",
"print([detect_problems(path) for path in all_paths]) # Adding an additional test to check if there are any files across the entire dataset."
]
},
{
Expand Down Expand Up @@ -314,7 +406,8 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "python-env",
"language": "python",
"name": "python3"
},
"language_info": {
Expand All @@ -327,7 +420,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.11.14"
}
},
"nbformat": 4,
Expand Down
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