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219 changes: 197 additions & 22 deletions 02_activities/assignments/assignment_2.ipynb
Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@
"id": "JhJAJb1m-nkn"
},
"source": [
"## 1. Reading and Displaying Data from the First File\n",
"## 1. Read 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",
Expand All @@ -76,8 +76,145 @@
"metadata": {
"id": "n0m48JsS-nMC"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"\n"
]
}
],
"source": [
"'''Python program to evaluate the effectiveness of a medication got reducing arthritis inflammation flare-ups. \n",
"Data from 60 patients, across 40 days for 12 session files is available.\n",
"User can select and display session data, one file at a time (Part 1).\n",
"Summary statistics (mean, max, and min) are calulated by patient record (Part 2).\n",
"Program returns an bool value of True if an irregularity exists in the pateient data (Part 3).\n",
"Data irregularity is defined as a session file with one or more patient data with a mean inflammation of zero.\n",
"'''\n",
"\n",
"# Part 1: Display data from selected file in named file path\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 +231,13 @@
"]\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"
" # Read the .csv file into the variable file_contents\n",
" file_contents = f.readlines()\n",
"\n",
" # Iterate through the variable file_contents using a for loop and print each row for inspection\n",
" # Use \"for row in\"command to print list items in rows for inspection ease\n",
" for row in file_contents:\n",
" print(row)\n"
]
},
{
Expand Down Expand Up @@ -136,27 +277,36 @@
},
"outputs": [],
"source": [
"# Part 2: Use Python numpy library to calculate three summary statistics on a speccified patient (n) by day (p) session (file) within a function named patient_summary\n",
"\n",
"# Import Numerical Python into this program application under the np alias\n",
"import numpy as np\n",
"\n",
"# Create a function named patient_summary to compute summary statistics for each patient's data over a 40-day period for the file session specified in Part 1\n",
"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",
" # Implement the specific operation based on the 'operation' argument\n",
" # Use the mean, max, and min numpy summary operations for each patient over 40 days\n",
" if operation == 'mean':\n",
" # YOUR CODE HERE: Calculate the mean (average) number of flare-ups for each patient\n",
" # Calculate the mean (average) number of flare-ups for each patient\n",
" summary_values = np.mean(data, axis=1)\n",
"\n",
" elif operation == 'max':\n",
" # YOUR CODE HERE: Calculate the maximum number of flare-ups experienced by each patient\n",
" # Calculate the maximum number of flare-ups experienced by each patient\n",
" summary_values = np.max(data, axis=1)\n",
"\n",
" elif operation == 'min':\n",
" # YOUR CODE HERE: Calculate the minimum number of flare-ups experienced by each patient\n",
" # Calculate the minimum number of flare-ups experienced by each patient\n",
" summary_values = np.min(data, axis=1)\n",
"\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"
" return summary_values\n",
" "
]
},
{
Expand All @@ -165,9 +315,17 @@
"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",
"# Test data file output to ensure 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(len(data_min))"
Expand Down Expand Up @@ -234,7 +392,7 @@
},
"outputs": [],
"source": [
"# Run this cell so you can use this helper function\n",
"# Run this cell to use it as helper function in Part 3\n",
"\n",
"def check_zeros(x):\n",
" '''\n",
Expand All @@ -257,23 +415,39 @@
},
"outputs": [],
"source": [
"# Define your function `detect_problems` here\n",
"# Part 3: Evaluate if the session file contains one or more patient mean inflammation scores of zero.\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"
" # Use the patient_summary() fucntion to get the session patient mean scores and use the check_zeros() to evaluate whether at least one or more patient man scores > 0\n",
" # Read patient data from the session file, specified in the Part 1 file_path\n",
" # Assign the patient_summary mean operation value to mean_result\n",
" mean_result = patient_summary(file_path, operation = 'mean')\n",
"\n",
" # Use check-zeros output to decide if the mean_result function evaluates as True (at least one patient mean inflammation score in the array is equal to zero)\n",
" return check_zeros(mean_result)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Test out your code here\n",
"# Your output for the first file should be False\n",
"print(detect_problems(all_paths[0]))"
"# The detect_problems function returns a check_zeros bool value = True if an irregularity exists in the pateient data;\n",
"# Otherwise, check_zeros bool value = False\n",
"detect_problems(all_paths[0])"
]
},
{
Expand Down Expand Up @@ -314,7 +488,8 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "python-env",
"language": "python",
"name": "python3"
},
"language_info": {
Expand All @@ -327,7 +502,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.11.13"
}
},
"nbformat": 4,
Expand Down