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Assignment 2 #2

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a020d82
Add Assignment #1 – Anagram Checker solution
katariamonika Oct 18, 2025
be8d061
Complete Assignment #2 - Arthritis Drug Efficacy Analysis
katariamonika Oct 26, 2025
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102 changes: 102 additions & 0 deletions assignment_1.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a7768fe5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"False\n",
"True\n"
]
}
],
"source": [
"# Part 1: Basic Anagram Checker\n",
"\n",
"def anagram_checker(word_a, word_b):\n",
" \"\"\"\n",
" Check if two words are anagrams of each other.\n",
" Ignores case sensitivity.\n",
" \"\"\"\n",
" # Convert both words to lowercase for comparison\n",
" word_a = word_a.lower()\n",
" word_b = word_b.lower()\n",
"\n",
" # Sort letters and compare\n",
" return sorted(word_a) == sorted(word_b)\n",
"\n",
"# Testing Part 1\n",
"print(anagram_checker(\"Silent\", \"listen\")) # True\n",
"print(anagram_checker(\"Silent\", \"Night\")) # False\n",
"print(anagram_checker(\"night\", \"Thing\")) # True\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9f4c4b83",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"False\n",
"True\n"
]
}
],
"source": [
"# Part 2: Expanding the Anagram Checker with Case Sensitivity\n",
"\n",
"def anagram_checker(word_a, word_b, is_case_sensitive=False):\n",
" \"\"\"\n",
" Check if two words are anagrams of each other.\n",
" Case sensitivity can be toggled with 'is_case_sensitive'.\n",
" \"\"\"\n",
"\n",
" # If not case-sensitive, convert both words to lowercase\n",
" if not is_case_sensitive:\n",
" word_a = word_a.lower()\n",
" word_b = word_b.lower()\n",
"\n",
" # Sort letters and compare\n",
" return sorted(word_a) == sorted(word_b)\n",
"\n",
"\n",
"# Testing Part 2\n",
"print(anagram_checker(\"Silent\", \"listen\", False)) # ✅ True (ignores case)\n",
"print(anagram_checker(\"Silent\", \"Listen\", True)) # ❌ False (case-sensitive)\n",
"print(anagram_checker(\"night\", \"Thing\", False)) # ✅ True\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
248 changes: 248 additions & 0 deletions assignment_2.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "806a8681",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '../../05_src/data/assignment_2_data/inflammation_01.csv'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 20\u001b[0m\n\u001b[1;32m 4\u001b[0m all_paths \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../../05_src/data/assignment_2_data/inflammation_01.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../../05_src/data/assignment_2_data/inflammation_02.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../../05_src/data/assignment_2_data/inflammation_12.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 17\u001b[0m ]\n\u001b[1;32m 19\u001b[0m \u001b[38;5;66;03m# Read and display the first file\u001b[39;00m\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(all_paths[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 21\u001b[0m lines \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mreadlines()\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Display first few rows for inspection\u001b[39;00m\n",
"File \u001b[0;32m/opt/anaconda3/lib/python3.12/site-packages/IPython/core/interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 319\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 322\u001b[0m )\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m io_open(file, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../../05_src/data/assignment_2_data/inflammation_01.csv'"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# All file paths (provided)\n",
"all_paths = [\n",
" \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_02.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_03.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_04.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_05.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_06.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_07.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_08.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_09.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_10.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_11.csv\",\n",
" \"../../05_src/data/assignment_2_data/inflammation_12.csv\"\n",
"]\n",
"\n",
"# Read and display the first file\n",
"with open(all_paths[0], 'r') as f:\n",
" lines = f.readlines()\n",
"\n",
"# Display first few rows for inspection\n",
"for line in lines[:5]:\n",
" print(line.strip())\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7871951f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/moli/Desktop/dsi projects/python\n"
]
}
],
"source": [
"import os\n",
"print(os.getcwd())\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "b6e7873e",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"id": "26d6c074",
"metadata": {},
"outputs": [],
"source": [
"all_paths = [\n",
" \"05_src/data/assignment_2_data/inflammation_01.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_02.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_03.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_04.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_05.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_06.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_07.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_08.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_09.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_10.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_11.csv\",\n",
" \"05_src/data/assignment_2_data/inflammation_12.csv\"\n",
"]\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "87c23235",
"metadata": {},
"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"
]
}
],
"source": [
"with open(all_paths[0], 'r') as f:\n",
" lines = f.readlines()\n",
"\n",
"for line in lines[:5]:\n",
" print(line.strip())\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f73d710f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of patients: 60\n",
"First 5 minimum values: [0. 0. 0. 0. 0.]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"def patient_summary(file_path, operation):\n",
" \"\"\"\n",
" Compute summary statistics (mean, max, or min) per patient.\n",
" Each row in the file corresponds to one patient.\n",
" \"\"\"\n",
" # Load the data (60 patients × 40 days)\n",
" data = np.loadtxt(fname=file_path, delimiter=',')\n",
" ax = 1 # perform operation across columns (days) for each patient\n",
"\n",
" # Choose operation type\n",
" if operation == 'mean':\n",
" summary_values = np.mean(data, axis=ax)\n",
" elif operation == 'max':\n",
" summary_values = np.max(data, axis=ax)\n",
" elif operation == 'min':\n",
" summary_values = np.min(data, axis=ax)\n",
" else:\n",
" raise ValueError(\"Invalid operation. Please choose 'mean', 'max', or 'min'.\")\n",
"\n",
" return summary_values\n",
"\n",
"# ✅ Test the function\n",
"data_min = patient_summary(all_paths[0], 'min')\n",
"print(\"Number of patients:\", len(data_min))\n",
"print(\"First 5 minimum values:\", data_min[:5])\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "1f01f8cf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Any zero-mean patients? False\n"
]
}
],
"source": [
"def check_zeros(x):\n",
" \"\"\"\n",
" Given an array x, check whether any values in x equal 0.\n",
" Return True if any values found, else False.\n",
" \"\"\"\n",
" flag = np.where(x == 0)[0]\n",
" return len(flag) > 0\n",
"\n",
"def detect_problems(file_path):\n",
" \"\"\"\n",
" Detects if any patient has a mean inflammation of 0.\n",
" Returns True if such a patient exists, False otherwise.\n",
" \"\"\"\n",
" means = patient_summary(file_path, 'mean')\n",
" has_zero = check_zeros(means)\n",
" return has_zero\n",
"\n",
"# ✅ Test\n",
"print(\"Any zero-mean patients?\", detect_problems(all_paths[0]))\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "38e43fba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean values (first 5): [5.45 5.425 6.1 5.9 5.55 ]\n",
"Max values (first 5): [18. 18. 19. 17. 17.]\n"
]
}
],
"source": [
"print(\"Mean values (first 5):\", patient_summary(all_paths[0], 'mean')[:5])\n",
"print(\"Max values (first 5):\", patient_summary(all_paths[0], 'max')[:5])\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}