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Assignment 2 Completed #2

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177 changes: 142 additions & 35 deletions 02_activities/assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,31 +72,103 @@
},
{
"cell_type": "code",
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"execution_count": 3,
"metadata": {
"id": "n0m48JsS-nMC"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"all_paths = [\n",
" \"python/05_src/data/assignment_2_data/inflammation_01.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_02.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_03.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_04.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_05.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_06.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_07.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_08.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_09.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_10.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_11.csv\",\n",
" \"python/05_src/data/assignment_2_data/inflammation_12.csv\"\n",
"]\n",
"# I have changed the code a bit to dynamically get the path location depending on where the python directory is located.\n",
"import os\n",
"import csv \n",
"\n",
"# I created a function to find where the python directory is\n",
"def find_python_directory():\n",
" current_dir = os.getcwd() \n",
" while current_dir != os.path.dirname(current_dir): \n",
" if os.path.basename(current_dir).lower() == \"python\": \n",
" return current_dir\n",
" else:\n",
" current_dir = os.path.dirname(current_dir) \n",
" return None #if directory does not exist in path\n",
"\n",
"base_path = find_python_directory()\n",
"\n",
"# I can now generate all_paths based on directory location.\n",
"all_paths = [os.path.join(base_path, \"05_src\", \"data\", \"assignment_2_data\", f\"inflammation_{str(i).zfill(2)}.csv\") for i in range(1, 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",
" data = f.readlines()\n",
" \n",
" # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection"
"for line in data:\n",
" print(line.strip())"
]
},
{
Expand Down Expand Up @@ -130,10 +202,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "82-bk4CBB1w4"
},
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
Expand All @@ -144,28 +214,37 @@
"\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",
" summary_values = np.mean(data, axis=ax)\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, axis=ax)\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, axis=ax)\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",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"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 +307,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {
"id": "_svDiRkdIwiT"
},
Expand All @@ -251,25 +330,53 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 29,
"metadata": {
"id": "LEYPM5v4JT0i"
},
"outputs": [],
"source": [
"# Define your function `detect_problems` here\n",
"import pandas as pd\n",
"\n",
"# Define your function `detect_problems` here\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",
" data = np.loadtxt(fname=file_path, delimiter=',')\n",
" df = pd.DataFrame(data)\n",
"\n",
" problems = {\n",
" \"missing_values\": df.isnull().sum().sum(),\n",
" \"negative_values\": (df < 0).sum().sum(),\n",
" \"extreme_outliers\": ((df > df.mean() + 3 * df.std()) | (df < df.mean() - 3 * df.std())).sum().sum(),\n",
" \"zero_mean_patients\": check_zeros(df.mean(axis=1))\n",
" }\n",
" \n",
" print(f\"Problems detected in {file_path}:\")\n",
" print(f\"Missing values: {problems['missing_values']}\")\n",
" print(f\"Negative values: {problems['negative_values']}\")\n",
" print(f\"Extreme outliers: {problems['extreme_outliers']}\")\n",
" print(f\"Patients with zero mean inflammation: {problems['zero_mean_patients']}\")\n",
"\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 31,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Problems detected in c:\\Users\\chels\\Documents\\python\\05_src\\data\\assignment_2_data\\inflammation_01.csv:\n",
"Missing values: 0\n",
"Negative values: 0\n",
"Extreme outliers: 0\n",
"Patients with zero mean inflammation: False\n",
"None\n"
]
}
],
"source": [
"# Test out your code here\n",
"# Your output for the first file should be False\n",
Expand Down Expand Up @@ -327,7 +434,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.13.2"
}
},
"nbformat": 4,
Expand Down