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finished assignment #3

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161 changes: 134 additions & 27 deletions 02_activities/assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -50,33 +50,100 @@
{
"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"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 26,
"metadata": {
"id": "n0m48JsS-nMC"
},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"all_paths = [\n",
" \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n",
Expand All @@ -94,8 +161,12 @@
"]\n",
"\n",
"with open(all_paths[0], 'r') as f:\n",
" lines = f.readlines()\n",
" \n",
" # YOUR CODE HERE: Use the readline() or readlines() method to read the .csv file into a variable\n",
" \n",
" \n",
" for line in lines:\n",
" print(line.strip()) \n",
" # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection"
]
},
Expand Down Expand Up @@ -130,7 +201,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 49,
"metadata": {
"id": "82-bk4CBB1w4"
},
Expand All @@ -145,32 +216,48 @@
" # 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",
" \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",
"\n",
" elif operation == 'min':\n",
" # YOUR CODE HERE: Calculate the minimum number of flare-ups experienced by each patient\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"
" \n",
" return summary_values\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 51,
"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",
"data_min = patient_summary(all_paths[0], 'min')\n",
"print(len(data_min))"
"data_min = patient_summary(all_paths[0], 'mean')\n",
"print(len(data_min))\n",
"\n"
]
},
{
Expand Down Expand Up @@ -228,7 +315,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 40,
"metadata": {
"id": "_svDiRkdIwiT"
},
Expand All @@ -251,7 +338,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 70,
"metadata": {
"id": "LEYPM5v4JT0i"
},
Expand All @@ -260,16 +347,35 @@
"# Define your function `detect_problems` here\n",
"\n",
"def detect_problems(file_path):\n",
" # getting the mean array from patient files\n",
" data_max = patient_summary(file_path, \"mean\")\n",
"\n",
" # default is that there are no problems \n",
" problem = False \n",
" # for loop to check if any of the means are zero, then assigning the problem variable to = false\n",
" for x in data_max:\n",
" if check_zeros(np.array([x])): # if it reads true\n",
" problem = True\n",
" break\n",
" \n",
" #YOUR CODE HERE: Use patient_summary() to get the means and check_zeros() to check for zeros in the means\n",
"\n",
" return"
" return problem"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 73,
"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",
Expand Down Expand Up @@ -314,7 +420,8 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "python-env",
"language": "python",
"name": "python3"
},
"language_info": {
Expand All @@ -327,7 +434,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.11.1"
}
},
"nbformat": 4,
Expand Down