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Assignment-2 completed for python #2

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200 changes: 184 additions & 16 deletions 02_activities/assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,11 +72,138 @@
},
{
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"metadata": {
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"\n",
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"\n"
]
}
],
"source": [
"all_paths = [\n",
" \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n",
Expand All @@ -95,8 +222,11 @@
"\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() # This reads the contents of the file in to data as a list of lines\n",
" \n",
" # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection"
" # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection\n",
" for row in data:\n",
" print(row) # This prints each row of the file to the console"
]
},
{
Expand Down Expand Up @@ -139,18 +269,29 @@
"import numpy as np\n",
"\n",
"def patient_summary(file_path, operation):\n",
" \"\"\"\n",
" Accepts a file path to a .csv file containing patient data and an operation to perform on the data. \n",
" Parameters:\n",
" file_path(str): The path to .csv file containing patient data\n",
" operation(str): The operation to perform on the data. Must be one of 'mean', 'max', or 'min'.\n",
" Returns:\n",
" An array containing the result of the specified operation for each patient.\n",
"\n",
" \"\"\"\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",
" if operation == 'mean':\n",
" # YOUR CODE HERE: Calculate the mean (average) number of flare-ups for each patient\n",
"\n",
" summary_values = np.mean(data, axis=ax)\n",
" elif operation == 'max':\n",
" # YOUR CODE HERE: Calculate the maximum number of flare-ups experienced by each patient\n",
"\n",
" summary_values = np.max(data, axis=ax)\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",
Expand All @@ -161,16 +302,24 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"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))"
"print(len(data_min))\n"
]
},
{
Expand Down Expand Up @@ -228,7 +377,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {
"id": "_svDiRkdIwiT"
},
Expand All @@ -251,7 +400,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 16,
"metadata": {
"id": "LEYPM5v4JT0i"
},
Expand All @@ -261,15 +410,33 @@
"\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"
" \"\"\"\n",
" Accepts a file path to a .csv file containing patient data and uses the patient_summary() function to calculate the mean number of flare ups for each patient, then uses the check_zeros() function to check if any patient data has abnormnalities.\n",
" Parameters: \n",
" file_path(str): The path to .csv file containing patient data.\n",
" Returns:\n",
" Boolean indicating whether any patients have abnormal data (i.e a mean of 0 flare-ups).\n",
"\n",
" \"\"\"\n",
" \n",
" means = patient_summary(file_path, 'mean') # Get the mean flare-ups for each patient\n",
" has_problems = check_zeros(means) # Check if any of the means are zero, which would flag a problem\n",
" return has_problems"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 17,
"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 +481,8 @@
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"display_name": "python-env (3.11.15)",
"language": "python",
"name": "python3"
},
"language_info": {
Expand All @@ -327,7 +495,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.11.15"
}
},
"nbformat": 4,
Expand Down
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