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UofT-DSI | python - Assignment 2 #2

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225 changes: 184 additions & 41 deletions 02_assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -90,16 +90,159 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"id": "n0m48JsS-nMC"
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{
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"\n",
"0,0,0,2,2,1,3,4,5,5,6,5,5,12,13,5,7,5,11,15,18,7,9,10,14,12,11,9,10,3,2,9,6,2,2,5,3,0,0,1\n",
"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
"0,0,0,2,4,4,5,3,3,3,10,4,4,4,14,11,15,13,10,14,11,17,9,11,11,7,10,12,10,10,10,8,7,5,2,2,4,1,2,1\n",
"\n",
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"\n",
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"\n",
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"\n",
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"\n",
"0,1,1,1,4,1,6,4,6,3,6,5,6,4,14,13,13,9,12,19,9,10,15,10,9,10,10,7,5,6,8,6,6,4,3,5,2,1,1,1\n",
"\n",
"0,0,0,1,4,5,6,3,8,7,9,10,8,6,5,12,15,5,10,5,8,13,18,17,14,9,13,4,10,11,10,8,8,6,5,5,2,0,2,0\n",
"\n",
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"\n"
]
}
],
"source": [
"all_paths = [\n",
" \"../05_data/assignment_2_data/inflammation_01.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_02.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_03.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_04.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_05.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_06.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_07.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_08.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_09.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_10.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_11.csv\",\n",
" \"../05_data/assignment_2_data/inflammation_12.csv\"\n",
"] # list of file paths\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 'contents'\n",
" \n",
" # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection"
" contents = f.readlines() # read first file into 'contents'\n",
"\n",
"for i in range(len(contents)):\n",
" print(contents[i]) # print each row of 'contents'"
]
},
{
Expand Down Expand Up @@ -133,7 +276,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {
"id": "82-bk4CBB1w4"
},
Expand All @@ -142,39 +285,42 @@
"import numpy as np\n",
"\n",
"def patient_summary(file_path, operation):\n",
" # load the data from the file\n",
" data = np.loadtxt(fname=file_path, delimiter=',')\n",
" ax = 1 # this specifies that the operation should be done for each row (patient)\n",
" data = np.loadtxt(fname=file_path, delimiter=',') # load data from file\n",
" ax = 1 # specify axis of operations (row)\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",
" summary_values = np.mean(data, ax) # calculate the mean for each row or patient\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, ax) # calculate the max for each row or patient\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, ax) # calculate the min for each row or patient\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",
" raise ValueError(\"Invalid operation. Please choose 'mean', 'max', or 'min'.\") # raise error if operation is invalid\n",
"\n",
" return summary_values"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"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)) # 60"
]
},
{
Expand Down Expand Up @@ -232,52 +378,49 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {
"id": "_svDiRkdIwiT"
},
"outputs": [],
"source": [
"# Run this cell so you can use this helper function\n",
"\n",
"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 returns False.\n",
" '''\n",
" # np.where() checks every value in x against the condition (x == 0) and returns a tuple of indices where it was True (i.e. x was 0)\n",
" flag = np.where(x == 0)[0]\n",
"\n",
" # Checks if there are any objects in flag (i.e. not empty)\n",
" # If not empty, it found at least one zero so flag is True, and vice-versa.\n",
" return len(flag) > 0"
" flag = np.where(x == 0)[0] # check every value in x against the condition (x == 0)\n",
" return len(flag) > 0 # check if flag is not empty meaning there is at least one zero"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {
"id": "LEYPM5v4JT0i"
},
"outputs": [],
"source": [
"# Define your function `detect_problems` here\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"
" data_mean = patient_summary(file_path, 'mean') # calculate the means\n",
" return check_zeros(data_mean) # check for zeros in the means"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"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",
"print(detect_problems(all_paths[0]))"
"print(detect_problems(all_paths[0])) # False"
]
},
{
Expand Down Expand Up @@ -331,7 +474,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.9.15"
}
},
"nbformat": 4,
Expand Down