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UofT-DSI | Python - Assignment #2: Efficacy Analysis of a Hypothetical Arthritis Drug #5

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Merged
hitechparadigm merged 1 commit into from
May 16, 2024
Merged

UofT-DSI | Python - Assignment #2: Efficacy Analysis of a Hypothetical Arthritis Drug #5

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209 changes: 194 additions & 15 deletions 02_assignments/assignment_2.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -90,16 +90,161 @@
},
{
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"metadata": {
"id": "n0m48JsS-nMC"
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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",
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"\n",
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"\n",
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"\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",
"]\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",
" contents = f.readlines()\n",
" \n",
" # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection"
" # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection\n",
" for row in contents:\n",
" print(row)"
]
},
{
Expand Down Expand Up @@ -133,7 +278,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 60,
"metadata": {
"id": "82-bk4CBB1w4"
},
Expand All @@ -145,16 +290,23 @@
" # 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",
" # print(data)\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, axis=ax)\n",
" # print(summary_values)\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",
" # print(summary_values)\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",
" # print(summary_values)\n",
"\n",
" else:\n",
" # if the operation is not one of the expected values, raise an error\n",
Expand All @@ -165,15 +317,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 61,
"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",
"data_min = patient_summary(all_paths[0], 'mean')\n",
"print(len(data_min))"
]
},
Expand Down Expand Up @@ -232,7 +392,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 67,
"metadata": {
"id": "_svDiRkdIwiT"
},
Expand All @@ -247,15 +407,16 @@
" '''\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",
" \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",
" \n",
" return len(flag) > 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 76,
"metadata": {
"id": "LEYPM5v4JT0i"
},
Expand All @@ -265,15 +426,33 @@
"\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"
" means = patient_summary(file_path, operation='mean')\n",
" print(means) # print the means to check that it's correct\n",
" \n",
" if check_zeros(means): # check if any of the means are zero\n",
" return True\n",
" else: \n",
" return False\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 77,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5.45 5.425 6.1 5.9 5.55 6.225 5.975 6.65 6.625 6.525 6.775 5.8\n",
" 6.225 5.75 5.225 6.3 6.55 5.7 5.85 6.55 5.775 5.825 6.175 6.1\n",
" 5.8 6.425 6.05 6.025 6.175 6.55 6.175 6.35 6.725 6.125 7.075 5.725\n",
" 5.925 6.15 6.075 5.75 5.975 5.725 6.3 5.9 6.75 5.925 7.225 6.15\n",
" 5.95 6.275 5.7 6.1 6.825 5.975 6.725 5.7 6.25 6.4 7.05 5.9 ]\n",
"False\n"
]
}
],
"source": [
"# Test out your code here\n",
"# Your output for the first file should be False\n",
Expand Down Expand Up @@ -331,7 +510,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.9.15"
}
},
"nbformat": 4,
Expand Down