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0582a5c
updated assignment.md file
lisafeeney33 Sep 1, 2024
4341e9b
updated assignment 2
lisafeeney33 Sep 9, 2024
4ac30d7
changes to assignment 2
lisafeeney33 Sep 9, 2024
7dddf16
test drive 2
lisafeeney33 Sep 9, 2024
e484b0f
Updated Python Assignment 1
lisafeeney33 Sep 13, 2024
a3800c2
Changes to assignment 1
lisafeeney33 Sep 13, 2024
fb325c4
Final attempt on Python Assignment 1
lisafeeney33 Sep 13, 2024
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131 changes: 112 additions & 19 deletions 02_activities/assignments/assignment_1.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -56,34 +56,86 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# This is a function, which we will learn more about next week. For testing purposes, we will write our code in the function\n",
"def anagram_checker(word_a, word_b):\n",
" # Your code here\n",
" word_a = word_a.lower()\n",
" word_b = word_b.lower()\n",
"\n",
"# Run your code to check using the words below:\n",
"anagram_checker(\"Silent\", \"listen\")"
"# sort letters of the two words\n",
" sorted_word_a = sorted(word_a)\n",
" sorted_word_b = sorted(word_b)\n",
" \n",
"# determine if sorted words are the same \n",
" return sorted_word_a == sorted_word_b"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anagram_checker(\"Silent\", \"Night\")"
"# compare \"Silent\" and \"listen\"\n",
"result = anagram_checker(\"Silent\", \"listen\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anagram_checker(\"night\", \"Thing\")"
"# compare \"Silent\" and \"Night\"\n",
"result = anagram_checker(\"Silent\", \"Night\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# compare \"night\" and \"Thing\"\n",
"result = anagram_checker(\"night\", \"Thing\")\n",
"result"
]
},
{
Expand All @@ -97,24 +149,65 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def anagram_checker(word_a, word_b, is_case_sensitive):\n",
" # Modify your existing code here\n",
" # sort the words\n",
" # sorted_worda = sorted(worda)\n",
" # sorted_wordb = sorted(wordb)\n",
"\n",
" # determine if sorted words are the same\n",
" if not is_case_sensitive:\n",
" word_a = word_a.lower()\n",
" word_b = word_b.lower()\n",
"\n",
"# Run your code to check using the words below:\n",
"anagram_checker(\"Silent\", \"listen\", False) # True"
" sorted_word_a = sorted(word_a)\n",
" sorted_word_b = sorted(word_b)\n",
"\n",
" if sorted_word_a == sorted_word_b:\n",
" return True\n",
" else:\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"# Compare \"Silent\" and \"listen\" to determine if they are anagrams\n",
"anagram_checker(\"Silent\", \"listen\", False)\n",
"print(anagram_checker(\"Silent\", \"listen\", False))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"anagram_checker(\"Silent\", \"Listen\", True) # False"
"# Step 6 - Compare \"Silent\" and \"Listen\" to determine if they are anagrams \n",
"anagram_checker(\"Silent\", \"Listen\", True)\n",
"print(anagram_checker(\"Silent\", \"listen\", True))"
]
},
{
Expand Down Expand Up @@ -144,7 +237,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.9.15"
}
},
"nbformat": 4,
Expand Down
78 changes: 69 additions & 9 deletions 02_activities/assignments/assignment_2.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -96,10 +96,37 @@
},
"outputs": [],
"source": [
"all_paths = [ \n",
"\"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_02.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_03.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_04.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_05.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_06.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_07.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_08.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_09.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_10.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_11.csv\",\n",
"\"../../05_src/data/assignment_2_data/inflammation_12.csv\" \n",
"]\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.readline()\n",
" print('Printing from the file, but just the first line:', contents)\n",
" \n",
" # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection"
"\n",
" # YOUR CODE HERE: Iterate through 'contents' using a for loop and print each row for inspection\n",
"\n",
" if not contents:\n",
" print(\"Contents are empty\")\n",
" else:\n",
" print('These lines are from the same file:')\n",
" contents = f.readlines()\n",
" for row in contents:\n",
" print(row) \n"
]
},
{
Expand Down Expand Up @@ -134,33 +161,62 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a = 1\n",
"print(type(a))\n",
"b = float(a)\n",
"print(type(b))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"id": "82-bk4CBB1w4"
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def patient_summary(file_path, operation):\n",
"def patient_summary(all_paths, operation):\n",
" #summary_values = np.zeros(60)\n",
" \n",
" # load the data from the file\n",
" data = np.loadtxt(fname=file_path, delimiter=',')\n",
" for idx, file_path in enumerate(all_paths):\n",
" data = np.loadtxt(fname=all_paths, delimiter=',')\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",
" result = 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",
" result = 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",
" result = 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"
" #summary_values[idx] = result\n",
" return result\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" mean_values = patient_summary(all_paths[0], 'mean')\n",
" print(mean_values)"
]
},
{
Expand Down Expand Up @@ -238,7 +294,7 @@
},
"outputs": [],
"source": [
"# Run this cell so you can use this helper function\n",
"# Run this cell so you can use this helper function - updated\n",
"\n",
"def check_zeros(x):\n",
" '''\n",
Expand All @@ -255,7 +311,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 53,
"metadata": {
"id": "LEYPM5v4JT0i"
},
Expand All @@ -265,8 +321,12 @@
"\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"
" mean_scores = patient_summary(file_path, 'mean') \n",
" has_zeros = check_zeros(mean_scores)\n",
" if has_zeros:\n",
" return True\n",
" else:\n",
" return False "
]
},
{
Expand Down Expand Up @@ -331,7 +391,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.9.15"
}
},
"nbformat": 4,
Expand Down