Skip to content

Assignment 2 #2

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
M-Howorucha wants to merge 3 commits into
base: main
Choose a base branch
from
Draft

Assignment 2 #2

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0ab3d66
created anagram_checker functions to compare two words and determine …
M-Howorucha Mar 12, 2026
33cd798
Created Functions to Analyze Patient Data
M-Howorucha Mar 19, 2026
40f2d45
Merge branch 'main' of https://github.com/M-Howorucha/python into ass…
M-Howorucha Mar 19, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
119 changes: 104 additions & 15 deletions 02_activities/assignments/assignment_1.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -56,32 +56,74 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"# For testing purposes, we will write our code in the function\n",
"def anagram_checker(word_a, word_b):\n",
" # Your code here\n",
" # find the unique characters in each word and their counts\n",
" chars1, counts = np.unique(np.array(list(word_a.lower()), dtype='U1'), return_counts=True)\n",
" chars2, counts2 = np.unique(np.array(list(word_b.lower()), dtype='U1'), return_counts=True)\n",
" # create dictionaries of the characters and their counts for each word\n",
" dict1 = dict(zip(chars1, counts))\n",
" dict2 = dict(zip(chars2, counts2))\n",
" # return whether the two dictionaries are equal (i.e. the words are anagrams)\n",
" return dict1 == dict2\n",
"\n",
"# Run your code to check using the words below:\n",
"anagram_checker(\"Silent\", \"listen\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anagram_checker(\"Silent\", \"Night\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 7,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anagram_checker(\"night\", \"Thing\")"
]
Expand All @@ -99,29 +141,76 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#import numpy as np\n",
"\n",
"def anagram_checker(word_a, word_b, is_case_sensitive):\n",
" # Modify your existing code here\n",
" # is we are not considering case sensitivity, convert both words to lowercase\n",
" if not is_case_sensitive:\n",
" word_a = word_a.lower()\n",
" word_b = word_b.lower()\n",
"\n",
" # find the unique characters in each word and their counts\n",
" chars1, counts = np.unique(np.array(list(word_a), dtype='U1'), return_counts=True)\n",
" chars2, counts2 = np.unique(np.array(list(word_b), dtype='U1'), return_counts=True)\n",
" # create dictionaries of the characters and their counts for each word\n",
" dict1 = dict(zip(chars1, counts))\n",
" dict2 = dict(zip(chars2, counts2))\n",
" # return whether the two dictionaries are equal (i.e. the words are anagrams)\n",
" return dict1 == dict2\n",
"\n",
"# Run your code to check using the words below:\n",
"anagram_checker(\"Silent\", \"listen\", False) # True"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 9,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anagram_checker(\"Silent\", \"listen\", True) # False"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"anagram_checker(\"Silent\", \"Listen\", True) # False"
]
Expand All @@ -139,7 +228,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "new-learner",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
Expand All @@ -153,7 +242,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.12.3"
}
},
"nbformat": 4,
Expand Down
Loading