diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index bee48d5a0..d5112def3 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -56,32 +56,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
+    "\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",
     "\n",
+    "  ''' Given input strings, word_a and word_b, returns True if the words are anagrams, and \n",
+    "  False if they are not\n",
+    "  Input:\n",
+    "    word_a: str, strings of letters\n",
+    "    word_b: str, strings of letters\n",
+    "  Output:\n",
+    "    bool\n",
+    "  '''\n",
+    "  ## Put both strings in the same case\n",
+    "  word_a = word_a.lower()\n",
+    "  word_b = word_b.lower()\n",
+    "\n",
+    "  ## Changes both strings into lists of 1 letter strings, and sorts them\n",
+    "  letter_list_a = sorted(list(word_a))\n",
+    "  letter_list_b = sorted(list(word_b))\n",
+    "\n",
+    "  ## checks if each of these sorted letter lists are equal and returns boolean\n",
+    "  return(letter_list_a == letter_list_b)\n",
+    "\n",
     "# Run your code to check using the words below:\n",
     "anagram_checker(\"Silent\", \"listen\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "anagram_checker(\"Silent\", \"Night\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "anagram_checker(\"night\", \"Thing\")"
    ]
@@ -97,12 +151,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def anagram_checker(word_a, word_b, is_case_sensitive):\n",
-    "  # Modify your existing code here\n",
+    "  # Your code here\n",
+    "\n",
+    "  '''Given input strings, word_a and word_b, returns True if the words are anagrams, and \n",
+    "  False if they are not. Can choose to only accept anagrams where all cases match with is_case_sensitive\n",
+    "  Input:\n",
+    "    word_a: str, strings of letters\n",
+    "    word_b: str, strings of letters\n",
+    "    is_case_sensitive: bool, if true, only checks for anagrams where all cases match\n",
+    "  Output:\n",
+    "    bool\n",
+    "  '''\n",
+    "\n",
+    "  ## Put both strings in the same case if is_case_sensitive is False\n",
+    "  if is_case_sensitive == False:\n",
+    "    word_a = word_a.lower()\n",
+    "    word_b = word_b.lower()\n",
+    "\n",
+    "  ## Changes both strings into lists of 1 letter strings, and sorts them\n",
+    "  letter_list_a = sorted(list(word_a))\n",
+    "  letter_list_b = sorted(list(word_b))\n",
+    "\n",
+    "  ## checks if each of these sorted letter lists are equal and returns boolean\n",
+    "  return(letter_list_a == letter_list_b)\n",
+    "\n",
     "\n",
     "# Run your code to check using the words below:\n",
     "anagram_checker(\"Silent\", \"listen\", False) # True"
@@ -110,9 +198,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "anagram_checker(\"Silent\", \"Listen\", True) # False"
    ]
@@ -130,7 +229,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "new-learner",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -144,7 +243,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
diff --git a/02_activities/assignments/assignment_2.ipynb b/02_activities/assignments/assignment_2.ipynb
index 36a3e2bb7..79377203c 100644
--- a/02_activities/assignments/assignment_2.ipynb
+++ b/02_activities/assignments/assignment_2.ipynb
@@ -72,11 +72,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "id": "n0m48JsS-nMC"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0\n",
+      "\n",
+      "0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1\n",
+      "\n",
+      "0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1\n",
+      "\n",
+      "0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1\n",
+      "\n",
+      "0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1\n",
+      "\n",
+      "0,0,1,2,2,4,2,1,6,4,7,6,6,9,9,15,4,16,18,12,12,5,18,9,5,3,10,3,12,7,8,4,7,3,5,4,4,3,2,1\n",
+      "\n",
+      "0,0,2,2,4,2,2,5,5,8,6,5,11,9,4,13,5,12,10,6,9,17,15,8,9,3,13,7,8,2,8,8,4,2,3,5,4,1,1,1\n",
+      "\n",
+      "0,0,1,2,3,1,2,3,5,3,7,8,8,5,10,9,15,11,18,19,20,8,5,13,15,10,6,10,6,7,4,9,3,5,2,5,3,2,2,1\n",
+      "\n",
+      "0,0,0,3,1,5,6,5,5,8,2,4,11,12,10,11,9,10,17,11,6,16,12,6,8,14,6,13,10,11,4,6,4,7,6,3,2,1,0,0\n",
+      "\n",
+      "0,1,1,2,1,3,5,3,5,8,6,8,12,5,13,6,13,8,16,8,18,15,16,14,12,7,3,8,9,11,2,5,4,5,1,4,1,2,0,0\n",
+      "\n",
+      "0,1,0,0,4,3,3,5,5,4,5,8,7,10,13,3,7,13,15,18,8,15,15,16,11,14,12,4,10,10,4,3,4,5,5,3,3,2,2,1\n",
+      "\n",
+      "0,1,0,0,3,4,2,7,8,5,2,8,11,5,5,8,14,11,6,11,9,16,18,6,12,5,4,3,5,7,8,3,5,4,5,5,4,0,1,1\n",
+      "\n",
+      "0,0,2,1,4,3,6,4,6,7,9,9,3,11,6,12,4,17,13,15,13,12,8,7,4,7,12,9,5,6,5,4,7,3,5,4,2,3,0,1\n",
+      "\n",
+      "0,0,0,0,1,3,1,6,6,5,5,6,3,6,13,3,10,13,9,16,15,9,11,4,6,4,11,11,12,3,5,8,7,4,6,4,1,3,0,0\n",
+      "\n",
+      "0,1,2,1,1,1,4,1,5,2,3,3,10,7,13,5,7,17,6,9,12,13,10,4,12,4,6,7,6,10,8,2,5,1,3,4,2,0,2,0\n",
+      "\n",
+      "0,1,1,0,1,2,4,3,6,4,7,5,5,7,5,10,7,8,18,17,9,8,12,11,11,11,14,6,11,2,10,9,5,6,5,3,4,2,2,0\n",
+      "\n",
+      "0,0,0,0,2,3,6,5,7,4,3,2,10,7,9,11,12,5,12,9,13,19,14,17,5,13,8,11,5,10,9,8,7,5,3,1,4,0,2,1\n",
+      "\n",
+      "0,0,0,1,2,1,4,3,6,7,4,2,12,6,12,4,14,7,8,14,13,19,6,9,12,6,4,13,6,7,2,3,6,5,4,2,3,0,1,0\n",
+      "\n",
+      "0,0,2,1,2,5,4,2,7,8,4,7,11,9,8,11,15,17,11,12,7,12,7,6,7,4,13,5,7,6,6,9,2,1,1,2,2,0,1,0\n",
+      "\n",
+      "0,1,2,0,1,4,3,2,2,7,3,3,12,13,11,13,6,5,9,16,9,19,16,11,8,9,14,12,11,9,6,6,6,1,1,2,4,3,1,1\n",
+      "\n",
+      "0,1,1,3,1,4,4,1,8,2,2,3,12,12,10,15,13,6,5,5,18,19,9,6,11,12,7,6,3,6,3,2,4,3,1,5,4,2,2,0\n",
+      "\n",
+      "0,0,2,3,2,3,2,6,3,8,7,4,6,6,9,5,12,12,8,5,12,10,16,7,14,12,5,4,6,9,8,5,6,6,1,4,3,0,2,0\n",
+      "\n",
+      "0,0,0,3,4,5,1,7,7,8,2,5,12,4,10,14,5,5,17,13,16,15,13,6,12,9,10,3,3,7,4,4,8,2,6,5,1,0,1,0\n",
+      "\n",
+      "0,1,1,1,1,3,3,2,6,3,9,7,8,8,4,13,7,14,11,15,14,13,5,13,7,14,9,10,5,11,5,3,5,1,1,4,4,1,2,0\n",
+      "\n",
+      "0,1,1,1,2,3,5,3,6,3,7,10,3,8,12,4,12,9,15,5,17,16,5,10,10,15,7,5,3,11,5,5,6,1,1,1,1,0,2,1\n",
+      "\n",
+      "0,0,2,1,3,3,2,7,4,4,3,8,12,9,12,9,5,16,8,17,7,11,14,7,13,11,7,12,12,7,8,5,7,2,2,4,1,1,1,0\n",
+      "\n",
+      "0,0,1,2,4,2,2,3,5,7,10,5,5,12,3,13,4,13,7,15,9,12,18,14,16,12,3,11,3,2,7,4,8,2,2,1,3,0,1,1\n",
+      "\n",
+      "0,0,1,1,1,5,1,5,2,2,4,10,4,8,14,6,15,6,12,15,15,13,7,17,4,5,11,4,8,7,9,4,5,3,2,5,4,3,2,1\n",
+      "\n",
+      "0,0,2,2,3,4,6,3,7,6,4,5,8,4,7,7,6,11,12,19,20,18,9,5,4,7,14,8,4,3,7,7,8,3,5,4,1,3,1,0\n",
+      "\n",
+      "0,0,0,1,4,4,6,3,8,6,4,10,12,3,3,6,8,7,17,16,14,15,17,4,14,13,4,4,12,11,6,9,5,5,2,5,2,1,0,1\n",
+      "\n",
+      "0,1,1,0,3,2,4,6,8,6,2,3,11,3,14,14,12,8,8,16,13,7,6,9,15,7,6,4,10,8,10,4,2,6,5,5,2,3,2,1\n",
+      "\n",
+      "0,0,2,3,3,4,5,3,6,7,10,5,10,13,14,3,8,10,9,9,19,15,15,6,8,8,11,5,5,7,3,6,6,4,5,2,2,3,0,0\n",
+      "\n",
+      "0,1,2,2,2,3,6,6,6,7,6,3,11,12,13,15,15,10,14,11,11,8,6,12,10,5,12,7,7,11,5,8,5,2,5,5,2,0,2,1\n",
+      "\n",
+      "0,0,2,1,3,5,6,7,5,8,9,3,12,10,12,4,12,9,13,10,10,6,10,11,4,15,13,7,3,4,2,9,7,2,4,2,1,2,1,1\n",
+      "\n",
+      "0,0,1,2,4,1,5,5,2,3,4,8,8,12,5,15,9,17,7,19,14,18,12,17,14,4,13,13,8,11,5,6,6,2,3,5,2,1,1,1\n",
+      "\n",
+      "0,0,0,3,1,3,6,4,3,4,8,3,4,8,3,11,5,7,10,5,15,9,16,17,16,3,8,9,8,3,3,9,5,1,6,5,4,2,2,0\n",
+      "\n",
+      "0,1,2,2,2,5,5,1,4,6,3,6,5,9,6,7,4,7,16,7,16,13,9,16,12,6,7,9,10,3,6,4,5,4,6,3,4,3,2,1\n",
+      "\n",
+      "0,1,1,2,3,1,5,1,2,2,5,7,6,6,5,10,6,7,17,13,15,16,17,14,4,4,10,10,10,11,9,9,5,4,4,2,1,0,1,0\n",
+      "\n",
+      "0,1,0,3,2,4,1,1,5,9,10,7,12,10,9,15,12,13,13,6,19,9,10,6,13,5,13,6,7,2,5,5,2,1,1,1,1,3,0,1\n",
+      "\n",
+      "0,1,1,3,1,1,5,5,3,7,2,2,3,12,4,6,8,15,16,16,15,4,14,5,13,10,7,10,6,3,2,3,6,3,3,5,4,3,2,1\n",
+      "\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",
+      "0,0,1,3,3,1,2,1,8,9,2,8,10,3,8,6,10,13,11,17,19,6,4,11,6,12,7,5,5,4,4,8,2,6,6,4,2,2,0,0\n",
+      "\n",
+      "0,1,1,3,4,5,2,1,3,7,9,6,10,5,8,15,11,12,15,6,12,16,6,4,14,3,12,9,6,11,5,8,5,5,6,1,2,1,2,0\n",
+      "\n",
+      "0,0,1,3,1,4,3,6,7,8,5,7,11,3,6,11,6,10,6,19,18,14,6,10,7,9,8,5,8,3,10,2,5,1,5,4,2,1,0,1\n",
+      "\n",
+      "0,1,1,3,3,4,4,6,3,4,9,9,7,6,8,15,12,15,6,11,6,18,5,14,15,12,9,8,3,6,10,6,8,7,2,5,4,3,1,1\n",
+      "\n",
+      "0,1,2,2,4,3,1,4,8,9,5,10,10,3,4,6,7,11,16,6,14,9,11,10,10,7,10,8,8,4,5,8,4,4,5,2,4,1,1,0\n",
+      "\n",
+      "0,0,2,3,4,5,4,6,2,9,7,4,9,10,8,11,16,12,15,17,19,10,18,13,15,11,8,4,7,11,6,7,6,5,1,3,1,0,0,0\n",
+      "\n",
+      "0,1,1,3,1,4,6,2,8,2,10,3,11,9,13,15,5,15,6,10,10,5,14,15,12,7,4,5,11,4,6,9,5,6,1,1,2,1,2,1\n",
+      "\n",
+      "0,0,1,3,2,5,1,2,7,6,6,3,12,9,4,14,4,6,12,9,12,7,11,7,16,8,13,6,7,6,10,7,6,3,1,5,4,3,0,0\n",
+      "\n",
+      "0,0,1,2,3,4,5,7,5,4,10,5,12,12,5,4,7,9,18,16,16,10,15,15,10,4,3,7,5,9,4,6,2,4,1,4,2,2,2,1\n",
+      "\n",
+      "0,1,2,1,1,3,5,3,6,3,10,10,11,10,13,10,13,6,6,14,5,4,5,5,9,4,12,7,7,4,7,9,3,3,6,3,4,1,2,0\n",
+      "\n",
+      "0,1,2,2,3,5,2,4,5,6,8,3,5,4,3,15,15,12,16,7,20,15,12,8,9,6,12,5,8,3,8,5,4,1,3,2,1,3,1,0\n",
+      "\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",
+      "0,0,2,1,1,4,4,7,2,9,4,10,12,7,6,6,11,12,9,15,15,6,6,13,5,12,9,6,4,7,7,6,5,4,1,4,2,2,2,1\n",
+      "\n",
+      "0,1,2,1,1,4,5,4,4,5,9,7,10,3,13,13,8,9,17,16,16,15,12,13,5,12,10,9,11,9,4,5,5,2,2,5,1,0,0,1\n",
+      "\n",
+      "0,0,1,3,2,3,6,4,5,7,2,4,11,11,3,8,8,16,5,13,16,5,8,8,6,9,10,10,9,3,3,5,3,5,4,5,3,3,0,1\n",
+      "\n",
+      "0,1,1,2,2,5,1,7,4,2,5,5,4,6,6,4,16,11,14,16,14,14,8,17,4,14,13,7,6,3,7,7,5,6,3,4,2,2,1,1\n",
+      "\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",
+      "0,0,1,0,3,2,5,4,8,2,9,3,3,10,12,9,14,11,13,8,6,18,11,9,13,11,8,5,5,2,8,5,3,5,4,1,3,1,1,0\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "all_paths = [\n",
     "  \"../../05_src/data/assignment_2_data/inflammation_01.csv\",\n",
@@ -92,11 +219,15 @@
     "  \"../../05_src/data/assignment_2_data/inflammation_11.csv\",\n",
     "  \"../../05_src/data/assignment_2_data/inflammation_12.csv\"\n",
     "]\n",
-    "\n",
+    "import numpy as np\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",
     "    \n",
-    "    # YOUR CODE HERE: Iterate through the variable using a for loop and print each row for inspection"
+    "    ## reads .csv files located in index 0 of all_paths list into a list called 'patent_01_data'\n",
+    "    patent_01_data = f.readlines()\n",
+    "    \n",
+    "    ## itterates rows lines in patent_01_data and prints them\n",
+    "    for i in np.arange(len(patent_01_data)):\n",
+    "        print(patent_01_data[i])"
    ]
   },
   {
@@ -130,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "id": "82-bk4CBB1w4"
    },
@@ -139,21 +270,33 @@
     "import numpy as np\n",
     "\n",
     "def patient_summary(file_path, operation):\n",
+    "    '''\n",
+    "    Takes a given file_path containing a csv file containig integers and reads it into a numpy array of integers\n",
+    "    Computes a summary statistic specified by the operation string\n",
+    "    \n",
+    "    input: \n",
+    "    file_path: string containing location of .csv file containing a matrix of numbers\n",
+    "    operation: string that specifies the operation to be applied on each row of the .csv file.\n",
+    "    can take one of three values; mean, max, and min.\n",
+    "    \n",
+    "    output:\n",
+    "    a 1d array of summary value floats\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",
+    "        summary_values = 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",
+    "        summary_values = np.nanmax(data,axis=ax)\n",
     "\n",
     "    elif operation == 'min':\n",
-    "        # YOUR CODE HERE: Calculate the minimum number of flare-ups experienced by each patient\n",
+    "        summary_values = np.nanmin(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"
@@ -161,11 +304,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "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",
@@ -228,7 +379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {
     "id": "_svDiRkdIwiT"
    },
@@ -246,12 +397,50 @@
     "\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"
+    "    return len(flag) > 0\n",
+    "\n",
+    "def check_zeros_myversion(x):\n",
+    "    '''\n",
+    "    Does the same thing as check_zeros, just wanted to play around\n",
+    "    '''\n",
+    "    \n",
+    "    ## Find the minimum magnitude of values in array x\n",
+    "    min_magnitude = np.min(np.abs(x))\n",
+    "    \n",
+    "    ## Returns True if this minimum value is zero. Returns false otherwise\n",
+    "    return(min_magnitude == 0)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.   , 4.225, 3.9  , 3.7  , 4.075, 3.95 , 4.55 , 3.45 , 3.975,\n",
+       "       4.525, 4.425, 4.225, 3.85 , 4.925, 4.5  , 3.225, 4.4  , 4.275,\n",
+       "       4.5  , 4.125, 4.7  , 5.9  , 3.975, 4.   , 5.275, 4.075, 4.475,\n",
+       "       3.7  , 3.775, 3.7  , 3.925, 4.525, 4.125, 4.025, 4.1  , 4.675,\n",
+       "       5.025, 4.9  , 4.7  , 4.75 , 3.975, 5.325, 3.925, 4.4  , 4.35 ,\n",
+       "       4.65 , 4.1  , 4.   , 4.4  , 4.575, 3.9  , 4.65 , 3.725, 4.   ,\n",
+       "       4.   , 5.2  , 4.325, 3.575, 4.075, 0.   ])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "patient_summary(all_paths[2], 'mean')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "id": "LEYPM5v4JT0i"
    },
@@ -260,22 +449,59 @@
     "# 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",
+    "    Takes a given file_path containing a csv file containig integers and reads it into a numpy array of integer.\n",
+    "    Checks if any column in this file contails only zeros. Returns False if none do, and True if any do.\n",
+    "    \n",
+    "    input: \n",
+    "    file_path: string containing location of .csv file containing a matrix of numbers\n",
+    "    \n",
+    "    output:\n",
+    "    Boolean\n",
+    "    '''\n",
+    "    mean_scores = patient_summary(file_path, 'mean') ## loads file and takes mean of each row\n",
+    "    is_bad = check_zeros(mean_scores) ## Checks if the mean of each row is zero\n",
     "\n",
-    "  return"
+    "    return(is_bad)\n",
+    "  #YOUR CODE HERE: Use patient_summary() to get the means and check_zeros() to check for zeros in the means"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "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]))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(detect_problems(all_paths[2]))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -314,7 +540,8 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -327,9 +554,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 4
 }