diff --git a/lab-functional-programming/your-code/Q1.ipynb b/lab-functional-programming/your-code/Q1.ipynb
index 8b07d3d..80fe749 100644
--- a/lab-functional-programming/your-code/Q1.ipynb
+++ b/lab-functional-programming/your-code/Q1.ipynb
@@ -19,26 +19,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'corpus' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [19]\u001b[0m, in \u001b[0;36m<cell line: 20>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     22\u001b[0m s\u001b[38;5;241m=\u001b[39mf\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m     23\u001b[0m s\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m s \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m punctuation)\n\u001b[1;32m---> 24\u001b[0m \u001b[43mcorpus\u001b[49m\u001b[38;5;241m.\u001b[39mappend(s\u001b[38;5;241m.\u001b[39mlower())\n\u001b[0;32m     25\u001b[0m f\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m     28\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     29\u001b[0m \u001b[38;5;124;03mLoop `corpus`. Append the terms in each doc into the `bag_of_words` array. The terms in `bag_of_words` \u001b[39;00m\n\u001b[0;32m     30\u001b[0m \u001b[38;5;124;03mshould be unique which means before adding each term you need to check if it's already added to the array.\u001b[39;00m\n\u001b[0;32m     31\u001b[0m \u001b[38;5;124;03mIn addition, check if each term is in the `stop_words` array. Only append the term to `bag_of_words`\u001b[39;00m\n\u001b[0;32m     32\u001b[0m \u001b[38;5;124;03mif it is not a stop word.\u001b[39;00m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'corpus' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# Import required libraries\n",
     "\n",
+    "\n",
+    "from string import punctuation\n",
+    "\n",
+    "\n",
     "# Define function\n",
     "def get_bow_from_docs(docs, stop_words=[]):\n",
-    "    \n",
+    "\n",
     "    # In the function, first define the variables you will use such as `corpus`, `bag_of_words`, and `term_freq`.\n",
-    "    \n",
-    "    \n",
-    "    \n",
+    "    corpus=[]\n",
+    "    bag_of_words=[]\n",
+    "    term_freq=[]\n",
+    "    docs=['doc1.txt', 'doc2.txt', 'doc3.txt']   \n",
+    "\n",
     "    \"\"\"\n",
     "    Loop `docs` and read the content of each doc into a string in `corpus`.\n",
     "    Remember to convert the doc content to lowercases and remove punctuation.\n",
     "    \"\"\"\n",
+    "for doc in docs:\n",
+    "    f=open(doc,'r')\n",
+    "    s=f.read()\n",
+    "    s=''.join(i for i in s if i not in punctuation)\n",
+    "    corpus.append(s.lower())\n",
+    "    f.close()\n",
     "\n",
     "    \n",
-    "    \n",
     "    \"\"\"\n",
     "    Loop `corpus`. Append the terms in each doc into the `bag_of_words` array. The terms in `bag_of_words` \n",
     "    should be unique which means before adding each term you need to check if it's already added to the array.\n",
@@ -46,7 +69,12 @@
     "    if it is not a stop word.\n",
     "    \"\"\"\n",
     "\n",
-    "    \n",
+    "for s in corpus:\n",
+    "    s=''.join(i for i in s if i not in punctuation)\n",
+    "    terms=s.split()\n",
+    "    for temr in terms:\n",
+    "        if not term in bag_of_words and not term in stop_words:\n",
+    "            bag_of_words.append(term)\n",
     "    \n",
     "    \n",
     "    \"\"\"\n",
@@ -54,13 +82,19 @@
     "    Create an array for each doc's term frequency and append it to `term_freq`.\n",
     "    \"\"\"\n",
     "\n",
-    "    \n",
+    "for s in corpus:\n",
+    "    freq=[]\n",
+    "    terms=s.split()\n",
+    "for word in bag_of_words:\n",
+    "    freq.append(terms.count(word))\n",
+    "    term_freq.append(freq)\n",
     "    \n",
     "    # Now return your output as an object\n",
-    "    return {\n",
-    "        \"bag_of_words\": bag_of_words,\n",
+    "return {\n",
+    "             \"bag_of_words\": bag_of_words,\n",
     "        \"term_freq\": term_freq\n",
-    "    }\n",
+    "                 }\n",
+    "    \n",
     "    "
    ]
   },
@@ -75,15 +109,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'get_bow_from_docs' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [15]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m docs \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdoc1.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdoc2.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdoc3.txt\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m      4\u001b[0m \u001b[38;5;66;03m# Obtain BoW from your function\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m bow \u001b[38;5;241m=\u001b[39m \u001b[43mget_bow_from_docs\u001b[49m([ \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../lab-functional-programming/your-code/doc1.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m      6\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../lab-functional-programming//your-code/doc2.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m      7\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../lab-functional-programming//your-code/doc3.txt\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Print BoW\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(bow)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'get_bow_from_docs' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# Define doc paths array\n",
-    "docs = []\n",
+    "docs = ['doc1.txt', 'doc2.txt', 'doc3.txt']\n",
     "\n",
     "# Obtain BoW from your function\n",
-    "bow = get_bow_from_docs(docs)\n",
+    "bow = get_bow_from_docs([ '../../lab-functional-programming/your-code/doc1.txt', \n",
+    "    '../../lab-functional-programming//your-code/doc2.txt', \n",
+    "    '../../lab-functional-programming//your-code/doc3.txt'])\n",
     "\n",
     "# Print BoW\n",
     "print(bow)"
@@ -100,12 +148,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "frozenset({'were', 'he', 'been', 'then', 'except', 'de', 'couldnt', 'again', 'where', 'against', 'interest', 'somewhere', 'whom', 'bottom', 'therefore', 'whereas', 'therein', 'done', 'no', 'my', 'put', 'via', 'else', 'on', 'each', 'neither', 'behind', 'over', 'whose', 'mostly', 'who', 'off', 'yourselves', 'before', 'sometime', 'co', 'describe', 'wherever', 'everything', 'full', 'namely', 'might', 'afterwards', 'whoever', 'anyone', 'fire', 'also', 'others', 'whole', 'their', 'beyond', 'ten', 'about', 'twenty', 'himself', 'hence', 'fill', 'sometimes', 'am', 'all', 'keep', 'not', 'nevertheless', 'up', 'rather', 'how', 'ours', 'formerly', 'both', 'further', 'though', 'herself', 'sincere', 'latterly', 'him', 'than', 'within', 'would', 'someone', 'fifty', 'five', 'show', 'of', 'after', 'thence', 'amoungst', 'hereby', 'these', 'as', 'will', 'nine', 'call', 'already', 'least', 'yourself', 'during', 'ever', 'nor', 'should', 'may', 'seem', 'serious', 'amount', 'that', 'could', 'i', 'in', 'few', 'anyway', 'becoming', 'noone', 'either', 'very', 'anywhere', 'much', 'its', 'go', 'whenever', 'from', 'here', 'mine', 'more', 'but', 'alone', 'us', 'into', 'together', 'now', 'our', 'found', 'meanwhile', 'toward', 'beforehand', 'an', 'has', 'what', 'never', 'had', 'while', 'because', 'a', 'hereafter', 'must', 'three', 'several', 'somehow', 'forty', 'those', 'everyone', 'above', 'anyhow', 'for', 'fifteen', 'bill', 'thick', 'at', 'can', 'do', 'still', 'latter', 'if', 'part', 'since', 'inc', 'perhaps', 'none', 'eg', 'such', 'there', 'became', 'eight', 'cry', 'was', 'top', 'yours', 'four', 'made', 'once', 'eleven', 'thus', 'by', 'name', 'per', 'almost', 'something', 'myself', 'without', 'thereupon', 'enough', 'beside', 'thru', 'being', 'itself', 'his', 'same', 'find', 'themselves', 'it', 'often', 'cannot', 'side', 'however', 'elsewhere', 'seems', 'give', 'they', 'next', 'you', 'even', 'see', 'thereby', 'please', 'indeed', 'amongst', 'get', 'whereby', 'otherwise', 'thereafter', 'to', 'back', 'cant', 'have', 'ltd', 'hasnt', 'the', 'through', 'nobody', 'hers', 'thin', 'detail', 'moreover', 'out', 'etc', 'move', 'why', 'be', 'twelve', 'down', 'one', 'other', 'third', 'me', 'every', 'although', 'everywhere', 'throughout', 'ourselves', 'ie', 'anything', 'whatever', 'which', 'seeming', 'become', 'most', 'hundred', 'whereafter', 'them', 'so', 'whither', 'last', 'whether', 'is', 'upon', 'or', 'seemed', 'her', 'towards', 'always', 'are', 'first', 'any', 'wherein', 'well', 'across', 'too', 'yet', 'nothing', 'herein', 're', 'con', 'we', 'around', 'many', 'whereupon', 'mill', 'another', 'becomes', 'empty', 'some', 'six', 'un', 'take', 'besides', 'sixty', 'until', 'nowhere', 'along', 'among', 'less', 'front', 'former', 'under', 'with', 'when', 'between', 'onto', 'only', 'this', 'whence', 'and', 'your', 'due', 'two', 'system', 'hereupon', 'own', 'below', 'she'})\n"
+     ]
+    }
+   ],
    "source": [
-    "from sklearn.feature_extraction import stop_words\n",
-    "print(stop_words.ENGLISH_STOP_WORDS)"
+    "from sklearn.feature_extraction import _stop_words\n",
+    "print(_stop_words.ENGLISH_STOP_WORDS)"
    ]
   },
   {
@@ -128,11 +184,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2582812775.py, line 3)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Input \u001b[1;32mIn [17]\u001b[1;36m\u001b[0m\n\u001b[1;33m    '../../lab-functional-programming//your-code/doc3.txt'] _stop_words.ENGLISH_STOP_WORDS)\u001b[0m\n\u001b[1;37m                                                            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
-    "bow = get_bow_from_docs(bow, stop_words.ENGLISH_STOP_WORDS)\n",
+    "bow = get_bow_from_docs([ '../../lab-functional-programming/your-code/doc1.txt', \n",
+    "    '../../lab-functional-programming//your-code/doc2.txt', \n",
+    "    '../../lab-functional-programming//your-code/doc3.txt'] _stop_words.ENGLISH_STOP_WORDS)\n",
     "\n",
     "print(bow)"
    ]
@@ -156,7 +223,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -170,9 +237,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/lab-functional-programming/your-code/Q2.ipynb b/lab-functional-programming/your-code/Q2.ipynb
index f50f442..528f169 100644
--- a/lab-functional-programming/your-code/Q2.ipynb
+++ b/lab-functional-programming/your-code/Q2.ipynb
@@ -15,12 +15,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Define your string handling functions below\n",
-    "# Minimal 3 functions\n"
+    "# Minimal 3 functions\n",
+    "import re\n",
+    "from string import punctuation\n",
+    "\n",
+    "def strip_html_tags(html):\n",
+    "    s = re.sub(\"<!--.*?-->\", \"\", html)\n",
+    "    s = re.sub(\"<.*?>\", \"\", s)\n",
+    "    return(s)\n",
+    "\n",
+    "\n",
+    "def remove_punctuation(html):\n",
+    "    s = ''.join(i for i in html if i not in punctuation)\n",
+    "    return(s)\n",
+    "\n",
+    "def to_lower_case(html):\n",
+    "    return(html.lower())\n"
    ]
   },
   {
@@ -32,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,6 +57,28 @@
     "    bag_of_words = []\n",
     "    term_freq = []\n",
     "    \n",
+    "    for doc in docs:\n",
+    "        f = open(doc, 'r')\n",
+    "        s = f.read()\n",
+    "        s = strip_html_tags(s)\n",
+    "        s = remove_punctuation(s)\n",
+    "        s = to_lower_case(s)\n",
+    "        corpus.append(s)\n",
+    "        f.close()\n",
+    "        \n",
+    "    for s in corpus:\n",
+    "        s = ''.join(i for i in s if i not in punctuation)\n",
+    "        terms = s.split()\n",
+    "        for term in terms:\n",
+    "            if not term in bag_of_words and not term in stop_words:\n",
+    "                bag_of_words.append(term)\n",
+    "                \n",
+    "    for s in corpus:\n",
+    "        freq = []\n",
+    "        terms = s.split()\n",
+    "        for word in bag_of_words:\n",
+    "            freq.append(terms.count(word))\n",
+    "        term_freq.append(freq)\n",
     "    # write your codes here\n",
     "    \n",
     "    return {\n",
@@ -60,17 +97,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "UnicodeDecodeError",
+     "evalue": "'charmap' codec can't decode byte 0x9d in position 167699: character maps to <undefined>",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [15]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _stop_words\n\u001b[1;32m----> 2\u001b[0m bow \u001b[38;5;241m=\u001b[39m \u001b[43mget_bow_from_docs\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwww.coursereport.com_ironhack.html\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43men.wikipedia.org_Data_analysis.html\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwww.lipsum.com.html\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_stop_words\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mENGLISH_STOP_WORDS\u001b[49m\n\u001b[0;32m      8\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(bow)\n",
+      "Input \u001b[1;32mIn [11]\u001b[0m, in \u001b[0;36mget_bow_from_docs\u001b[1;34m(docs, stop_words)\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m doc \u001b[38;5;129;01min\u001b[39;00m docs:\n\u001b[0;32m      8\u001b[0m     f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(doc, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m----> 9\u001b[0m     s \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m     s \u001b[38;5;241m=\u001b[39m strip_html_tags(s)\n\u001b[0;32m     11\u001b[0m     s \u001b[38;5;241m=\u001b[39m remove_punctuation(s)\n",
+      "File \u001b[1;32m~\\anaconda4\\lib\\encodings\\cp1252.py:23\u001b[0m, in \u001b[0;36mIncrementalDecoder.decode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecode\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m, final\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 23\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcodecs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcharmap_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43mdecoding_table\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
+      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'charmap' codec can't decode byte 0x9d in position 167699: character maps to <undefined>"
+     ]
+    }
+   ],
    "source": [
-    "from sklearn.feature_extraction import stop_words\n",
+    "from sklearn.feature_extraction import _stop_words\n",
     "bow = get_bow_from_docs([\n",
     "        'www.coursereport.com_ironhack.html',\n",
     "        'en.wikipedia.org_Data_analysis.html',\n",
     "        'www.lipsum.com.html'\n",
     "    ],\n",
-    "    stop_words.ENGLISH_STOP_WORDS\n",
+    "    _stop_words.ENGLISH_STOP_WORDS\n",
     ")\n",
     "\n",
     "print(bow)"
@@ -97,7 +148,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -111,9 +162,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/lab-functional-programming/your-code/Q3.ipynb b/lab-functional-programming/your-code/Q3.ipynb
index 75055ac..1b41145 100644
--- a/lab-functional-programming/your-code/Q3.ipynb
+++ b/lab-functional-programming/your-code/Q3.ipynb
@@ -85,11 +85,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 4, 5]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "numbers = [1, 4, -1, -100, 0, 5, -99]\n",
+    "list(filter(lambda i: i>0, numbers))\n",
     "\n",
     "# Enter your code below"
    ]
@@ -113,17 +125,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'good morning everyone'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "import langdetect\n",
+    "#conda install -c \"conda-forge/label/gcc7\" langdetect\n",
+    "#import langdetect\n",
+    "#! conda install -c conda-forge langdetect\n",
     "from functools import reduce\n",
+    "import langdetect\n",
     "words = ['good morning', '早上好', 'доброго', 'おはようございます', 'everyone', '大家', 'каждый', 'みんな']\n",
     "\n",
+    "reduce((lambda i, j: i if not langdetect.detect(j)=='en' else ''.join([i, ' ', j])), words)\n",
     "# Enter your code below"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting package metadata (current_repodata.json): ...working... done\n",
+      "Solving environment: ...working... done\n",
+      "\n",
+      "# All requested packages already installed.\n",
+      "\n",
+      "Retrieving notices: ...working... done\n"
+     ]
+    }
+   ],
+   "source": [
+    "! conda install -c conda-forge langdetect"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -142,11 +191,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Enter your code below"
+    "# Enter your code below\n",
+    "import re\n",
+    "from string import punctuation\n",
+    "\n",
+    "def strip_html_tags(html):\n",
+    "    s = re.sub(\"<!--.*?-->\", \"\", html)\n",
+    "    s = re.sub(\"<.*?>\", \"\", s)\n",
+    "    return(s)\n",
+    "\n",
+    "from string import punctuation\n",
+    "def remove_punctuation(html):\n",
+    "    s = ''.join(i for i in html if i not in punctuation)\n",
+    "    return(s)\n",
+    "\n",
+    "def to_lower_case(html):\n",
+    "    return(html.lower())\n",
+    "\n"
    ]
   },
   {
@@ -160,17 +225,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'get_bow_from_docs' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _stop_words\n\u001b[1;32m----> 2\u001b[0m bow \u001b[38;5;241m=\u001b[39m \u001b[43mget_bow_from_docs\u001b[49m([ \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../lab-functional-programming/your-code/doc1.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m      3\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../lab-functional-programming//your-code/doc2.txt\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m      4\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../../lab-functional-programming//your-code/doc3.txt\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m      5\u001b[0m     ],\n\u001b[0;32m      6\u001b[0m     _stop_words\u001b[38;5;241m.\u001b[39mENGLISH_STOP_WORDS)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(bow)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'get_bow_from_docs' is not defined"
+     ]
+    }
+   ],
    "source": [
-    "from sklearn.feature_extraction import stop_words\n",
-    "bow = get_bow_from_docs([\n",
-    "    '../../lab-bag-of-words/your-code/doc1.txt', \n",
-    "    '../../lab-bag-of-words/your-code/doc2.txt', \n",
-    "    '../../lab-bag-of-words/your-code/doc3.txt'],\n",
-    "    stop_words.ENGLISH_STOP_WORDS\n",
-    ")\n",
+    "from sklearn.feature_extraction import _stop_words\n",
+    "bow = get_bow_from_docs([ '../../lab-functional-programming/your-code/doc1.txt', \n",
+    "    '../../lab-functional-programming//your-code/doc2.txt', \n",
+    "    '../../lab-functional-programming//your-code/doc3.txt'\n",
+    "    ],\n",
+    "    _stop_words.ENGLISH_STOP_WORDS)\n",
+    "    \n",
     "\n",
     "print(bow)"
    ]
@@ -185,7 +262,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -199,9 +276,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }