diff --git a/.github/workflows/testing.yaml b/.github/workflows/testing.yaml
index 686d0dc..9851650 100644
--- a/.github/workflows/testing.yaml
+++ b/.github/workflows/testing.yaml
@@ -27,7 +27,7 @@ jobs:
           flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
           # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
           flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-          codespell --ignore-words-list="nd" --skip="*.css,*.js,*.svg"
+          codespell --ignore-words-list="nd" --skip="*.css,*.js,*.svg,*ipynb"
       - name: Test with pytest
         run: |
           pytest
diff --git a/examples/jupyter/ransomware_report_usecases1.ipynb b/examples/jupyter/ransomware_report_usecases1.ipynb
new file mode 100644
index 0000000..b1fe9a0
--- /dev/null
+++ b/examples/jupyter/ransomware_report_usecases1.ipynb
@@ -0,0 +1,765 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mncPcdFCmU-7"
+   },
+   "source": [
+    "# Jupyter Notebook - Ransomware report use cases 1\n",
+    "\n",
+    "Copyright © 2021 Google\n",
+    "\n",
+    "Welcome to our Ransomware in a Global Context Jupyter notebook!\n",
+    "\n",
+    "You can find this and other very interesting posts in our [blog](https://blog.virustotal.com/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "fQ8iCKRpo_mu"
+   },
+   "outputs": [],
+   "source": [
+    "#@markdown Please, insert your VT API Key*:\n",
+    "\n",
+    "API_KEY = ''  #@param  {type: \"string\"}\n",
+    "\n",
+    "#@markdown **The API key should have Premium permissions, otherwise some of the use cases might not provide the expected results.*\n",
+    "\n",
+    "#@markdown \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "62mNyWJ-0_J_"
+   },
+   "source": [
+    "\n",
+    "\n",
+    "---\n",
+    "---\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "yZYLFG3kAB_a"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install vt-py nest_asyncio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qqOSPhLwsRLT"
+   },
+   "source": [
+    "# Use Case: Distribution vectors extraction\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tioTJg2cGu4v"
+   },
+   "source": [
+    "\n",
+    "Given a couple of searches like these ones:\n",
+    "\n",
+    "       engines:gandcrab fs:2020-02-01+ fs:2020-05-01- (type:peexe or type:pedll) tag:exploit (have:compressed_parents OR have:execution_parents OR have:pcap_parents OR have:email_parents)\n",
+    "       engines:gandcrab fs:2020-02-01+ fs:2020-05-01- (type:peexe or type:pedll) have:in_the_wild\n",
+    "\n",
+    "You can easily extract the distribution vectors of those matches thanks to the different relationships related to those observables. More concretely, we are going to focus on these relationships:\n",
+    "\n",
+    "*   [itw_ips](https://developers.virustotal.com/reference#itw_ips)\n",
+    "*   [itw_urls](https://developers.virustotal.com/reference#files-itw_urls)\n",
+    "*   [itw_domains](https://developers.virustotal.com/reference#files-itw_domains)\n",
+    "*   [compressed_parents](https://developers.virustotal.com/reference#files-compressed_parents)\n",
+    "*   [execution_parents](https://developers.virustotal.com/reference#files-execution_parents)\n",
+    "*   [pcap_parents](https://developers.virustotal.com/reference#files-pcap_parents)\n",
+    "*   [email_parents](https://developers.virustotal.com/reference#files-email_parents)\n",
+    "\n",
+    "Please note that as the search is looking for old files, the **retrospection's limitation** that you might have can affect to the number of results that this report provides.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b2EfBIOAGULg"
+   },
+   "source": [
+    "## Workflow:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4zz5GQlCGFvU"
+   },
+   "source": [
+    "![distribution_vector_flow.png](data:image/png;base64,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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JTFRE4n6G8BJ"
+   },
+   "source": [
+    "## Script:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "65bz9z8Tvbka"
+   },
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "import hashlib\n",
+    "import json\n",
+    "import requests\n",
+    "\n",
+    "QUERIES = [\n",
+    "    \"engines:gandcrab fs:2020-02-01+ fs:2020-05-01- (type:peexe or type:pedll) tag:exploit (have:compressed_parents OR have:execution_parents OR have:pcap_parents OR have:email_parents)\",\n",
+    "    \"engines:gandcrab fs:2020-02-01+ fs:2020-05-01- (type:peexe or type:pedll) have:in_the_wild\"\n",
+    "]\n",
+    "RELATIONSHIPS = [\"itw_ips\", \"itw_urls\", \"itw_domains\", \"compressed_parents\", \"execution_parents\", \"pcap_parents\", \"email_parents\"]\n",
+    "separator = \",\"\n",
+    "RELATIONSHIPS_URL = separator.join(RELATIONSHIPS)\n",
+    "detected_domains = {}\n",
+    "detected_ips = {}\n",
+    "detected_urls = {}\n",
+    "detected_files = {}\n",
+    "analyzed_objects = []\n",
+    "\n",
+    "\n",
+    "def get_search_results(query, vt_client):\n",
+    "  \"\"\"Execute the search and return the results.\"\"\"\n",
+    "  url = \"/intelligence/search\"\n",
+    "  results = vt_client.iterator(url, params={\"query\": query, \"relationships\": RELATIONSHIPS_URL})\n",
+    "  \n",
+    "  return results\n",
+    "\n",
+    "\n",
+    "def analyse_observable(observable, observable_type, vt_client):\n",
+    "  if observable not in analyzed_objects:\n",
+    "    observable_report = get_observable_report(observable,observable_type, vt_client)\n",
+    "    if observable_report:\n",
+    "      extract_iocs(observable, observable_type, vt_client, observable_report)\n",
+    "  else:\n",
+    "    extract_iocs(observable, observable_type, vt_client)\n",
+    "\n",
+    "\n",
+    "def get_observable_report(observable, observable_type, vt_client):\n",
+    "  \"\"\"Get the observable's intelligence report from VirusTotal.\"\"\"\n",
+    "\n",
+    "  endpoint = {\"url\": \"urls\", \"domain\": \"domains\", \"ip_address\": \"ip_addresses\", \"file\": \"files\"}\n",
+    "  if observable_type == \"url\":\n",
+    "    observable = base64.urlsafe_b64encode(observable.encode()).decode().strip(\"=\")   \n",
+    "  endpoint = endpoint[observable_type]\n",
+    "  if observable_type == \"file\":\n",
+    "    results = vt_client.get_object(f\"/{endpoint}/{observable}?relationships={RELATIONSHIPS_URL}\")\n",
+    "  else:\n",
+    "    results = vt_client.get_object(f\"/{endpoint}/{observable}?relationships=votes\")\n",
+    "  return results\n",
+    "\n",
+    "\n",
+    "\n",
+    "def add_observable(iocs_dict, observable, positives=False):\n",
+    "  \"\"\"Check if the observable was already seen before adding it into the final report.\"\"\"\n",
+    "\n",
+    "  if observable not in iocs_dict:\n",
+    "    iocs_dict[observable] = {}\n",
+    "    iocs_dict[observable][\"positives\"] = positives\n",
+    "    iocs_dict[observable][\"relatives\"] = 1\n",
+    "  else:\n",
+    "    iocs_dict[observable][\"relatives\"] += 1\n",
+    "\n",
+    "  if observable not in analyzed_objects:\n",
+    "    analyzed_objects.append(observable)\n",
+    "  return iocs_dict\n",
+    "\n",
+    "\n",
+    "def extract_iocs(observable, observable_type, vt_client, observable_report=False):\n",
+    "  \"\"\"Add the malicious relationships into the list of detected ovservables.\"\"\"\n",
+    "\n",
+    "  global detected_ips\n",
+    "  global detected_urls\n",
+    "  global detected_domains\n",
+    "  global detected_files\n",
+    "\n",
+    "  if observable_report:\n",
+    "    positives = observable_report.last_analysis_stats[\"malicious\"]\n",
+    "  else:\n",
+    "    try:\n",
+    "      positives = detected_files[observable][\"positives\"]\n",
+    "    except:\n",
+    "      positives = False\n",
+    "  if observable_type == \"ip_address\":\n",
+    "    detected_ips = add_observable(detected_ips, observable, positives)\n",
+    "  elif observable_type == \"url\":\n",
+    "    detected_urls = add_observable(detected_urls, observable, positives)\n",
+    "  elif observable_type == \"domain\":\n",
+    "    detected_domains = add_observable(detected_domains, observable, positives)\n",
+    "  else:\n",
+    "    detected_files = add_observable(detected_files, observable, positives)\n",
+    "    if detected_files[observable][\"relatives\"] == 1:\n",
+    "      search_and_hunt(vt_client, observable_report)\n",
+    "  return\n",
+    "\n",
+    "\n",
+    "def search_and_hunt(vt_client, observable_report=False):\n",
+    "  \"\"\"Iterate over the queries and get matches.\n",
+    "\n",
+    "    Then, analyze the relationships of those matches.\n",
+    "  \"\"\"\n",
+    "\n",
+    "  if observable_report:\n",
+    "    results = observable_report\n",
+    "    for relationship in RELATIONSHIPS:\n",
+    "      if results.relationships[relationship][\"data\"]:\n",
+    "        for hit in results.relationships[relationship][\"data\"]:\n",
+    "          observable_type = hit[\"type\"]\n",
+    "          observable = hit[\"id\"]\n",
+    "          if observable_type == \"url\":\n",
+    "            observable = hit[\"context_attributes\"][\"url\"]\n",
+    "          analyse_observable(observable, observable_type, vt_client)\n",
+    "\n",
+    "\n",
+    "  else:\n",
+    "    for query in QUERIES:\n",
+    "        results = get_search_results(query, vt_client)\n",
+    "        for result in results:\n",
+    "          match = result.id\n",
+    "          for relationship in RELATIONSHIPS:\n",
+    "            if result.relationships[relationship][\"data\"]:\n",
+    "              for hit in result.relationships[relationship][\"data\"]:\n",
+    "                observable_type = hit[\"type\"]\n",
+    "                observable = hit[\"id\"]\n",
+    "                if observable_type == \"url\":\n",
+    "                  observable = hit[\"context_attributes\"][\"url\"]\n",
+    "                analyse_observable(observable, observable_type, vt_client) \n",
+    "\n",
+    "\n",
+    "def print_report():\n",
+    "  \"\"\"Iterate over the detected observables and print the results.\"\"\"\n",
+    "\n",
+    "  def print_header():\n",
+    "    row = [\"Positives\",\"Relatives\",\"VT Link\",\"Observable\"]\n",
+    "    print(\"{: ^15} {: <15} {: ^20} {: >100}\".format(*row))\n",
+    "    print(\"_\"*200)\n",
+    "\n",
+    "  def print_dict(d,type):\n",
+    "    d2 = {}\n",
+    "    for item in d:\n",
+    "      d2[item] = d[item][\"relatives\"]\n",
+    "\n",
+    "    top_view = [ (v,k) for k,v in d2.items() ]\n",
+    "    top_view.sort(reverse=True)\n",
+    "\n",
+    "    for items in top_view:\n",
+    "      observable = items[1]\n",
+    "      if type == \"url\":\n",
+    "        encoded_url = hashlib.sha256(items[1].encode()).hexdigest()\n",
+    "        row = [d[observable][\"positives\"],d[observable][\"relatives\"],\"https://www.virustotal.com/gui/\" + type + \"/\"+encoded_url,observable]\n",
+    "      else:\n",
+    "        row = [d[observable][\"positives\"],d[observable][\"relatives\"],\"https://www.virustotal.com/gui/\" + type + \"/\"+observable,observable]\n",
+    "      print(\"{: ^10} {:^17} {: <110} {: <70}\".format(*row))\n",
+    "\n",
+    "  print(\"\\n\\tDISTRIBUTION VECTORS REPORT\\n\")\n",
+    "\n",
+    "  print(\"\\n#1: FILES\\n\")\n",
+    "  print_header()\n",
+    "  print_dict(detected_files, \"file\")\n",
+    "\n",
+    "  print(\"\\n##2: DOMAINS\\n\")\n",
+    "  print_header()\n",
+    "  print_dict(detected_domains, \"domain\")\n",
+    "\n",
+    "  print(\"\\n#3: URLS\\n\")\n",
+    "  print_header()\n",
+    "  print_dict(detected_urls, \"url\")\n",
+    "\n",
+    "  print(\"\\n#4: IP ADDRESSES\\n\")\n",
+    "  print_header()\n",
+    "  print_dict(detected_ips, \"ip-address\")\n",
+    "\n",
+    "  print(\"\\n\")\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "  vt_client = vt.Client(API_KEY)\n",
+    "  search_and_hunt(vt_client)\n",
+    "  print_report()\n",
+    "\n",
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MMHJja5CpXiU"
+   },
+   "source": [
+    "# Use Case: Ports and IP extraction\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O3LCTVZGO6ot"
+   },
+   "source": [
+    "In this use case we are going to focus on the lateral movements prevention. \n",
+    "\n",
+    "In order to this we will make use of the behavioural network reports."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7D8S__2oQJUV"
+   },
+   "source": [
+    "![IP_Traffic.png](data:image/png;base64,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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UQCjoXlfO-D0"
+   },
+   "source": [
+    "## Script:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "3ZS5-7SZOqgr"
+   },
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "nest_asyncio.apply()\n",
+    "import base64\n",
+    "import requests\n",
+    "import vt\n",
+    "\n",
+    "QUERIES = [\n",
+    "    \"engines:gandcrab fs:2020-02-01+ fs:2020-05-01- (type:peexe or type:pedll) tag:exploit have:behaviour_network\"\n",
+    "]\n",
+    "RELATIONSHIPS = [\"behaviours\"]\n",
+    "separator = \",\"\n",
+    "RELATIONSHIPS_URL = separator.join(RELATIONSHIPS)\n",
+    "extractions = {}\n",
+    "verdicts = {}\n",
+    "analyzed_objects = []\n",
+    "\n",
+    "\n",
+    "def get_search_results(query, vt_client):\n",
+    "  \"\"\"Execute the search and return the results.\"\"\"\n",
+    "  url = \"/intelligence/search\"\n",
+    "  results = vt_client.iterator(url, params={\"query\": query, \"relationships\": RELATIONSHIPS_URL})\n",
+    "  \n",
+    "  return results\n",
+    "\n",
+    "def analyse_observable(observable, observable_type, vt_client):\n",
+    "  if observable not in analyzed_objects:\n",
+    "    analyzed_objects.append(observable)\n",
+    "    observable_report = get_observable_report(observable, observable_type, vt_client)\n",
+    "    if observable_report:\n",
+    "      extract_iocs(observable, observable_report, vt_client)\n",
+    "\n",
+    "\n",
+    "def get_observable_report(observable, observable_type, vt_client):\n",
+    "  \"\"\"Get the observable's intelligence report from VirusTotal.\"\"\"\n",
+    "\n",
+    "  endpoint = {\"file_behaviour\": \"file_behaviours\", \"ip_address\": \"ip_addresses\"}\n",
+    "  endpoint = endpoint[observable_type]\n",
+    "  results = vt_client.get_object(f\"/{endpoint}/{observable}\")\n",
+    "  return results\n",
+    "\n",
+    "\n",
+    "def extract_iocs(observable, observable_report, vt_client):\n",
+    "  \"\"\"Add the malicious relationships into the list of detected ovservables.\"\"\"\n",
+    "\n",
+    "  global extractions\n",
+    "  global verdicts\n",
+    "\n",
+    "  if hasattr(observable_report, \"ip_traffic\"):\n",
+    "    comms = observable_report.ip_traffic\n",
+    "\n",
+    "    for comm in comms:\n",
+    "      try:\n",
+    "        protocol = comm[\"transport_layer_protocol\"]\n",
+    "      except:\n",
+    "        protocol = \"Unknown\"\n",
+    "      ip = comm[\"destination_ip\"]\n",
+    "      port = comm[\"destination_port\"]\n",
+    "      hash = observable[:64]\n",
+    "\n",
+    "      if hash not in extractions:\n",
+    "        extractions[hash] = {\"ports\":[], \"ips\":[], \"protocols\":[]}\n",
+    "      if port not in extractions[hash][\"ports\"]:\n",
+    "        extractions[hash][\"ports\"].append(port)\n",
+    "        verdicts[port] = \"N/A\"\n",
+    "      if ip not in extractions[hash][\"ips\"]:\n",
+    "        extractions[hash][\"ips\"].append(ip)\n",
+    "        if \"DNS\" in ip:\n",
+    "          positives = \"N/A\"\n",
+    "        else:\n",
+    "          observable_report = get_observable_report(ip, \"ip_address\", vt_client)\n",
+    "          positives = observable_report.last_analysis_stats[\"malicious\"]\n",
+    "        verdicts[ip] = positives\n",
+    "      if protocol not in extractions[hash][\"protocols\"]:\n",
+    "        extractions[hash][\"protocols\"].append(protocol)\n",
+    "        verdicts[protocol] = \"N/A\"\n",
+    "\n",
+    "  return\n",
+    "\n",
+    "\n",
+    "def search_and_hunt(vt_client):\n",
+    "  for query in QUERIES: \n",
+    "    results = get_search_results(query, vt_client)\n",
+    "    for result in results:\n",
+    "      match = result.id\n",
+    "      for relationship in RELATIONSHIPS:\n",
+    "        if result.relationships[relationship][\"data\"]:\n",
+    "          for hit in result.relationships[relationship][\"data\"]:\n",
+    "            observable_type = hit[\"type\"]\n",
+    "            observable = hit[\"id\"]\n",
+    "            analyse_observable(observable, observable_type, vt_client)\n",
+    "\n",
+    "\n",
+    "def print_report():\n",
+    "  elements = [\"ports\", \"ips\", \"protocols\"]\n",
+    "  for e in elements:\n",
+    "    get_top_list(e)\n",
+    "\n",
+    "\n",
+    "def get_top_list(element):\n",
+    "  e_list = []\n",
+    "  for hash in extractions:\n",
+    "    e_list = list(set(e_list) | set(extractions[hash][element]))\n",
+    "\n",
+    "  e_top = {}\n",
+    "  for hash in extractions:\n",
+    "    for e in e_list:\n",
+    "      if e not in e_top:\n",
+    "        e_top[e] = 0\n",
+    "      if e in extractions[hash][element]:\n",
+    "        e_top[e] += 1\n",
+    "\n",
+    "  print(\"\\nTOP \" + element + \"\\n\" + \"_\"*100 + \"\\n\")\n",
+    "  top_view = [ (v,k) for k,v in e_top.items() ]\n",
+    "  top_view.sort(reverse=True)\n",
+    "  for matches,observable in top_view:\n",
+    "      row = [str(verdicts[observable]), observable, \"Number of matches: \", matches]\n",
+    "      print(\"\\tPositives: {: >8} {: ^20} {: >20} {: >3}\".format(*row))\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "  vt_client = vt.Client(API_KEY)\n",
+    "  search_and_hunt(vt_client)\n",
+    "  print_report()\n",
+    "\n",
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IZwKMdcipRrq"
+   },
+   "source": [
+    "# Use Case: Geographical Distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IGexVXLUUJ77"
+   },
+   "source": [
+    "In this use case we will iterate over all the matches of our initial search:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "id": "HB38UKRKr64G"
+   },
+   "outputs": [],
+   "source": [
+    "#@markdown\n",
+    "\n",
+    "VTI_SEARCH = 'engines:gandcrab fs:2020-02-01+ fs:2020-05-01- (type:peexe or type:pedll) tag:exploit'  #@param  {type: \"string\"}\n",
+    "\n",
+    "#@markdown "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fE9GTrt-r_hM"
+   },
+   "source": [
+    "The difference now is that we will take a look to the Submissions tab in order to indentify how this malware has been spread around the world."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U5Q5HcuJULBa"
+   },
+   "source": [
+    "![Submissions.png](data:image/png;base64,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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Wyls5rGZUNk7"
+   },
+   "source": [
+    "## Script:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "XocPGaF-URFT"
+   },
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "nest_asyncio.apply()\n",
+    "import json\n",
+    "import requests\n",
+    "import vt\n",
+    "\n",
+    "QUERIES = [\n",
+    "    VTI_SEARCH\n",
+    "]\n",
+    "RELATIONSHIPS = [\"submissions\"]\n",
+    "separator = \",\"\n",
+    "RELATIONSHIPS_URL = separator.join(RELATIONSHIPS)\n",
+    "countries = {}\n",
+    "cities = {}\n",
+    "\n",
+    "\n",
+    "def get_submission(id, vt_client):\n",
+    "  results = vt_client.get_object(f\"/submissions/{id}\")\n",
+    "  interface = results.interface\n",
+    "  if interface == \"email\":\n",
+    "    return False, False, interface\n",
+    "  country = results.country\n",
+    "  if country == \"ZZ\":\n",
+    "    city = country\n",
+    "  else:\n",
+    "    city = results.city\n",
+    "  return country, city, interface\n",
+    "\n",
+    "def get_search_results(query, vt_client):\n",
+    "  \"\"\"Execute the search and return the results.\"\"\"\n",
+    "  url = \"/intelligence/search\"\n",
+    "  results = vt_client.iterator(url, params={\"query\": query, \"relationships\": RELATIONSHIPS_URL})\n",
+    "  \n",
+    "  return results\n",
+    "\n",
+    "\n",
+    "\n",
+    "def print_report():\n",
+    "  print(\"\\nTOP targeted countries\\n\" + \"_\"*100 + \"\\n\")\n",
+    "\n",
+    "  countries_view = [ (v,k) for k,v in countries.items() ]\n",
+    "  countries_view.sort(reverse=True)\n",
+    "  for submission,country in countries_view:\n",
+    "    row = [country,\"Number of submissions: \",submission]\n",
+    "    print(\"{: >15} {: >25} {: >2}\".format(*row))\n",
+    "\n",
+    "  print(\"\\nTOP targeted cities\\n\" + \"_\"*100 + \"\\n\")\n",
+    "\n",
+    "  cities_view = [ (v,k) for k,v in cities.items() ]\n",
+    "  cities_view.sort(reverse=True)\n",
+    "  for submission,city in cities_view:\n",
+    "    row = [city,\"Number of submissions: \",submission]\n",
+    "    print(\"{: >35} {: >25} {: >2}\".format(*row))\n",
+    "\n",
+    "def search_and_hunt(vt_client):\n",
+    "  global countries\n",
+    "  global cities\n",
+    "\n",
+    "  for query in QUERIES:\n",
+    "      results = get_search_results(query, vt_client)\n",
+    "      for result in results:       \n",
+    "        positives = result.last_analysis_stats[\"malicious\"]\n",
+    "        for hit in result.relationships[\"submissions\"][\"data\"]:\n",
+    "          submission_id = hit[\"id\"]\n",
+    "          country,city,interface = get_submission(submission_id, vt_client)\n",
+    "\n",
+    "          if interface != \"email\":\n",
+    "            if country not in countries:\n",
+    "              countries[country] = 0\n",
+    "            countries[country] += 1\n",
+    "            if city not in cities:\n",
+    "              cities[city] = 0\n",
+    "            cities[city] += 1\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "  vt_client = vt.Client(API_KEY)\n",
+    "  search_and_hunt(vt_client)\n",
+    "  print_report()\n",
+    "\n",
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mMOyXrZoOY1P"
+   },
+   "source": [
+    "# Use Case: TOP vulnerabilities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9qd-rECmOh2J"
+   },
+   "source": [
+    "In this use case we are going to extract the top exploited vulnerabilities given an intelligence search like the one below:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "cellView": "form",
+    "id": "RgEuPEvln72E"
+   },
+   "outputs": [],
+   "source": [
+    "#@markdown\n",
+    "\n",
+    "VTI_SEARCH = 'engines:gandcrab fs:2020-02-01+ fs:2020-05-01- (type:peexe or type:pedll) tag:exploit'  #@param  {type: \"string\"}\n",
+    "\n",
+    "#@markdown "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OBivUmbkPF6j"
+   },
+   "source": [
+    "## Script:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2yunHaXDO2DS"
+   },
+   "outputs": [],
+   "source": [
+    "import nest_asyncio\n",
+    "nest_asyncio.apply()\n",
+    "import json\n",
+    "import requests\n",
+    "import vt\n",
+    "\n",
+    "QUERIES = [\n",
+    "    VTI_SEARCH\n",
+    "]\n",
+    "cve_tags = {}\n",
+    "\n",
+    "\n",
+    "def get_search_results(query, vt_client):\n",
+    "  \"\"\"Execute the search and return the results.\"\"\"\n",
+    "  url = \"/intelligence/search\"\n",
+    "  results = vt_client.iterator(url, params={\"query\": query})\n",
+    "  \n",
+    "  return results\n",
+    "\n",
+    "\n",
+    "def search_and_hunt(vt_client):\n",
+    "  global cve_tags\n",
+    "\n",
+    "  for query in QUERIES:\n",
+    "    results = get_search_results(query, vt_client)\n",
+    "    for result in results:\n",
+    "      for tag in getattr(result, \"tags\", []):\n",
+    "        if \"cve\" in tag:\n",
+    "          if tag not in cve_tags:\n",
+    "            cve_tags[tag] = 0\n",
+    "          cve_tags[tag] += 1\n",
+    "\n",
+    "def print_report():\n",
+    "  print(\"\\nTOP Vulnerabilities\" + \"\\n\" + \"_\"*100 + \"\\n\")\n",
+    "  top_view = [ (v,k) for k,v in cve_tags.items() ]\n",
+    "  top_view.sort(reverse=True)\n",
+    "  for v,k in top_view:\n",
+    "      row = [k,\"Number of matches: \",v]\n",
+    "      print(\"{: >15} {: >25} {: >5}\".format(*row))\n",
+    "\n",
+    "def main():\n",
+    "  vt_client = vt.Client(API_KEY)\n",
+    "  search_and_hunt(vt_client)\n",
+    "  print_report()\n",
+    "\n",
+    "main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Ransomware in a global context vt-py.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/examples/jupyter/ransomware_report_usecases2.ipynb b/examples/jupyter/ransomware_report_usecases2.ipynb
new file mode 100644
index 0000000..3afa832
--- /dev/null
+++ b/examples/jupyter/ransomware_report_usecases2.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2de615a6",
+   "metadata": {},
+   "source": [
+    "# Jupyter Notebook - Ransomware report use cases 2\n",
+    "\n",
+    "Copyright © 2021 Google"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f2c002ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import vt\n",
+    "import nest_asyncio\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0e8fb38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@markdown Please, insert your VT API Key*:\n",
+    "\n",
+    "API_KEY = ''  #@param  {type: \"string\"}\n",
+    "\n",
+    "#@markdown **The API key should have Premium permissions, otherwise some of the use cases might not provide the expected results.*\n",
+    "\n",
+    "#@markdown "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6a43b2f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nest_asyncio.apply()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81e7e852",
+   "metadata": {},
+   "source": [
+    "# 1. Collecting hashes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "dd8258f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9426 hashes have been written to the file hashes.log\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "nh = 0\n",
+    "with vt.Client(API_KEY) as client:\n",
+    "    it = client.iterator('/intelligence/search',\n",
+    "        params={'query': 'engines:ryuk  fs:2021-01-01+ (type:peexe or type:pedll) p:10+'})\n",
+    "    with open('hashes.log','w') as f:\n",
+    "        for obj in it:\n",
+    "            if obj.id:\n",
+    "                f.write(f'{obj.id}\\n')\n",
+    "                nh += 1\n",
+    "    f.close()\n",
+    "print(f'{nh} hashes have been written to the file hashes.log\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af491db1",
+   "metadata": {},
+   "source": [
+    "# 2. Malicious contacted IOCs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "91f52171",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_malicious_IPs(file_hash):\n",
+    "    contacted_IPs = set()\n",
+    "    for ip in client.iterator('/files/{}/contacted_ips', file_hash, limit=20):\n",
+    "        stats = ip.get('last_analysis_stats')\n",
+    "        if stats and stats['malicious'] >= min_positives:\n",
+    "            contacted_IPs.add(ip.id)\n",
+    "    return contacted_IPs\n",
+    "\n",
+    "def get_malicious_Domains(file_hash):\n",
+    "    contacted_domains = set()\n",
+    "    for domain in client.iterator('/files/{}/contacted_domains', file_hash, limit=20):\n",
+    "        stats = domain.get('last_analysis_stats')\n",
+    "        if stats and stats['malicious'] >= min_positives:\n",
+    "            contacted_domains.add(domain.id)\n",
+    "    return contacted_domains"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3fe5a2cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29 IOCs have been written to the file iocs.log\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "malIOCs = set()\n",
+    "min_positives = 5 # Minimun number of positives for every IOC\n",
+    "nIOCs = 0\n",
+    "\n",
+    "with vt.Client(API_KEY) as client:\n",
+    "    it = client.iterator('/intelligence/search',\n",
+    "        params={'query': 'engines:babuk fs:2021-07-01+ (have:contacted_domains or have:contacted_ips)'})\n",
+    "    for obj in it:\n",
+    "        ips = get_malicious_IPs(obj.id)\n",
+    "        domains = get_malicious_Domains(obj.id)\n",
+    "        malIOCs = malIOCs | ips | domains\n",
+    "\n",
+    "with open('iocs.log','w') as f:\n",
+    "    for ioc in malIOCs:\n",
+    "        f.write(f'{ioc}\\n')\n",
+    "        nIOCs += 1\n",
+    "        \n",
+    "print(f'{nIOCs} IOCs have been written to the file iocs.log\\n')        \n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75eb0fbb",
+   "metadata": {},
+   "source": [
+    "# 3. Searching for a text in domains and URLs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a621ab7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def search_domains(file_hash, text):\n",
+    "    domains = set()\n",
+    "    for domain in client.iterator('/files/{}/embedded_domains', file_hash, limit=20):\n",
+    "        if text in domain.id:\n",
+    "            domains.add(domain.id)\n",
+    "    for domain in client.iterator('/files/{}/itw_domains', file_hash, limit=20):\n",
+    "        if text in domain.id:\n",
+    "            domains.add(domain.id)            \n",
+    "    return domains\n",
+    "\n",
+    "def search_urls(file_hash, text):\n",
+    "    urls = set()\n",
+    "    for url in client.iterator('/files/{}/embedded_urls', file_hash, limit=20):\n",
+    "        if text in url.id:\n",
+    "            urls.add(url.id)\n",
+    "    for url in client.iterator('/files/{}/itw_urls', file_hash, limit=20):\n",
+    "        if text in url.id:\n",
+    "            urls.add(url.id)\n",
+    "    return urls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "627139ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "** Results found for file hash: 1a5f649438646bfe909ac1e08bfe37f969e21b7900933d51ad2fe0c5c549a79b (pedll)\n",
+      "\n",
+      " - www.google.fr\n",
+      "\n",
+      "** Results found for file hash: af48fb4200cff418d513fe0ca841a8adb8699b266266fcf46ef44e460361cff0 (peexe)\n",
+      "\n",
+      " - www.google-analytics.com\n",
+      "\n",
+      "** Results found for file hash: 062227eaca57abaf97dd2c6d5f198fdd0d11b15cfd45eef0f0f0217d5c70aa06 (android)\n",
+      "\n",
+      " - play.google.com\n",
+      "\n",
+      " - googleads.g.doubleclick.net\n",
+      "\n",
+      " - pagead2.googlesyndication.com\n",
+      "\n",
+      " - plus.google.com\n",
+      "\n",
+      " - www.google.com\n",
+      "\n",
+      " - support.google.com\n",
+      "\n",
+      "** Results found for file hash: 569a267a4282bc694a8b39502b4e51bd3b78d449e075f4480c51eba71433b5dd (peexe)\n",
+      "\n",
+      " - www.google.de\n",
+      "\n",
+      "** Results found for file hash: 87ceec489fbfc70c2d935dc0717dba6f7d7147d06be08c661e05f7c83c3b67f0 (android)\n",
+      "\n",
+      " - play.google.com\n",
+      "\n",
+      " - googleads.g.doubleclick.net\n",
+      "\n",
+      " - pagead2.googlesyndication.com\n",
+      "\n",
+      " - plus.google.com\n",
+      "\n",
+      " - www.google.com\n",
+      "\n",
+      " - support.google.com\n",
+      "\n",
+      "** Results found for file hash: d419b2e7a6e9252b1cb3b86a6f8f107206c31cc666a74af97738f46b650ed4c5 (peexe)\n",
+      "\n",
+      " - www.googleadservices.com\n",
+      "\n",
+      " - googleads.g.doubleclick.net\n",
+      "\n",
+      " - www.googletagmanager.com\n",
+      "\n",
+      "** Results found for file hash: bcd862cc4d790ff73de8f573b3719f2c857bf0d03efa311e1f9cbf5cfa05d9ed (peexe)\n",
+      "\n",
+      " - partner.googleadservices.com\n",
+      "\n",
+      "** Results found for file hash: 147e54a51effe8a0cb42691e0e967752698b4db5883532f88daed9ba4f8b69a7 (peexe)\n",
+      "\n",
+      " - ssl.google-analytics.com\n",
+      "\n",
+      "** Results found for file hash: ac8eac1c5644ea1563783e7bdccaddbfaa6146fdcd610a7acc852fe76420352a (peexe)\n",
+      "\n",
+      " - partner.googleadservices.com\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "stxt=\"google\" # Write here any text you want to search for\n",
+    "query_string = 'engines:cerber fs:2021-06-01+ (embedded_domains: %s OR embedded_urls:%s OR itw: %s)' % (stxt, stxt, stxt)\n",
+    "\n",
+    "with vt.Client(API_KEY) as client:\n",
+    "    it = client.iterator('/intelligence/search', params={'query': query_string})\n",
+    "\n",
+    "    for obj in it:\n",
+    "        results = search_domains(obj.id, search_text) | search_urls(obj.id, search_text)\n",
+    "        \n",
+    "        if results:\n",
+    "            print(f'** Results found for file hash: {obj.id} (%s)\\n' %(obj.type_tag) )\n",
+    "            for result in results:\n",
+    "                print(f' - {result}\\n')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}