PY0101EN-5.2_API_2.v2.ipynb

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    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Simple APIs\n",
    "\n",
    "## Random User and Fruitvice API Examples\n",
    "\n",
    "Estimated time needed: **25** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Load and use RandomUser API, using `RandomUser()` Python library\n",
    "*   Load and use Fruitvice API, using `requests` Python library\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "The purpose of this notebook is to provide more examples on how to use simple APIs. As you have already learned from previous videos and notebooks, API stands for Application Programming Interface and is a software intermediary that allows two applications to talk to each other.\n",
    "\n",
    "The advantages of using APIs:\n",
    "\n",
    "*   **Automation**. Less human effort is required and workflows can be easily updated to become faster and more\\\n",
    "    productive.\n",
    "*   **Efficiency**. It allows to use the capabilities of one of the already developed APIs than to try to\n",
    "    independently implement some functionality from scratch.\n",
    "\n",
    "The disadvantage of using APIs:\n",
    "\n",
    "*   **Security**. If the API is poorly integrated, it means it will be vulnerable to attacks, resulting in data\\\n",
    "    breaches or losses having financial or reputation implications.\n",
    "\n",
    "One of the applications we will use in this notebook is Random User Generator. RandomUser is an open-source, free API providing developers with randomly generated users to be used as placeholders for testing purposes. This makes the tool similar to Lorem Ipsum, but is a placeholder for people instead of text. The API can return multiple results, as well as specify generated user details such as gender, email, image, username, address, title, first and last name, and more. More information on [RandomUser](https://randomuser.me/documentation?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2022-01-01#intro) can be found here.\n",
    "\n",
    "Another example of simple API we will use in this notebook is Fruitvice application. The Fruitvice API webservice which provides data for all kinds of fruit! You can use Fruitvice to find out interesting information about fruit and educate yourself. The webservice is completely free to use and contribute to.\n"
   ]
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    "## Example 1: RandomUser API\n",
    "\n",
    "Below are Get Methods parameters that we can generate. For more information on the parameters, please visit this [documentation](https://randomuser.me/documentation?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2022-01-01) page.\n"
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    "## **Get Methods**\n",
    "\n",
    "*   get_cell()\n",
    "*   get_city()\n",
    "*   get_dob()\n",
    "*   get_email()\n",
    "*   get_first_name()\n",
    "*   get_full_name()\n",
    "*   get_gender()\n",
    "*   get_id()\n",
    "*   get_id_number()\n",
    "*   get_id_type()\n",
    "*   get_info()\n",
    "*   get_last_name()\n",
    "*   get_login_md5()\n",
    "*   get_login_salt()\n",
    "*   get_login_sha1()\n",
    "*   get_login_sha256()\n",
    "*   get_nat()\n",
    "*   get_password()\n",
    "*   get_phone()\n",
    "*   get_picture()\n",
    "*   get_postcode()\n",
    "*   get_registered()\n",
    "*   get_state()\n",
    "*   get_street()\n",
    "*   get_username()\n",
    "*   get_zipcode()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "272de567-28e5-41e5-a826-6004cb99599e",
   "metadata": {},
   "source": [
    "To start using the API you can install the `randomuser` library running the `pip install` command.\n"
   ]
  },
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     "text": [
      "Collecting randomuser\n",
      "  Downloading randomuser-1.6.tar.gz (5.0 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hBuilding wheels for collected packages: randomuser\n",
      "  Building wheel for randomuser (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for randomuser: filename=randomuser-1.6-py3-none-any.whl size=5067 sha256=9ec2c6e67dd3745cb61026fabc6e9fe92e6113a9e20a587a1e1ba4dff6d6e7eb\n",
      "  Stored in directory: /home/jupyterlab/.cache/pip/wheels/21/10/7b/c13bc3e24a3c1000a34554802ad8aa0ff27df56366998a0473\n",
      "Successfully built randomuser\n",
      "Installing collected packages: randomuser\n",
      "Successfully installed randomuser-1.6\n"
     ]
    }
   ],
   "source": [
    "!pip install randomuser"
   ]
  },
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   "id": "2e943801-2287-43e0-a0de-2e88c7ab2997",
   "metadata": {},
   "source": [
    "Then, we will load the necessary libraries.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e6fb3ac-4b0a-4fd4-bafe-9ababb5cfef4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from randomuser import RandomUser\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609eb1c1-3c16-4381-acf0-896800a6afb6",
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   "source": [
    "First, we will create a random user object, r.\n"
   ]
  },
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   "cell_type": "code",
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   "id": "6d875f14-ef54-4c7a-9bfd-be93619558ac",
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   "source": [
    "r = RandomUser()"
   ]
  },
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   "cell_type": "markdown",
   "id": "990d2b02-a1dd-4305-ba21-8729d929ab53",
   "metadata": {},
   "source": [
    "Then, using `generate_users()` function, we get a list of random 10 users.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "719dd960-4819-4f29-8ce5-59d4560d9b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "some_list = r.generate_users(10)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "565f9b93-5dc0-4d59-a0f6-30b829a3748f",
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    {
     "data": {
      "text/plain": [
       "[<randomuser.RandomUser at 0x7fab985a1fd0>,\n",
       " <randomuser.RandomUser at 0x7fab985a1610>,\n",
       " <randomuser.RandomUser at 0x7fab985a1210>,\n",
       " <randomuser.RandomUser at 0x7fab985a1790>,\n",
       " <randomuser.RandomUser at 0x7fab985a1d90>,\n",
       " <randomuser.RandomUser at 0x7fab985a1b50>,\n",
       " <randomuser.RandomUser at 0x7fab985a1150>,\n",
       " <randomuser.RandomUser at 0x7fab985a1190>,\n",
       " <randomuser.RandomUser at 0x7fab985a1950>,\n",
       " <randomuser.RandomUser at 0x7fab985a1ad0>]"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "some_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29191364-9ae7-40f1-bdce-14ea874c9db9",
   "metadata": {},
   "source": [
    "The **\"Get Methods\"** functions mentioned at the beginning of this notebook, can generate the required parameters to construct a dataset. For example, to get full name, we call `get_full_name()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "98303bd3-435b-4487-8507-a4fa385237c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = r.get_full_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e7ddac-414a-40b4-aec0-b2c93c6688d7",
   "metadata": {},
   "source": [
    "Let's say we only need 10 users with full names and their email addresses. We can write a \"for-loop\" to print these 10 users.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc4df431-d0ee-403c-8ca7-725c082ead97",
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    {
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     "text": [
      "نیایش نكو نظر   nyysh.nkwnzr@example.com\n",
      "Maia Hughes   maia.hughes@example.com\n",
      "Anania Porto   anania.porto@example.com\n",
      "Évelyne Jean   evelyne.jean@example.com\n",
      "Anna Olsen   anna.olsen@example.com\n",
      "سارینا حیدری   sryn.hydry@example.com\n",
      "Janne Nordstrøm   janne.nordstrom@example.com\n",
      "Jesse Young   jesse.young@example.com\n",
      "Ellen Peterson   ellen.peterson@example.com\n",
      "Coşkun Tokatlıoğlu   coskun.tokatlioglu@example.com\n"
     ]
    }
   ],
   "source": [
    "for user in some_list:\n",
    "    print (user.get_full_name(),\" \",user.get_email())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d42721-4e99-45a9-9b4b-8ba40e067af2",
   "metadata": {},
   "source": [
    "## Exercise 1\n",
    "\n",
    "In this Exercise, generate photos of the random 5 users.\n"
   ]
  },
  {
   "cell_type": "code",
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   "id": "aee864df-938c-41f8-b662-721bdb2fa07f",
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     "text": [
      "https://randomuser.me/api/portraits/women/30.jpg\n",
      "https://randomuser.me/api/portraits/women/30.jpg\n",
      "https://randomuser.me/api/portraits/men/22.jpg\n",
      "https://randomuser.me/api/portraits/women/92.jpg\n",
      "https://randomuser.me/api/portraits/women/22.jpg\n",
      "https://randomuser.me/api/portraits/women/63.jpg\n",
      "https://randomuser.me/api/portraits/women/70.jpg\n",
      "https://randomuser.me/api/portraits/men/62.jpg\n",
      "https://randomuser.me/api/portraits/women/65.jpg\n",
      "https://randomuser.me/api/portraits/men/21.jpg\n"
     ]
    }
   ],
   "source": [
    "## Write your code here\n",
    "for user in some_list:\n",
    "    print (user.get_picture())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2d2f60d-38bb-4cc0-a3e1-632a2de2bac9",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "for user in some_list:\n",
    "    print (user.get_picture())\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb049fa-6ae1-4844-88c8-9b4cb71945c1",
   "metadata": {},
   "source": [
    "To generate a table with information about the users, we can write a function containing all desirable parameters. For example, name, gender, city, etc. The parameters will depend on the requirements of the test to be performed. We call the Get Methods, listed at the beginning of this notebook. Then, we return pandas dataframe with the users.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5bef8951-0a24-4f52-83e2-7bdfe5249114",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_users():\n",
    "    users =[]\n",
    "     \n",
    "    for user in RandomUser.generate_users(10):\n",
    "        users.append({\"Name\":user.get_full_name(),\"Gender\":user.get_gender(),\"City\":user.get_city(),\"State\":user.get_state(),\"Email\":user.get_email(), \"DOB\":user.get_dob(),\"Picture\":user.get_picture()})\n",
    "      \n",
    "    return pd.DataFrame(users)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c7d05c65-826e-4d96-9815-ebfbc5bae1a2",
   "metadata": {},
   "outputs": [
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Email</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Picture</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vandana Van der Zwan</td>\n",
       "      <td>female</td>\n",
       "      <td>Voorst</td>\n",
       "      <td>Overijssel</td>\n",
       "      <td>vandana.vanderzwan@example.com</td>\n",
       "      <td>1975-10-22T21:40:43.495Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/23.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Theresa Porter</td>\n",
       "      <td>female</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>Maine</td>\n",
       "      <td>theresa.porter@example.com</td>\n",
       "      <td>1956-01-19T11:00:56.266Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/60.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pam Niks</td>\n",
       "      <td>female</td>\n",
       "      <td>Alblasserdam</td>\n",
       "      <td>Overijssel</td>\n",
       "      <td>pam.niks@example.com</td>\n",
       "      <td>1992-10-21T06:13:55.481Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/72.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Annie Theunissen</td>\n",
       "      <td>female</td>\n",
       "      <td>Born</td>\n",
       "      <td>Limburg</td>\n",
       "      <td>annie.theunissen@example.com</td>\n",
       "      <td>1959-01-21T08:06:06.484Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/93.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Romek Bolt</td>\n",
       "      <td>male</td>\n",
       "      <td>Afferden</td>\n",
       "      <td>Friesland</td>\n",
       "      <td>romek.bolt@example.com</td>\n",
       "      <td>1963-10-15T15:55:10.882Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/men/97.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ella Korpi</td>\n",
       "      <td>female</td>\n",
       "      <td>Tarvasjoki</td>\n",
       "      <td>Central Ostrobothnia</td>\n",
       "      <td>ella.korpi@example.com</td>\n",
       "      <td>1962-04-05T21:08:23.576Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/68.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Anna Jackson</td>\n",
       "      <td>female</td>\n",
       "      <td>Whanganui</td>\n",
       "      <td>Hawke'S Bay</td>\n",
       "      <td>anna.jackson@example.com</td>\n",
       "      <td>1945-10-25T05:05:48.612Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/82.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>آیناز کوتی</td>\n",
       "      <td>female</td>\n",
       "      <td>تبریز</td>\n",
       "      <td>لرستان</td>\n",
       "      <td>aynz.khwty@example.com</td>\n",
       "      <td>1951-01-27T06:15:52.232Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/95.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sofia Green</td>\n",
       "      <td>female</td>\n",
       "      <td>Gisborne</td>\n",
       "      <td>Marlborough</td>\n",
       "      <td>sofia.green@example.com</td>\n",
       "      <td>1952-09-05T17:53:54.658Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/women/46.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Alfredo Santana</td>\n",
       "      <td>male</td>\n",
       "      <td>Torrente</td>\n",
       "      <td>Asturias</td>\n",
       "      <td>alfredo.santana@example.com</td>\n",
       "      <td>1964-07-25T19:15:44.395Z</td>\n",
       "      <td>https://randomuser.me/api/portraits/men/75.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name  Gender          City                 State  \\\n",
       "0  Vandana Van der Zwan  female        Voorst            Overijssel   \n",
       "1        Theresa Porter  female   Bakersfield                 Maine   \n",
       "2              Pam Niks  female  Alblasserdam            Overijssel   \n",
       "3      Annie Theunissen  female          Born               Limburg   \n",
       "4            Romek Bolt    male      Afferden             Friesland   \n",
       "5            Ella Korpi  female    Tarvasjoki  Central Ostrobothnia   \n",
       "6          Anna Jackson  female     Whanganui           Hawke'S Bay   \n",
       "7            آیناز کوتی  female         تبریز                لرستان   \n",
       "8           Sofia Green  female      Gisborne           Marlborough   \n",
       "9       Alfredo Santana    male      Torrente              Asturias   \n",
       "\n",
       "                            Email                       DOB  \\\n",
       "0  vandana.vanderzwan@example.com  1975-10-22T21:40:43.495Z   \n",
       "1      theresa.porter@example.com  1956-01-19T11:00:56.266Z   \n",
       "2            pam.niks@example.com  1992-10-21T06:13:55.481Z   \n",
       "3    annie.theunissen@example.com  1959-01-21T08:06:06.484Z   \n",
       "4          romek.bolt@example.com  1963-10-15T15:55:10.882Z   \n",
       "5          ella.korpi@example.com  1962-04-05T21:08:23.576Z   \n",
       "6        anna.jackson@example.com  1945-10-25T05:05:48.612Z   \n",
       "7          aynz.khwty@example.com  1951-01-27T06:15:52.232Z   \n",
       "8         sofia.green@example.com  1952-09-05T17:53:54.658Z   \n",
       "9     alfredo.santana@example.com  1964-07-25T19:15:44.395Z   \n",
       "\n",
       "                                            Picture  \n",
       "0  https://randomuser.me/api/portraits/women/23.jpg  \n",
       "1  https://randomuser.me/api/portraits/women/60.jpg  \n",
       "2  https://randomuser.me/api/portraits/women/72.jpg  \n",
       "3  https://randomuser.me/api/portraits/women/93.jpg  \n",
       "4    https://randomuser.me/api/portraits/men/97.jpg  \n",
       "5  https://randomuser.me/api/portraits/women/68.jpg  \n",
       "6  https://randomuser.me/api/portraits/women/82.jpg  \n",
       "7  https://randomuser.me/api/portraits/women/95.jpg  \n",
       "8  https://randomuser.me/api/portraits/women/46.jpg  \n",
       "9    https://randomuser.me/api/portraits/men/75.jpg  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_users()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a2cda2a-a364-4e71-8c3a-39acac424728",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(get_users())  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c8eac9-b382-4df2-aaf3-82837d111ba2",
   "metadata": {},
   "source": [
    "Now we have a *pandas* dataframe that can be used for any testing purposes that the tester might have.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48bed39a-4530-4a43-bfee-dda5c4ed8680",
   "metadata": {},
   "source": [
    "## Example 2: Fruitvice API\n",
    "\n",
    "Another, more common way to use APIs, is through `requests` library. The next lab, Requests and HTTP, will contain more information about requests.\n",
    "\n",
    "We will start by importing all required libraries.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b870d2df-34b7-40ab-8590-3f332c98dd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc03067a-d7fc-4fec-b40f-edbc377edf70",
   "metadata": {},
   "source": [
    "We will obtain the [fruitvice](https://www.fruityvice.com/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2022-01-01) API data using `requests.get(\"url\")` function. The data is in a json format.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6666070-6137-4fb0-8ae0-685ca9fa6263",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = requests.get(\"https://www.fruityvice.com/api/fruit/all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602ef509-8362-4351-81f4-37d36d0faf3b",
   "metadata": {},
   "source": [
    "We will retrieve results using `json.loads()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e55d1575-5106-46bf-a24f-870d6f166592",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = json.loads(data.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864df24c-6982-4ccd-9feb-8ff361fe00b5",
   "metadata": {},
   "source": [
    "We will convert our json data into *pandas* data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc5b0a48-1711-45c5-92cc-1082f63c6d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genus</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>nutritions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Malus</td>\n",
       "      <td>Apple</td>\n",
       "      <td>6</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 11.4, 'protein': 0.3, 'fat':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prunus</td>\n",
       "      <td>Apricot</td>\n",
       "      <td>35</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 3.9, 'protein': 0.5, 'fat': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Musa</td>\n",
       "      <td>Banana</td>\n",
       "      <td>1</td>\n",
       "      <td>Musaceae</td>\n",
       "      <td>Zingiberales</td>\n",
       "      <td>{'carbohydrates': 22, 'protein': 1, 'fat': 0.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rubus</td>\n",
       "      <td>Blackberry</td>\n",
       "      <td>64</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 9, 'protein': 1.3, 'fat': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fragaria</td>\n",
       "      <td>Blueberry</td>\n",
       "      <td>33</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 5.5, 'protein': 0, 'fat': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prunus</td>\n",
       "      <td>Cherry</td>\n",
       "      <td>9</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>None</td>\n",
       "      <td>{'carbohydrates': 12, 'protein': 1, 'fat': 0.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Durio</td>\n",
       "      <td>Durian</td>\n",
       "      <td>60</td>\n",
       "      <td>Malvaceae</td>\n",
       "      <td>Malvales</td>\n",
       "      <td>{'carbohydrates': 27.1, 'protein': 1.5, 'fat':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ficus</td>\n",
       "      <td>Fig</td>\n",
       "      <td>68</td>\n",
       "      <td>Moraceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 19, 'protein': 0.8, 'fat': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ribes</td>\n",
       "      <td>Gooseberry</td>\n",
       "      <td>69</td>\n",
       "      <td>Grossulariaceae</td>\n",
       "      <td>Saxifragales</td>\n",
       "      <td>{'carbohydrates': 10, 'protein': 0.9, 'fat': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vitis</td>\n",
       "      <td>Grapes</td>\n",
       "      <td>47</td>\n",
       "      <td>Vitaceae</td>\n",
       "      <td>Vitales</td>\n",
       "      <td>{'carbohydrates': 18.1, 'protein': 0.72, 'fat'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Malus</td>\n",
       "      <td>GreenApple</td>\n",
       "      <td>72</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 3.1, 'protein': 0.4, 'fat': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Psidium</td>\n",
       "      <td>Guava</td>\n",
       "      <td>37</td>\n",
       "      <td>Myrtaceae</td>\n",
       "      <td>Myrtales</td>\n",
       "      <td>{'carbohydrates': 14, 'protein': 2.6, 'fat': 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Apteryx</td>\n",
       "      <td>Kiwi</td>\n",
       "      <td>66</td>\n",
       "      <td>Actinidiaceae</td>\n",
       "      <td>Struthioniformes</td>\n",
       "      <td>{'carbohydrates': 15, 'protein': 1.1, 'fat': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Citrus</td>\n",
       "      <td>Lemon</td>\n",
       "      <td>26</td>\n",
       "      <td>Rutaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>{'carbohydrates': 9, 'protein': 1.1, 'fat': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Citrus</td>\n",
       "      <td>Lime</td>\n",
       "      <td>44</td>\n",
       "      <td>Rutaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>{'carbohydrates': 8.4, 'protein': 0.3, 'fat': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Vaccinium</td>\n",
       "      <td>Lingonberry</td>\n",
       "      <td>65</td>\n",
       "      <td>Ericaceae</td>\n",
       "      <td>Ericales</td>\n",
       "      <td>{'carbohydrates': 11.3, 'protein': 0.75, 'fat'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Litchi</td>\n",
       "      <td>Lychee</td>\n",
       "      <td>67</td>\n",
       "      <td>Sapindaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>{'carbohydrates': 17, 'protein': 0.8, 'fat': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mangifera</td>\n",
       "      <td>Mango</td>\n",
       "      <td>27</td>\n",
       "      <td>Anacardiaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>{'carbohydrates': 15, 'protein': 0.82, 'fat': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cucumis</td>\n",
       "      <td>Melon</td>\n",
       "      <td>41</td>\n",
       "      <td>Cucurbitaceae</td>\n",
       "      <td>Cucurbitaceae</td>\n",
       "      <td>{'carbohydrates': 8, 'protein': 0, 'fat': 0, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Citrus</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2</td>\n",
       "      <td>Rutaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>{'carbohydrates': 8.3, 'protein': 1, 'fat': 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Carica</td>\n",
       "      <td>Papaya</td>\n",
       "      <td>42</td>\n",
       "      <td>Caricaceae</td>\n",
       "      <td>Caricacea</td>\n",
       "      <td>{'carbohydrates': 11, 'protein': 0, 'fat': 0.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Passiflora</td>\n",
       "      <td>Passionfruit</td>\n",
       "      <td>70</td>\n",
       "      <td>Passifloraceae</td>\n",
       "      <td>Malpighiales</td>\n",
       "      <td>{'carbohydrates': 22.4, 'protein': 2.2, 'fat':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Pyrus</td>\n",
       "      <td>Pear</td>\n",
       "      <td>4</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 15, 'protein': 0.4, 'fat': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Diospyros</td>\n",
       "      <td>Persimmon</td>\n",
       "      <td>52</td>\n",
       "      <td>Ebenaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 18, 'protein': 0, 'fat': 0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ananas</td>\n",
       "      <td>Pineapple</td>\n",
       "      <td>10</td>\n",
       "      <td>Bromeliaceae</td>\n",
       "      <td>Poales</td>\n",
       "      <td>{'carbohydrates': 13.12, 'protein': 0.54, 'fat...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Prunus</td>\n",
       "      <td>Plum</td>\n",
       "      <td>71</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 11.4, 'protein': 0.7, 'fat':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rubus</td>\n",
       "      <td>Raspberry</td>\n",
       "      <td>23</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 12, 'protein': 1.2, 'fat': 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fragaria</td>\n",
       "      <td>Strawberry</td>\n",
       "      <td>3</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>{'carbohydrates': 5.5, 'protein': 0.8, 'fat': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Solanum</td>\n",
       "      <td>Tomato</td>\n",
       "      <td>5</td>\n",
       "      <td>Solanaceae</td>\n",
       "      <td>Solanales</td>\n",
       "      <td>{'carbohydrates': 3.9, 'protein': 0.9, 'fat': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Spondias</td>\n",
       "      <td>Umbu</td>\n",
       "      <td>73</td>\n",
       "      <td>Anacardiaceae</td>\n",
       "      <td>0</td>\n",
       "      <td>{'carbohydrates': 0, 'protein': 0, 'fat': 0, '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Citrullus</td>\n",
       "      <td>Watermelon</td>\n",
       "      <td>25</td>\n",
       "      <td>Cucurbitaceae</td>\n",
       "      <td>Cucurbitales</td>\n",
       "      <td>{'carbohydrates': 8, 'protein': 0.6, 'fat': 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         genus          name  id           family             order  \\\n",
       "0        Malus         Apple   6         Rosaceae           Rosales   \n",
       "1       Prunus       Apricot  35         Rosaceae           Rosales   \n",
       "2         Musa        Banana   1         Musaceae      Zingiberales   \n",
       "3        Rubus    Blackberry  64         Rosaceae           Rosales   \n",
       "4     Fragaria     Blueberry  33         Rosaceae           Rosales   \n",
       "5       Prunus        Cherry   9         Rosaceae              None   \n",
       "6        Durio        Durian  60        Malvaceae          Malvales   \n",
       "7        Ficus           Fig  68         Moraceae           Rosales   \n",
       "8        Ribes    Gooseberry  69  Grossulariaceae      Saxifragales   \n",
       "9        Vitis        Grapes  47         Vitaceae           Vitales   \n",
       "10       Malus    GreenApple  72         Rosaceae           Rosales   \n",
       "11     Psidium         Guava  37        Myrtaceae          Myrtales   \n",
       "12     Apteryx          Kiwi  66    Actinidiaceae  Struthioniformes   \n",
       "13      Citrus         Lemon  26         Rutaceae        Sapindales   \n",
       "14      Citrus          Lime  44         Rutaceae        Sapindales   \n",
       "15   Vaccinium   Lingonberry  65        Ericaceae          Ericales   \n",
       "16      Litchi        Lychee  67      Sapindaceae        Sapindales   \n",
       "17   Mangifera         Mango  27    Anacardiaceae        Sapindales   \n",
       "18     Cucumis         Melon  41    Cucurbitaceae     Cucurbitaceae   \n",
       "19      Citrus        Orange   2         Rutaceae        Sapindales   \n",
       "20      Carica        Papaya  42       Caricaceae         Caricacea   \n",
       "21  Passiflora  Passionfruit  70   Passifloraceae      Malpighiales   \n",
       "22       Pyrus          Pear   4         Rosaceae           Rosales   \n",
       "23   Diospyros     Persimmon  52        Ebenaceae           Rosales   \n",
       "24      Ananas     Pineapple  10     Bromeliaceae            Poales   \n",
       "25      Prunus          Plum  71         Rosaceae           Rosales   \n",
       "26       Rubus     Raspberry  23         Rosaceae           Rosales   \n",
       "27    Fragaria    Strawberry   3         Rosaceae           Rosales   \n",
       "28     Solanum        Tomato   5       Solanaceae         Solanales   \n",
       "29    Spondias          Umbu  73    Anacardiaceae                 0   \n",
       "30   Citrullus    Watermelon  25    Cucurbitaceae      Cucurbitales   \n",
       "\n",
       "                                           nutritions  \n",
       "0   {'carbohydrates': 11.4, 'protein': 0.3, 'fat':...  \n",
       "1   {'carbohydrates': 3.9, 'protein': 0.5, 'fat': ...  \n",
       "2   {'carbohydrates': 22, 'protein': 1, 'fat': 0.2...  \n",
       "3   {'carbohydrates': 9, 'protein': 1.3, 'fat': 0....  \n",
       "4   {'carbohydrates': 5.5, 'protein': 0, 'fat': 0....  \n",
       "5   {'carbohydrates': 12, 'protein': 1, 'fat': 0.3...  \n",
       "6   {'carbohydrates': 27.1, 'protein': 1.5, 'fat':...  \n",
       "7   {'carbohydrates': 19, 'protein': 0.8, 'fat': 0...  \n",
       "8   {'carbohydrates': 10, 'protein': 0.9, 'fat': 0...  \n",
       "9   {'carbohydrates': 18.1, 'protein': 0.72, 'fat'...  \n",
       "10  {'carbohydrates': 3.1, 'protein': 0.4, 'fat': ...  \n",
       "11  {'carbohydrates': 14, 'protein': 2.6, 'fat': 1...  \n",
       "12  {'carbohydrates': 15, 'protein': 1.1, 'fat': 0...  \n",
       "13  {'carbohydrates': 9, 'protein': 1.1, 'fat': 0....  \n",
       "14  {'carbohydrates': 8.4, 'protein': 0.3, 'fat': ...  \n",
       "15  {'carbohydrates': 11.3, 'protein': 0.75, 'fat'...  \n",
       "16  {'carbohydrates': 17, 'protein': 0.8, 'fat': 0...  \n",
       "17  {'carbohydrates': 15, 'protein': 0.82, 'fat': ...  \n",
       "18  {'carbohydrates': 8, 'protein': 0, 'fat': 0, '...  \n",
       "19  {'carbohydrates': 8.3, 'protein': 1, 'fat': 0....  \n",
       "20  {'carbohydrates': 11, 'protein': 0, 'fat': 0.4...  \n",
       "21  {'carbohydrates': 22.4, 'protein': 2.2, 'fat':...  \n",
       "22  {'carbohydrates': 15, 'protein': 0.4, 'fat': 0...  \n",
       "23  {'carbohydrates': 18, 'protein': 0, 'fat': 0, ...  \n",
       "24  {'carbohydrates': 13.12, 'protein': 0.54, 'fat...  \n",
       "25  {'carbohydrates': 11.4, 'protein': 0.7, 'fat':...  \n",
       "26  {'carbohydrates': 12, 'protein': 1.2, 'fat': 0...  \n",
       "27  {'carbohydrates': 5.5, 'protein': 0.8, 'fat': ...  \n",
       "28  {'carbohydrates': 3.9, 'protein': 0.9, 'fat': ...  \n",
       "29  {'carbohydrates': 0, 'protein': 0, 'fat': 0, '...  \n",
       "30  {'carbohydrates': 8, 'protein': 0.6, 'fat': 0....  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c88b2014-6130-4cbd-ae14-9256f9f0e1be",
   "metadata": {},
   "source": [
    "The result is in a nested json format. The 'nutrition' column contains multiple subcolumns, so the data needs to be 'flattened' or normalized.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b209d6c5-ab68-42c0-a17e-ee416afc84fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.json_normalize(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e76145a4-f77c-42c6-9b75-1f380b734de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genus</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>family</th>\n",
       "      <th>order</th>\n",
       "      <th>nutritions.carbohydrates</th>\n",
       "      <th>nutritions.protein</th>\n",
       "      <th>nutritions.fat</th>\n",
       "      <th>nutritions.calories</th>\n",
       "      <th>nutritions.sugar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Malus</td>\n",
       "      <td>Apple</td>\n",
       "      <td>6</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>11.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.40</td>\n",
       "      <td>52</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prunus</td>\n",
       "      <td>Apricot</td>\n",
       "      <td>35</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>3.90</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.10</td>\n",
       "      <td>15</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Musa</td>\n",
       "      <td>Banana</td>\n",
       "      <td>1</td>\n",
       "      <td>Musaceae</td>\n",
       "      <td>Zingiberales</td>\n",
       "      <td>22.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>96</td>\n",
       "      <td>17.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rubus</td>\n",
       "      <td>Blackberry</td>\n",
       "      <td>64</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.40</td>\n",
       "      <td>40</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fragaria</td>\n",
       "      <td>Blueberry</td>\n",
       "      <td>33</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>29</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Prunus</td>\n",
       "      <td>Cherry</td>\n",
       "      <td>9</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>None</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>50</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Durio</td>\n",
       "      <td>Durian</td>\n",
       "      <td>60</td>\n",
       "      <td>Malvaceae</td>\n",
       "      <td>Malvales</td>\n",
       "      <td>27.10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>5.30</td>\n",
       "      <td>147</td>\n",
       "      <td>6.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ficus</td>\n",
       "      <td>Fig</td>\n",
       "      <td>68</td>\n",
       "      <td>Moraceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.30</td>\n",
       "      <td>74</td>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ribes</td>\n",
       "      <td>Gooseberry</td>\n",
       "      <td>69</td>\n",
       "      <td>Grossulariaceae</td>\n",
       "      <td>Saxifragales</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.60</td>\n",
       "      <td>44</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vitis</td>\n",
       "      <td>Grapes</td>\n",
       "      <td>47</td>\n",
       "      <td>Vitaceae</td>\n",
       "      <td>Vitales</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.16</td>\n",
       "      <td>69</td>\n",
       "      <td>15.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Malus</td>\n",
       "      <td>GreenApple</td>\n",
       "      <td>72</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.10</td>\n",
       "      <td>21</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Psidium</td>\n",
       "      <td>Guava</td>\n",
       "      <td>37</td>\n",
       "      <td>Myrtaceae</td>\n",
       "      <td>Myrtales</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.00</td>\n",
       "      <td>68</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Apteryx</td>\n",
       "      <td>Kiwi</td>\n",
       "      <td>66</td>\n",
       "      <td>Actinidiaceae</td>\n",
       "      <td>Struthioniformes</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.50</td>\n",
       "      <td>61</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Citrus</td>\n",
       "      <td>Lemon</td>\n",
       "      <td>26</td>\n",
       "      <td>Rutaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>29</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Citrus</td>\n",
       "      <td>Lime</td>\n",
       "      <td>44</td>\n",
       "      <td>Rutaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.10</td>\n",
       "      <td>25</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Vaccinium</td>\n",
       "      <td>Lingonberry</td>\n",
       "      <td>65</td>\n",
       "      <td>Ericaceae</td>\n",
       "      <td>Ericales</td>\n",
       "      <td>11.30</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.34</td>\n",
       "      <td>50</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Litchi</td>\n",
       "      <td>Lychee</td>\n",
       "      <td>67</td>\n",
       "      <td>Sapindaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.44</td>\n",
       "      <td>66</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mangifera</td>\n",
       "      <td>Mango</td>\n",
       "      <td>27</td>\n",
       "      <td>Anacardiaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.38</td>\n",
       "      <td>60</td>\n",
       "      <td>13.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cucumis</td>\n",
       "      <td>Melon</td>\n",
       "      <td>41</td>\n",
       "      <td>Cucurbitaceae</td>\n",
       "      <td>Cucurbitaceae</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Citrus</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2</td>\n",
       "      <td>Rutaceae</td>\n",
       "      <td>Sapindales</td>\n",
       "      <td>8.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>43</td>\n",
       "      <td>8.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Carica</td>\n",
       "      <td>Papaya</td>\n",
       "      <td>42</td>\n",
       "      <td>Caricaceae</td>\n",
       "      <td>Caricacea</td>\n",
       "      <td>11.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>43</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Passiflora</td>\n",
       "      <td>Passionfruit</td>\n",
       "      <td>70</td>\n",
       "      <td>Passifloraceae</td>\n",
       "      <td>Malpighiales</td>\n",
       "      <td>22.40</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.70</td>\n",
       "      <td>97</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Pyrus</td>\n",
       "      <td>Pear</td>\n",
       "      <td>4</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.10</td>\n",
       "      <td>57</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Diospyros</td>\n",
       "      <td>Persimmon</td>\n",
       "      <td>52</td>\n",
       "      <td>Ebenaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>18.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>81</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ananas</td>\n",
       "      <td>Pineapple</td>\n",
       "      <td>10</td>\n",
       "      <td>Bromeliaceae</td>\n",
       "      <td>Poales</td>\n",
       "      <td>13.12</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.12</td>\n",
       "      <td>50</td>\n",
       "      <td>9.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Prunus</td>\n",
       "      <td>Plum</td>\n",
       "      <td>71</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>11.40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.28</td>\n",
       "      <td>46</td>\n",
       "      <td>9.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rubus</td>\n",
       "      <td>Raspberry</td>\n",
       "      <td>23</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.70</td>\n",
       "      <td>53</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fragaria</td>\n",
       "      <td>Strawberry</td>\n",
       "      <td>3</td>\n",
       "      <td>Rosaceae</td>\n",
       "      <td>Rosales</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.40</td>\n",
       "      <td>29</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Solanum</td>\n",
       "      <td>Tomato</td>\n",
       "      <td>5</td>\n",
       "      <td>Solanaceae</td>\n",
       "      <td>Solanales</td>\n",
       "      <td>3.90</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.20</td>\n",
       "      <td>74</td>\n",
       "      <td>2.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Spondias</td>\n",
       "      <td>Umbu</td>\n",
       "      <td>73</td>\n",
       "      <td>Anacardiaceae</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Citrullus</td>\n",
       "      <td>Watermelon</td>\n",
       "      <td>25</td>\n",
       "      <td>Cucurbitaceae</td>\n",
       "      <td>Cucurbitales</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.20</td>\n",
       "      <td>30</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         genus          name  id           family             order  \\\n",
       "0        Malus         Apple   6         Rosaceae           Rosales   \n",
       "1       Prunus       Apricot  35         Rosaceae           Rosales   \n",
       "2         Musa        Banana   1         Musaceae      Zingiberales   \n",
       "3        Rubus    Blackberry  64         Rosaceae           Rosales   \n",
       "4     Fragaria     Blueberry  33         Rosaceae           Rosales   \n",
       "5       Prunus        Cherry   9         Rosaceae              None   \n",
       "6        Durio        Durian  60        Malvaceae          Malvales   \n",
       "7        Ficus           Fig  68         Moraceae           Rosales   \n",
       "8        Ribes    Gooseberry  69  Grossulariaceae      Saxifragales   \n",
       "9        Vitis        Grapes  47         Vitaceae           Vitales   \n",
       "10       Malus    GreenApple  72         Rosaceae           Rosales   \n",
       "11     Psidium         Guava  37        Myrtaceae          Myrtales   \n",
       "12     Apteryx          Kiwi  66    Actinidiaceae  Struthioniformes   \n",
       "13      Citrus         Lemon  26         Rutaceae        Sapindales   \n",
       "14      Citrus          Lime  44         Rutaceae        Sapindales   \n",
       "15   Vaccinium   Lingonberry  65        Ericaceae          Ericales   \n",
       "16      Litchi        Lychee  67      Sapindaceae        Sapindales   \n",
       "17   Mangifera         Mango  27    Anacardiaceae        Sapindales   \n",
       "18     Cucumis         Melon  41    Cucurbitaceae     Cucurbitaceae   \n",
       "19      Citrus        Orange   2         Rutaceae        Sapindales   \n",
       "20      Carica        Papaya  42       Caricaceae         Caricacea   \n",
       "21  Passiflora  Passionfruit  70   Passifloraceae      Malpighiales   \n",
       "22       Pyrus          Pear   4         Rosaceae           Rosales   \n",
       "23   Diospyros     Persimmon  52        Ebenaceae           Rosales   \n",
       "24      Ananas     Pineapple  10     Bromeliaceae            Poales   \n",
       "25      Prunus          Plum  71         Rosaceae           Rosales   \n",
       "26       Rubus     Raspberry  23         Rosaceae           Rosales   \n",
       "27    Fragaria    Strawberry   3         Rosaceae           Rosales   \n",
       "28     Solanum        Tomato   5       Solanaceae         Solanales   \n",
       "29    Spondias          Umbu  73    Anacardiaceae                 0   \n",
       "30   Citrullus    Watermelon  25    Cucurbitaceae      Cucurbitales   \n",
       "\n",
       "    nutritions.carbohydrates  nutritions.protein  nutritions.fat  \\\n",
       "0                      11.40                0.30            0.40   \n",
       "1                       3.90                0.50            0.10   \n",
       "2                      22.00                1.00            0.20   \n",
       "3                       9.00                1.30            0.40   \n",
       "4                       5.50                0.00            0.40   \n",
       "5                      12.00                1.00            0.30   \n",
       "6                      27.10                1.50            5.30   \n",
       "7                      19.00                0.80            0.30   \n",
       "8                      10.00                0.90            0.60   \n",
       "9                      18.10                0.72            0.16   \n",
       "10                      3.10                0.40            0.10   \n",
       "11                     14.00                2.60            1.00   \n",
       "12                     15.00                1.10            0.50   \n",
       "13                      9.00                1.10            0.30   \n",
       "14                      8.40                0.30            0.10   \n",
       "15                     11.30                0.75            0.34   \n",
       "16                     17.00                0.80            0.44   \n",
       "17                     15.00                0.82            0.38   \n",
       "18                      8.00                0.00            0.00   \n",
       "19                      8.30                1.00            0.20   \n",
       "20                     11.00                0.00            0.40   \n",
       "21                     22.40                2.20            0.70   \n",
       "22                     15.00                0.40            0.10   \n",
       "23                     18.00                0.00            0.00   \n",
       "24                     13.12                0.54            0.12   \n",
       "25                     11.40                0.70            0.28   \n",
       "26                     12.00                1.20            0.70   \n",
       "27                      5.50                0.80            0.40   \n",
       "28                      3.90                0.90            0.20   \n",
       "29                      0.00                0.00            0.00   \n",
       "30                      8.00                0.60            0.20   \n",
       "\n",
       "    nutritions.calories  nutritions.sugar  \n",
       "0                    52             10.30  \n",
       "1                    15              3.20  \n",
       "2                    96             17.20  \n",
       "3                    40              4.50  \n",
       "4                    29              5.40  \n",
       "5                    50              8.00  \n",
       "6                   147              6.75  \n",
       "7                    74             16.00  \n",
       "8                    44              0.00  \n",
       "9                    69             15.48  \n",
       "10                   21              6.40  \n",
       "11                   68              9.00  \n",
       "12                   61              9.00  \n",
       "13                   29              2.50  \n",
       "14                   25              1.70  \n",
       "15                   50              5.74  \n",
       "16                   66             15.00  \n",
       "17                   60             13.70  \n",
       "18                   34              8.00  \n",
       "19                   43              8.20  \n",
       "20                   43              1.00  \n",
       "21                   97             11.20  \n",
       "22                   57             10.00  \n",
       "23                   81             18.00  \n",
       "24                   50              9.85  \n",
       "25                   46              9.92  \n",
       "26                   53              4.40  \n",
       "27                   29              5.40  \n",
       "28                   74              2.60  \n",
       "29                    0              0.00  \n",
       "30                   30              6.00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8f04a8-37c8-49a1-984a-7a4e748ca58d",
   "metadata": {},
   "source": [
    "Let's see if we can extract some information from this dataframe. Perhaps, we need to know the family and genus of a cherry.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8a425265-e4b9-4451-a0bb-7f8f41a84cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Rosaceae', 'Prunus')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cherry = df2.loc[df2[\"name\"] == 'Cherry']\n",
    "(cherry.iloc[0]['family']) , (cherry.iloc[0]['genus'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6e6e78b-a6e4-4c1c-bd7d-a312f7fa0b2e",
   "metadata": {},
   "source": [
    "## Exercise 2\n",
    "\n",
    "In this Exercise, find out how many calories are contained in a banana.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "98c1ffd5-6dfd-4b7c-8481-6eaff8caa42a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here\n",
    "banana = df2.loc[df2[\"name\"] == \"Banana\"]\n",
    "(banana.iloc[0][\"nutritions.calories\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095b6c19-6864-4b2e-91c3-61c2a7ee7e46",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "cal_banana = df2.loc[df2[\"name\"] == 'Banana']\n",
    "cal_banana.iloc[0]['nutritions.carbohydrates']\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963fbca0-2671-4bd3-a194-3e8d7a03316d",
   "metadata": {},
   "source": [
    "## Exercise 3\n",
    "\n",
    "This [page](https://github.com/public-apis/public-apis#animals) contains a list of free public APIs. Choose any API of your interest and use it to load/extract some information, as shown in the example above.\n",
    "\n",
    "1.  Using `requests.get(\"url\")` function, load your data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a8170667-71cd-43d9-b57e-3cd8f7688cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code here\n",
    "dogdata = requests.get(\"https://dog.ceo/api/breeds/list/all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9626816-af9c-4d70-84bc-ba650b79ca74",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "data2 = requests.get(\"https://www.fishwatch.gov/api/species\")\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94bb99b-a701-498b-ae22-cf091c3d6be7",
   "metadata": {},
   "source": [
    "2.  Retrieve results using `json.loads()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dc098952-1a2a-4790-8440-e4c310cef86d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write your code here\n",
    "dogbreeds = json.loads(dogdata.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7945c3a0-6349-4180-ac0b-30db1caf72f6",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "results2 = json.loads(data2.text)\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751dfb83-972c-49e9-9e93-a92af4c111a9",
   "metadata": {},
   "source": [
    "3.  Convert json data into *pandas* data frame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f1cf0945-2383-4554-b924-95e7abf1c30b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>message</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>affenpinscher</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>african</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airedale</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>akita</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>appenzeller</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vizsla</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterdog</th>\n",
       "      <td>[spanish]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weimaraner</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>whippet</th>\n",
       "      <td>[]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wolfhound</th>\n",
       "      <td>[irish]</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 message   status\n",
       "affenpinscher         []  success\n",
       "african               []  success\n",
       "airedale              []  success\n",
       "akita                 []  success\n",
       "appenzeller           []  success\n",
       "...                  ...      ...\n",
       "vizsla                []  success\n",
       "waterdog       [spanish]  success\n",
       "weimaraner            []  success\n",
       "whippet               []  success\n",
       "wolfhound        [irish]  success\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your code here\n",
    "dogdf = pd.DataFrame(dogbreeds)\n",
    "dogdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bcd9fe8-9ee8-44eb-a02e-36a8c3c6023c",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "df3 = pd.DataFrame(results2)\n",
    "df3\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a05264-ae66-4eb0-bf17-6da7dcf329d3",
   "metadata": {},
   "source": [
    "# Congratulations! - You have completed the lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2605ca-4f86-4c26-8d36-4c4632f9071e",
   "metadata": {},
   "source": [
    "## Author\n",
    "\n",
    "[Svitlana Kramar](https://www.linkedin.com/in/svitlana-kramar?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2022-01-01)\n",
    "\n",
    "Svitlana is a master’s degree Data Science and Analytics student at University of Calgary, who enjoys travelling, learning new languages and cultures and loves spreading her passion for Data Science.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47f20b7-8df8-47d1-ab82-c14dbde12258",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
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