![\"cognitiveclass.ai](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/IDSNlogo.png\")
\n",
+ "\n",
+ "![\"logistic](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/pandas_api.png\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "When you call the method `head` the dataframe communicates with the API displaying the first few rows of the dataframe.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "| \n", + " | a | \n", + "b | \n", + "
|---|---|---|
| 0 | \n", + "11 | \n", + "12 | \n", + "
| 1 | \n", + "21 | \n", + "22 | \n", + "
| 2 | \n", + "31 | \n", + "32 | \n", + "
Rest APIs function by sending a request, the request is communicated via HTTP message. The HTTP message usually contains a JSON file. This contains instructions for what operation we would like the service or resource to perform. In a similar manner, API returns a response, via an HTTP message, this response is usually contained within a JSON.
\n", + "In this lab, we will use the NBA API to determine how well the Golden State Warriors performed against the Toronto Raptors. We will use the API to determine the number of points the Golden State Warriors won or lost by for each game. So if the value is three, the Golden State Warriors won by three points. Similarly it the Golden State Warriors lost by two points the result will be negative two. The API will handle a lot of the details, such a Endpoints and Authentication.
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's quite simple to use the nba api to make a request for a specific team. We don't require a JSON, all we require is an id. This information is stored locally in the API. We import the module `teams`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting nba_api\n", + " Downloading nba_api-1.1.13-py3-none-any.whl (255 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m255.2/255.2 kB\u001b[0m \u001b[31m217.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: numpy in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nba_api) (1.21.6)\n", + "Requirement already satisfied: requests in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nba_api) (2.29.0)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests->nba_api) (3.1.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests->nba_api) (3.4)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests->nba_api) (1.26.15)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests->nba_api) (2023.5.7)\n", + "Installing collected packages: nba_api\n", + "Successfully installed nba_api-1.1.13\n" + ] + } + ], + "source": [ + "!pip install nba_api" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from nba_api.stats.static import teams\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The one_dict() function takes a list of dictionaries (each representing one team's details) and combines them into a single dictionary where each key contains a list of all corresponding values.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def one_dict(list_dict):\n", + " keys=list_dict[0].keys()\n", + " out_dict={key:[] for key in keys}\n", + " for dict_ in list_dict:\n", + " for key, value in dict_.items():\n", + " out_dict[key].append(value)\n", + " return out_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The methodget_teams() returns a list of dictionaries.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "nba_teams = teams.get_teams()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The dictionary key id has a unique identifier for each team as a value. Let's look at the first three elements of the list:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "nba_teams[0:3]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To make things easier, we can convert the dictionary to a table. First, we use the function one dict, to create a dictionary. We use the common keys for each team as the keys, the value is a list; each element of the list corresponds to the values for each team.\n",
+ "We then convert the dictionary to a dataframe, each row contains the information for a different team.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "dict_nba_team=one_dict(nba_teams)\n",
+ "df_teams=pd.DataFrame(dict_nba_team)\n",
+ "df_teams.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Will use the team's nickname to find the unique id, we can see the row that contains the warriors by using the column nickname as follows:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_warriors=df_teams[df_teams['nickname']=='Warriors']\n",
+ "df_warriors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can use the following line of code to access the first column of the DataFrame:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "id_warriors=df_warriors[['id']].values[0][0]\n",
+ "# we now have an integer that can be used to request the Warriors information \n",
+ "id_warriors"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The function \"League Game Finder \" will make an API call, it's in the module stats.endpoints.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nba_api.stats.endpoints import leaguegamefinder"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The parameter team_id_nullable is the unique ID for the warriors. Under the hood, the NBA API is making a HTTP request.\\\n",
+ "The information requested is provided and is transmitted via an HTTP response this is assigned to the object game finder.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Since https://stats.nba.com does not allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\n",
+ "# The following code is commented out, you can run it on jupyter labs on your own computer.\n",
+ "# gamefinder = leaguegamefinder.LeagueGameFinder(team_id_nullable=id_warriors)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see the json file by running the following line of code.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Since https://stats.nba.com does not allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\n",
+ "# The following code is commented out, you can run it on jupyter labs on your own computer.\n",
+ "# gamefinder.get_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The game finder object has a method get_data_frames(), that returns a dataframe. If we view the dataframe, we can see it contains information about all the games the Warriors played. The PLUS_MINUS column contains information on the score, if the value is negative, the Warriors lost by that many points, if the value is positive, the warriors won by that amount of points. The column MATCHUP has the team the Warriors were playing, GSW stands for Golden State Warriors and TOR means Toronto Raptors. vs signifies it was a home game and the @ symbol means an away game.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Since https://stats.nba.com does not allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.\n",
+ "# The following code is comment out, you can run it on jupyter labs on your own computer.\n",
+ "# games = gamefinder.get_data_frames()[0]\n",
+ "# games.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can download the dataframe from the API call for Golden State and run the rest like a video.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import requests\n",
+ "\n",
+ "filename = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%205/Labs/Golden_State.pkl\"\n",
+ "\n",
+ "def download(url, filename):\n",
+ " response = requests.get(url)\n",
+ " if response.status_code == 200:\n",
+ " with open(filename, \"wb\") as f:\n",
+ " f.write(response.content)\n",
+ "\n",
+ "download(filename, \"Golden_State.pkl\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "file_name = \"Golden_State.pkl\"\n",
+ "games = pd.read_pickle(file_name)\n",
+ "games.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can create two dataframes, one for the games that the Warriors faced the raptors at home, and the second for away games.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "games_home=games[games['MATCHUP']=='GSW vs. TOR']\n",
+ "games_away=games[games['MATCHUP']=='GSW @ TOR']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can calculate the mean for the column PLUS_MINUS for the dataframes games_home and games_away:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "games_home['PLUS_MINUS'].mean()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "games_away['PLUS_MINUS'].mean()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can plot out the PLUS MINUS column for the dataframes games_home and games_away.\n",
+ "We see the warriors played better at home.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "\n",
+ "games_away.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)\n",
+ "games_home.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)\n",
+ "ax.legend([\"away\", \"home\"])\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quiz\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Calculate the mean for the column PTS for the dataframes games_home and games_away:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'games_home' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m/tmp/ipykernel_68/926849432.py\u001b[0m in \u001b[0;36m![\"cognitiveclass.ai](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\")
\n",
+ "| \n", + " | Name | \n", + "Gender | \n", + "City | \n", + "State | \n", + "DOB | \n", + "Picture | \n", + "|
|---|---|---|---|---|---|---|---|
| 0 | \n", + "Irène Renard | \n", + "female | \n", + "Morcote | \n", + "Graubünden | \n", + "irene.renard@example.com | \n", + "1968-07-05T03:08:20.465Z | \n", + "https://randomuser.me/api/portraits/women/36.jpg | \n", + "
| 1 | \n", + "Ida Schnoor | \n", + "female | \n", + "Kranichfeld | \n", + "Saarland | \n", + "ida.schnoor@example.com | \n", + "1969-09-22T11:23:25.734Z | \n", + "https://randomuser.me/api/portraits/women/51.jpg | \n", + "
| 2 | \n", + "Siebrigje Verhoog | \n", + "female | \n", + "Terborg | \n", + "Flevoland | \n", + "siebrigje.verhoog@example.com | \n", + "1986-06-12T19:23:24.048Z | \n", + "https://randomuser.me/api/portraits/women/61.jpg | \n", + "
| 3 | \n", + "Anolido Nunes | \n", + "male | \n", + "Resende | \n", + "Distrito Federal | \n", + "anolido.nunes@example.com | \n", + "1946-02-08T04:26:32.531Z | \n", + "https://randomuser.me/api/portraits/men/68.jpg | \n", + "
| 4 | \n", + "Maël Mathieu | \n", + "male | \n", + "Saint-Pierre | \n", + "Loire-Atlantique | \n", + "mael.mathieu@example.com | \n", + "1983-09-04T11:24:22.303Z | \n", + "https://randomuser.me/api/portraits/men/57.jpg | \n", + "
| 5 | \n", + "Christina Saur | \n", + "female | \n", + "Freising | \n", + "Hessen | \n", + "christina.saur@example.com | \n", + "1951-07-03T13:24:53.323Z | \n", + "https://randomuser.me/api/portraits/women/28.jpg | \n", + "
| 6 | \n", + "Siete Spoelstra | \n", + "male | \n", + "Leeuwarderadeel | \n", + "Utrecht | \n", + "siete.spoelstra@example.com | \n", + "1948-04-08T03:02:35.102Z | \n", + "https://randomuser.me/api/portraits/men/74.jpg | \n", + "
| 7 | \n", + "Ron Spencer | \n", + "male | \n", + "Roscommon | \n", + "Fingal | \n", + "ron.spencer@example.com | \n", + "1958-10-06T19:58:05.229Z | \n", + "https://randomuser.me/api/portraits/men/58.jpg | \n", + "
| 8 | \n", + "Nathan Garrett | \n", + "male | \n", + "Sunnyvale | \n", + "Wyoming | \n", + "nathan.garrett@example.com | \n", + "1995-05-09T02:32:53.349Z | \n", + "https://randomuser.me/api/portraits/men/31.jpg | \n", + "
| 9 | \n", + "Claire George | \n", + "female | \n", + "Leixlip | \n", + "Wexford | \n", + "claire.george@example.com | \n", + "1965-08-04T08:19:42.828Z | \n", + "https://randomuser.me/api/portraits/women/83.jpg | \n", + "
| \n", + " | name | \n", + "id | \n", + "family | \n", + "order | \n", + "genus | \n", + "nutritions | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Persimmon | \n", + "52 | \n", + "Ebenaceae | \n", + "Rosales | \n", + "Diospyros | \n", + "{'calories': 81, 'fat': 0.0, 'sugar': 18.0, 'c... | \n", + "
| 1 | \n", + "Strawberry | \n", + "3 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "{'calories': 29, 'fat': 0.4, 'sugar': 5.4, 'ca... | \n", + "
| 2 | \n", + "Banana | \n", + "1 | \n", + "Musaceae | \n", + "Zingiberales | \n", + "Musa | \n", + "{'calories': 96, 'fat': 0.2, 'sugar': 17.2, 'c... | \n", + "
| 3 | \n", + "Tomato | \n", + "5 | \n", + "Solanaceae | \n", + "Solanales | \n", + "Solanum | \n", + "{'calories': 74, 'fat': 0.2, 'sugar': 2.6, 'ca... | \n", + "
| 4 | \n", + "Pear | \n", + "4 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Pyrus | \n", + "{'calories': 57, 'fat': 0.1, 'sugar': 10.0, 'c... | \n", + "
| 5 | \n", + "Durian | \n", + "60 | \n", + "Malvaceae | \n", + "Malvales | \n", + "Durio | \n", + "{'calories': 147, 'fat': 5.3, 'sugar': 6.75, '... | \n", + "
| 6 | \n", + "Blackberry | \n", + "64 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "{'calories': 40, 'fat': 0.4, 'sugar': 4.5, 'ca... | \n", + "
| 7 | \n", + "Lingonberry | \n", + "65 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "{'calories': 50, 'fat': 0.34, 'sugar': 5.74, '... | \n", + "
| 8 | \n", + "Kiwi | \n", + "66 | \n", + "Actinidiaceae | \n", + "Struthioniformes | \n", + "Apteryx | \n", + "{'calories': 61, 'fat': 0.5, 'sugar': 9.0, 'ca... | \n", + "
| 9 | \n", + "Lychee | \n", + "67 | \n", + "Sapindaceae | \n", + "Sapindales | \n", + "Litchi | \n", + "{'calories': 66, 'fat': 0.44, 'sugar': 15.0, '... | \n", + "
| 10 | \n", + "Pineapple | \n", + "10 | \n", + "Bromeliaceae | \n", + "Poales | \n", + "Ananas | \n", + "{'calories': 50, 'fat': 0.12, 'sugar': 9.85, '... | \n", + "
| 11 | \n", + "Fig | \n", + "68 | \n", + "Moraceae | \n", + "Rosales | \n", + "Ficus | \n", + "{'calories': 74, 'fat': 0.3, 'sugar': 16.0, 'c... | \n", + "
| 12 | \n", + "Gooseberry | \n", + "69 | \n", + "Grossulariaceae | \n", + "Saxifragales | \n", + "Ribes | \n", + "{'calories': 44, 'fat': 0.6, 'sugar': 0.0, 'ca... | \n", + "
| 13 | \n", + "Passionfruit | \n", + "70 | \n", + "Passifloraceae | \n", + "Malpighiales | \n", + "Passiflora | \n", + "{'calories': 97, 'fat': 0.7, 'sugar': 11.2, 'c... | \n", + "
| 14 | \n", + "Plum | \n", + "71 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 46, 'fat': 0.28, 'sugar': 9.92, '... | \n", + "
| 15 | \n", + "Orange | \n", + "2 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 43, 'fat': 0.2, 'sugar': 8.2, 'ca... | \n", + "
| 16 | \n", + "GreenApple | \n", + "72 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "{'calories': 21, 'fat': 0.1, 'sugar': 6.4, 'ca... | \n", + "
| 17 | \n", + "Raspberry | \n", + "23 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "{'calories': 53, 'fat': 0.7, 'sugar': 4.4, 'ca... | \n", + "
| 18 | \n", + "Watermelon | \n", + "25 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Citrullus | \n", + "{'calories': 30, 'fat': 0.2, 'sugar': 6.0, 'ca... | \n", + "
| 19 | \n", + "Lemon | \n", + "26 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 29, 'fat': 0.3, 'sugar': 2.5, 'ca... | \n", + "
| 20 | \n", + "Mango | \n", + "27 | \n", + "Anacardiaceae | \n", + "Sapindales | \n", + "Mangifera | \n", + "{'calories': 60, 'fat': 0.38, 'sugar': 13.7, '... | \n", + "
| 21 | \n", + "Blueberry | \n", + "33 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "{'calories': 29, 'fat': 0.4, 'sugar': 5.4, 'ca... | \n", + "
| 22 | \n", + "Apple | \n", + "6 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "{'calories': 52, 'fat': 0.4, 'sugar': 10.3, 'c... | \n", + "
| 23 | \n", + "Guava | \n", + "37 | \n", + "Myrtaceae | \n", + "Myrtales | \n", + "Psidium | \n", + "{'calories': 68, 'fat': 1.0, 'sugar': 9.0, 'ca... | \n", + "
| 24 | \n", + "Apricot | \n", + "35 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 15, 'fat': 0.1, 'sugar': 3.2, 'ca... | \n", + "
| 25 | \n", + "Melon | \n", + "41 | \n", + "Cucurbitaceae | \n", + "Cucurbitaceae | \n", + "Cucumis | \n", + "{'calories': 34, 'fat': 0.0, 'sugar': 8.0, 'ca... | \n", + "
| 26 | \n", + "Tangerine | \n", + "77 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 45, 'fat': 0.4, 'sugar': 9.1, 'ca... | \n", + "
| 27 | \n", + "Pitahaya | \n", + "78 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Cactaceae | \n", + "{'calories': 36, 'fat': 0.4, 'sugar': 3.0, 'ca... | \n", + "
| 28 | \n", + "Lime | \n", + "44 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 25, 'fat': 0.1, 'sugar': 1.7, 'ca... | \n", + "
| 29 | \n", + "Pomegranate | \n", + "79 | \n", + "Lythraceae | \n", + "Myrtales | \n", + "Punica | \n", + "{'calories': 83, 'fat': 1.2, 'sugar': 13.7, 'c... | \n", + "
| 30 | \n", + "Dragonfruit | \n", + "80 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Selenicereus | \n", + "{'calories': 60, 'fat': 1.5, 'sugar': 8.0, 'ca... | \n", + "
| 31 | \n", + "Grape | \n", + "81 | \n", + "Vitaceae | \n", + "Vitales | \n", + "Vitis | \n", + "{'calories': 69, 'fat': 0.16, 'sugar': 16.0, '... | \n", + "
| 32 | \n", + "Morus | \n", + "82 | \n", + "Moraceae | \n", + "Rosales | \n", + "Morus | \n", + "{'calories': 43, 'fat': 0.39, 'sugar': 8.1, 'c... | \n", + "
| 33 | \n", + "Feijoa | \n", + "76 | \n", + "Myrtaceae | \n", + "Myrtoideae | \n", + "Sellowiana | \n", + "{'calories': 44, 'fat': 0.4, 'sugar': 3.0, 'ca... | \n", + "
| 34 | \n", + "Avocado | \n", + "84 | \n", + "Lauraceae | \n", + "Laurales | \n", + "Persea | \n", + "{'calories': 160, 'fat': 14.66, 'sugar': 0.66,... | \n", + "
| 35 | \n", + "Kiwifruit | \n", + "85 | \n", + "Actinidiaceae | \n", + "Ericales | \n", + "Actinidia | \n", + "{'calories': 61, 'fat': 0.5, 'sugar': 8.9, 'ca... | \n", + "
| 36 | \n", + "Cranberry | \n", + "87 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "{'calories': 46, 'fat': 0.1, 'sugar': 4.0, 'ca... | \n", + "
| 37 | \n", + "Cherry | \n", + "9 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 50, 'fat': 0.3, 'sugar': 8.0, 'ca... | \n", + "
| 38 | \n", + "Peach | \n", + "86 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 39, 'fat': 0.25, 'sugar': 8.4, 'c... | \n", + "
| 39 | \n", + "Jackfruit | \n", + "94 | \n", + "Moraceae | \n", + "Rosales | \n", + "Artocarpus | \n", + "{'calories': 95, 'fat': 0.0, 'sugar': 19.1, 'c... | \n", + "
| 40 | \n", + "Horned Melon | \n", + "95 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucumis | \n", + "{'calories': 44, 'fat': 1.26, 'sugar': 0.5, 'c... | \n", + "
| 41 | \n", + "Hazelnut | \n", + "96 | \n", + "Betulaceae | \n", + "Fagales | \n", + "Corylus | \n", + "{'calories': 628, 'fat': 61.0, 'sugar': 4.3, '... | \n", + "
| 42 | \n", + "Pomelo | \n", + "98 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 37, 'fat': 0.0, 'sugar': 8.5, 'ca... | \n", + "
| 43 | \n", + "Mangosteen | \n", + "99 | \n", + "Clusiaceae | \n", + "Malpighiales | \n", + "Garcinia | \n", + "{'calories': 73, 'fat': 0.58, 'sugar': 16.11, ... | \n", + "
| 44 | \n", + "Pumpkin | \n", + "100 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucurbita | \n", + "{'calories': 25, 'fat': 0.3, 'sugar': 3.3, 'ca... | \n", + "
| 45 | \n", + "Japanese Persimmon | \n", + "101 | \n", + "Ebenaceae | \n", + "Ericales | \n", + "Diospyros | \n", + "{'calories': 70, 'fat': 0.2, 'sugar': 13.0, 'c... | \n", + "
| 46 | \n", + "Papaya | \n", + "42 | \n", + "Caricaceae | \n", + "Brassicales | \n", + "Carica | \n", + "{'calories': 39, 'fat': 0.3, 'sugar': 4.4, 'ca... | \n", + "
| 47 | \n", + "Annona | \n", + "103 | \n", + "Annonaceae | \n", + "Rosales | \n", + "Annonas | \n", + "{'calories': 92, 'fat': 0.29, 'sugar': 3.4, 'c... | \n", + "
| 48 | \n", + "Ceylon Gooseberry | \n", + "104 | \n", + "Salicaceae | \n", + "Malpighiales | \n", + "Dovyalis | \n", + "{'calories': 47, 'fat': 0.3, 'sugar': 8.1, 'ca... | \n", + "
| \n", + " | name | \n", + "id | \n", + "family | \n", + "order | \n", + "genus | \n", + "nutritions.calories | \n", + "nutritions.fat | \n", + "nutritions.sugar | \n", + "nutritions.carbohydrates | \n", + "nutritions.protein | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Persimmon | \n", + "52 | \n", + "Ebenaceae | \n", + "Rosales | \n", + "Diospyros | \n", + "81 | \n", + "0.00 | \n", + "18.00 | \n", + "18.00 | \n", + "0.00 | \n", + "
| 1 | \n", + "Strawberry | \n", + "3 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "29 | \n", + "0.40 | \n", + "5.40 | \n", + "5.50 | \n", + "0.80 | \n", + "
| 2 | \n", + "Banana | \n", + "1 | \n", + "Musaceae | \n", + "Zingiberales | \n", + "Musa | \n", + "96 | \n", + "0.20 | \n", + "17.20 | \n", + "22.00 | \n", + "1.00 | \n", + "
| 3 | \n", + "Tomato | \n", + "5 | \n", + "Solanaceae | \n", + "Solanales | \n", + "Solanum | \n", + "74 | \n", + "0.20 | \n", + "2.60 | \n", + "3.90 | \n", + "0.90 | \n", + "
| 4 | \n", + "Pear | \n", + "4 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Pyrus | \n", + "57 | \n", + "0.10 | \n", + "10.00 | \n", + "15.00 | \n", + "0.40 | \n", + "
| 5 | \n", + "Durian | \n", + "60 | \n", + "Malvaceae | \n", + "Malvales | \n", + "Durio | \n", + "147 | \n", + "5.30 | \n", + "6.75 | \n", + "27.10 | \n", + "1.50 | \n", + "
| 6 | \n", + "Blackberry | \n", + "64 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "40 | \n", + "0.40 | \n", + "4.50 | \n", + "9.00 | \n", + "1.30 | \n", + "
| 7 | \n", + "Lingonberry | \n", + "65 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "50 | \n", + "0.34 | \n", + "5.74 | \n", + "11.30 | \n", + "0.75 | \n", + "
| 8 | \n", + "Kiwi | \n", + "66 | \n", + "Actinidiaceae | \n", + "Struthioniformes | \n", + "Apteryx | \n", + "61 | \n", + "0.50 | \n", + "9.00 | \n", + "15.00 | \n", + "1.10 | \n", + "
| 9 | \n", + "Lychee | \n", + "67 | \n", + "Sapindaceae | \n", + "Sapindales | \n", + "Litchi | \n", + "66 | \n", + "0.44 | \n", + "15.00 | \n", + "17.00 | \n", + "0.80 | \n", + "
| 10 | \n", + "Pineapple | \n", + "10 | \n", + "Bromeliaceae | \n", + "Poales | \n", + "Ananas | \n", + "50 | \n", + "0.12 | \n", + "9.85 | \n", + "13.12 | \n", + "0.54 | \n", + "
| 11 | \n", + "Fig | \n", + "68 | \n", + "Moraceae | \n", + "Rosales | \n", + "Ficus | \n", + "74 | \n", + "0.30 | \n", + "16.00 | \n", + "19.00 | \n", + "0.80 | \n", + "
| 12 | \n", + "Gooseberry | \n", + "69 | \n", + "Grossulariaceae | \n", + "Saxifragales | \n", + "Ribes | \n", + "44 | \n", + "0.60 | \n", + "0.00 | \n", + "10.00 | \n", + "0.90 | \n", + "
| 13 | \n", + "Passionfruit | \n", + "70 | \n", + "Passifloraceae | \n", + "Malpighiales | \n", + "Passiflora | \n", + "97 | \n", + "0.70 | \n", + "11.20 | \n", + "22.40 | \n", + "2.20 | \n", + "
| 14 | \n", + "Plum | \n", + "71 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "46 | \n", + "0.28 | \n", + "9.92 | \n", + "11.40 | \n", + "0.70 | \n", + "
| 15 | \n", + "Orange | \n", + "2 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "43 | \n", + "0.20 | \n", + "8.20 | \n", + "8.30 | \n", + "1.00 | \n", + "
| 16 | \n", + "GreenApple | \n", + "72 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "21 | \n", + "0.10 | \n", + "6.40 | \n", + "3.10 | \n", + "0.40 | \n", + "
| 17 | \n", + "Raspberry | \n", + "23 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "53 | \n", + "0.70 | \n", + "4.40 | \n", + "12.00 | \n", + "1.20 | \n", + "
| 18 | \n", + "Watermelon | \n", + "25 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Citrullus | \n", + "30 | \n", + "0.20 | \n", + "6.00 | \n", + "8.00 | \n", + "0.60 | \n", + "
| 19 | \n", + "Lemon | \n", + "26 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "29 | \n", + "0.30 | \n", + "2.50 | \n", + "9.00 | \n", + "1.10 | \n", + "
| 20 | \n", + "Mango | \n", + "27 | \n", + "Anacardiaceae | \n", + "Sapindales | \n", + "Mangifera | \n", + "60 | \n", + "0.38 | \n", + "13.70 | \n", + "15.00 | \n", + "0.82 | \n", + "
| 21 | \n", + "Blueberry | \n", + "33 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "29 | \n", + "0.40 | \n", + "5.40 | \n", + "5.50 | \n", + "0.00 | \n", + "
| 22 | \n", + "Apple | \n", + "6 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "52 | \n", + "0.40 | \n", + "10.30 | \n", + "11.40 | \n", + "0.30 | \n", + "
| 23 | \n", + "Guava | \n", + "37 | \n", + "Myrtaceae | \n", + "Myrtales | \n", + "Psidium | \n", + "68 | \n", + "1.00 | \n", + "9.00 | \n", + "14.00 | \n", + "2.60 | \n", + "
| 24 | \n", + "Apricot | \n", + "35 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "15 | \n", + "0.10 | \n", + "3.20 | \n", + "3.90 | \n", + "0.50 | \n", + "
| 25 | \n", + "Melon | \n", + "41 | \n", + "Cucurbitaceae | \n", + "Cucurbitaceae | \n", + "Cucumis | \n", + "34 | \n", + "0.00 | \n", + "8.00 | \n", + "8.00 | \n", + "0.00 | \n", + "
| 26 | \n", + "Tangerine | \n", + "77 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "45 | \n", + "0.40 | \n", + "9.10 | \n", + "8.30 | \n", + "0.00 | \n", + "
| 27 | \n", + "Pitahaya | \n", + "78 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Cactaceae | \n", + "36 | \n", + "0.40 | \n", + "3.00 | \n", + "7.00 | \n", + "1.00 | \n", + "
| 28 | \n", + "Lime | \n", + "44 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "25 | \n", + "0.10 | \n", + "1.70 | \n", + "8.40 | \n", + "0.30 | \n", + "
| 29 | \n", + "Pomegranate | \n", + "79 | \n", + "Lythraceae | \n", + "Myrtales | \n", + "Punica | \n", + "83 | \n", + "1.20 | \n", + "13.70 | \n", + "18.70 | \n", + "1.70 | \n", + "
| 30 | \n", + "Dragonfruit | \n", + "80 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Selenicereus | \n", + "60 | \n", + "1.50 | \n", + "8.00 | \n", + "9.00 | \n", + "9.00 | \n", + "
| 31 | \n", + "Grape | \n", + "81 | \n", + "Vitaceae | \n", + "Vitales | \n", + "Vitis | \n", + "69 | \n", + "0.16 | \n", + "16.00 | \n", + "18.10 | \n", + "0.72 | \n", + "
| 32 | \n", + "Morus | \n", + "82 | \n", + "Moraceae | \n", + "Rosales | \n", + "Morus | \n", + "43 | \n", + "0.39 | \n", + "8.10 | \n", + "9.80 | \n", + "1.44 | \n", + "
| 33 | \n", + "Feijoa | \n", + "76 | \n", + "Myrtaceae | \n", + "Myrtoideae | \n", + "Sellowiana | \n", + "44 | \n", + "0.40 | \n", + "3.00 | \n", + "8.00 | \n", + "0.60 | \n", + "
| 34 | \n", + "Avocado | \n", + "84 | \n", + "Lauraceae | \n", + "Laurales | \n", + "Persea | \n", + "160 | \n", + "14.66 | \n", + "0.66 | \n", + "8.53 | \n", + "2.00 | \n", + "
| 35 | \n", + "Kiwifruit | \n", + "85 | \n", + "Actinidiaceae | \n", + "Ericales | \n", + "Actinidia | \n", + "61 | \n", + "0.50 | \n", + "8.90 | \n", + "14.60 | \n", + "1.14 | \n", + "
| 36 | \n", + "Cranberry | \n", + "87 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "46 | \n", + "0.10 | \n", + "4.00 | \n", + "12.20 | \n", + "0.40 | \n", + "
| 37 | \n", + "Cherry | \n", + "9 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "50 | \n", + "0.30 | \n", + "8.00 | \n", + "12.00 | \n", + "1.00 | \n", + "
| 38 | \n", + "Peach | \n", + "86 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "39 | \n", + "0.25 | \n", + "8.40 | \n", + "9.50 | \n", + "0.90 | \n", + "
| 39 | \n", + "Jackfruit | \n", + "94 | \n", + "Moraceae | \n", + "Rosales | \n", + "Artocarpus | \n", + "95 | \n", + "0.00 | \n", + "19.10 | \n", + "23.20 | \n", + "1.72 | \n", + "
| 40 | \n", + "Horned Melon | \n", + "95 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucumis | \n", + "44 | \n", + "1.26 | \n", + "0.50 | \n", + "7.56 | \n", + "1.78 | \n", + "
| 41 | \n", + "Hazelnut | \n", + "96 | \n", + "Betulaceae | \n", + "Fagales | \n", + "Corylus | \n", + "628 | \n", + "61.00 | \n", + "4.30 | \n", + "17.00 | \n", + "15.00 | \n", + "
| 42 | \n", + "Pomelo | \n", + "98 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "37 | \n", + "0.00 | \n", + "8.50 | \n", + "9.67 | \n", + "0.82 | \n", + "
| 43 | \n", + "Mangosteen | \n", + "99 | \n", + "Clusiaceae | \n", + "Malpighiales | \n", + "Garcinia | \n", + "73 | \n", + "0.58 | \n", + "16.11 | \n", + "17.91 | \n", + "0.41 | \n", + "
| 44 | \n", + "Pumpkin | \n", + "100 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucurbita | \n", + "25 | \n", + "0.30 | \n", + "3.30 | \n", + "4.60 | \n", + "1.10 | \n", + "
| 45 | \n", + "Japanese Persimmon | \n", + "101 | \n", + "Ebenaceae | \n", + "Ericales | \n", + "Diospyros | \n", + "70 | \n", + "0.20 | \n", + "13.00 | \n", + "19.00 | \n", + "0.60 | \n", + "
| 46 | \n", + "Papaya | \n", + "42 | \n", + "Caricaceae | \n", + "Brassicales | \n", + "Carica | \n", + "39 | \n", + "0.30 | \n", + "4.40 | \n", + "5.80 | \n", + "0.50 | \n", + "
| 47 | \n", + "Annona | \n", + "103 | \n", + "Annonaceae | \n", + "Rosales | \n", + "Annonas | \n", + "92 | \n", + "0.29 | \n", + "3.40 | \n", + "19.10 | \n", + "1.50 | \n", + "
| 48 | \n", + "Ceylon Gooseberry | \n", + "104 | \n", + "Salicaceae | \n", + "Malpighiales | \n", + "Dovyalis | \n", + "47 | \n", + "0.30 | \n", + "8.10 | \n", + "9.60 | \n", + "1.20 | \n", + "
\n",
+ "| \n", + " | Name | \n", + "Gender | \n", + "City | \n", + "State | \n", + "DOB | \n", + "Picture | \n", + "|
|---|---|---|---|---|---|---|---|
| 0 | \n", + "Irène Renard | \n", + "female | \n", + "Morcote | \n", + "Graubünden | \n", + "irene.renard@example.com | \n", + "1968-07-05T03:08:20.465Z | \n", + "https://randomuser.me/api/portraits/women/36.jpg | \n", + "
| 1 | \n", + "Ida Schnoor | \n", + "female | \n", + "Kranichfeld | \n", + "Saarland | \n", + "ida.schnoor@example.com | \n", + "1969-09-22T11:23:25.734Z | \n", + "https://randomuser.me/api/portraits/women/51.jpg | \n", + "
| 2 | \n", + "Siebrigje Verhoog | \n", + "female | \n", + "Terborg | \n", + "Flevoland | \n", + "siebrigje.verhoog@example.com | \n", + "1986-06-12T19:23:24.048Z | \n", + "https://randomuser.me/api/portraits/women/61.jpg | \n", + "
| 3 | \n", + "Anolido Nunes | \n", + "male | \n", + "Resende | \n", + "Distrito Federal | \n", + "anolido.nunes@example.com | \n", + "1946-02-08T04:26:32.531Z | \n", + "https://randomuser.me/api/portraits/men/68.jpg | \n", + "
| 4 | \n", + "Maël Mathieu | \n", + "male | \n", + "Saint-Pierre | \n", + "Loire-Atlantique | \n", + "mael.mathieu@example.com | \n", + "1983-09-04T11:24:22.303Z | \n", + "https://randomuser.me/api/portraits/men/57.jpg | \n", + "
| 5 | \n", + "Christina Saur | \n", + "female | \n", + "Freising | \n", + "Hessen | \n", + "christina.saur@example.com | \n", + "1951-07-03T13:24:53.323Z | \n", + "https://randomuser.me/api/portraits/women/28.jpg | \n", + "
| 6 | \n", + "Siete Spoelstra | \n", + "male | \n", + "Leeuwarderadeel | \n", + "Utrecht | \n", + "siete.spoelstra@example.com | \n", + "1948-04-08T03:02:35.102Z | \n", + "https://randomuser.me/api/portraits/men/74.jpg | \n", + "
| 7 | \n", + "Ron Spencer | \n", + "male | \n", + "Roscommon | \n", + "Fingal | \n", + "ron.spencer@example.com | \n", + "1958-10-06T19:58:05.229Z | \n", + "https://randomuser.me/api/portraits/men/58.jpg | \n", + "
| 8 | \n", + "Nathan Garrett | \n", + "male | \n", + "Sunnyvale | \n", + "Wyoming | \n", + "nathan.garrett@example.com | \n", + "1995-05-09T02:32:53.349Z | \n", + "https://randomuser.me/api/portraits/men/31.jpg | \n", + "
| 9 | \n", + "Claire George | \n", + "female | \n", + "Leixlip | \n", + "Wexford | \n", + "claire.george@example.com | \n", + "1965-08-04T08:19:42.828Z | \n", + "https://randomuser.me/api/portraits/women/83.jpg | \n", + "
| \n", + " | name | \n", + "id | \n", + "family | \n", + "order | \n", + "genus | \n", + "nutritions | \n", + "
|---|---|---|---|---|---|---|
| 0 | \n", + "Persimmon | \n", + "52 | \n", + "Ebenaceae | \n", + "Rosales | \n", + "Diospyros | \n", + "{'calories': 81, 'fat': 0.0, 'sugar': 18.0, 'c... | \n", + "
| 1 | \n", + "Strawberry | \n", + "3 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "{'calories': 29, 'fat': 0.4, 'sugar': 5.4, 'ca... | \n", + "
| 2 | \n", + "Banana | \n", + "1 | \n", + "Musaceae | \n", + "Zingiberales | \n", + "Musa | \n", + "{'calories': 96, 'fat': 0.2, 'sugar': 17.2, 'c... | \n", + "
| 3 | \n", + "Tomato | \n", + "5 | \n", + "Solanaceae | \n", + "Solanales | \n", + "Solanum | \n", + "{'calories': 74, 'fat': 0.2, 'sugar': 2.6, 'ca... | \n", + "
| 4 | \n", + "Pear | \n", + "4 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Pyrus | \n", + "{'calories': 57, 'fat': 0.1, 'sugar': 10.0, 'c... | \n", + "
| 5 | \n", + "Durian | \n", + "60 | \n", + "Malvaceae | \n", + "Malvales | \n", + "Durio | \n", + "{'calories': 147, 'fat': 5.3, 'sugar': 6.75, '... | \n", + "
| 6 | \n", + "Blackberry | \n", + "64 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "{'calories': 40, 'fat': 0.4, 'sugar': 4.5, 'ca... | \n", + "
| 7 | \n", + "Lingonberry | \n", + "65 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "{'calories': 50, 'fat': 0.34, 'sugar': 5.74, '... | \n", + "
| 8 | \n", + "Kiwi | \n", + "66 | \n", + "Actinidiaceae | \n", + "Struthioniformes | \n", + "Apteryx | \n", + "{'calories': 61, 'fat': 0.5, 'sugar': 9.0, 'ca... | \n", + "
| 9 | \n", + "Lychee | \n", + "67 | \n", + "Sapindaceae | \n", + "Sapindales | \n", + "Litchi | \n", + "{'calories': 66, 'fat': 0.44, 'sugar': 15.0, '... | \n", + "
| 10 | \n", + "Pineapple | \n", + "10 | \n", + "Bromeliaceae | \n", + "Poales | \n", + "Ananas | \n", + "{'calories': 50, 'fat': 0.12, 'sugar': 9.85, '... | \n", + "
| 11 | \n", + "Fig | \n", + "68 | \n", + "Moraceae | \n", + "Rosales | \n", + "Ficus | \n", + "{'calories': 74, 'fat': 0.3, 'sugar': 16.0, 'c... | \n", + "
| 12 | \n", + "Gooseberry | \n", + "69 | \n", + "Grossulariaceae | \n", + "Saxifragales | \n", + "Ribes | \n", + "{'calories': 44, 'fat': 0.6, 'sugar': 0.0, 'ca... | \n", + "
| 13 | \n", + "Passionfruit | \n", + "70 | \n", + "Passifloraceae | \n", + "Malpighiales | \n", + "Passiflora | \n", + "{'calories': 97, 'fat': 0.7, 'sugar': 11.2, 'c... | \n", + "
| 14 | \n", + "Plum | \n", + "71 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 46, 'fat': 0.28, 'sugar': 9.92, '... | \n", + "
| 15 | \n", + "Orange | \n", + "2 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 43, 'fat': 0.2, 'sugar': 8.2, 'ca... | \n", + "
| 16 | \n", + "GreenApple | \n", + "72 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "{'calories': 21, 'fat': 0.1, 'sugar': 6.4, 'ca... | \n", + "
| 17 | \n", + "Raspberry | \n", + "23 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "{'calories': 53, 'fat': 0.7, 'sugar': 4.4, 'ca... | \n", + "
| 18 | \n", + "Watermelon | \n", + "25 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Citrullus | \n", + "{'calories': 30, 'fat': 0.2, 'sugar': 6.0, 'ca... | \n", + "
| 19 | \n", + "Lemon | \n", + "26 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 29, 'fat': 0.3, 'sugar': 2.5, 'ca... | \n", + "
| 20 | \n", + "Mango | \n", + "27 | \n", + "Anacardiaceae | \n", + "Sapindales | \n", + "Mangifera | \n", + "{'calories': 60, 'fat': 0.38, 'sugar': 13.7, '... | \n", + "
| 21 | \n", + "Blueberry | \n", + "33 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "{'calories': 29, 'fat': 0.4, 'sugar': 5.4, 'ca... | \n", + "
| 22 | \n", + "Apple | \n", + "6 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "{'calories': 52, 'fat': 0.4, 'sugar': 10.3, 'c... | \n", + "
| 23 | \n", + "Guava | \n", + "37 | \n", + "Myrtaceae | \n", + "Myrtales | \n", + "Psidium | \n", + "{'calories': 68, 'fat': 1.0, 'sugar': 9.0, 'ca... | \n", + "
| 24 | \n", + "Apricot | \n", + "35 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 15, 'fat': 0.1, 'sugar': 3.2, 'ca... | \n", + "
| 25 | \n", + "Melon | \n", + "41 | \n", + "Cucurbitaceae | \n", + "Cucurbitaceae | \n", + "Cucumis | \n", + "{'calories': 34, 'fat': 0.0, 'sugar': 8.0, 'ca... | \n", + "
| 26 | \n", + "Tangerine | \n", + "77 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 45, 'fat': 0.4, 'sugar': 9.1, 'ca... | \n", + "
| 27 | \n", + "Pitahaya | \n", + "78 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Cactaceae | \n", + "{'calories': 36, 'fat': 0.4, 'sugar': 3.0, 'ca... | \n", + "
| 28 | \n", + "Lime | \n", + "44 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 25, 'fat': 0.1, 'sugar': 1.7, 'ca... | \n", + "
| 29 | \n", + "Pomegranate | \n", + "79 | \n", + "Lythraceae | \n", + "Myrtales | \n", + "Punica | \n", + "{'calories': 83, 'fat': 1.2, 'sugar': 13.7, 'c... | \n", + "
| 30 | \n", + "Dragonfruit | \n", + "80 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Selenicereus | \n", + "{'calories': 60, 'fat': 1.5, 'sugar': 8.0, 'ca... | \n", + "
| 31 | \n", + "Grape | \n", + "81 | \n", + "Vitaceae | \n", + "Vitales | \n", + "Vitis | \n", + "{'calories': 69, 'fat': 0.16, 'sugar': 16.0, '... | \n", + "
| 32 | \n", + "Morus | \n", + "82 | \n", + "Moraceae | \n", + "Rosales | \n", + "Morus | \n", + "{'calories': 43, 'fat': 0.39, 'sugar': 8.1, 'c... | \n", + "
| 33 | \n", + "Feijoa | \n", + "76 | \n", + "Myrtaceae | \n", + "Myrtoideae | \n", + "Sellowiana | \n", + "{'calories': 44, 'fat': 0.4, 'sugar': 3.0, 'ca... | \n", + "
| 34 | \n", + "Avocado | \n", + "84 | \n", + "Lauraceae | \n", + "Laurales | \n", + "Persea | \n", + "{'calories': 160, 'fat': 14.66, 'sugar': 0.66,... | \n", + "
| 35 | \n", + "Kiwifruit | \n", + "85 | \n", + "Actinidiaceae | \n", + "Ericales | \n", + "Actinidia | \n", + "{'calories': 61, 'fat': 0.5, 'sugar': 8.9, 'ca... | \n", + "
| 36 | \n", + "Cranberry | \n", + "87 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "{'calories': 46, 'fat': 0.1, 'sugar': 4.0, 'ca... | \n", + "
| 37 | \n", + "Cherry | \n", + "9 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 50, 'fat': 0.3, 'sugar': 8.0, 'ca... | \n", + "
| 38 | \n", + "Peach | \n", + "86 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "{'calories': 39, 'fat': 0.25, 'sugar': 8.4, 'c... | \n", + "
| 39 | \n", + "Jackfruit | \n", + "94 | \n", + "Moraceae | \n", + "Rosales | \n", + "Artocarpus | \n", + "{'calories': 95, 'fat': 0.0, 'sugar': 19.1, 'c... | \n", + "
| 40 | \n", + "Horned Melon | \n", + "95 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucumis | \n", + "{'calories': 44, 'fat': 1.26, 'sugar': 0.5, 'c... | \n", + "
| 41 | \n", + "Hazelnut | \n", + "96 | \n", + "Betulaceae | \n", + "Fagales | \n", + "Corylus | \n", + "{'calories': 628, 'fat': 61.0, 'sugar': 4.3, '... | \n", + "
| 42 | \n", + "Pomelo | \n", + "98 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "{'calories': 37, 'fat': 0.0, 'sugar': 8.5, 'ca... | \n", + "
| 43 | \n", + "Mangosteen | \n", + "99 | \n", + "Clusiaceae | \n", + "Malpighiales | \n", + "Garcinia | \n", + "{'calories': 73, 'fat': 0.58, 'sugar': 16.11, ... | \n", + "
| 44 | \n", + "Pumpkin | \n", + "100 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucurbita | \n", + "{'calories': 25, 'fat': 0.3, 'sugar': 3.3, 'ca... | \n", + "
| 45 | \n", + "Japanese Persimmon | \n", + "101 | \n", + "Ebenaceae | \n", + "Ericales | \n", + "Diospyros | \n", + "{'calories': 70, 'fat': 0.2, 'sugar': 13.0, 'c... | \n", + "
| 46 | \n", + "Papaya | \n", + "42 | \n", + "Caricaceae | \n", + "Brassicales | \n", + "Carica | \n", + "{'calories': 39, 'fat': 0.3, 'sugar': 4.4, 'ca... | \n", + "
| 47 | \n", + "Annona | \n", + "103 | \n", + "Annonaceae | \n", + "Rosales | \n", + "Annonas | \n", + "{'calories': 92, 'fat': 0.29, 'sugar': 3.4, 'c... | \n", + "
| 48 | \n", + "Ceylon Gooseberry | \n", + "104 | \n", + "Salicaceae | \n", + "Malpighiales | \n", + "Dovyalis | \n", + "{'calories': 47, 'fat': 0.3, 'sugar': 8.1, 'ca... | \n", + "
| \n", + " | name | \n", + "id | \n", + "family | \n", + "order | \n", + "genus | \n", + "nutritions.calories | \n", + "nutritions.fat | \n", + "nutritions.sugar | \n", + "nutritions.carbohydrates | \n", + "nutritions.protein | \n", + "
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", + "Persimmon | \n", + "52 | \n", + "Ebenaceae | \n", + "Rosales | \n", + "Diospyros | \n", + "81 | \n", + "0.00 | \n", + "18.00 | \n", + "18.00 | \n", + "0.00 | \n", + "
| 1 | \n", + "Strawberry | \n", + "3 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "29 | \n", + "0.40 | \n", + "5.40 | \n", + "5.50 | \n", + "0.80 | \n", + "
| 2 | \n", + "Banana | \n", + "1 | \n", + "Musaceae | \n", + "Zingiberales | \n", + "Musa | \n", + "96 | \n", + "0.20 | \n", + "17.20 | \n", + "22.00 | \n", + "1.00 | \n", + "
| 3 | \n", + "Tomato | \n", + "5 | \n", + "Solanaceae | \n", + "Solanales | \n", + "Solanum | \n", + "74 | \n", + "0.20 | \n", + "2.60 | \n", + "3.90 | \n", + "0.90 | \n", + "
| 4 | \n", + "Pear | \n", + "4 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Pyrus | \n", + "57 | \n", + "0.10 | \n", + "10.00 | \n", + "15.00 | \n", + "0.40 | \n", + "
| 5 | \n", + "Durian | \n", + "60 | \n", + "Malvaceae | \n", + "Malvales | \n", + "Durio | \n", + "147 | \n", + "5.30 | \n", + "6.75 | \n", + "27.10 | \n", + "1.50 | \n", + "
| 6 | \n", + "Blackberry | \n", + "64 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "40 | \n", + "0.40 | \n", + "4.50 | \n", + "9.00 | \n", + "1.30 | \n", + "
| 7 | \n", + "Lingonberry | \n", + "65 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "50 | \n", + "0.34 | \n", + "5.74 | \n", + "11.30 | \n", + "0.75 | \n", + "
| 8 | \n", + "Kiwi | \n", + "66 | \n", + "Actinidiaceae | \n", + "Struthioniformes | \n", + "Apteryx | \n", + "61 | \n", + "0.50 | \n", + "9.00 | \n", + "15.00 | \n", + "1.10 | \n", + "
| 9 | \n", + "Lychee | \n", + "67 | \n", + "Sapindaceae | \n", + "Sapindales | \n", + "Litchi | \n", + "66 | \n", + "0.44 | \n", + "15.00 | \n", + "17.00 | \n", + "0.80 | \n", + "
| 10 | \n", + "Pineapple | \n", + "10 | \n", + "Bromeliaceae | \n", + "Poales | \n", + "Ananas | \n", + "50 | \n", + "0.12 | \n", + "9.85 | \n", + "13.12 | \n", + "0.54 | \n", + "
| 11 | \n", + "Fig | \n", + "68 | \n", + "Moraceae | \n", + "Rosales | \n", + "Ficus | \n", + "74 | \n", + "0.30 | \n", + "16.00 | \n", + "19.00 | \n", + "0.80 | \n", + "
| 12 | \n", + "Gooseberry | \n", + "69 | \n", + "Grossulariaceae | \n", + "Saxifragales | \n", + "Ribes | \n", + "44 | \n", + "0.60 | \n", + "0.00 | \n", + "10.00 | \n", + "0.90 | \n", + "
| 13 | \n", + "Passionfruit | \n", + "70 | \n", + "Passifloraceae | \n", + "Malpighiales | \n", + "Passiflora | \n", + "97 | \n", + "0.70 | \n", + "11.20 | \n", + "22.40 | \n", + "2.20 | \n", + "
| 14 | \n", + "Plum | \n", + "71 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "46 | \n", + "0.28 | \n", + "9.92 | \n", + "11.40 | \n", + "0.70 | \n", + "
| 15 | \n", + "Orange | \n", + "2 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "43 | \n", + "0.20 | \n", + "8.20 | \n", + "8.30 | \n", + "1.00 | \n", + "
| 16 | \n", + "GreenApple | \n", + "72 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "21 | \n", + "0.10 | \n", + "6.40 | \n", + "3.10 | \n", + "0.40 | \n", + "
| 17 | \n", + "Raspberry | \n", + "23 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Rubus | \n", + "53 | \n", + "0.70 | \n", + "4.40 | \n", + "12.00 | \n", + "1.20 | \n", + "
| 18 | \n", + "Watermelon | \n", + "25 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Citrullus | \n", + "30 | \n", + "0.20 | \n", + "6.00 | \n", + "8.00 | \n", + "0.60 | \n", + "
| 19 | \n", + "Lemon | \n", + "26 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "29 | \n", + "0.30 | \n", + "2.50 | \n", + "9.00 | \n", + "1.10 | \n", + "
| 20 | \n", + "Mango | \n", + "27 | \n", + "Anacardiaceae | \n", + "Sapindales | \n", + "Mangifera | \n", + "60 | \n", + "0.38 | \n", + "13.70 | \n", + "15.00 | \n", + "0.82 | \n", + "
| 21 | \n", + "Blueberry | \n", + "33 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Fragaria | \n", + "29 | \n", + "0.40 | \n", + "5.40 | \n", + "5.50 | \n", + "0.00 | \n", + "
| 22 | \n", + "Apple | \n", + "6 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Malus | \n", + "52 | \n", + "0.40 | \n", + "10.30 | \n", + "11.40 | \n", + "0.30 | \n", + "
| 23 | \n", + "Guava | \n", + "37 | \n", + "Myrtaceae | \n", + "Myrtales | \n", + "Psidium | \n", + "68 | \n", + "1.00 | \n", + "9.00 | \n", + "14.00 | \n", + "2.60 | \n", + "
| 24 | \n", + "Apricot | \n", + "35 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "15 | \n", + "0.10 | \n", + "3.20 | \n", + "3.90 | \n", + "0.50 | \n", + "
| 25 | \n", + "Melon | \n", + "41 | \n", + "Cucurbitaceae | \n", + "Cucurbitaceae | \n", + "Cucumis | \n", + "34 | \n", + "0.00 | \n", + "8.00 | \n", + "8.00 | \n", + "0.00 | \n", + "
| 26 | \n", + "Tangerine | \n", + "77 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "45 | \n", + "0.40 | \n", + "9.10 | \n", + "8.30 | \n", + "0.00 | \n", + "
| 27 | \n", + "Pitahaya | \n", + "78 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Cactaceae | \n", + "36 | \n", + "0.40 | \n", + "3.00 | \n", + "7.00 | \n", + "1.00 | \n", + "
| 28 | \n", + "Lime | \n", + "44 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "25 | \n", + "0.10 | \n", + "1.70 | \n", + "8.40 | \n", + "0.30 | \n", + "
| 29 | \n", + "Pomegranate | \n", + "79 | \n", + "Lythraceae | \n", + "Myrtales | \n", + "Punica | \n", + "83 | \n", + "1.20 | \n", + "13.70 | \n", + "18.70 | \n", + "1.70 | \n", + "
| 30 | \n", + "Dragonfruit | \n", + "80 | \n", + "Cactaceae | \n", + "Caryophyllales | \n", + "Selenicereus | \n", + "60 | \n", + "1.50 | \n", + "8.00 | \n", + "9.00 | \n", + "9.00 | \n", + "
| 31 | \n", + "Grape | \n", + "81 | \n", + "Vitaceae | \n", + "Vitales | \n", + "Vitis | \n", + "69 | \n", + "0.16 | \n", + "16.00 | \n", + "18.10 | \n", + "0.72 | \n", + "
| 32 | \n", + "Morus | \n", + "82 | \n", + "Moraceae | \n", + "Rosales | \n", + "Morus | \n", + "43 | \n", + "0.39 | \n", + "8.10 | \n", + "9.80 | \n", + "1.44 | \n", + "
| 33 | \n", + "Feijoa | \n", + "76 | \n", + "Myrtaceae | \n", + "Myrtoideae | \n", + "Sellowiana | \n", + "44 | \n", + "0.40 | \n", + "3.00 | \n", + "8.00 | \n", + "0.60 | \n", + "
| 34 | \n", + "Avocado | \n", + "84 | \n", + "Lauraceae | \n", + "Laurales | \n", + "Persea | \n", + "160 | \n", + "14.66 | \n", + "0.66 | \n", + "8.53 | \n", + "2.00 | \n", + "
| 35 | \n", + "Kiwifruit | \n", + "85 | \n", + "Actinidiaceae | \n", + "Ericales | \n", + "Actinidia | \n", + "61 | \n", + "0.50 | \n", + "8.90 | \n", + "14.60 | \n", + "1.14 | \n", + "
| 36 | \n", + "Cranberry | \n", + "87 | \n", + "Ericaceae | \n", + "Ericales | \n", + "Vaccinium | \n", + "46 | \n", + "0.10 | \n", + "4.00 | \n", + "12.20 | \n", + "0.40 | \n", + "
| 37 | \n", + "Cherry | \n", + "9 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "50 | \n", + "0.30 | \n", + "8.00 | \n", + "12.00 | \n", + "1.00 | \n", + "
| 38 | \n", + "Peach | \n", + "86 | \n", + "Rosaceae | \n", + "Rosales | \n", + "Prunus | \n", + "39 | \n", + "0.25 | \n", + "8.40 | \n", + "9.50 | \n", + "0.90 | \n", + "
| 39 | \n", + "Jackfruit | \n", + "94 | \n", + "Moraceae | \n", + "Rosales | \n", + "Artocarpus | \n", + "95 | \n", + "0.00 | \n", + "19.10 | \n", + "23.20 | \n", + "1.72 | \n", + "
| 40 | \n", + "Horned Melon | \n", + "95 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucumis | \n", + "44 | \n", + "1.26 | \n", + "0.50 | \n", + "7.56 | \n", + "1.78 | \n", + "
| 41 | \n", + "Hazelnut | \n", + "96 | \n", + "Betulaceae | \n", + "Fagales | \n", + "Corylus | \n", + "628 | \n", + "61.00 | \n", + "4.30 | \n", + "17.00 | \n", + "15.00 | \n", + "
| 42 | \n", + "Pomelo | \n", + "98 | \n", + "Rutaceae | \n", + "Sapindales | \n", + "Citrus | \n", + "37 | \n", + "0.00 | \n", + "8.50 | \n", + "9.67 | \n", + "0.82 | \n", + "
| 43 | \n", + "Mangosteen | \n", + "99 | \n", + "Clusiaceae | \n", + "Malpighiales | \n", + "Garcinia | \n", + "73 | \n", + "0.58 | \n", + "16.11 | \n", + "17.91 | \n", + "0.41 | \n", + "
| 44 | \n", + "Pumpkin | \n", + "100 | \n", + "Cucurbitaceae | \n", + "Cucurbitales | \n", + "Cucurbita | \n", + "25 | \n", + "0.30 | \n", + "3.30 | \n", + "4.60 | \n", + "1.10 | \n", + "
| 45 | \n", + "Japanese Persimmon | \n", + "101 | \n", + "Ebenaceae | \n", + "Ericales | \n", + "Diospyros | \n", + "70 | \n", + "0.20 | \n", + "13.00 | \n", + "19.00 | \n", + "0.60 | \n", + "
| 46 | \n", + "Papaya | \n", + "42 | \n", + "Caricaceae | \n", + "Brassicales | \n", + "Carica | \n", + "39 | \n", + "0.30 | \n", + "4.40 | \n", + "5.80 | \n", + "0.50 | \n", + "
| 47 | \n", + "Annona | \n", + "103 | \n", + "Annonaceae | \n", + "Rosales | \n", + "Annonas | \n", + "92 | \n", + "0.29 | \n", + "3.40 | \n", + "19.10 | \n", + "1.50 | \n", + "
| 48 | \n", + "Ceylon Gooseberry | \n", + "104 | \n", + "Salicaceae | \n", + "Malpighiales | \n", + "Dovyalis | \n", + "47 | \n", + "0.30 | \n", + "8.10 | \n", + "9.60 | \n", + "1.20 | \n", + "
| \n", + " | setup | \n", + "punchline | \n", + "
|---|---|---|
| 0 | \n", + "Why are graveyards so noisy? | \n", + "Because of all the coffin. | \n", + "
| 1 | \n", + "Why do bananas have to put on sunscreen before... | \n", + "Because they might peel! | \n", + "
| 2 | \n", + "What do vegetarian zombies eat? | \n", + "Grrrrrainnnnnssss. | \n", + "
| 3 | \n", + "What did the digital clock say to the grandfat... | \n", + "Look, no hands! | \n", + "
| 4 | \n", + "Dad, can you put my shoes on? | \n", + "I don't think they'll fit me. | \n", + "
| 5 | \n", + "What did one ocean say to the other ocean? | \n", + "Nothing, they just waved. | \n", + "
| 6 | \n", + "What is the most used language in programming? | \n", + "Profanity. | \n", + "
| 7 | \n", + "What do you give a sick lemon? | \n", + "Lemonaid. | \n", + "
| 8 | \n", + "While I was sleeping my friends decided to wri... | \n", + "You should have seen the expression on my face... | \n", + "
| 9 | \n", + "Knock knock. \\n Who's there? \\n Opportunity. | \n", + "That is impossible. Opportunity doesn’t come k... | \n", + "
\n",
+ "| \n", + " | Name | \n", + "Gender | \n", + "City | \n", + "State | \n", + "DOB | \n", + "Picture | \n", + "|
|---|---|---|---|---|---|---|---|
| 0 | \n", + "Floyd Rogers | \n", + "male | \n", + "Dundee | \n", + "County Londonderry | \n", + "floyd.rogers@example.com | \n", + "1959-02-08T13:15:30.999Z | \n", + "https://randomuser.me/api/portraits/men/79.jpg | \n", + "
| 1 | \n", + "Marie Kristensen | \n", + "female | \n", + "Lintrup | \n", + "Midtjylland | \n", + "marie.kristensen@example.com | \n", + "1955-08-11T03:30:19.966Z | \n", + "https://randomuser.me/api/portraits/women/71.jpg | \n", + "
| 2 | \n", + "Clifton Knight | \n", + "male | \n", + "Orange | \n", + "Western Australia | \n", + "clifton.knight@example.com | \n", + "1967-01-11T15:24:56.289Z | \n", + "https://randomuser.me/api/portraits/men/83.jpg | \n", + "
| 3 | \n", + "Alison Carroll | \n", + "female | \n", + "Mountmellick | \n", + "Carlow | \n", + "alison.carroll@example.com | \n", + "1997-07-23T02:50:30.973Z | \n", + "https://randomuser.me/api/portraits/women/23.jpg | \n", + "
| 4 | \n", + "Kadir Doğan | \n", + "male | \n", + "Ağrı | \n", + "Bursa | \n", + "kadir.dogan@example.com | \n", + "1994-08-11T04:25:43.693Z | \n", + "https://randomuser.me/api/portraits/men/40.jpg | \n", + "
| 5 | \n", + "Fatih Çağıran | \n", + "male | \n", + "Erzurum | \n", + "Çorum | \n", + "fatih.cagiran@example.com | \n", + "1950-05-17T03:51:43.648Z | \n", + "https://randomuser.me/api/portraits/men/67.jpg | \n", + "
| 6 | \n", + "Leroy Powell | \n", + "male | \n", + "Bendigo | \n", + "Northern Territory | \n", + "leroy.powell@example.com | \n", + "1947-08-07T21:50:01.248Z | \n", + "https://randomuser.me/api/portraits/men/43.jpg | \n", + "
| 7 | \n", + "Alexander Johansen | \n", + "male | \n", + "Vipperød | \n", + "Syddanmark | \n", + "alexander.johansen@example.com | \n", + "1951-11-28T16:07:53.979Z | \n", + "https://randomuser.me/api/portraits/men/61.jpg | \n", + "
| 8 | \n", + "Vicky Schmidt | \n", + "female | \n", + "Portsmouth | \n", + "Fife | \n", + "vicky.schmidt@example.com | \n", + "1974-10-07T03:07:52.920Z | \n", + "https://randomuser.me/api/portraits/women/27.jpg | \n", + "
| 9 | \n", + "Bill King | \n", + "male | \n", + "Newark | \n", + "Illinois | \n", + "bill.king@example.com | \n", + "1962-07-20T01:16:35.497Z | \n", + "https://randomuser.me/api/portraits/men/59.jpg | \n", + "
\nA file format is a standard way in which information is encoded for storage in a file. First, the file format specifies whether the file is a binary or ASCII file. Second, it shows how the information is organized. For example, the comma-separated values (CSV) file format stores tabular data in plain text.\n\nTo identify a file format, you can usually look at the file extension to get an idea. For example, a file saved with name \"Data\" in \"CSV\" format will appear as **Data.csv**. By noticing the **.csv** extension, we can clearly identify that it is a **CSV** file and the data is stored in a tabular format.
\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "There are various formats for a dataset, .csv, .json, .xlsx etc. The dataset can be stored in different places, on your local machine or sometimes online.\n\n**In this section, you will learn how to load a dataset into our Jupyter Notebook.**\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "Now, we will look at some file formats and how to read them in Python:\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "# Comma-separated values (CSV) file format\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "The **Comma-separated values** file format falls under a spreadsheet file format.\n\nIn a spreadsheet file format, data is stored in cells. Each cell is organized in rows and columns. A column in the spreadsheet file can have different types. For example, a column can be of string type, a date type, or an integer type.\n\nEach line in CSV file represents an observation, or commonly called a record. Each record may contain one or more fields which are separated by a comma.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "## Reading data from CSV in Python\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "The **Pandas** Library is a useful tool that enables us to read various datasets into a Pandas data frame\n\nLet us look at how to read a CSV file in Pandas Library.\n\nWe use **pandas.read_csv()** function to read the csv file. In the parentheses, we put the file path along with a quotation mark as an argument, so that pandas will read the file into a data frame from that address. The file path can be either a URL or your local file address.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "import piplite\nawait piplite.install(['seaborn', 'lxml', 'openpyxl'])\n\nimport pandas as pd", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "from pyodide.http import pyfetch\n\nfilename = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/data/addresses.csv\"\n\nasync def download(url, filename):\n response = await pyfetch(url)\n if response.status == 200:\n with open(filename, \"wb\") as f:\n f.write(await response.bytes())\n\nawait download(filename, \"addresses.csv\")\n\ndf = pd.read_csv(\"addresses.csv\", header=None)", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "df", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "#### Adding column name to the DataFrame\n\nWe can add columns to an existing DataFrame using its **columns** attribute.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "df.columns =['First Name', 'Last Name', 'Location ', 'City','State','Area Code']", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "df", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "#### Selecting a single column\n\nTo select the first column 'First Name', you can pass the column name as a string to the indexing operator.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "df[\"First Name\"]", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "#### Selecting multiple columns\n\nTo select multiple columns, you can pass a list of column names to the indexing operator.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "df = df[['First Name', 'Last Name', 'Location ', 'City','State','Area Code']]\ndf", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "#### Selecting rows using .iloc and .loc\n\nNow, let's see how to use .loc for selecting rows from our DataFrame.\n\n**loc() : loc() is label based data selecting method which means that we have to pass the name of the row or column which we want to select.**\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "# To select the first row\ndf.loc[0]", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "# To select the 0th,1st and 2nd row of \"First Name\" column only\ndf.loc[[0,1,2], \"First Name\" ]", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "Now, let's see how to use .iloc for selecting rows from our DataFrame.\n\n**iloc() : iloc() is a indexed based selecting method which means that we have to pass integer index in the method to select specific row/column.**\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "# To select the 0th,1st and 2nd row of \"First Name\" column only\ndf.iloc[[0,1,2], 0]", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "For more information please read the [documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01).\n\nLet's perform some basic transformation in pandas.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### Transform Function in Pandas\n\nPython's Transform function returns a self-produced dataframe with transformed values after applying the function specified in its parameter.\n\nLet's see how Transform function works.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "#import library\nimport pandas as pd\nimport numpy as np", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "#creating a dataframe\ndf=pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), columns=['a', 'b', 'c'])\ndf", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "Let’s say we want to add 10 to each element in a dataframe:\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "#applying the transform function\ndf = df.transform(func = lambda x : x + 10)\ndf", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "Now we will use DataFrame.transform() function to find the square root to each element of the dataframe.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "result = df.transform(func = ['sqrt'])", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "result", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "For more information about the **transform()** function please read the [documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.transform.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01).\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "# JSON file Format\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "**JSON (JavaScript Object Notation)** is a lightweight data-interchange format. It is easy for humans to read and write.\n\nJSON is built on two structures:\n\n1. A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array.\n\n2. An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence.\n\nJSON is a language-independent data format. It was derived from JavaScript, but many modern programming languages include code to generate and parse JSON-format data. It is a very common data format with a diverse range of applications.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "The text in JSON is done through quoted string which contains the values in key-value mappings within { }. It is similar to the dictionary in Python.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "Python supports JSON through a built-in package called **json**. To use this feature, we import the json package in Python script.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "import json", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "# Writing JSON to a File\n\nThis is usually called **serialization**. It is the process of converting an object into a special format which is suitable for transmitting over the network or storing in file or database.\n\nTo handle the data flow in a file, the JSON library in Python uses the **dump()** or **dumps()** function to convert the Python objects into their respective JSON object. This makes it easy to write data to files.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "import json\nperson = {\n 'first_name' : 'Mark',\n 'last_name' : 'abc',\n 'age' : 27,\n 'address': {\n \"streetAddress\": \"21 2nd Street\",\n \"city\": \"New York\",\n \"state\": \"NY\",\n \"postalCode\": \"10021-3100\"\n }\n}", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "#### serialization using dump() function\n\n**json.dump()** method can be used for writing to JSON file.\n\nSyntax: json.dump(dict, file_pointer)\n\nParameters:\n\n1. **dictionary** – name of the dictionary which should be converted to JSON object.\n2. **file pointer** – pointer of the file opened in write or append mode.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "with open('person.json', 'w') as f: # writing JSON object\n json.dump(person, f)", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "#### serialization using dumps() function\n\n**json.dumps()** that helps in converting a dictionary to a JSON object.\n\nIt takes two parameters:\n\n1. **dictionary** – name of the dictionary which should be converted to JSON object.\n2. **indent** – defines the number of units for indentation\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "# Serializing json \njson_object = json.dumps(person, indent = 4) \n \n# Writing to sample.json \nwith open(\"sample.json\", \"w\") as outfile: \n outfile.write(json_object) ", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "print(json_object)", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "Our Python objects are now serialized to the file. For deserialize it back to the Python object, we use the load() function.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "# Reading JSON to a File\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "This process is usually called **Deserialization** - it is the reverse of serialization. It converts the special format returned by the serialization back into a usable object.\n\n### Using json.load()\n\nThe JSON package has json.load() function that loads the json content from a json file into a dictionary.\n\nIt takes one parameter:\n\n**File pointer** : A file pointer that points to a JSON file.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "import json \n \n# Opening JSON file \nwith open('sample.json', 'r') as openfile: \n \n # Reading from json file \n json_object = json.load(openfile) \n \nprint(json_object) \nprint(type(json_object)) ", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "# XLSX file format\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "**XLSX** is a Microsoft Excel Open XML file format. It is another type of Spreadsheet file format.\n\nIn XLSX data is organized under the cells and columns in a sheet.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "## Reading the data from XLSX file\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "Let's load the data from XLSX file and define the sheet name. For loading the data you can use the Pandas library in python.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "import pandas as pd", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "# Not needed unless you're running locally\n# import urllib.request\n# urllib.request.urlretrieve(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/data/file_example_XLSX_10.xlsx\", \"sample.xlsx\")\n\nfilename = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/data/file_example_XLSX_10.xlsx\"\n\nasync def download(url, filename):\n response = await pyfetch(url)\n if response.status == 200:\n with open(filename, \"wb\") as f:\n f.write(await response.bytes())\n\nawait download(filename, \"file_example_XLSX_10.xlsx\")\n\ndf = pd.read_excel(\"file_example_XLSX_10.xlsx\")", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "df", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "# XML file format\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "**XML is also known as Extensible Markup Language**. As the name suggests, it is a markup language. It has certain rules for encoding data. XML file format is a human-readable and machine-readable file format.\n\nWe will take a look at how we can use other modules to read data from an XML file, and load it into a Pandas DataFrame.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### Writing with xml.etree.ElementTree\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "The **xml.etree.ElementTree** module comes built-in with Python. It provides functionality for parsing and creating XML documents. **ElementTree** represents the XML document as a tree. We can move across the document using nodes which are elements and sub-elements of the XML file.\n\nFor more information please read the [xml.etree.ElementTree](https://docs.python.org/3/library/xml.etree.elementtree.html?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01) documentation.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "import xml.etree.ElementTree as ET\n\n# create the file structure\nemployee = ET.Element('employee')\ndetails = ET.SubElement(employee, 'details')\nfirst = ET.SubElement(details, 'firstname')\nsecond = ET.SubElement(details, 'lastname')\nthird = ET.SubElement(details, 'age')\nfirst.text = 'Shiv'\nsecond.text = 'Mishra'\nthird.text = '23'\n\n# create a new XML file with the results\nmydata1 = ET.ElementTree(employee)\n# myfile = open(\"items2.xml\", \"wb\")\n# myfile.write(mydata)\nwith open(\"new_sample.xml\", \"wb\") as files:\n mydata1.write(files)", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "### Reading with xml.etree.ElementTree\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "Let's have a look at a one way to read XML data and put it in a Pandas DataFrame. You can see the XML file in the Notepad of your local machine.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "# Not needed unless running locally\n# !wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/data/Sample-employee-XML-file.xml\n\nimport xml.etree.ElementTree as etree\n\nfilename = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/data/Sample-employee-XML-file.xml\"\n\nasync def download(url, filename):\n response = await pyfetch(url)\n if response.status == 200:\n with open(filename, \"wb\") as f:\n f.write(await response.bytes())\n\nawait download(filename, \"Sample-employee-XML-file.xml\")", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "You would need to firstly parse an XML file and create a list of columns for data frame, then extract useful information from the XML file and add to a pandas data frame.\n\nHere is a sample code that you can use.:\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "# Parse the XML file\ntree = etree.parse(\"Sample-employee-XML-file.xml\")\n\n# Get the root of the XML tree\nroot = tree.getroot()\n\n# Define the columns for the DataFrame\ncolumns = [\"firstname\", \"lastname\", \"title\", \"division\", \"building\", \"room\"]\n\n# Initialize an empty DataFrame\ndatatframe = pd.DataFrame(columns=columns)\n\n# Iterate through each node in the XML root\nfor node in root:\n # Extract text from each element\n firstname = node.find(\"firstname\").text\n lastname = node.find(\"lastname\").text\n title = node.find(\"title\").text\n division = node.find(\"division\").text\n building = node.find(\"building\").text\n room = node.find(\"room\").text\n \n # Create a DataFrame for the current row\n row_df = pd.DataFrame([[firstname, lastname, title, division, building, room]], columns=columns)\n \n # Concatenate with the existing DataFrame\n datatframe = pd.concat([datatframe, row_df], ignore_index=True)\n\n", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "datatframe", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "### Reading xml file using pandas.read_xml function\n\nWe can also read the downloaded xml file using the read_xml function present in the pandas library which returns a Dataframe object.\n\nFor more information read the pandas.read_xml documentation.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "# Herein xpath we mention the set of xml nodes to be considered for migrating to the dataframe which in this case is details node under employees.\ndf=pd.read_xml(\"Sample-employee-XML-file.xml\", xpath=\"/employees/details\") ", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "### Save Data\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "Correspondingly, Pandas enables us to save the dataset to csv by using the **dataframe.to_csv()** method, you can add the file path and name along with quotation marks in the parentheses.\n\nFor example, if you would save the dataframe df as **employee.csv** to your local machine, you may use the syntax below:\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "datatframe.to_csv(\"employee.csv\", index=False)", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "We can also read and save other file formats, we can use similar functions to **`pd.read_csv()`** and **`df.to_csv()`** for other data formats. The functions are listed in the following table:\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "\nUsing a for loop in Python, we can quickly figure out the number of missing values in each column. As mentioned above, \"True\" represents a missing value, \"False\" means the value is present in the dataset. In the body of the for loop the method \".value_counts()\" counts the number of \"True\" values. \n
\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "for column in missing_data.columns.values.tolist():\n print(column)\n print (missing_data[column].value_counts())\n print(\"\") ", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "As you can see above, there is no missing values in the dataset.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "Check all data is in the correct format (int, float, text or other).
\n\nIn Pandas, we use\n\n.dtype() to check the data type
\n.astype() to change the data type
\n\nNumerical variables should have type **'float'** or **'int'**.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "df.dtypes", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "As we can see above, All columns have the correct data type.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "# Visualization\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "**Visualization** is one of the best way to get insights from the dataset. **Seaborn** and **Matplotlib** are two of Python's most powerful visualization libraries.\n", + "metadata": {} + }, + { + "cell_type": "code", + "source": "# import libraries\nimport matplotlib.pyplot as plt\nimport seaborn as sns", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "source": "labels= 'Not Diabetic','Diabetic'\nplt.pie(df['Outcome'].value_counts(),labels=labels,autopct='%0.02f%%')\nplt.legend()\nplt.show()", + "metadata": {}, + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "source": "As you can see above, 65.10% females are not Diabetic and 34.90% are Diabetic.\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "# Thank you for completing this Notebook\n", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "\n", + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/PY0101EN-5-2-Numpy2D.ipynb b/PY0101EN-5-2-Numpy2D.ipynb new file mode 100644 index 0000000..d8647f5 --- /dev/null +++ b/PY0101EN-5-2-Numpy2D.ipynb @@ -0,0 +1,481 @@ +{ + "metadata": { + "kernelspec": { + "display_name": "Python", + "language": "python", + "name": "conda-env-python-py" + }, + "language_info": { + "name": "" + } + }, + "nbformat_minor": 4, + "nbformat": 4, + "cells": [ + { + "cell_type": "markdown", + "source": "\n \n \n \n
a, which contains three nested lists **each of equal size**. \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a list\n\na = [[11, 12, 13], [21, 22, 23], [31, 32, 33]]\na",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can cast the list to a Numpy Array as follows:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Convert list to Numpy Array\n# Every element is the same type\n\nA = np.array(a)\nA",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can use the attribute ndim to obtain the number of axes or dimensions, referred to as the rank. \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Show the numpy array dimensions\n\nA.ndim",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "Attribute shape returns a tuple corresponding to the size or number of each dimension.\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Show the numpy array shape\n\nA.shape",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "The total number of elements in the array is given by the attribute size.\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Show the numpy array size\n\nA.size",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoEg.png\")
\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can access the 2nd-row, 3rd column as shown in the following figure:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoFT.png\")
\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": " We simply use the square brackets and the indices corresponding to the element we would like:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Access the element on the second row and third column\n\nA[1, 2]",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": " We can also use the following notation to obtain the elements: \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Access the element on the second row and third column\n\nA[1][2]",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": " Consider the elements shown in the following figure \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoFF.png\")
\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can access the element as follows: \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Access the element on the first row and first column\n\nA[0][0]",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can also use slicing in numpy arrays. Consider the following figure. We would like to obtain the first two columns in the first row\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoFSF.png\")
\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": " This can be done with the following syntax: \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Access the element on the first row and first and second columns\n\nA[0][0:2]",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "Similarly, we can obtain the first two rows of the 3rd column as follows:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Access the element on the first and second rows and third column\n\nA[0:2, 2]",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "Corresponding to the following figure: \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/2D_numpy.png\")
X and Y is shown in the following figure:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoAdd.png\")
\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "The numpy array is given by X and Y\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a numpy array X\n\nX = np.array([[1, 0], [0, 1]]) \nX",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a numpy array Y\n\nY = np.array([[2, 1], [1, 2]]) \nY",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": " We can add the numpy arrays as follows.\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Add X and Y\n\nZ = X + Y\nZ",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "Multiplying a numpy array by a scaler is identical to multiplying a matrix by a scaler. If we multiply the matrix Y by the scaler 2, we simply multiply every element in the matrix by 2, as shown in the figure.\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoDb.png\")
\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can perform the same operation in numpy as follows \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a numpy array Y\n\nY = np.array([[2, 1], [1, 2]]) \nY",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "code",
+ "source": "# Multiply Y with 2\n\nZ = 2 * Y\nZ",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "Multiplication of two arrays corresponds to an element-wise product or Hadamard product. Consider matrix X and Y. The Hadamard product corresponds to multiplying each of the elements in the same position, i.e. multiplying elements contained in the same color boxes together. The result is a new matrix that is the same size as matrix Y or X, as shown in the following figure.\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumTwoMul.png\")
\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can perform element-wise product of the array X and Y as follows:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a numpy array Y\n\nY = np.array([[2, 1], [1, 2]]) \nY",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a numpy array X\n\nX = np.array([[1, 0], [0, 1]]) \nX",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "code",
+ "source": "# Multiply X with Y\n\nZ = X * Y\nZ",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We can also perform matrix multiplication with the numpy arrays A and B as follows:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": "First, we define matrix A and B:\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a matrix A\n\nA = np.array([[0, 1, 1], [1, 0, 1]])\nA",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a matrix B\n\nB = np.array([[1, 1], [1, 1], [-1, 1]])\nB",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We use the numpy function dot to multiply the arrays together.\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Calculate the dot product\n\nZ = np.dot(A,B)\nZ",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "code",
+ "source": "# Calculate the sine of Z\n\nnp.sin(Z)",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "We use the numpy attribute T to calculate the transposed matrix\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Create a matrix C\n\nC = np.array([[1,1],[2,2],[3,3]])\nC",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "code",
+ "source": "# Get the transposed of C\n\nC.T",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "a, convert it to Numpy Array. \n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Write your code below and press Shift+Enter to execute\n\na = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "A and B.\n",
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "source": "# Write your code below and press Shift+Enter to execute\n\nB = np.array([[0, 1], [1, 0], [1, 1], [-1, 0]])",
+ "metadata": {},
+ "outputs": [],
+ "execution_count": null
+ },
+ {
+ "cell_type": "markdown",
+ "source": "Congratulations, you have completed your first lesson and hands-on lab in Python. \n