api_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "424f968d-bcb6-41fa-a2af-83b98cee9958",
   "metadata": {},
   "source": [
    "## Example API Call\n",
    "This is an example notebook that calls an API and puts the response into a dataframe for additional processing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52143d9b-483d-45df-b695-771f95914dff",
   "metadata": {},
   "source": [
    "#### Import Dependencies\n",
    "The code below installs and imports the dependencies you need to run the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5ea5ba6-f2bc-40d8-a20b-022aec604618",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: requests in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (2.31.0)\n",
      "Collecting pandas\n",
      "  Obtaining dependency information for pandas from https://files.pythonhosted.org/packages/f9/57/5fbdd9f42204691adac1280a9731abba77df604c7998b6c10433219abcad/pandas-2.1.1-cp39-cp39-macosx_10_9_x86_64.whl.metadata\n",
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      "  Obtaining dependency information for numpy>=1.22.4 from https://files.pythonhosted.org/packages/2a/11/c074f7530bac91294b09988c3ff7b024bf13bf6c19f751551fa1e700c27d/numpy-1.26.0-cp39-cp39-macosx_10_9_x86_64.whl.metadata\n",
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      "\u001b[?25hInstalling collected packages: pytz, tzdata, numpy, pandas\n",
      "\u001b[33m  WARNING: The script f2py is installed in '/Library/Frameworks/Python.framework/Versions/3.9/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
      "\u001b[0mSuccessfully installed numpy-1.26.0 pandas-2.1.1 pytz-2023.3.post1 tzdata-2023.3\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install requests pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "818fa712-d797-4378-83b2-93440a028660",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from getpass import getpass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc90d376-3935-415c-97bc-fc83481da96b",
   "metadata": {},
   "source": [
    "#### Call the API and Format Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c2cd077c-154f-489b-8150-c359b714ed43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ········\n"
     ]
    }
   ],
   "source": [
    "# prompt for the api key as it might be a secret\n",
    "api_key = getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f0b0c95-024b-4e2e-852c-731158ce9200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# format the API by appending api_key to the end of the url\n",
    "api = f\"https://feeds.datagolf.com/preds/in-play?tour=pga&dead_heat=yes&odds_format=percent&file_format=json&key={api_key}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c007e6f2-bfd3-4a10-8630-0a409595cbf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# call the API and save the response as variable `response`\n",
    "response = requests.get(url=api)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aba1b507-2877-437b-8042-df7d6aa43029",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the API returns json (so response.json) then all the data is in the `data` key of the json response\n",
    "data = pd.DataFrame.from_records(response.json()['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "88f8b8bf-c977-45a1-a54c-7c991712beaa",
   "metadata": {},
   "outputs": [
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       "     R1    R2  R3    R4 country course current_pos  current_score  dg_id  \\\n",
       "0  63.0  69.0 NaN  None     USA     SM          T4            -10  21554   \n",
       "1  63.0  67.0 NaN  None     USA     SM          T1            -12  23542   \n",
       "2  64.0  66.0 NaN  None     USA     SM          T1            -12  15330   \n",
       "3  65.0  67.0 NaN  None     CAN     SM          T4            -10  13126   \n",
       "4  62.0  71.0 NaN  None     USA     SM          T7             -9  15470   \n",
       "5  68.0  65.0 NaN  None     SWE     SM          T7             -9  10419   \n",
       "6  67.0  67.0 NaN  None     AUS     SM         T12             -8  17786   \n",
       "7  67.0  64.0 NaN  None     SWE     SM           3            -11  11657   \n",
       "8  65.0  68.0 NaN  None     USA     SM          T7             -9   7960   \n",
       "9  69.0  64.0 NaN  None     KOR     SM          T7             -9  13497   \n",
       "\n",
       "   end_hole  make_cut        player_name  round  thru  today    top_10  \\\n",
       "0        18       1.0       Poston, J.T.      3     0      0  0.576314   \n",
       "1        18       1.0     Champ, Cameron      3     0      0  0.553234   \n",
       "2        18       1.0     Griffin, Lanto      3     0      0  0.495107   \n",
       "3        18       1.0       Taylor, Nick      3     0      0  0.454693   \n",
       "4        18       1.0      Hossler, Beau      3     0      0  0.445594   \n",
       "5        18       1.0        Noren, Alex      3     0      0  0.464978   \n",
       "6        18       1.0     Davis, Cameron      3     0      0  0.412119   \n",
       "7        18       1.0  Norlander, Henrik      3     0      0  0.389537   \n",
       "8        18       1.0         List, Luke      3     0      0  0.356756   \n",
       "9        18       1.0    Lee, Kyounghoon      3     0      0  0.355806   \n",
       "\n",
       "     top_20     top_5       win  \n",
       "0  0.785967  0.382027  0.116015  \n",
       "1  0.765643  0.362590  0.108237  \n",
       "2  0.718353  0.310069  0.084479  \n",
       "3  0.686971  0.271946  0.066209  \n",
       "4  0.678040  0.266069  0.065212  \n",
       "5  0.699535  0.276307  0.064275  \n",
       "6  0.649646  0.237762  0.054150  \n",
       "7  0.628933  0.216186  0.046267  \n",
       "8  0.596543  0.195237  0.039332  \n",
       "9  0.597919  0.193113  0.037385  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the data\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dedc7e77-3e9b-4818-b85d-861a81fae9c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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