dune_to_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': {'get_result': {'job_id': None, 'result_id': '9c1dd153-b8e5-4071-bd51-dbabbbded3cf', '__typename': 'get_result_response'}}}\n",
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'date': '2021-04-13T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 322631.2536275816}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 289827.79630288173, 'date': '2021-04-12T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 289827.79630288173}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 254087.00400352172, 'date': '2021-04-11T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 254087.00400352172}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 217549.26105238104, 'date': '2021-04-10T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 217549.26105238104}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 195595.4799958634, 'date': '2021-04-09T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 195595.4799958634}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 160603.10804241928, 'date': '2021-04-08T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 160603.10804241928}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 139819.74566993685, 'date': '2021-04-07T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 139819.74566993685}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 129363.27507106852, 'date': '2021-04-06T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 129363.27507106852}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 119779.65849718533, 'date': '2021-04-05T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 119779.65849718533}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 114283.45388047466, 'date': '2021-04-04T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 114283.45388047466}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 98692.00160391557, 'date': '2021-04-03T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 98692.00160391557}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 93424.52622129551, 'date': '2021-04-02T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 93424.52622129551}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 91461.32768662869, 'date': '2021-04-01T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 91461.32768662869}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 74782.25517506119, 'date': '2021-03-31T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 74782.25517506119}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 73720.45530116593, 'date': '2021-03-30T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 73720.45530116593}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 72525.23528896805, 'date': '2021-03-29T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 72525.23528896805}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 71193.61305578159, 'date': '2021-03-28T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 71193.61305578159}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 70637.27058589339, 'date': '2021-03-27T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 70637.27058589339}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 69741.78140463473, 'date': '2021-03-26T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 69741.78140463473}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 68962.80579351318, 'date': '2021-03-25T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 68962.80579351318}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 68260.0, 'date': '2021-03-24T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 68260.0}, '__typename': 'get_result_template'}, {'data': {'dai_rfv': 68260.0, 'date': '2021-03-23T00:00:00+00:00', 'frax_rfv': 0.0, 'lusd_rfv': 0.0, 'treasury_rfv': 68260.0}, '__typename': 'get_result_template'}]}}\n"
     ]
    }
   ],
   "source": [
    "# Packages: We only need two packages for this script. The first is the dune analytics package which can be found here:\n",
    "# https://github.com/itzmestar/duneanalytics\n",
    "# Massive credit to the creator of the duneanalytics package (itzmestar) . your work has helped me significantly\n",
    "# The second is Pandas\n",
    "\n",
    "from duneanalytics import DuneAnalytics\n",
    "import pandas as pd\n",
    "# initialize client. This simply means log into your dune analytics account. ('username','password')\n",
    "dune = DuneAnalytics('Tachikoma000', 'iZ7kUnw!B8!XUTe')\n",
    "# try to login\n",
    "dune.login()\n",
    "# fetch token\n",
    "dune.fetch_auth_token()\n",
    "# fetch query result id using query id. https://dune.xyz/queries/29153/58862 . Observe the location of the query number on\n",
    "# the url\n",
    "result_id = dune.query_result_id(query_id=29153)\n",
    "# fetch query result\n",
    "data = dune.query_result(result_id)\n",
    "# assign the results to a pandas data frame. You can stop here technically. But depending on your query, you might\n",
    "# need to do some more unpacking and clean up\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "# I know the data comes in as a dict of dicts, so I have cleaned up the data and created columns\n",
    "df = df['data']['get_result_by_result_id']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "# More unpacking and formatting\n",
    "df2 = pd.DataFrame(df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "0      {'dai_rfv': 86577803.37424314, 'date': '2021-1...\n1      {'dai_rfv': 85876939.47986348, 'date': '2021-1...\n2      {'dai_rfv': 85829411.65676825, 'date': '2021-1...\n3      {'dai_rfv': 85021767.84609343, 'date': '2021-1...\n4      {'dai_rfv': 84425756.02922261, 'date': '2021-1...\n                             ...                        \n248    {'dai_rfv': 70637.27058589339, 'date': '2021-0...\n249    {'dai_rfv': 69741.78140463473, 'date': '2021-0...\n250    {'dai_rfv': 68962.80579351318, 'date': '2021-0...\n251    {'dai_rfv': 68260.0, 'date': '2021-03-24T00:00...\n252    {'dai_rfv': 68260.0, 'date': '2021-03-23T00:00...\nName: data, Length: 253, dtype: object"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the data and see if we like the form\n",
    "df2['data']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "# I like to work with serialized data\n",
    "df2 = df2[\"data\"].apply(pd.Series)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "          dai_rfv                       date      frax_rfv      lusd_rfv  \\\n0    8.657780e+07  2021-11-30T00:00:00+00:00  1.529077e+07  7.960061e+07   \n1    8.587694e+07  2021-11-29T00:00:00+00:00  1.528686e+07  7.960052e+07   \n2    8.582941e+07  2021-11-28T00:00:00+00:00  1.513665e+07  7.960027e+07   \n3    8.502177e+07  2021-11-27T00:00:00+00:00  1.500515e+07  7.960014e+07   \n4    8.442576e+07  2021-11-26T00:00:00+00:00  1.495060e+07  7.960010e+07   \n..            ...                        ...           ...           ...   \n248  7.063727e+04  2021-03-27T00:00:00+00:00  0.000000e+00  0.000000e+00   \n249  6.974178e+04  2021-03-26T00:00:00+00:00  0.000000e+00  0.000000e+00   \n250  6.896281e+04  2021-03-25T00:00:00+00:00  0.000000e+00  0.000000e+00   \n251  6.826000e+04  2021-03-24T00:00:00+00:00  0.000000e+00  0.000000e+00   \n252  6.826000e+04  2021-03-23T00:00:00+00:00  0.000000e+00  0.000000e+00   \n\n     treasury_rfv  \n0    1.814692e+08  \n1    1.807643e+08  \n2    1.805663e+08  \n3    1.796271e+08  \n4    1.789765e+08  \n..            ...  \n248  7.063727e+04  \n249  6.974178e+04  \n250  6.896281e+04  \n251  6.826000e+04  \n252  6.826000e+04  \n\n[253 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>dai_rfv</th>\n      <th>date</th>\n      <th>frax_rfv</th>\n      <th>lusd_rfv</th>\n      <th>treasury_rfv</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>8.657780e+07</td>\n      <td>2021-11-30T00:00:00+00:00</td>\n      <td>1.529077e+07</td>\n      <td>7.960061e+07</td>\n      <td>1.814692e+08</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>8.587694e+07</td>\n      <td>2021-11-29T00:00:00+00:00</td>\n      <td>1.528686e+07</td>\n      <td>7.960052e+07</td>\n      <td>1.807643e+08</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8.582941e+07</td>\n      <td>2021-11-28T00:00:00+00:00</td>\n      <td>1.513665e+07</td>\n      <td>7.960027e+07</td>\n      <td>1.805663e+08</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.502177e+07</td>\n      <td>2021-11-27T00:00:00+00:00</td>\n      <td>1.500515e+07</td>\n      <td>7.960014e+07</td>\n      <td>1.796271e+08</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>8.442576e+07</td>\n      <td>2021-11-26T00:00:00+00:00</td>\n      <td>1.495060e+07</td>\n      <td>7.960010e+07</td>\n      <td>1.789765e+08</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>248</th>\n      <td>7.063727e+04</td>\n      <td>2021-03-27T00:00:00+00:00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>7.063727e+04</td>\n    </tr>\n    <tr>\n      <th>249</th>\n      <td>6.974178e+04</td>\n      <td>2021-03-26T00:00:00+00:00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>6.974178e+04</td>\n    </tr>\n    <tr>\n      <th>250</th>\n      <td>6.896281e+04</td>\n      <td>2021-03-25T00:00:00+00:00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>6.896281e+04</td>\n    </tr>\n    <tr>\n      <th>251</th>\n      <td>6.826000e+04</td>\n      <td>2021-03-24T00:00:00+00:00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>6.826000e+04</td>\n    </tr>\n    <tr>\n      <th>252</th>\n      <td>6.826000e+04</td>\n      <td>2021-03-23T00:00:00+00:00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>6.826000e+04</td>\n    </tr>\n  </tbody>\n</table>\n<p>253 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "df2 = df2[['date','treasury_rfv','lusd_rfv','frax_rfv','dai_rfv']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:xlabel='date'>"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2\n",
    "rslt_df = df2.sort_values(by = 'date')\n",
    "rslt_df.plot(x=\"date\", y=[\"treasury_rfv\", \"lusd_rfv\", \"frax_rfv\", 'dai_rfv'], kind=\"line\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "ename": "StreamlitAPIException",
     "evalue": "(\"Expected bytes, got a 'float' object\", 'Conversion failed for column value with type object')",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mArrowTypeError\u001B[0m                            Traceback (most recent call last)",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/streamlit/type_util.py\u001B[0m in \u001B[0;36mdata_frame_to_bytes\u001B[0;34m(df)\u001B[0m\n\u001B[1;32m    378\u001B[0m     \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 379\u001B[0;31m         \u001B[0mtable\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpa\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mTable\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfrom_pandas\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    380\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mpyarrow_table_to_bytes\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtable\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pyarrow/table.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.Table.from_pandas\u001B[0;34m()\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pyarrow/pandas_compat.py\u001B[0m in \u001B[0;36mdataframe_to_arrays\u001B[0;34m(df, schema, preserve_index, nthreads, columns, safe)\u001B[0m\n\u001B[1;32m    606\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0misinstance\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmaybe_fut\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfutures\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mFuture\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 607\u001B[0;31m                 \u001B[0marrays\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0mi\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmaybe_fut\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mresult\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    608\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/concurrent/futures/_base.py\u001B[0m in \u001B[0;36mresult\u001B[0;34m(self, timeout)\u001B[0m\n\u001B[1;32m    431\u001B[0m             \u001B[0;32melif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_state\u001B[0m \u001B[0;34m==\u001B[0m \u001B[0mFINISHED\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 432\u001B[0;31m                 \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__get_result\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    433\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/concurrent/futures/_base.py\u001B[0m in \u001B[0;36m__get_result\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    387\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_exception\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 388\u001B[0;31m             \u001B[0;32mraise\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_exception\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    389\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/concurrent/futures/thread.py\u001B[0m in \u001B[0;36mrun\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m     56\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 57\u001B[0;31m             \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfn\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     58\u001B[0m         \u001B[0;32mexcept\u001B[0m \u001B[0mBaseException\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mexc\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pyarrow/pandas_compat.py\u001B[0m in \u001B[0;36mconvert_column\u001B[0;34m(col, field)\u001B[0m\n\u001B[1;32m    580\u001B[0m                        .format(col.name, col.dtype),)\n\u001B[0;32m--> 581\u001B[0;31m             \u001B[0;32mraise\u001B[0m \u001B[0me\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    582\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0mfield_nullable\u001B[0m \u001B[0;32mand\u001B[0m \u001B[0mresult\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnull_count\u001B[0m \u001B[0;34m>\u001B[0m \u001B[0;36m0\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pyarrow/pandas_compat.py\u001B[0m in \u001B[0;36mconvert_column\u001B[0;34m(col, field)\u001B[0m\n\u001B[1;32m    574\u001B[0m         \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 575\u001B[0;31m             \u001B[0mresult\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpa\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marray\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mcol\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtype\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mtype_\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mfrom_pandas\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msafe\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0msafe\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    576\u001B[0m         except (pa.ArrowInvalid,\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pyarrow/array.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.array\u001B[0;34m()\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pyarrow/array.pxi\u001B[0m in \u001B[0;36mpyarrow.lib._ndarray_to_array\u001B[0;34m()\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pyarrow/error.pxi\u001B[0m in \u001B[0;36mpyarrow.lib.check_status\u001B[0;34m()\u001B[0m\n",
      "\u001B[0;31mArrowTypeError\u001B[0m: (\"Expected bytes, got a 'float' object\", 'Conversion failed for column value with type object')",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[0;31mStreamlitAPIException\u001B[0m                     Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-52-73c5519ede4f>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mstreamlit\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mst\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mst\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marea_chart\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mrslt_df\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/streamlit/elements/dataframe_selector.py\u001B[0m in \u001B[0;36marea_chart\u001B[0;34m(self, data, width, height, use_container_width)\u001B[0m\n\u001B[1;32m    212\u001B[0m         \"\"\"\n\u001B[1;32m    213\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0m_use_arrow\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 214\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_arrow_area_chart\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mwidth\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mheight\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0muse_container_width\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    215\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    216\u001B[0m             \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_legacy_area_chart\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mwidth\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mheight\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0muse_container_width\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/streamlit/elements/arrow_altair.py\u001B[0m in \u001B[0;36m_arrow_area_chart\u001B[0;34m(self, data, width, height, use_container_width)\u001B[0m\n\u001B[1;32m    145\u001B[0m         \u001B[0mproto\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mArrowVegaLiteChartProto\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    146\u001B[0m         \u001B[0mchart\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0m_generate_chart\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mChartType\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mAREA\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdata\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mwidth\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mheight\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 147\u001B[0;31m         \u001B[0mmarshall\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mproto\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mchart\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0muse_container_width\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    148\u001B[0m         \u001B[0mlast_index\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlast_index_for_melted_dataframes\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    149\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/streamlit/elements/arrow_altair.py\u001B[0m in \u001B[0;36mmarshall\u001B[0;34m(vega_lite_chart, altair_chart, use_container_width, **kwargs)\u001B[0m\n\u001B[1;32m    373\u001B[0m         \u001B[0mchart_dict\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"datasets\"\u001B[0m\u001B[0;34m]\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdatasets\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    374\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 375\u001B[0;31m         arrow_vega_lite.marshall(\n\u001B[0m\u001B[1;32m    376\u001B[0m             \u001B[0mvega_lite_chart\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    377\u001B[0m             \u001B[0mchart_dict\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/streamlit/elements/arrow_vega_lite.py\u001B[0m in \u001B[0;36mmarshall\u001B[0;34m(proto, data, spec, use_container_width, **kwargs)\u001B[0m\n\u001B[1;32m    149\u001B[0m             \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mname\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mstr\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mk\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    150\u001B[0m             \u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mhas_name\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;32mTrue\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 151\u001B[0;31m             \u001B[0marrow\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmarshall\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdataset\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mv\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    152\u001B[0m         \u001B[0;32mdel\u001B[0m \u001B[0mspec\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;34m\"datasets\"\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    153\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/streamlit/elements/arrow.py\u001B[0m in \u001B[0;36mmarshall\u001B[0;34m(proto, data, default_uuid)\u001B[0m\n\u001B[1;32m    158\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    159\u001B[0m         \u001B[0mdf\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtype_util\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mconvert_anything_to_df\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdata\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 160\u001B[0;31m         \u001B[0mproto\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdata\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtype_util\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdata_frame_to_bytes\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdf\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    161\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    162\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/lib/python3.8/site-packages/streamlit/type_util.py\u001B[0m in \u001B[0;36mdata_frame_to_bytes\u001B[0;34m(df)\u001B[0m\n\u001B[1;32m    390\u001B[0m             )\n\u001B[1;32m    391\u001B[0m         \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 392\u001B[0;31m             \u001B[0;32mraise\u001B[0m \u001B[0merrors\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mStreamlitAPIException\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0me\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m    393\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    394\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mStreamlitAPIException\u001B[0m: (\"Expected bytes, got a 'float' object\", 'Conversion failed for column value with type object')"
     ]
    }
   ],
   "source": [
    "import streamlit as st\n",
    "st.area_chart(rslt_df)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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