{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "with open('data/pun_anno/pun_het.json') as f:\n",
    "    data = json.load(f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create a DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "# df switch columns to rows\n",
    "df = df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# hist 'human_rating'\n",
    "df['human_rating'].hist()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}