bukowa/pycharmbug.ipynb

## pycharmbug.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-10-22T17:23:24.863174216Z",
     "start_time": "2023-10-22T17:23:24.661190925Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame.from_dict({\n",
    "    'price': list(range(0,100))\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "    price\n0       0\n1       1\n2       2\n3       3\n4       4\n..    ...\n95     95\n96     96\n97     97\n98     98\n99     99\n\n[100 rows x 1 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>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>99</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-22T17:23:25.952154771Z",
     "start_time": "2023-10-22T17:23:25.937457744Z"
    }
   },
   "id": "e867346de998962d"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "array([<Axes: >], dtype=object)"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(subplots=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-22T17:23:26.747082117Z",
     "start_time": "2023-10-22T17:23:26.343326882Z"
    }
   },
   "id": "a6029c1e51b1f8f9"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "    price\n0       0\n1       1\n2       2\n3       3\n4       4\n..    ...\n95     95\n96     96\n97     97\n98     98\n99     99\n\n[100 rows x 1 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>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>95</th>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>96</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>99</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-22T17:23:26.882695701Z",
     "start_time": "2023-10-22T17:23:26.877783236Z"
    }
   },
   "id": "12f6d6df960508de"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "fb7e40cabc8a2282"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}