first_video.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-07-22T11:58:47.421397200Z",
     "start_time": "2023-07-22T11:58:47.401464500Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 800 entries, 0 to 799\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   #           800 non-null    int64 \n",
      " 1   Name        800 non-null    object\n",
      " 2   Type 1      800 non-null    object\n",
      " 3   Type 2      414 non-null    object\n",
      " 4   HP          800 non-null    int64 \n",
      " 5   Attack      800 non-null    int64 \n",
      " 6   Defense     800 non-null    int64 \n",
      " 7   Sp. Atk     800 non-null    int64 \n",
      " 8   Sp. Def     800 non-null    int64 \n",
      " 9   Speed       800 non-null    int64 \n",
      " 10  Generation  800 non-null    int64 \n",
      " 11  Legendary   800 non-null    bool  \n",
      "dtypes: bool(1), int64(8), object(3)\n",
      "memory usage: 69.7+ KB\n",
      "None\n",
      "--------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": "       #                   Name   Type 1  Type 2  HP  Attack  Defense  \\\n0      1              Bulbasaur    Grass  Poison  45      49       49   \n1      2                Ivysaur    Grass  Poison  60      62       63   \n2      3               Venusaur    Grass  Poison  80      82       83   \n3      3  VenusaurMega Venusaur    Grass  Poison  80     100      123   \n4      4             Charmander     Fire     NaN  39      52       43   \n..   ...                    ...      ...     ...  ..     ...      ...   \n795  719                Diancie     Rock   Fairy  50     100      150   \n796  719    DiancieMega Diancie     Rock   Fairy  50     160      110   \n797  720    HoopaHoopa Confined  Psychic   Ghost  80     110       60   \n798  720     HoopaHoopa Unbound  Psychic    Dark  80     160       60   \n799  721              Volcanion     Fire   Water  80     110      120   \n\n     Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n0         65       65     45           1      False  \n1         80       80     60           1      False  \n2        100      100     80           1      False  \n3        122      120     80           1      False  \n4         60       50     65           1      False  \n..       ...      ...    ...         ...        ...  \n795      100      150     50           6       True  \n796      160      110    110           6       True  \n797      150      130     70           6       True  \n798      170      130     80           6       True  \n799      130       90     70           6       True  \n\n[800 rows x 12 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>#</th>\n      <th>Name</th>\n      <th>Type 1</th>\n      <th>Type 2</th>\n      <th>HP</th>\n      <th>Attack</th>\n      <th>Defense</th>\n      <th>Sp. Atk</th>\n      <th>Sp. Def</th>\n      <th>Speed</th>\n      <th>Generation</th>\n      <th>Legendary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Bulbasaur</td>\n      <td>Grass</td>\n      <td>Poison</td>\n      <td>45</td>\n      <td>49</td>\n      <td>49</td>\n      <td>65</td>\n      <td>65</td>\n      <td>45</td>\n      <td>1</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Ivysaur</td>\n      <td>Grass</td>\n      <td>Poison</td>\n      <td>60</td>\n      <td>62</td>\n      <td>63</td>\n      <td>80</td>\n      <td>80</td>\n      <td>60</td>\n      <td>1</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Venusaur</td>\n      <td>Grass</td>\n      <td>Poison</td>\n      <td>80</td>\n      <td>82</td>\n      <td>83</td>\n      <td>100</td>\n      <td>100</td>\n      <td>80</td>\n      <td>1</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>VenusaurMega Venusaur</td>\n      <td>Grass</td>\n      <td>Poison</td>\n      <td>80</td>\n      <td>100</td>\n      <td>123</td>\n      <td>122</td>\n      <td>120</td>\n      <td>80</td>\n      <td>1</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>Charmander</td>\n      <td>Fire</td>\n      <td>NaN</td>\n      <td>39</td>\n      <td>52</td>\n      <td>43</td>\n      <td>60</td>\n      <td>50</td>\n      <td>65</td>\n      <td>1</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>795</th>\n      <td>719</td>\n      <td>Diancie</td>\n      <td>Rock</td>\n      <td>Fairy</td>\n      <td>50</td>\n      <td>100</td>\n      <td>150</td>\n      <td>100</td>\n      <td>150</td>\n      <td>50</td>\n      <td>6</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>796</th>\n      <td>719</td>\n      <td>DiancieMega Diancie</td>\n      <td>Rock</td>\n      <td>Fairy</td>\n      <td>50</td>\n      <td>160</td>\n      <td>110</td>\n      <td>160</td>\n      <td>110</td>\n      <td>110</td>\n      <td>6</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>797</th>\n      <td>720</td>\n      <td>HoopaHoopa Confined</td>\n      <td>Psychic</td>\n      <td>Ghost</td>\n      <td>80</td>\n      <td>110</td>\n      <td>60</td>\n      <td>150</td>\n      <td>130</td>\n      <td>70</td>\n      <td>6</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>798</th>\n      <td>720</td>\n      <td>HoopaHoopa Unbound</td>\n      <td>Psychic</td>\n      <td>Dark</td>\n      <td>80</td>\n      <td>160</td>\n      <td>60</td>\n      <td>170</td>\n      <td>130</td>\n      <td>80</td>\n      <td>6</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>799</th>\n      <td>721</td>\n      <td>Volcanion</td>\n      <td>Fire</td>\n      <td>Water</td>\n      <td>80</td>\n      <td>110</td>\n      <td>120</td>\n      <td>130</td>\n      <td>90</td>\n      <td>70</td>\n      <td>6</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>800 rows × 12 columns</p>\n</div>"
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "pokemon_data=pd.read_csv('pokemon_data.csv')\n",
    "print(pokemon_data.info())\n",
    "print(\"--------------------\")\n",
    "#healthy_pokemons = pokemon_data[pokemon_data[\"HP\"] > pokemon_data[\"HP\"].median()]\n",
    "#healthy_pokemons.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Series name: None\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "5 non-null      object\n",
      "dtypes: object(1)\n",
      "memory usage: 172.0+ bytes\n",
      "a    hru walked; ye; fuc\n",
      "b                 bab; e\n",
      "c             oooohs;as;\n",
      "dtype: object\n",
      "0    1\n",
      "1    1\n",
      "2    8\n",
      "3    7\n",
      "4    0\n",
      "5    5\n",
      "6    8\n",
      "7    1\n",
      "8    4\n",
      "9    9\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "d = {'a': \"hru walked; ye; fuc\", 'b': \"bab; e\", 'c': \"oooohs;as;\"}\n",
    "d_ser = pd.Series(d)\n",
    "rows_vs_columns.info()\n",
    "print(d_ser)\n",
    "ser = pd.Series(data=np.random.randint(0, 10, size=10))\n",
    "print(ser)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-22T12:00:09.903380900Z",
     "start_time": "2023-07-22T12:00:09.897000700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "avg_hundred = pd.read_csv(\"csTimerExport_20230721_135612.csv\")\n",
    "avg_hundred.rename(\n",
    "    columns={\"No.;Time;Comment;Scramble;Date;P.1\": \"main\"},\n",
    "    inplace=True\n",
    ")\n",
    "avg_hundred[['number', 'time', 'unknown', 'scramble', 'date', 'other_time']] =\\\n",
    "    avg_hundred['main'].str.split(';', expand=True)\n",
    "avg_hundred.drop(\n",
    "    columns={\"main\", \"unknown\", \"other_time\"},\n",
    "    inplace=True\n",
    ")\n",
    "times=avg_hundred[\"time\"]\n",
    "times = times.apply(float)\n",
    "times.std()\n",
    "max_val = math.ceil(times.max())\n",
    "average = times.mean()\n",
    "\n",
    "# Create bins from 6 to max_val with a step of 0.5\n",
    "bins = np.arange(6, max_val, 0.2)\n",
    "\n",
    "# Plot the histogram\n",
    "plt.hist(times, bins=bins, edgecolor='black')\n",
    "\n",
    "plt.title(\"{:.2f} ao100 times distribution\".format(times.mean()))\n",
    "plt.xlabel('Interval')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-22T12:14:02.634623Z",
     "start_time": "2023-07-22T12:14:02.500526300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
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