{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Matematikai Algoritmusok és Felfedezések I.\n",
    "\n",
    "## 11. Előadás: Pandas\n",
    "\n",
    "### 2022 április 27."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grafikonokhoz\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# grafikonok stílusának beállítása\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (15, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Adatok beolvasása egy csv fájlból\n",
    "\n",
    "\n",
    "Szegedi időjárási adatokat gyűjtöttünk össze a `weather.csv` fálba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "idojaras = pd.read_csv('weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Formatted Date</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006-04-01 00:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006-04-01 01:00:00.000 +0200</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006-04-01 02:00:00.000 +0200</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Formatted Date        Summary Precip Type  Temperature (C)  \\\n",
       "0  2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "1  2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2  2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "\n",
       "   Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "0                  7.388889      0.89            14.1197   \n",
       "1                  7.227778      0.86            14.2646   \n",
       "2                  9.377778      0.89             3.9284   \n",
       "\n",
       "   Wind Bearing (degrees)  Visibility (km)  Loud Cover  Pressure (millibars)  \\\n",
       "0                   251.0          15.8263         0.0               1015.13   \n",
       "1                   259.0          15.8263         0.0               1015.63   \n",
       "2                   204.0          14.9569         0.0               1015.94   \n",
       "\n",
       "                       Daily Summary  \n",
       "0  Partly cloudy throughout the day.  \n",
       "1  Partly cloudy throughout the day.  \n",
       "2  Partly cloudy throughout the day.  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "`pandas.read_csv(filepath_or_buffer, sep=<object object>, delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Tipikus beállítások:\n",
    "\n",
    "* `sep`: oszlop elválasztó karakter\n",
    "* `encoding`: karater kódolás (tipikusan `'latin1'` vagy `'utf8'`)\n",
    "* `index_col`: Az index legyen a megjelölt oszlop\n",
    "* `parse_dates`: A megadott oszlopkat értelmezze idő adatként"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-04-01 00:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 01:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 02:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                              \n",
       "2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "\n",
       "                               Apparent Temperature (C)  Humidity  \\\n",
       "Formatted Date                                                      \n",
       "2006-04-01 00:00:00.000 +0200                  7.388889      0.89   \n",
       "2006-04-01 01:00:00.000 +0200                  7.227778      0.86   \n",
       "2006-04-01 02:00:00.000 +0200                  9.377778      0.89   \n",
       "\n",
       "                               Wind Speed (km/h)  Wind Bearing (degrees)  \\\n",
       "Formatted Date                                                             \n",
       "2006-04-01 00:00:00.000 +0200            14.1197                   251.0   \n",
       "2006-04-01 01:00:00.000 +0200            14.2646                   259.0   \n",
       "2006-04-01 02:00:00.000 +0200             3.9284                   204.0   \n",
       "\n",
       "                               Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                               \n",
       "2006-04-01 00:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 01:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 02:00:00.000 +0200          14.9569         0.0   \n",
       "\n",
       "                               Pressure (millibars)  \\\n",
       "Formatted Date                                        \n",
       "2006-04-01 00:00:00.000 +0200               1015.13   \n",
       "2006-04-01 01:00:00.000 +0200               1015.63   \n",
       "2006-04-01 02:00:00.000 +0200               1015.94   \n",
       "\n",
       "                                                   Daily Summary  \n",
       "Formatted Date                                                    \n",
       "2006-04-01 00:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 01:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 02:00:00.000 +0200  Partly cloudy throughout the day.  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras = pd.read_csv('weather.csv', sep=',', encoding='latin1',index_col='Formatted Date')\n",
    "idojaras[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Adatok lekérdezése"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amikor beolvasunk egy CSV fájlt egy ` DataFrame` objektum jön létre, ami oszlopokból és sorokból áll. Egy oszlophoz hasonlóan férhetünk hozzá, mint egy dictionaryben egy elemhez."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.000000</td>\n",
       "      <td>96453.0</td>\n",
       "      <td>96453.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.932678</td>\n",
       "      <td>10.855029</td>\n",
       "      <td>0.734899</td>\n",
       "      <td>10.810640</td>\n",
       "      <td>187.509232</td>\n",
       "      <td>10.347325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1003.235956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.551546</td>\n",
       "      <td>10.696847</td>\n",
       "      <td>0.195473</td>\n",
       "      <td>6.913571</td>\n",
       "      <td>107.383428</td>\n",
       "      <td>4.192123</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.969906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-21.822222</td>\n",
       "      <td>-27.716667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.688889</td>\n",
       "      <td>2.311111</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.828200</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>8.339800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>9.965900</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>10.046400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.838889</td>\n",
       "      <td>18.838889</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>14.135800</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>14.812000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1021.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>39.905556</td>\n",
       "      <td>39.344444</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.852600</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1046.380000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Temperature (C)  Apparent Temperature (C)      Humidity  \\\n",
       "count     96453.000000              96453.000000  96453.000000   \n",
       "mean         11.932678                 10.855029      0.734899   \n",
       "std           9.551546                 10.696847      0.195473   \n",
       "min         -21.822222                -27.716667      0.000000   \n",
       "25%           4.688889                  2.311111      0.600000   \n",
       "50%          12.000000                 12.000000      0.780000   \n",
       "75%          18.838889                 18.838889      0.890000   \n",
       "max          39.905556                 39.344444      1.000000   \n",
       "\n",
       "       Wind Speed (km/h)  Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "count       96453.000000            96453.000000     96453.000000     96453.0   \n",
       "mean           10.810640              187.509232        10.347325         0.0   \n",
       "std             6.913571              107.383428         4.192123         0.0   \n",
       "min             0.000000                0.000000         0.000000         0.0   \n",
       "25%             5.828200              116.000000         8.339800         0.0   \n",
       "50%             9.965900              180.000000        10.046400         0.0   \n",
       "75%            14.135800              290.000000        14.812000         0.0   \n",
       "max            63.852600              359.000000        16.100000         0.0   \n",
       "\n",
       "       Pressure (millibars)  \n",
       "count          96453.000000  \n",
       "mean            1003.235956  \n",
       "std              116.969906  \n",
       "min                0.000000  \n",
       "25%             1011.900000  \n",
       "50%             1016.450000  \n",
       "75%             1021.090000  \n",
       "max             1046.380000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Summary                      object\n",
       "Precip Type                  object\n",
       "Temperature (C)             float64\n",
       "Apparent Temperature (C)    float64\n",
       "Humidity                    float64\n",
       "Wind Speed (km/h)           float64\n",
       "Wind Bearing (degrees)      float64\n",
       "Visibility (km)             float64\n",
       "Loud Cover                  float64\n",
       "Pressure (millibars)        float64\n",
       "Daily Summary                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Egy oszlop egy `Series` objektumnak felel meg. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Formatted Date\n",
       "2006-04-01 00:00:00.000 +0200     9.472222\n",
       "2006-04-01 01:00:00.000 +0200     9.355556\n",
       "2006-04-01 02:00:00.000 +0200     9.377778\n",
       "2006-04-01 03:00:00.000 +0200     8.288889\n",
       "2006-04-01 04:00:00.000 +0200     8.755556\n",
       "                                   ...    \n",
       "2016-09-09 19:00:00.000 +0200    26.016667\n",
       "2016-09-09 20:00:00.000 +0200    24.583333\n",
       "2016-09-09 21:00:00.000 +0200    22.038889\n",
       "2016-09-09 22:00:00.000 +0200    21.522222\n",
       "2016-09-09 23:00:00.000 +0200    20.438889\n",
       "Name: Temperature (C), Length: 96453, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras['Temperature (C)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(idojaras['Temperature (C)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Egy `DataFrame`-re érdemes úgy gondolni, mint olyan `Series`-ekből álló készlet, amiknek megegyezik az indexe.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Earth      9.80\n",
       "Mars       3.72\n",
       "Saturn    10.44\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gravity=pd.Series([9.8,3.72,10.44 ],index=['Earth','Mars','Saturn'])\n",
    "gravity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Earth     29.780\n",
       "Mars      24.007\n",
       "Saturn     9.680\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orbital_speed=pd.Series([29.78,24.007,9.68],index=['Earth','Mars','Saturn'])\n",
    "orbital_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>orbital speed</th>\n",
       "      <th>gravity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Earth</th>\n",
       "      <td>29.780</td>\n",
       "      <td>9.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mars</th>\n",
       "      <td>24.007</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturn</th>\n",
       "      <td>9.680</td>\n",
       "      <td>10.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        orbital speed  gravity\n",
       "Earth          29.780     9.80\n",
       "Mars           24.007     3.72\n",
       "Saturn          9.680    10.44"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_data=pd.DataFrame({\n",
    "     \"orbital speed\": orbital_speed,\n",
    "     \"gravity\": gravity\n",
    " })\n",
    "planet_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `loc` és `iloc` függvényekkel tudunk sorokat lekérdezni."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orbital speed    29.78\n",
       "gravity           9.80\n",
       "Name: Earth, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_data.loc['Earth'] #  név szerint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orbital speed    24.007\n",
       "gravity           3.720\n",
       "Name: Mars, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet_data.iloc[1] # pozíció szerint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Sorokat a szokásos tömb indexeléssel is tudunk lekérdezni. Például az első nap adatait `idojaras[:24]`-ként kapjuk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-04-01 00:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 01:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 02:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 03:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.41</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 04:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 05:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.222222</td>\n",
       "      <td>7.111111</td>\n",
       "      <td>0.85</td>\n",
       "      <td>13.9587</td>\n",
       "      <td>258.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.66</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 06:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>7.733333</td>\n",
       "      <td>5.522222</td>\n",
       "      <td>0.95</td>\n",
       "      <td>12.3648</td>\n",
       "      <td>259.0</td>\n",
       "      <td>9.9820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.72</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 07:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.772222</td>\n",
       "      <td>6.527778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1519</td>\n",
       "      <td>260.0</td>\n",
       "      <td>9.9820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.84</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 08:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>10.822222</td>\n",
       "      <td>10.822222</td>\n",
       "      <td>0.82</td>\n",
       "      <td>11.3183</td>\n",
       "      <td>259.0</td>\n",
       "      <td>9.9820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.37</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 09:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>13.772222</td>\n",
       "      <td>13.772222</td>\n",
       "      <td>0.72</td>\n",
       "      <td>12.5258</td>\n",
       "      <td>279.0</td>\n",
       "      <td>9.9820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.22</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 10:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>16.016667</td>\n",
       "      <td>16.016667</td>\n",
       "      <td>0.67</td>\n",
       "      <td>17.5651</td>\n",
       "      <td>290.0</td>\n",
       "      <td>11.2056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.42</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 11:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>17.144444</td>\n",
       "      <td>17.144444</td>\n",
       "      <td>0.54</td>\n",
       "      <td>19.7869</td>\n",
       "      <td>316.0</td>\n",
       "      <td>11.4471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.74</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 12:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>21.9443</td>\n",
       "      <td>281.0</td>\n",
       "      <td>11.2700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.59</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 13:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>0.51</td>\n",
       "      <td>20.6885</td>\n",
       "      <td>289.0</td>\n",
       "      <td>11.2700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.48</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 14:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>18.877778</td>\n",
       "      <td>18.877778</td>\n",
       "      <td>0.47</td>\n",
       "      <td>15.3755</td>\n",
       "      <td>262.0</td>\n",
       "      <td>11.4471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1017.17</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 15:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>18.911111</td>\n",
       "      <td>18.911111</td>\n",
       "      <td>0.46</td>\n",
       "      <td>10.4006</td>\n",
       "      <td>288.0</td>\n",
       "      <td>11.2700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.47</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 16:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>15.388889</td>\n",
       "      <td>15.388889</td>\n",
       "      <td>0.60</td>\n",
       "      <td>14.4095</td>\n",
       "      <td>251.0</td>\n",
       "      <td>11.2700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.15</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 17:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>15.550000</td>\n",
       "      <td>0.63</td>\n",
       "      <td>11.1573</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.4471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.17</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 18:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>14.255556</td>\n",
       "      <td>14.255556</td>\n",
       "      <td>0.69</td>\n",
       "      <td>8.5169</td>\n",
       "      <td>163.0</td>\n",
       "      <td>11.2056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.82</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 19:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>13.144444</td>\n",
       "      <td>13.144444</td>\n",
       "      <td>0.70</td>\n",
       "      <td>7.6314</td>\n",
       "      <td>139.0</td>\n",
       "      <td>11.2056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.83</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 20:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>11.550000</td>\n",
       "      <td>11.550000</td>\n",
       "      <td>0.77</td>\n",
       "      <td>7.3899</td>\n",
       "      <td>147.0</td>\n",
       "      <td>11.0285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.85</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 21:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>11.183333</td>\n",
       "      <td>11.183333</td>\n",
       "      <td>0.76</td>\n",
       "      <td>4.9266</td>\n",
       "      <td>160.0</td>\n",
       "      <td>9.9820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.77</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 22:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>10.116667</td>\n",
       "      <td>10.116667</td>\n",
       "      <td>0.79</td>\n",
       "      <td>6.6493</td>\n",
       "      <td>163.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.40</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 23:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.77</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>152.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                              \n",
       "2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "2006-04-01 03:00:00.000 +0200  Partly Cloudy        rain         8.288889   \n",
       "2006-04-01 04:00:00.000 +0200  Mostly Cloudy        rain         8.755556   \n",
       "2006-04-01 05:00:00.000 +0200  Partly Cloudy        rain         9.222222   \n",
       "2006-04-01 06:00:00.000 +0200  Partly Cloudy        rain         7.733333   \n",
       "2006-04-01 07:00:00.000 +0200  Partly Cloudy        rain         8.772222   \n",
       "2006-04-01 08:00:00.000 +0200  Partly Cloudy        rain        10.822222   \n",
       "2006-04-01 09:00:00.000 +0200  Partly Cloudy        rain        13.772222   \n",
       "2006-04-01 10:00:00.000 +0200  Partly Cloudy        rain        16.016667   \n",
       "2006-04-01 11:00:00.000 +0200  Partly Cloudy        rain        17.144444   \n",
       "2006-04-01 12:00:00.000 +0200  Partly Cloudy        rain        17.800000   \n",
       "2006-04-01 13:00:00.000 +0200  Partly Cloudy        rain        17.333333   \n",
       "2006-04-01 14:00:00.000 +0200  Partly Cloudy        rain        18.877778   \n",
       "2006-04-01 15:00:00.000 +0200  Partly Cloudy        rain        18.911111   \n",
       "2006-04-01 16:00:00.000 +0200  Partly Cloudy        rain        15.388889   \n",
       "2006-04-01 17:00:00.000 +0200  Mostly Cloudy        rain        15.550000   \n",
       "2006-04-01 18:00:00.000 +0200  Mostly Cloudy        rain        14.255556   \n",
       "2006-04-01 19:00:00.000 +0200  Mostly Cloudy        rain        13.144444   \n",
       "2006-04-01 20:00:00.000 +0200  Mostly Cloudy        rain        11.550000   \n",
       "2006-04-01 21:00:00.000 +0200  Mostly Cloudy        rain        11.183333   \n",
       "2006-04-01 22:00:00.000 +0200  Partly Cloudy        rain        10.116667   \n",
       "2006-04-01 23:00:00.000 +0200  Mostly Cloudy        rain        10.200000   \n",
       "\n",
       "                               Apparent Temperature (C)  Humidity  \\\n",
       "Formatted Date                                                      \n",
       "2006-04-01 00:00:00.000 +0200                  7.388889      0.89   \n",
       "2006-04-01 01:00:00.000 +0200                  7.227778      0.86   \n",
       "2006-04-01 02:00:00.000 +0200                  9.377778      0.89   \n",
       "2006-04-01 03:00:00.000 +0200                  5.944444      0.83   \n",
       "2006-04-01 04:00:00.000 +0200                  6.977778      0.83   \n",
       "2006-04-01 05:00:00.000 +0200                  7.111111      0.85   \n",
       "2006-04-01 06:00:00.000 +0200                  5.522222      0.95   \n",
       "2006-04-01 07:00:00.000 +0200                  6.527778      0.89   \n",
       "2006-04-01 08:00:00.000 +0200                 10.822222      0.82   \n",
       "2006-04-01 09:00:00.000 +0200                 13.772222      0.72   \n",
       "2006-04-01 10:00:00.000 +0200                 16.016667      0.67   \n",
       "2006-04-01 11:00:00.000 +0200                 17.144444      0.54   \n",
       "2006-04-01 12:00:00.000 +0200                 17.800000      0.55   \n",
       "2006-04-01 13:00:00.000 +0200                 17.333333      0.51   \n",
       "2006-04-01 14:00:00.000 +0200                 18.877778      0.47   \n",
       "2006-04-01 15:00:00.000 +0200                 18.911111      0.46   \n",
       "2006-04-01 16:00:00.000 +0200                 15.388889      0.60   \n",
       "2006-04-01 17:00:00.000 +0200                 15.550000      0.63   \n",
       "2006-04-01 18:00:00.000 +0200                 14.255556      0.69   \n",
       "2006-04-01 19:00:00.000 +0200                 13.144444      0.70   \n",
       "2006-04-01 20:00:00.000 +0200                 11.550000      0.77   \n",
       "2006-04-01 21:00:00.000 +0200                 11.183333      0.76   \n",
       "2006-04-01 22:00:00.000 +0200                 10.116667      0.79   \n",
       "2006-04-01 23:00:00.000 +0200                 10.200000      0.77   \n",
       "\n",
       "                               Wind Speed (km/h)  Wind Bearing (degrees)  \\\n",
       "Formatted Date                                                             \n",
       "2006-04-01 00:00:00.000 +0200            14.1197                   251.0   \n",
       "2006-04-01 01:00:00.000 +0200            14.2646                   259.0   \n",
       "2006-04-01 02:00:00.000 +0200             3.9284                   204.0   \n",
       "2006-04-01 03:00:00.000 +0200            14.1036                   269.0   \n",
       "2006-04-01 04:00:00.000 +0200            11.0446                   259.0   \n",
       "2006-04-01 05:00:00.000 +0200            13.9587                   258.0   \n",
       "2006-04-01 06:00:00.000 +0200            12.3648                   259.0   \n",
       "2006-04-01 07:00:00.000 +0200            14.1519                   260.0   \n",
       "2006-04-01 08:00:00.000 +0200            11.3183                   259.0   \n",
       "2006-04-01 09:00:00.000 +0200            12.5258                   279.0   \n",
       "2006-04-01 10:00:00.000 +0200            17.5651                   290.0   \n",
       "2006-04-01 11:00:00.000 +0200            19.7869                   316.0   \n",
       "2006-04-01 12:00:00.000 +0200            21.9443                   281.0   \n",
       "2006-04-01 13:00:00.000 +0200            20.6885                   289.0   \n",
       "2006-04-01 14:00:00.000 +0200            15.3755                   262.0   \n",
       "2006-04-01 15:00:00.000 +0200            10.4006                   288.0   \n",
       "2006-04-01 16:00:00.000 +0200            14.4095                   251.0   \n",
       "2006-04-01 17:00:00.000 +0200            11.1573                   230.0   \n",
       "2006-04-01 18:00:00.000 +0200             8.5169                   163.0   \n",
       "2006-04-01 19:00:00.000 +0200             7.6314                   139.0   \n",
       "2006-04-01 20:00:00.000 +0200             7.3899                   147.0   \n",
       "2006-04-01 21:00:00.000 +0200             4.9266                   160.0   \n",
       "2006-04-01 22:00:00.000 +0200             6.6493                   163.0   \n",
       "2006-04-01 23:00:00.000 +0200             3.9284                   152.0   \n",
       "\n",
       "                               Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                               \n",
       "2006-04-01 00:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 01:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 02:00:00.000 +0200          14.9569         0.0   \n",
       "2006-04-01 03:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 04:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 05:00:00.000 +0200          14.9569         0.0   \n",
       "2006-04-01 06:00:00.000 +0200           9.9820         0.0   \n",
       "2006-04-01 07:00:00.000 +0200           9.9820         0.0   \n",
       "2006-04-01 08:00:00.000 +0200           9.9820         0.0   \n",
       "2006-04-01 09:00:00.000 +0200           9.9820         0.0   \n",
       "2006-04-01 10:00:00.000 +0200          11.2056         0.0   \n",
       "2006-04-01 11:00:00.000 +0200          11.4471         0.0   \n",
       "2006-04-01 12:00:00.000 +0200          11.2700         0.0   \n",
       "2006-04-01 13:00:00.000 +0200          11.2700         0.0   \n",
       "2006-04-01 14:00:00.000 +0200          11.4471         0.0   \n",
       "2006-04-01 15:00:00.000 +0200          11.2700         0.0   \n",
       "2006-04-01 16:00:00.000 +0200          11.2700         0.0   \n",
       "2006-04-01 17:00:00.000 +0200          11.4471         0.0   \n",
       "2006-04-01 18:00:00.000 +0200          11.2056         0.0   \n",
       "2006-04-01 19:00:00.000 +0200          11.2056         0.0   \n",
       "2006-04-01 20:00:00.000 +0200          11.0285         0.0   \n",
       "2006-04-01 21:00:00.000 +0200           9.9820         0.0   \n",
       "2006-04-01 22:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 23:00:00.000 +0200          14.9569         0.0   \n",
       "\n",
       "                               Pressure (millibars)  \\\n",
       "Formatted Date                                        \n",
       "2006-04-01 00:00:00.000 +0200               1015.13   \n",
       "2006-04-01 01:00:00.000 +0200               1015.63   \n",
       "2006-04-01 02:00:00.000 +0200               1015.94   \n",
       "2006-04-01 03:00:00.000 +0200               1016.41   \n",
       "2006-04-01 04:00:00.000 +0200               1016.51   \n",
       "2006-04-01 05:00:00.000 +0200               1016.66   \n",
       "2006-04-01 06:00:00.000 +0200               1016.72   \n",
       "2006-04-01 07:00:00.000 +0200               1016.84   \n",
       "2006-04-01 08:00:00.000 +0200               1017.37   \n",
       "2006-04-01 09:00:00.000 +0200               1017.22   \n",
       "2006-04-01 10:00:00.000 +0200               1017.42   \n",
       "2006-04-01 11:00:00.000 +0200               1017.74   \n",
       "2006-04-01 12:00:00.000 +0200               1017.59   \n",
       "2006-04-01 13:00:00.000 +0200               1017.48   \n",
       "2006-04-01 14:00:00.000 +0200               1017.17   \n",
       "2006-04-01 15:00:00.000 +0200               1016.47   \n",
       "2006-04-01 16:00:00.000 +0200               1016.15   \n",
       "2006-04-01 17:00:00.000 +0200               1016.17   \n",
       "2006-04-01 18:00:00.000 +0200               1015.82   \n",
       "2006-04-01 19:00:00.000 +0200               1015.83   \n",
       "2006-04-01 20:00:00.000 +0200               1015.85   \n",
       "2006-04-01 21:00:00.000 +0200               1015.77   \n",
       "2006-04-01 22:00:00.000 +0200               1015.40   \n",
       "2006-04-01 23:00:00.000 +0200               1015.51   \n",
       "\n",
       "                                                   Daily Summary  \n",
       "Formatted Date                                                    \n",
       "2006-04-01 00:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 01:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 02:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 03:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 04:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 05:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 06:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 07:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 08:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 09:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 10:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 11:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 12:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 13:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 14:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 15:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 16:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 17:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 18:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 19:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 20:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 21:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 22:00:00.000 +0200  Partly cloudy throughout the day.  \n",
       "2006-04-01 23:00:00.000 +0200  Partly cloudy throughout the day.  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras[:24]\n",
    "idojaras.iloc[:24]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Grafikon készítés"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Egyszerűen `.plot()`-ot rakunk a végére."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Formatted Date'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idojaras['Temperature (C)'][:7*24].plot()\n",
    "idojaras['Apparent Temperature (C)'][:7*24].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Egymásik példa\n",
    "\n",
    "20000 sakjátszmát és a hozzájuk tartozó adatokat tartalmazó adatbázis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "chess = pd.read_csv('chess.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha kiíratunk egy `DataFrame`-et csak néhány sort mutat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>id</th>\n",
       "      <th>rated</th>\n",
       "      <th>created_at</th>\n",
       "      <th>last_move_at</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>increment_code</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_eco</th>\n",
       "      <th>opening_name</th>\n",
       "      <th>opening_ply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TZJHLljE</td>\n",
       "      <td>False</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>13</td>\n",
       "      <td>outoftime</td>\n",
       "      <td>white</td>\n",
       "      <td>15+2</td>\n",
       "      <td>bourgris</td>\n",
       "      <td>1500</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1191</td>\n",
       "      <td>d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...</td>\n",
       "      <td>D10</td>\n",
       "      <td>Slav Defense: Exchange Variation</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>l1NXvwaE</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>16</td>\n",
       "      <td>resign</td>\n",
       "      <td>black</td>\n",
       "      <td>5+10</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1322</td>\n",
       "      <td>skinnerua</td>\n",
       "      <td>1261</td>\n",
       "      <td>d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...</td>\n",
       "      <td>B00</td>\n",
       "      <td>Nimzowitsch Defense: Kennedy Variation</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mIICvQHh</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>61</td>\n",
       "      <td>mate</td>\n",
       "      <td>white</td>\n",
       "      <td>5+10</td>\n",
       "      <td>ischia</td>\n",
       "      <td>1496</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1500</td>\n",
       "      <td>e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...</td>\n",
       "      <td>C20</td>\n",
       "      <td>King's Pawn Game: Leonardis Variation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  rated    created_at  last_move_at  turns victory_status winner  \\\n",
       "0  TZJHLljE  False  1.504210e+12  1.504210e+12     13      outoftime  white   \n",
       "1  l1NXvwaE   True  1.504130e+12  1.504130e+12     16         resign  black   \n",
       "2  mIICvQHh   True  1.504130e+12  1.504130e+12     61           mate  white   \n",
       "\n",
       "  increment_code  white_id  white_rating   black_id  black_rating  \\\n",
       "0           15+2  bourgris          1500       a-00          1191   \n",
       "1           5+10      a-00          1322  skinnerua          1261   \n",
       "2           5+10    ischia          1496       a-00          1500   \n",
       "\n",
       "                                               moves opening_eco  \\\n",
       "0  d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...         D10   \n",
       "1  d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...         B00   \n",
       "2  e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...         C20   \n",
       "\n",
       "                             opening_name  opening_ply  \n",
       "0        Slav Defense: Exchange Variation            5  \n",
       "1  Nimzowitsch Defense: Kennedy Variation            4  \n",
       "2   King's Pawn Game: Leonardis Variation            3  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Slav Defense: Exchange Variation\n",
       "1        Nimzowitsch Defense: Kennedy Variation\n",
       "2         King's Pawn Game: Leonardis Variation\n",
       "3        Queen's Pawn Game: Zukertort Variation\n",
       "4                              Philidor Defense\n",
       "                          ...                  \n",
       "20053                             Dutch Defense\n",
       "20054                              Queen's Pawn\n",
       "20055           Queen's Pawn Game: Mason Attack\n",
       "20056                              Pirc Defense\n",
       "20057           Queen's Pawn Game: Mason Attack\n",
       "Name: opening_name, Length: 20058, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess['opening_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "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>id</th>\n",
       "      <th>rated</th>\n",
       "      <th>created_at</th>\n",
       "      <th>last_move_at</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>increment_code</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_eco</th>\n",
       "      <th>opening_name</th>\n",
       "      <th>opening_ply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TZJHLljE</td>\n",
       "      <td>False</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>13</td>\n",
       "      <td>outoftime</td>\n",
       "      <td>white</td>\n",
       "      <td>15+2</td>\n",
       "      <td>bourgris</td>\n",
       "      <td>1500</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1191</td>\n",
       "      <td>d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...</td>\n",
       "      <td>D10</td>\n",
       "      <td>Slav Defense: Exchange Variation</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>l1NXvwaE</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>16</td>\n",
       "      <td>resign</td>\n",
       "      <td>black</td>\n",
       "      <td>5+10</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1322</td>\n",
       "      <td>skinnerua</td>\n",
       "      <td>1261</td>\n",
       "      <td>d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...</td>\n",
       "      <td>B00</td>\n",
       "      <td>Nimzowitsch Defense: Kennedy Variation</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mIICvQHh</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>61</td>\n",
       "      <td>mate</td>\n",
       "      <td>white</td>\n",
       "      <td>5+10</td>\n",
       "      <td>ischia</td>\n",
       "      <td>1496</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1500</td>\n",
       "      <td>e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...</td>\n",
       "      <td>C20</td>\n",
       "      <td>King's Pawn Game: Leonardis Variation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  rated    created_at  last_move_at  turns victory_status winner  \\\n",
       "0  TZJHLljE  False  1.504210e+12  1.504210e+12     13      outoftime  white   \n",
       "1  l1NXvwaE   True  1.504130e+12  1.504130e+12     16         resign  black   \n",
       "2  mIICvQHh   True  1.504130e+12  1.504130e+12     61           mate  white   \n",
       "\n",
       "  increment_code  white_id  white_rating   black_id  black_rating  \\\n",
       "0           15+2  bourgris          1500       a-00          1191   \n",
       "1           5+10      a-00          1322  skinnerua          1261   \n",
       "2           5+10    ischia          1496       a-00          1500   \n",
       "\n",
       "                                               moves opening_eco  \\\n",
       "0  d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...         D10   \n",
       "1  d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...         B00   \n",
       "2  e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...         C20   \n",
       "\n",
       "                             opening_name  opening_ply  \n",
       "0        Slav Defense: Exchange Variation            5  \n",
       "1  Nimzowitsch Defense: Kennedy Variation            4  \n",
       "2   King's Pawn Game: Leonardis Variation            3  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A két módszert kombinálhatjuk, tetszőleges sorrendben:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Slav Defense: Exchange Variation\n",
       "1    Nimzowitsch Defense: Kennedy Variation\n",
       "2     King's Pawn Game: Leonardis Variation\n",
       "3    Queen's Pawn Game: Zukertort Variation\n",
       "4                          Philidor Defense\n",
       "Name: opening_name, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess['opening_name'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Slav Defense: Exchange Variation\n",
       "1    Nimzowitsch Defense: Kennedy Variation\n",
       "2     King's Pawn Game: Leonardis Variation\n",
       "3    Queen's Pawn Game: Zukertort Variation\n",
       "4                          Philidor Defense\n",
       "Name: opening_name, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[:5]['opening_name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Több oszlop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>winner</th>\n",
       "      <th>white_id</th>\n",
       "      <th>black_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>white</td>\n",
       "      <td>bourgris</td>\n",
       "      <td>a-00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>black</td>\n",
       "      <td>a-00</td>\n",
       "      <td>skinnerua</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>white</td>\n",
       "      <td>ischia</td>\n",
       "      <td>a-00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>white</td>\n",
       "      <td>daniamurashov</td>\n",
       "      <td>adivanov2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>white</td>\n",
       "      <td>nik221107</td>\n",
       "      <td>adivanov2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20053</th>\n",
       "      <td>white</td>\n",
       "      <td>belcolt</td>\n",
       "      <td>jamboger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20054</th>\n",
       "      <td>black</td>\n",
       "      <td>jamboger</td>\n",
       "      <td>farrukhasomiddinov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20055</th>\n",
       "      <td>white</td>\n",
       "      <td>jamboger</td>\n",
       "      <td>schaaksmurf3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20056</th>\n",
       "      <td>white</td>\n",
       "      <td>marcodisogno</td>\n",
       "      <td>jamboger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20057</th>\n",
       "      <td>black</td>\n",
       "      <td>jamboger</td>\n",
       "      <td>ffbob</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20058 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      winner       white_id            black_id\n",
       "0      white       bourgris                a-00\n",
       "1      black           a-00           skinnerua\n",
       "2      white         ischia                a-00\n",
       "3      white  daniamurashov        adivanov2009\n",
       "4      white      nik221107        adivanov2009\n",
       "...      ...            ...                 ...\n",
       "20053  white        belcolt            jamboger\n",
       "20054  black       jamboger  farrukhasomiddinov\n",
       "20055  white       jamboger        schaaksmurf3\n",
       "20056  white   marcodisogno            jamboger\n",
       "20057  black       jamboger               ffbob\n",
       "\n",
       "[20058 rows x 3 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[['winner', 'white_id','black_id']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Melyik a leggyakoribb megnyitás?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `.value_counts()` metódust használhatjuk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Van't Kruijs Opening                                                                    368\n",
       "Sicilian Defense                                                                        358\n",
       "Sicilian Defense: Bowdler Attack                                                        296\n",
       "Scotch Game                                                                             271\n",
       "French Defense: Knight Variation                                                        271\n",
       "                                                                                       ... \n",
       "French Defense: MacCutcheon Variation |  Exchange Variation                               1\n",
       "French Defense: Rubinstein Variation |  Kasparov Attack                                   1\n",
       "English Opening: King's English Variation |  Four Knights Variation |  Korchnoi Line      1\n",
       "Sicilian Defense: Smith-Morra Gambit Declined |  Wing Formation                           1\n",
       "Ruy Lopez: Berlin Defense |  Closed Bernstein Variation                                   1\n",
       "Name: opening_name, Length: 1477, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess['opening_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Van't Kruijs Opening                             368\n",
       "Sicilian Defense                                 358\n",
       "Sicilian Defense: Bowdler Attack                 296\n",
       "Scotch Game                                      271\n",
       "French Defense: Knight Variation                 271\n",
       "Scandinavian Defense: Mieses-Kotroc Variation    259\n",
       "Queen's Pawn Game: Mason Attack                  232\n",
       "Queen's Pawn Game: Chigorin Variation            229\n",
       "Scandinavian Defense                             223\n",
       "Horwitz Defense                                  209\n",
       "Name: opening_name, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "chess_opening_count= chess['opening_name'].value_counts()\n",
    "chess_opening_count[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Sőt grafikont is könnyen tudunk rajzolni!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='opening_name'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "chess_opening_count[:10].plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Adatok szűrése"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Melyik mecccseken játszották a szicíliai megnyitást? (`'Sicilian Defense'`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>id</th>\n",
       "      <th>rated</th>\n",
       "      <th>created_at</th>\n",
       "      <th>last_move_at</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>increment_code</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_eco</th>\n",
       "      <th>opening_name</th>\n",
       "      <th>opening_ply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>FSkgvV2E</td>\n",
       "      <td>True</td>\n",
       "      <td>1.502780e+12</td>\n",
       "      <td>1.502780e+12</td>\n",
       "      <td>54</td>\n",
       "      <td>resign</td>\n",
       "      <td>white</td>\n",
       "      <td>10+10</td>\n",
       "      <td>vihaandumir</td>\n",
       "      <td>1203</td>\n",
       "      <td>shivangithegenius</td>\n",
       "      <td>1019</td>\n",
       "      <td>e4 c5 Nf3 d5 exd5 Qxd5 Nc3 Qe6+ Qe2 Qxe2+ Bxe2...</td>\n",
       "      <td>B27</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>BoDJGVyk</td>\n",
       "      <td>True</td>\n",
       "      <td>1.503450e+12</td>\n",
       "      <td>1.503450e+12</td>\n",
       "      <td>107</td>\n",
       "      <td>resign</td>\n",
       "      <td>white</td>\n",
       "      <td>10+10</td>\n",
       "      <td>jadesummer</td>\n",
       "      <td>1885</td>\n",
       "      <td>isachess</td>\n",
       "      <td>1856</td>\n",
       "      <td>e4 c5 Nf3 d6 Nc3 a6 a3 Nf6 Bc4 e6 d3 Nc6 Ba2 B...</td>\n",
       "      <td>B50</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0dm3RaQv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.503120e+12</td>\n",
       "      <td>1.503120e+12</td>\n",
       "      <td>105</td>\n",
       "      <td>outoftime</td>\n",
       "      <td>white</td>\n",
       "      <td>10+0</td>\n",
       "      <td>fikr</td>\n",
       "      <td>1895</td>\n",
       "      <td>isachess</td>\n",
       "      <td>1859</td>\n",
       "      <td>e4 c5 Nf3 d6 Bc4 Nf6 d3 a6 a3 g6 b4 Bg7 Bb2 O-...</td>\n",
       "      <td>B50</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  rated    created_at  last_move_at  turns victory_status winner  \\\n",
       "32   FSkgvV2E   True  1.502780e+12  1.502780e+12     54         resign  white   \n",
       "167  BoDJGVyk   True  1.503450e+12  1.503450e+12    107         resign  white   \n",
       "174  0dm3RaQv   True  1.503120e+12  1.503120e+12    105      outoftime  white   \n",
       "\n",
       "    increment_code     white_id  white_rating           black_id  \\\n",
       "32           10+10  vihaandumir          1203  shivangithegenius   \n",
       "167          10+10   jadesummer          1885           isachess   \n",
       "174           10+0         fikr          1895           isachess   \n",
       "\n",
       "     black_rating                                              moves  \\\n",
       "32           1019  e4 c5 Nf3 d5 exd5 Qxd5 Nc3 Qe6+ Qe2 Qxe2+ Bxe2...   \n",
       "167          1856  e4 c5 Nf3 d6 Nc3 a6 a3 Nf6 Bc4 e6 d3 Nc6 Ba2 B...   \n",
       "174          1859  e4 c5 Nf3 d6 Bc4 Nf6 d3 a6 a3 g6 b4 Bg7 Bb2 O-...   \n",
       "\n",
       "    opening_eco      opening_name  opening_ply  \n",
       "32          B27  Sicilian Defense            3  \n",
       "167         B50  Sicilian Defense            4  \n",
       "174         B50  Sicilian Defense            4  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sicilian = chess[chess['opening_name'] == \"Sicilian Defense\"]\n",
    "sicilian[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Mit történik a háttérben?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "20053    False\n",
       "20054    False\n",
       "20055    False\n",
       "20056    False\n",
       "20057    False\n",
       "Name: opening_name, Length: 20058, dtype: bool"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess['opening_name'] == \"Sicilian Defense\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A háttérben a Numpyból ismert indexelési trükkök működnek!\n",
    "\n",
    "Például össze éselhetünk feltételeket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>id</th>\n",
       "      <th>rated</th>\n",
       "      <th>created_at</th>\n",
       "      <th>last_move_at</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>increment_code</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_eco</th>\n",
       "      <th>opening_name</th>\n",
       "      <th>opening_ply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>FSkgvV2E</td>\n",
       "      <td>True</td>\n",
       "      <td>1.502780e+12</td>\n",
       "      <td>1.502780e+12</td>\n",
       "      <td>54</td>\n",
       "      <td>resign</td>\n",
       "      <td>white</td>\n",
       "      <td>10+10</td>\n",
       "      <td>vihaandumir</td>\n",
       "      <td>1203</td>\n",
       "      <td>shivangithegenius</td>\n",
       "      <td>1019</td>\n",
       "      <td>e4 c5 Nf3 d5 exd5 Qxd5 Nc3 Qe6+ Qe2 Qxe2+ Bxe2...</td>\n",
       "      <td>B27</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>BoDJGVyk</td>\n",
       "      <td>True</td>\n",
       "      <td>1.503450e+12</td>\n",
       "      <td>1.503450e+12</td>\n",
       "      <td>107</td>\n",
       "      <td>resign</td>\n",
       "      <td>white</td>\n",
       "      <td>10+10</td>\n",
       "      <td>jadesummer</td>\n",
       "      <td>1885</td>\n",
       "      <td>isachess</td>\n",
       "      <td>1856</td>\n",
       "      <td>e4 c5 Nf3 d6 Nc3 a6 a3 Nf6 Bc4 e6 d3 Nc6 Ba2 B...</td>\n",
       "      <td>B50</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0dm3RaQv</td>\n",
       "      <td>True</td>\n",
       "      <td>1.503120e+12</td>\n",
       "      <td>1.503120e+12</td>\n",
       "      <td>105</td>\n",
       "      <td>outoftime</td>\n",
       "      <td>white</td>\n",
       "      <td>10+0</td>\n",
       "      <td>fikr</td>\n",
       "      <td>1895</td>\n",
       "      <td>isachess</td>\n",
       "      <td>1859</td>\n",
       "      <td>e4 c5 Nf3 d6 Bc4 Nf6 d3 a6 a3 g6 b4 Bg7 Bb2 O-...</td>\n",
       "      <td>B50</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>PEIWrbTQ</td>\n",
       "      <td>True</td>\n",
       "      <td>1.471100e+12</td>\n",
       "      <td>1.471100e+12</td>\n",
       "      <td>29</td>\n",
       "      <td>mate</td>\n",
       "      <td>white</td>\n",
       "      <td>7+3</td>\n",
       "      <td>eideral</td>\n",
       "      <td>1513</td>\n",
       "      <td>atorius</td>\n",
       "      <td>1456</td>\n",
       "      <td>e4 c5 Nf3 d6 d4 Nc6 Bb5 a6 Bxc6+ bxc6 dxc5 Qa5...</td>\n",
       "      <td>B54</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>YcFsnSyR</td>\n",
       "      <td>True</td>\n",
       "      <td>1.502390e+12</td>\n",
       "      <td>1.502400e+12</td>\n",
       "      <td>75</td>\n",
       "      <td>resign</td>\n",
       "      <td>white</td>\n",
       "      <td>10+0</td>\n",
       "      <td>alihasanzadeh</td>\n",
       "      <td>1211</td>\n",
       "      <td>snaek161616</td>\n",
       "      <td>1230</td>\n",
       "      <td>e4 c5 Bb5 Nf6 Nc3 Nc6 Bxc6 bxc6 e5 Nd5 Nxd5 cx...</td>\n",
       "      <td>B20</td>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  rated    created_at  last_move_at  turns victory_status winner  \\\n",
       "32   FSkgvV2E   True  1.502780e+12  1.502780e+12     54         resign  white   \n",
       "167  BoDJGVyk   True  1.503450e+12  1.503450e+12    107         resign  white   \n",
       "174  0dm3RaQv   True  1.503120e+12  1.503120e+12    105      outoftime  white   \n",
       "270  PEIWrbTQ   True  1.471100e+12  1.471100e+12     29           mate  white   \n",
       "612  YcFsnSyR   True  1.502390e+12  1.502400e+12     75         resign  white   \n",
       "\n",
       "    increment_code       white_id  white_rating           black_id  \\\n",
       "32           10+10    vihaandumir          1203  shivangithegenius   \n",
       "167          10+10     jadesummer          1885           isachess   \n",
       "174           10+0           fikr          1895           isachess   \n",
       "270            7+3        eideral          1513            atorius   \n",
       "612           10+0  alihasanzadeh          1211        snaek161616   \n",
       "\n",
       "     black_rating                                              moves  \\\n",
       "32           1019  e4 c5 Nf3 d5 exd5 Qxd5 Nc3 Qe6+ Qe2 Qxe2+ Bxe2...   \n",
       "167          1856  e4 c5 Nf3 d6 Nc3 a6 a3 Nf6 Bc4 e6 d3 Nc6 Ba2 B...   \n",
       "174          1859  e4 c5 Nf3 d6 Bc4 Nf6 d3 a6 a3 g6 b4 Bg7 Bb2 O-...   \n",
       "270          1456  e4 c5 Nf3 d6 d4 Nc6 Bb5 a6 Bxc6+ bxc6 dxc5 Qa5...   \n",
       "612          1230  e4 c5 Bb5 Nf6 Nc3 Nc6 Bxc6 bxc6 e5 Nd5 Nxd5 cx...   \n",
       "\n",
       "    opening_eco      opening_name  opening_ply  \n",
       "32          B27  Sicilian Defense            3  \n",
       "167         B50  Sicilian Defense            4  \n",
       "174         B50  Sicilian Defense            4  \n",
       "270         B54  Sicilian Defense            5  \n",
       "612         B20  Sicilian Defense            2  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_sicilian = chess['opening_name'] == \"Sicilian Defense\"\n",
    "is_white_win = chess['winner'] == \"white\"\n",
    "chess[is_sicilian & is_white_win][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Minden kombinálható:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>opening_name</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>54</td>\n",
       "      <td>resign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>107</td>\n",
       "      <td>resign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>105</td>\n",
       "      <td>outoftime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>29</td>\n",
       "      <td>mate</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Sicilian Defense</td>\n",
       "      <td>75</td>\n",
       "      <td>resign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         opening_name  turns victory_status\n",
       "32   Sicilian Defense     54         resign\n",
       "167  Sicilian Defense    107         resign\n",
       "174  Sicilian Defense    105      outoftime\n",
       "270  Sicilian Defense     29           mate\n",
       "612  Sicilian Defense     75         resign"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[is_sicilian & is_white_win][['opening_name','turns','victory_status']][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Fehér vagy fekete figurákkal éri meg jobban játszani?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "white    10001\n",
       "black     9107\n",
       "draw       950\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess['winner'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fehér győzelmek hogyan értek véget?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resign       5844\n",
       "mate         3344\n",
       "outoftime     813\n",
       "Name: victory_status, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_white_win= chess[chess['winner'] == \"white\"]\n",
    "is_white_win['victory_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Adott játék vége típuson belül fehér győzelmek aránya?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "is_white_win= chess[chess['winner'] == \"white\"]\n",
    "white_win_victory_count=is_white_win['victory_status'].value_counts()\n",
    "all_games_victory_counts=chess['victory_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "draw              NaN\n",
       "mate         0.528696\n",
       "outoftime    0.483929\n",
       "resign       0.524267\n",
       "Name: victory_status, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_win_victory_count / all_games_victory_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAFVCAYAAACemBxaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAc8UlEQVR4nO3df3SW9X3/8RckOShFW5MIiNAqKZ2/urkQz5R5ToukPfTsrLC6wWnPPG7seLqyutnjsU5klh0PK+08cqqWeWYZs9qd0s7O7tjpukytE9QBSnUr9BB0KiMVSaRVfsc73z96vHfyRU34lfsT8nj8leu6r5O8Ofe57uTJ9WtUX19fXwAAACjG6FoPAAAAQH9CDQAAoDBCDQAAoDBCDQAAoDBCDQAAoDBCDQAAoDBCDQAAoDD1tfzh27dvr+WPZwDNzc3ZuXNnrceAYcs+BEfHPgRHxz5UvkmTJr3ja46oAQAAFEaoAQAAFEaoAQAAFEaoAQAAFEaoAQAAFEaoAQAAFEaoAQAAFEaoAQAAFEaoAQAAFEaoAQAAFEaoAQAAFKa+1gMAvOXNqz5Z6xGOqVdqPcAxVnfXP9d6BAAYMRxRAwAAKIwjagAAEGd2DAcj6ewOR9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKI9QAAAAKM6jnqG3cuDGrVq1KpVLJrFmzMnfu3H6vP/roo7nnnnvS2NiYJJk9e3ZmzZp1zIcFAAAYCQYMtUqlkpUrV2bx4sVpamrKDTfckLa2tkyePLnfdjNmzMgf/dEfHbdBAQAARooBT33s7OzMxIkTM2HChNTX12fGjBlZt27dUMwGAAAwIg14RK2npydNTU3V5aampmzZsuWQ7Z566qls2rQpZ5xxRq688so0Nzcf20kBAABGiEFdozaQ6dOn5zd/8zfT0NCQf/u3f8vXv/71fOlLXzpku46OjnR0dCRJli1bJuYKV19f7z1iSL1S6wF4Vz4PGGp+DzHU/B4q30j6TBgw1BobG9Pd3V1d7u7urt405C2nnHJK9etZs2bl3nvvfdvv1d7envb29uryzp07D3tghk5zc7P3CKjyecBQ83sI+P+daJ8JkyZNesfXBrxGraWlJV1dXdmxY0d6e3uzdu3atLW19dvmtddeq369fv36Q240AgAAwOANeEStrq4uCxYsyNKlS1OpVDJz5sxMmTIlq1evTktLS9ra2vLggw9m/fr1qaury7hx47Jw4cKhmB0AAOCENKhr1FpbW9Pa2tpv3fz586tff+Yzn8lnPvOZYzsZAADACDXgqY8AAAAMLaEGAABQGKEGAABQmGPyHDUAoPbevOqTtR7hmDoRn2lVd9c/13oEYJhwRA0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwQg0AAKAwgwq1jRs35s/+7M9y9dVX5/7773/H7Z588snMmzcvW7duPVbzAQAAjDgDhlqlUsnKlSuzaNGiLF++PGvWrMm2bdsO2W7v3r158MEHM23atOMyKAAAwEgxYKh1dnZm4sSJmTBhQurr6zNjxoysW7fukO1Wr16dOXPmpKGh4bgMCgAAMFIMGGo9PT1pamqqLjc1NaWnp6ffNs8//3x27tyZ1tbWYz8hAADACFN/tN+gUqnkm9/8ZhYuXDjgth0dHeno6EiSLFu2LM3NzUf74zmO6uvrvUcMqVdqPQDvyudB+exD5bMflc0+VL6RtA8NGGqNjY3p7u6uLnd3d6exsbG6vG/fvrz88sv5y7/8yyTJrl278tWvfjVf/OIX09LS0u97tbe3p729vbq8c+fOo/4HcPw0Nzd7j4Aqnwdw9OxHcHROtH1o0qRJ7/jagKHW0tKSrq6u7NixI42NjVm7dm3+9E//tPr62LFjs3LlyurykiVLcsUVVxwSaQAAAAzOgKFWV1eXBQsWZOnSpalUKpk5c2amTJmS1atXp6WlJW1tbUMxJwAAwIgxqGvUWltbD7lRyPz589922yVLlhz1UAAAACPZoB54DQAAwNARagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIURagAAAIWpH8xGGzduzKpVq1KpVDJr1qzMnTu33+s//OEP86//+q8ZPXp0TjrppHz2s5/N5MmTj8e8AAAAJ7wBQ61SqWTlypVZvHhxmpqacsMNN6Stra1fiF166aX5+Mc/niRZv3597r777tx4443Hb2oAAIAT2ICnPnZ2dmbixImZMGFC6uvrM2PGjKxbt67fNmPHjq1+vW/fvowaNerYTwoAADBCDHhEraenJ01NTdXlpqambNmy5ZDtHnroofzgBz9Ib29vbrrppmM7JQAAwAgyqGvUBmP27NmZPXt2Hn/88dx33335/Oc/f8g2HR0d6ejoSJIsW7Yszc3Nx+rHcxzU19d7jxhSr9R6AN6Vz4Py2YfKZz8qm32ofCNpHxow1BobG9Pd3V1d7u7uTmNj4ztuP2PGjNx1111v+1p7e3va29uryzt37jycWRlizc3N3iOgyucBHD37ERydE20fmjRp0ju+NuA1ai0tLenq6sqOHTvS29ubtWvXpq2trd82XV1d1a+ffvrpnHHGGUcxLgAAwMg24BG1urq6LFiwIEuXLk2lUsnMmTMzZcqUrF69Oi0tLWlra8tDDz2U5557LnV1dRk3blz+5E/+ZChmBwAAOCEN6hq11tbWtLa29ls3f/786td/+Id/eGynAgAAGMEGPPURAACAoSXUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAACiPUAAAAClM/mI02btyYVatWpVKpZNasWZk7d26/1x944IH8+7//e+rq6nLqqafmc5/7XE4//fTjMS8AAMAJb8AjapVKJStXrsyiRYuyfPnyrFmzJtu2beu3zVlnnZVly5bllltuycUXX5x77733uA0MAABwohsw1Do7OzNx4sRMmDAh9fX1mTFjRtatW9dvmwsuuCBjxoxJkkybNi09PT3HZ1oAAIARYMBQ6+npSVNTU3W5qanpXUPs4YcfzoUXXnhMhgMAABiJBnWN2mA99thjef7557NkyZK3fb2joyMdHR1JkmXLlqW5uflY/niOsfr6eu8RQ+qVWg/Au/J5UD77UPnsR2WzD5VvJO1DA4ZaY2Njuru7q8vd3d1pbGw8ZLtnn302//RP/5QlS5akoaHhbb9Xe3t72tvbq8s7d+48kpkZIs3Nzd4joMrnARw9+xEcnRNtH5o0adI7vjbgqY8tLS3p6urKjh070tvbm7Vr16atra3fNi+88ELuuuuufPGLX8x73/veo58YAABgBBvwiFpdXV0WLFiQpUuXplKpZObMmZkyZUpWr16dlpaWtLW15d57782+ffty6623JvnlkZjrr7/+uA8PAABwIhrUNWqtra1pbW3tt27+/PnVr//iL/7i2E4FAAAwgg146iMAAABDS6gBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAURqgBAAAUpn4wG23cuDGrVq1KpVLJrFmzMnfu3H6v/+QnP8ndd9+dF198Mddcc00uvvji4zErAADAiDDgEbVKpZKVK1dm0aJFWb58edasWZNt27b126a5uTkLFy7MpZdeetwGBQAAGCkGPKLW2dmZiRMnZsKECUmSGTNmZN26dZk8eXJ1m/HjxydJRo0adZzGBAAAGDkGPKLW09OTpqam6nJTU1N6enqO61AAAAAj2aCuUTtWOjo60tHRkSRZtmxZmpubh/LHc5jq6+u9RwypV2o9AO/K50H57EPlsx+VzT5UvpG0Dw0Yao2Njenu7q4ud3d3p7Gx8Yh+WHt7e9rb26vLO3fuPKLvw9Bobm72HgFVPg/g6NmP4OicaPvQpEmT3vG1AU99bGlpSVdXV3bs2JHe3t6sXbs2bW1tx3RAAAAA/s+AR9Tq6uqyYMGCLF26NJVKJTNnzsyUKVOyevXqtLS0pK2tLZ2dnbnllluye/fubNiwId/5zndy6623DsX8AAAAJ5xBXaPW2tqa1tbWfuvmz59f/fqDH/xg7rzzzmM7GQAAwAg14KmPAAAADC2hBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUBihBgAAUJj6wWy0cePGrFq1KpVKJbNmzcrcuXP7vX7w4MHccccdef7553PKKafkmmuuyfjx44/HvAAAACe8AY+oVSqVrFy5MosWLcry5cuzZs2abNu2rd82Dz/8cN7znvfk9ttvz2/91m/lW9/61nEbGAAA4EQ3YKh1dnZm4sSJmTBhQurr6zNjxoysW7eu3zbr16/PRz/60STJxRdfnP/6r/9KX1/fcRkYAADgRDdgqPX09KSpqam63NTUlJ6ennfcpq6uLmPHjs3rr79+jEcFAAAYGQZ1jdqx0tHRkY6OjiTJsmXLMmnSpKH88RwB7xFD6gfraz0BDG/2ITg69iEKMuARtcbGxnR3d1eXu7u709jY+I7bvPnmm9mzZ09OOeWUQ75Xe3t7li1blmXLlh3t3AyBP//zP6/1CDCs2Yfg6NiH4OjYh4a3AUOtpaUlXV1d2bFjR3p7e7N27dq0tbX122b69Ol59NFHkyRPPvlkzj///IwaNeq4DAwAAHCiG/DUx7q6uixYsCBLly5NpVLJzJkzM2XKlKxevTotLS1pa2vLZZddljvuuCNXX311xo0bl2uuuWYIRgcAADgxjepze0beQUdHR9rb22s9Bgxb9iE4OvYhODr2oeFNqAEAABRmwGvUAAAAGFpCDQAAoDBCDQAAoDBCjarnnnsuBw4cqPUYMOwdOHAg27dvr/UYMCzt378///iP/5g777wzSdLV1ZUNGzbUeCqAoTfg7fkZOX70ox/lG9/4RsaNG5dzzjkn5557bs4555yMGzeu1qPBsLF+/frcc8896e3tzde//vX8z//8T1avXp3rr7++1qPBsLBixYpMnTo1W7ZsSZI0Njbm1ltvzfTp02s8GQwPTz31VL71rW/l5z//eZKkr68vo0aNyt13313jyThcQo2qz3/+80mSnp6ePPnkk1m5cmVee+21fPvb367xZDB8fPe7382Xv/zlLFmyJEly1llnZceOHbUdCoaRV155JV/4wheyZs2aJMmYMWNqPBEML/fee2+uv/76TJ48udajcJSEGlWPPfZYNm/enJdeeimnnHJKZs+enXPPPbfWY8GwUl9fn7Fjx/ZbN2rUqBpNA8NPfX19Dhw4UN1vfvazn6W+3p8rMFjve9/7RNoJwicfVXfffXcmTJiQj33sYzn//PMzfvz4Wo8Ew87kyZPz+OOPp1KppKurKw8++GA+9KEP1XosGDbmzZuXpUuXZufOnbntttvy05/+NAsXLqz1WDBsTJ06NcuXL89FF12UhoaG6vrf+I3fqOFUHAkPvKafl19+OZs2bcrmzZvT1dWVSZMm5eqrr671WDBs7N+/P9/73vfy7LPPJkl+7dd+LZdffnm/X5bAu3v99dezZcuW9PX1Zdq0aTn11FNrPRIMGytWrHjb9f7DY/gRalTt2bMnP/3pT/OTn/wkmzdvzi9+8YtMmzateu0aMLAnnngil1xyyYDrgHf24osv5tVXX82bb75ZXedoADDSOPWRqptuuinnnHNOzjnnnMyePTtNTU21HgmGnfvvv/+QKHu7dcDbW7FiRV566aVMnjw5o0f/31OEhBoMzt/93d8dsm7s2LFpaWnJRRddVIOJOFJCjapbbrml1iPAsPXMM8/kmWeeSU9PT79fknv37u33xybw7rZs2ZLly5fXegwYtg4ePJjt27fn4osvTvLL2/WPHz8+L774Yv77v/87f/AHf1DbARk0oUbVL37xi3z/+9/Ptm3b+j34+ktf+lINp4Lh4bTTTsvUqVOzfv36TJ06tbr+5JNPzpVXXlnDyWB4+dCHPpRt27a5ax0coZdeeik333xz9T8JP/7xj+emm27KzTffnGuvvbbG03E4hBpVt912W2bMmJGnn346V111VR599FEXcMMgnXXWWTnrrLNy6aWXupU4HIWPfOQjufHGG/O+970vDQ0N1Yf1OusDBueNN97Ivn37qo+K2b9/f954442MHj3aja2GGX9NUPX666/nsssuy7/8y7/kvPPOy3nnnZcbbrih1mPBsPLqq6/mH/7hH7Jt27YcPHiwuv6OO+6o4VQwfPzN3/xNrr766rz//e/3DEI4AnPmzMl1112X888/P319fdm0aVN+53d+J/v27cuHP/zhWo/HYRBqVL11FOC0007L008/ndNOOy1vvPFGjaeC4WXFihWZN29e7r777ixatCiPPPJI3FwXBu/UU09NW1tbrceAYeuyyy7Lr//6r6ezszNJ8ulPfzqNjY1JkiuuuKKWo3GYhBpVn/rUp7Jnz55cccUVWbVqVfbs2ePaGjhMBw4cyIc//OH09fXl9NNPz7x583L99ddn/vz5tR4NhoWzzz47X/va1zJ9+nQP64XD8L//+78588wz8/zzzydJ9e7du3btyq5du/pdP83wINRIklQqlXR1dWX69Ol5//vf7wYicIQaGhpSqVRyxhln5KGHHkpjY2P27dtX67Fg2Dhw4EAaGhqqD41/i1CDd/fAAw/ks5/9bO655563fd3fdsOPB15TdcMNN+TLX/5yrceAYa2zszOTJ0/O7t27s3r16uzZsydz5szJtGnTaj0aADCMCDWq/v7v/z5vvvlmZsyYkTFjxlTXO1QOg7d169Z873vfy86dO9Pb25sk7lgHg/D9738/c+bMeduH9SbJggULhngiGJ6eeOKJXHjhhTn55JNz33335YUXXsjll1+es88+u9ajcZic+kjViy++mCT5zne+02+9Q+UweLfddluuuOIKd6yDw3TmmWcm8Z+DcLTuu+++XHLJJdm8eXOee+65fPKTn8xdd92Vv/qrv6r1aBwmoUYeeOCBJElra2tGjRrV7w51/tCEw+OOdXBk3tpvxowZk0suuaTfa0888UQtRoJh6a0HXT/99NNpb29Pa2trvv3tb9d4Ko6EUCN79+5Nkmzfvj1bt26t/rLcsGFDWlpaajkaDDvz5s3LnXfemQsuuMAd6+AI3H///YeE2tutA95eY2Nj/vZv/zbPPvts5syZk4MHD3pMzDAl1Mjv/d7vJfnlKY5f+cpXcvLJJ1fXL1u2rJajwbDzyCOPZPv27ent7a3+r2Yi1GAgzzzzTJ555pn09PT0u05t7969/fYl4N194QtfyMaNG/Pbv/3bec973pPXXnstv//7v1/rsTgCQo2qXbt2VR96nfzyAdi7du2q3UAwDG3dujVf+9rXaj0GDDvjxo3L1KlTs379+n7XqZ188sme6QmHYcyYMXnve9+bzZs354wzzkhdXV3OOOOMWo/FERBqVH3kIx/JokWLctFFFyVJ1q1bl49+9KO1HQqGmV/5lV/Jtm3bMnny5FqPAsPKN77xjXzlK1/Jj3/8Y7974Ch897vfzdatW9PV1ZWZM2emt7c3t99+e26++eZaj8ZhEmpUfepTn8qFF16YzZs3J0kWLlzoVq5wmLZs2ZLrrrsu48ePT0NDQ/r6+tyeHwaht7c3jz/+eDo7O/PUU08d8rrTh2Fw/vM//zNf/epXc/311yf55TVrb92PgOFFqNHP1KlT3RoZjsKiRYtqPQIMS1dddVX+4z/+I7t3786GDRsOeV2oweDU19dn1KhR1Tt379u3r8YTcaQ88BoAKMbDDz+cyy67rNZjwLDU19eX++67Lz09PXn22Wczd+7cPPLII7n00kvziU98otbjcZiEGgBQjN7e3vzwhz/Mpk2bkiTnnXdePvaxj/W72RXwzq699tpceeWV+fGPf5y+vr5ceOGF+dVf/dVaj8UREGoAQDHuvPPO9Pb2Vm8o8thjj2X06NH54z/+49oOBsPEHXfckdmzZ+eDH/xgrUfhKPnvKQCgGFu3bs1f//VfV5cvuOCCXHfddTWcCIaXzs7OLF68OKeffnrGjBlTXe+mVsOPUAMAijF69Oj87Gc/y8SJE5Mkr7zyigdew2G48cYbaz0Cx4hTHwGAYjz33HNZsWJFJkyYkL6+vuzcuTOf+9zncsEFF9R6NIAhJdQAgGIcPHgwSbJ9+/YkyaRJk5IkDQ0NNZsJoBacSwAAFGPx4sVpaGjIBz7wgXzgAx9IQ0NDFi9eXOuxAIaca9QAgJrbtWtXenp6cuDAgbzwwgt564SfvXv3Zv/+/TWeDmDoCTUAoOY2btyYH/3oR+nu7s43v/nN6vqTTjopn/70p2s4GUBtuEYNACjGk08+mYsvvrjWYwDUnCNqAEAxXn755Wzbtu2Q9b/7u79bg2kAakeoAQDFOOmkk6pfHzx4MBs2bMiZZ55Zw4kAasOpjwBAsQ4ePJilS5dmyZIltR4FYEi5PT8AUKz9+/enu7u71mMADDmnPgIAxbj22mszatSoJElfX19+/vOf5/LLL6/xVABDz6mPAEAxXn311ezevTubNm3K7t2709ramqlTp9Z6LIAh59RHAKAY69aty+23357XX389b775ZlasWJEHH3yw1mMBDDmnPgIAxXj44YezdOnS6t0f58yZk8WLF+cTn/hEjScDGFqOqAEAxejr68vo0f/358no0aPjKg1gJHJEDQAoxsyZM3PjjTfmoosuSvLLUyEvu+yyGk8FMPTcTAQAKMrzzz+fzZs3J0nOPffcnH322TWeCGDoCTUAAIDCuEYNAACgMEINAACgMEINAACgMEINAACgMEINAACgMP8Pz8bxltXFA/0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(white_win_victory_count / all_games_victory_counts).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## A DataFrame megváltoztatása\n",
    "\n",
    "Mennyi a két játékos közötti pontszámkülönbség?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>id</th>\n",
       "      <th>rated</th>\n",
       "      <th>created_at</th>\n",
       "      <th>last_move_at</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>increment_code</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_eco</th>\n",
       "      <th>opening_name</th>\n",
       "      <th>opening_ply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TZJHLljE</td>\n",
       "      <td>False</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>13</td>\n",
       "      <td>outoftime</td>\n",
       "      <td>white</td>\n",
       "      <td>15+2</td>\n",
       "      <td>bourgris</td>\n",
       "      <td>1500</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1191</td>\n",
       "      <td>d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...</td>\n",
       "      <td>D10</td>\n",
       "      <td>Slav Defense: Exchange Variation</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>l1NXvwaE</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>16</td>\n",
       "      <td>resign</td>\n",
       "      <td>black</td>\n",
       "      <td>5+10</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1322</td>\n",
       "      <td>skinnerua</td>\n",
       "      <td>1261</td>\n",
       "      <td>d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...</td>\n",
       "      <td>B00</td>\n",
       "      <td>Nimzowitsch Defense: Kennedy Variation</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mIICvQHh</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>61</td>\n",
       "      <td>mate</td>\n",
       "      <td>white</td>\n",
       "      <td>5+10</td>\n",
       "      <td>ischia</td>\n",
       "      <td>1496</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1500</td>\n",
       "      <td>e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...</td>\n",
       "      <td>C20</td>\n",
       "      <td>King's Pawn Game: Leonardis Variation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  rated    created_at  last_move_at  turns victory_status winner  \\\n",
       "0  TZJHLljE  False  1.504210e+12  1.504210e+12     13      outoftime  white   \n",
       "1  l1NXvwaE   True  1.504130e+12  1.504130e+12     16         resign  black   \n",
       "2  mIICvQHh   True  1.504130e+12  1.504130e+12     61           mate  white   \n",
       "\n",
       "  increment_code  white_id  white_rating   black_id  black_rating  \\\n",
       "0           15+2  bourgris          1500       a-00          1191   \n",
       "1           5+10      a-00          1322  skinnerua          1261   \n",
       "2           5+10    ischia          1496       a-00          1500   \n",
       "\n",
       "                                               moves opening_eco  \\\n",
       "0  d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...         D10   \n",
       "1  d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...         B00   \n",
       "2  e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...         C20   \n",
       "\n",
       "                             opening_name  opening_ply  \n",
       "0        Slav Defense: Exchange Variation            5  \n",
       "1  Nimzowitsch Defense: Kennedy Variation            4  \n",
       "2   King's Pawn Game: Leonardis Variation            3  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "chess['Difference']=chess['white_rating']-chess['black_rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "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>id</th>\n",
       "      <th>rated</th>\n",
       "      <th>created_at</th>\n",
       "      <th>last_move_at</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>increment_code</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_eco</th>\n",
       "      <th>opening_name</th>\n",
       "      <th>opening_ply</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TZJHLljE</td>\n",
       "      <td>False</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>13</td>\n",
       "      <td>outoftime</td>\n",
       "      <td>white</td>\n",
       "      <td>15+2</td>\n",
       "      <td>bourgris</td>\n",
       "      <td>1500</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1191</td>\n",
       "      <td>d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...</td>\n",
       "      <td>D10</td>\n",
       "      <td>Slav Defense: Exchange Variation</td>\n",
       "      <td>5</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>l1NXvwaE</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>16</td>\n",
       "      <td>resign</td>\n",
       "      <td>black</td>\n",
       "      <td>5+10</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1322</td>\n",
       "      <td>skinnerua</td>\n",
       "      <td>1261</td>\n",
       "      <td>d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...</td>\n",
       "      <td>B00</td>\n",
       "      <td>Nimzowitsch Defense: Kennedy Variation</td>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mIICvQHh</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>61</td>\n",
       "      <td>mate</td>\n",
       "      <td>white</td>\n",
       "      <td>5+10</td>\n",
       "      <td>ischia</td>\n",
       "      <td>1496</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1500</td>\n",
       "      <td>e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...</td>\n",
       "      <td>C20</td>\n",
       "      <td>King's Pawn Game: Leonardis Variation</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  rated    created_at  last_move_at  turns victory_status winner  \\\n",
       "0  TZJHLljE  False  1.504210e+12  1.504210e+12     13      outoftime  white   \n",
       "1  l1NXvwaE   True  1.504130e+12  1.504130e+12     16         resign  black   \n",
       "2  mIICvQHh   True  1.504130e+12  1.504130e+12     61           mate  white   \n",
       "\n",
       "  increment_code  white_id  white_rating   black_id  black_rating  \\\n",
       "0           15+2  bourgris          1500       a-00          1191   \n",
       "1           5+10      a-00          1322  skinnerua          1261   \n",
       "2           5+10    ischia          1496       a-00          1500   \n",
       "\n",
       "                                               moves opening_eco  \\\n",
       "0  d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...         D10   \n",
       "1  d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...         B00   \n",
       "2  e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...         C20   \n",
       "\n",
       "                             opening_name  opening_ply  Difference  \n",
       "0        Slav Defense: Exchange Variation            5         309  \n",
       "1  Nimzowitsch Defense: Kennedy Variation            4          61  \n",
       "2   King's Pawn Game: Leonardis Variation            3          -4  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Itt valami gond van:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Formatted Date'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idojaras['Temperature (C)'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "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>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-04-01 00:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 01:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 02:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 03:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.41</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 04:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 19:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>26.016667</td>\n",
       "      <td>26.016667</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.9963</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1014.36</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 20:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>24.583333</td>\n",
       "      <td>24.583333</td>\n",
       "      <td>0.48</td>\n",
       "      <td>10.0947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.5526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.16</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 21:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>22.038889</td>\n",
       "      <td>22.038889</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9838</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.66</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 22:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>21.522222</td>\n",
       "      <td>21.522222</td>\n",
       "      <td>0.60</td>\n",
       "      <td>10.5294</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.95</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 23:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>20.438889</td>\n",
       "      <td>20.438889</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.8765</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15.5204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.16</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96453 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                              \n",
       "2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "2006-04-01 03:00:00.000 +0200  Partly Cloudy        rain         8.288889   \n",
       "2006-04-01 04:00:00.000 +0200  Mostly Cloudy        rain         8.755556   \n",
       "...                                      ...         ...              ...   \n",
       "2016-09-09 19:00:00.000 +0200  Partly Cloudy        rain        26.016667   \n",
       "2016-09-09 20:00:00.000 +0200  Partly Cloudy        rain        24.583333   \n",
       "2016-09-09 21:00:00.000 +0200  Partly Cloudy        rain        22.038889   \n",
       "2016-09-09 22:00:00.000 +0200  Partly Cloudy        rain        21.522222   \n",
       "2016-09-09 23:00:00.000 +0200  Partly Cloudy        rain        20.438889   \n",
       "\n",
       "                               Apparent Temperature (C)  Humidity  \\\n",
       "Formatted Date                                                      \n",
       "2006-04-01 00:00:00.000 +0200                  7.388889      0.89   \n",
       "2006-04-01 01:00:00.000 +0200                  7.227778      0.86   \n",
       "2006-04-01 02:00:00.000 +0200                  9.377778      0.89   \n",
       "2006-04-01 03:00:00.000 +0200                  5.944444      0.83   \n",
       "2006-04-01 04:00:00.000 +0200                  6.977778      0.83   \n",
       "...                                                 ...       ...   \n",
       "2016-09-09 19:00:00.000 +0200                 26.016667      0.43   \n",
       "2016-09-09 20:00:00.000 +0200                 24.583333      0.48   \n",
       "2016-09-09 21:00:00.000 +0200                 22.038889      0.56   \n",
       "2016-09-09 22:00:00.000 +0200                 21.522222      0.60   \n",
       "2016-09-09 23:00:00.000 +0200                 20.438889      0.61   \n",
       "\n",
       "                               Wind Speed (km/h)  Wind Bearing (degrees)  \\\n",
       "Formatted Date                                                             \n",
       "2006-04-01 00:00:00.000 +0200            14.1197                   251.0   \n",
       "2006-04-01 01:00:00.000 +0200            14.2646                   259.0   \n",
       "2006-04-01 02:00:00.000 +0200             3.9284                   204.0   \n",
       "2006-04-01 03:00:00.000 +0200            14.1036                   269.0   \n",
       "2006-04-01 04:00:00.000 +0200            11.0446                   259.0   \n",
       "...                                          ...                     ...   \n",
       "2016-09-09 19:00:00.000 +0200            10.9963                    31.0   \n",
       "2016-09-09 20:00:00.000 +0200            10.0947                    20.0   \n",
       "2016-09-09 21:00:00.000 +0200             8.9838                    30.0   \n",
       "2016-09-09 22:00:00.000 +0200            10.5294                    20.0   \n",
       "2016-09-09 23:00:00.000 +0200             5.8765                    39.0   \n",
       "\n",
       "                               Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                               \n",
       "2006-04-01 00:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 01:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 02:00:00.000 +0200          14.9569         0.0   \n",
       "2006-04-01 03:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 04:00:00.000 +0200          15.8263         0.0   \n",
       "...                                        ...         ...   \n",
       "2016-09-09 19:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 20:00:00.000 +0200          15.5526         0.0   \n",
       "2016-09-09 21:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 22:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 23:00:00.000 +0200          15.5204         0.0   \n",
       "\n",
       "                               Pressure (millibars)  \\\n",
       "Formatted Date                                        \n",
       "2006-04-01 00:00:00.000 +0200               1015.13   \n",
       "2006-04-01 01:00:00.000 +0200               1015.63   \n",
       "2006-04-01 02:00:00.000 +0200               1015.94   \n",
       "2006-04-01 03:00:00.000 +0200               1016.41   \n",
       "2006-04-01 04:00:00.000 +0200               1016.51   \n",
       "...                                             ...   \n",
       "2016-09-09 19:00:00.000 +0200               1014.36   \n",
       "2016-09-09 20:00:00.000 +0200               1015.16   \n",
       "2016-09-09 21:00:00.000 +0200               1015.66   \n",
       "2016-09-09 22:00:00.000 +0200               1015.95   \n",
       "2016-09-09 23:00:00.000 +0200               1016.16   \n",
       "\n",
       "                                                        Daily Summary  \n",
       "Formatted Date                                                         \n",
       "2006-04-01 00:00:00.000 +0200       Partly cloudy throughout the day.  \n",
       "2006-04-01 01:00:00.000 +0200       Partly cloudy throughout the day.  \n",
       "2006-04-01 02:00:00.000 +0200       Partly cloudy throughout the day.  \n",
       "2006-04-01 03:00:00.000 +0200       Partly cloudy throughout the day.  \n",
       "2006-04-01 04:00:00.000 +0200       Partly cloudy throughout the day.  \n",
       "...                                                               ...  \n",
       "2016-09-09 19:00:00.000 +0200  Partly cloudy starting in the morning.  \n",
       "2016-09-09 20:00:00.000 +0200  Partly cloudy starting in the morning.  \n",
       "2016-09-09 21:00:00.000 +0200  Partly cloudy starting in the morning.  \n",
       "2016-09-09 22:00:00.000 +0200  Partly cloudy starting in the morning.  \n",
       "2016-09-09 23:00:00.000 +0200  Partly cloudy starting in the morning.  \n",
       "\n",
       "[96453 rows x 11 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Szedjük szét az időpontokat két oszlopba."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Formatted Date\n",
       "2006-04-01 00:00:00.000 +0200    [Partly, Cloudy]\n",
       "2006-04-01 01:00:00.000 +0200    [Partly, Cloudy]\n",
       "2006-04-01 02:00:00.000 +0200    [Mostly, Cloudy]\n",
       "2006-04-01 03:00:00.000 +0200    [Partly, Cloudy]\n",
       "2006-04-01 04:00:00.000 +0200    [Mostly, Cloudy]\n",
       "                                       ...       \n",
       "2016-09-09 19:00:00.000 +0200    [Partly, Cloudy]\n",
       "2016-09-09 20:00:00.000 +0200    [Partly, Cloudy]\n",
       "2016-09-09 21:00:00.000 +0200    [Partly, Cloudy]\n",
       "2016-09-09 22:00:00.000 +0200    [Partly, Cloudy]\n",
       "2016-09-09 23:00:00.000 +0200    [Partly, Cloudy]\n",
       "Name: Summary, Length: 96453, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras['Summary'].str.split(\" \") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "idojaras['Date'] = idojaras.index.str.split(\" \").str[0]\n",
    "idojaras['Hour']=idojaras.index.str.split(\" \").str[1].str.split(\":\").str[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "      <th>Date</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-04-01 00:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 01:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 02:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 03:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.41</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 04:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>04</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 19:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>26.016667</td>\n",
       "      <td>26.016667</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.9963</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1014.36</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 20:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>24.583333</td>\n",
       "      <td>24.583333</td>\n",
       "      <td>0.48</td>\n",
       "      <td>10.0947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.5526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.16</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 21:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>22.038889</td>\n",
       "      <td>22.038889</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9838</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.66</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 22:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>21.522222</td>\n",
       "      <td>21.522222</td>\n",
       "      <td>0.60</td>\n",
       "      <td>10.5294</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.95</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 23:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>20.438889</td>\n",
       "      <td>20.438889</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.8765</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15.5204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.16</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96453 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                              \n",
       "2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "2006-04-01 03:00:00.000 +0200  Partly Cloudy        rain         8.288889   \n",
       "2006-04-01 04:00:00.000 +0200  Mostly Cloudy        rain         8.755556   \n",
       "...                                      ...         ...              ...   \n",
       "2016-09-09 19:00:00.000 +0200  Partly Cloudy        rain        26.016667   \n",
       "2016-09-09 20:00:00.000 +0200  Partly Cloudy        rain        24.583333   \n",
       "2016-09-09 21:00:00.000 +0200  Partly Cloudy        rain        22.038889   \n",
       "2016-09-09 22:00:00.000 +0200  Partly Cloudy        rain        21.522222   \n",
       "2016-09-09 23:00:00.000 +0200  Partly Cloudy        rain        20.438889   \n",
       "\n",
       "                               Apparent Temperature (C)  Humidity  \\\n",
       "Formatted Date                                                      \n",
       "2006-04-01 00:00:00.000 +0200                  7.388889      0.89   \n",
       "2006-04-01 01:00:00.000 +0200                  7.227778      0.86   \n",
       "2006-04-01 02:00:00.000 +0200                  9.377778      0.89   \n",
       "2006-04-01 03:00:00.000 +0200                  5.944444      0.83   \n",
       "2006-04-01 04:00:00.000 +0200                  6.977778      0.83   \n",
       "...                                                 ...       ...   \n",
       "2016-09-09 19:00:00.000 +0200                 26.016667      0.43   \n",
       "2016-09-09 20:00:00.000 +0200                 24.583333      0.48   \n",
       "2016-09-09 21:00:00.000 +0200                 22.038889      0.56   \n",
       "2016-09-09 22:00:00.000 +0200                 21.522222      0.60   \n",
       "2016-09-09 23:00:00.000 +0200                 20.438889      0.61   \n",
       "\n",
       "                               Wind Speed (km/h)  Wind Bearing (degrees)  \\\n",
       "Formatted Date                                                             \n",
       "2006-04-01 00:00:00.000 +0200            14.1197                   251.0   \n",
       "2006-04-01 01:00:00.000 +0200            14.2646                   259.0   \n",
       "2006-04-01 02:00:00.000 +0200             3.9284                   204.0   \n",
       "2006-04-01 03:00:00.000 +0200            14.1036                   269.0   \n",
       "2006-04-01 04:00:00.000 +0200            11.0446                   259.0   \n",
       "...                                          ...                     ...   \n",
       "2016-09-09 19:00:00.000 +0200            10.9963                    31.0   \n",
       "2016-09-09 20:00:00.000 +0200            10.0947                    20.0   \n",
       "2016-09-09 21:00:00.000 +0200             8.9838                    30.0   \n",
       "2016-09-09 22:00:00.000 +0200            10.5294                    20.0   \n",
       "2016-09-09 23:00:00.000 +0200             5.8765                    39.0   \n",
       "\n",
       "                               Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                               \n",
       "2006-04-01 00:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 01:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 02:00:00.000 +0200          14.9569         0.0   \n",
       "2006-04-01 03:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 04:00:00.000 +0200          15.8263         0.0   \n",
       "...                                        ...         ...   \n",
       "2016-09-09 19:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 20:00:00.000 +0200          15.5526         0.0   \n",
       "2016-09-09 21:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 22:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 23:00:00.000 +0200          15.5204         0.0   \n",
       "\n",
       "                               Pressure (millibars)  \\\n",
       "Formatted Date                                        \n",
       "2006-04-01 00:00:00.000 +0200               1015.13   \n",
       "2006-04-01 01:00:00.000 +0200               1015.63   \n",
       "2006-04-01 02:00:00.000 +0200               1015.94   \n",
       "2006-04-01 03:00:00.000 +0200               1016.41   \n",
       "2006-04-01 04:00:00.000 +0200               1016.51   \n",
       "...                                             ...   \n",
       "2016-09-09 19:00:00.000 +0200               1014.36   \n",
       "2016-09-09 20:00:00.000 +0200               1015.16   \n",
       "2016-09-09 21:00:00.000 +0200               1015.66   \n",
       "2016-09-09 22:00:00.000 +0200               1015.95   \n",
       "2016-09-09 23:00:00.000 +0200               1016.16   \n",
       "\n",
       "                                                        Daily Summary  \\\n",
       "Formatted Date                                                          \n",
       "2006-04-01 00:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 01:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 02:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 03:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 04:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "...                                                               ...   \n",
       "2016-09-09 19:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 20:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 21:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 22:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 23:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "\n",
       "                                     Date Hour  \n",
       "Formatted Date                                  \n",
       "2006-04-01 00:00:00.000 +0200  2006-04-01   00  \n",
       "2006-04-01 01:00:00.000 +0200  2006-04-01   01  \n",
       "2006-04-01 02:00:00.000 +0200  2006-04-01   02  \n",
       "2006-04-01 03:00:00.000 +0200  2006-04-01   03  \n",
       "2006-04-01 04:00:00.000 +0200  2006-04-01   04  \n",
       "...                                   ...  ...  \n",
       "2016-09-09 19:00:00.000 +0200  2016-09-09   19  \n",
       "2016-09-09 20:00:00.000 +0200  2016-09-09   20  \n",
       "2016-09-09 21:00:00.000 +0200  2016-09-09   21  \n",
       "2016-09-09 22:00:00.000 +0200  2016-09-09   22  \n",
       "2016-09-09 23:00:00.000 +0200  2016-09-09   23  \n",
       "\n",
       "[96453 rows x 13 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Formatted Date'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idojaras.sort_values(by=['Date','Hour'])['Temperature (C)'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Formatted Date'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idojaras[(idojaras['Date'] > '2006-01-01') & (idojaras['Date'] <= '2006-12-31')].sort_values(by=['Date','Hour'])['Temperature (C)'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Aggregálás"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mennyi az adott megnyitást játszók átlagpontszáma?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>id</th>\n",
       "      <th>rated</th>\n",
       "      <th>created_at</th>\n",
       "      <th>last_move_at</th>\n",
       "      <th>turns</th>\n",
       "      <th>victory_status</th>\n",
       "      <th>winner</th>\n",
       "      <th>increment_code</th>\n",
       "      <th>white_id</th>\n",
       "      <th>white_rating</th>\n",
       "      <th>black_id</th>\n",
       "      <th>black_rating</th>\n",
       "      <th>moves</th>\n",
       "      <th>opening_eco</th>\n",
       "      <th>opening_name</th>\n",
       "      <th>opening_ply</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TZJHLljE</td>\n",
       "      <td>False</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>1.504210e+12</td>\n",
       "      <td>13</td>\n",
       "      <td>outoftime</td>\n",
       "      <td>white</td>\n",
       "      <td>15+2</td>\n",
       "      <td>bourgris</td>\n",
       "      <td>1500</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1191</td>\n",
       "      <td>d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...</td>\n",
       "      <td>D10</td>\n",
       "      <td>Slav Defense: Exchange Variation</td>\n",
       "      <td>5</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>l1NXvwaE</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>16</td>\n",
       "      <td>resign</td>\n",
       "      <td>black</td>\n",
       "      <td>5+10</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1322</td>\n",
       "      <td>skinnerua</td>\n",
       "      <td>1261</td>\n",
       "      <td>d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...</td>\n",
       "      <td>B00</td>\n",
       "      <td>Nimzowitsch Defense: Kennedy Variation</td>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mIICvQHh</td>\n",
       "      <td>True</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>1.504130e+12</td>\n",
       "      <td>61</td>\n",
       "      <td>mate</td>\n",
       "      <td>white</td>\n",
       "      <td>5+10</td>\n",
       "      <td>ischia</td>\n",
       "      <td>1496</td>\n",
       "      <td>a-00</td>\n",
       "      <td>1500</td>\n",
       "      <td>e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...</td>\n",
       "      <td>C20</td>\n",
       "      <td>King's Pawn Game: Leonardis Variation</td>\n",
       "      <td>3</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  rated    created_at  last_move_at  turns victory_status winner  \\\n",
       "0  TZJHLljE  False  1.504210e+12  1.504210e+12     13      outoftime  white   \n",
       "1  l1NXvwaE   True  1.504130e+12  1.504130e+12     16         resign  black   \n",
       "2  mIICvQHh   True  1.504130e+12  1.504130e+12     61           mate  white   \n",
       "\n",
       "  increment_code  white_id  white_rating   black_id  black_rating  \\\n",
       "0           15+2  bourgris          1500       a-00          1191   \n",
       "1           5+10      a-00          1322  skinnerua          1261   \n",
       "2           5+10    ischia          1496       a-00          1500   \n",
       "\n",
       "                                               moves opening_eco  \\\n",
       "0  d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5...         D10   \n",
       "1  d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6...         B00   \n",
       "2  e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc...         C20   \n",
       "\n",
       "                             opening_name  opening_ply  Difference  \n",
       "0        Slav Defense: Exchange Variation            5         309  \n",
       "1  Nimzowitsch Defense: Kennedy Variation            4          61  \n",
       "2   King's Pawn Game: Leonardis Variation            3          -4  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "A `groupby()` függvényt használhajuk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>white_rating</th>\n",
       "      <th>black_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opening_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ponziani Opening: Romanishin Variation</th>\n",
       "      <td>998.0</td>\n",
       "      <td>1016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King's Pawn Opening: Speers</th>\n",
       "      <td>1032.0</td>\n",
       "      <td>1140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King's Gambit Accepted</th>\n",
       "      <td>1064.0</td>\n",
       "      <td>1174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Opening: Achilles-Omega Gambit</th>\n",
       "      <td>1093.5</td>\n",
       "      <td>1211.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ruy Lopez: Morphy Defense |  Arkhangelsk Variation</th>\n",
       "      <td>1103.0</td>\n",
       "      <td>1283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Opening: Anglo-Indian Defense |  Old Indian Formation</th>\n",
       "      <td>2454.0</td>\n",
       "      <td>1848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gruenfeld Defense: Botvinnik Variation</th>\n",
       "      <td>2485.0</td>\n",
       "      <td>1802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarrasch Defense: Classical Variation |  Carlsbad Variation</th>\n",
       "      <td>2586.0</td>\n",
       "      <td>1618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queen's Gambit Declined: Westphalian Variation</th>\n",
       "      <td>2619.0</td>\n",
       "      <td>1927.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Game: Modern Attack |  Murrey Variation</th>\n",
       "      <td>2621.0</td>\n",
       "      <td>1613.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1477 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    white_rating  black_rating\n",
       "opening_name                                                                  \n",
       "Ponziani Opening: Romanishin Variation                     998.0        1016.0\n",
       "King's Pawn Opening: Speers                               1032.0        1140.0\n",
       "King's Gambit Accepted                                    1064.0        1174.0\n",
       "English Opening: Achilles-Omega Gambit                    1093.5        1211.5\n",
       "Ruy Lopez: Morphy Defense |  Arkhangelsk Variation        1103.0        1283.0\n",
       "...                                                          ...           ...\n",
       "English Opening: Anglo-Indian Defense |  Old In...        2454.0        1848.0\n",
       "Gruenfeld Defense: Botvinnik Variation                    2485.0        1802.0\n",
       "Tarrasch Defense: Classical Variation |  Carlsb...        2586.0        1618.0\n",
       "Queen's Gambit Declined: Westphalian Variation            2619.0        1927.0\n",
       "Russian Game: Modern Attack |  Murrey Variation           2621.0        1613.0\n",
       "\n",
       "[1477 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[['white_rating','black_rating','opening_name']].groupby('opening_name').mean().sort_values(by=['white_rating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Több aggregálás egyszerre elvégezhető:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "stats=chess[['white_rating','opening_name','black_rating']].groupby('opening_name').agg(['mean', 'count']).sort_values(by=[('white_rating','mean')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">white_rating</th>\n",
       "      <th colspan=\"2\" halign=\"left\">black_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opening_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ponziani Opening: Romanishin Variation</th>\n",
       "      <td>998.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King's Pawn Opening: Speers</th>\n",
       "      <td>1032.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King's Gambit Accepted</th>\n",
       "      <td>1064.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Opening: Achilles-Omega Gambit</th>\n",
       "      <td>1093.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1211.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ruy Lopez: Morphy Defense |  Arkhangelsk Variation</th>\n",
       "      <td>1103.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English Opening: Anglo-Indian Defense |  Old Indian Formation</th>\n",
       "      <td>2454.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1848.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gruenfeld Defense: Botvinnik Variation</th>\n",
       "      <td>2485.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tarrasch Defense: Classical Variation |  Carlsbad Variation</th>\n",
       "      <td>2586.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queen's Gambit Declined: Westphalian Variation</th>\n",
       "      <td>2619.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1927.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russian Game: Modern Attack |  Murrey Variation</th>\n",
       "      <td>2621.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1613.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1477 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   white_rating        \\\n",
       "                                                           mean count   \n",
       "opening_name                                                            \n",
       "Ponziani Opening: Romanishin Variation                    998.0     1   \n",
       "King's Pawn Opening: Speers                              1032.0     1   \n",
       "King's Gambit Accepted                                   1064.0     1   \n",
       "English Opening: Achilles-Omega Gambit                   1093.5     2   \n",
       "Ruy Lopez: Morphy Defense |  Arkhangelsk Variation       1103.0     1   \n",
       "...                                                         ...   ...   \n",
       "English Opening: Anglo-Indian Defense |  Old In...       2454.0     1   \n",
       "Gruenfeld Defense: Botvinnik Variation                   2485.0     1   \n",
       "Tarrasch Defense: Classical Variation |  Carlsb...       2586.0     1   \n",
       "Queen's Gambit Declined: Westphalian Variation           2619.0     2   \n",
       "Russian Game: Modern Attack |  Murrey Variation          2621.0     1   \n",
       "\n",
       "                                                   black_rating        \n",
       "                                                           mean count  \n",
       "opening_name                                                           \n",
       "Ponziani Opening: Romanishin Variation                   1016.0     1  \n",
       "King's Pawn Opening: Speers                              1140.0     1  \n",
       "King's Gambit Accepted                                   1174.0     1  \n",
       "English Opening: Achilles-Omega Gambit                   1211.5     2  \n",
       "Ruy Lopez: Morphy Defense |  Arkhangelsk Variation       1283.0     1  \n",
       "...                                                         ...   ...  \n",
       "English Opening: Anglo-Indian Defense |  Old In...       1848.0     1  \n",
       "Gruenfeld Defense: Botvinnik Variation                   1802.0     1  \n",
       "Tarrasch Defense: Classical Variation |  Carlsb...       1618.0     1  \n",
       "Queen's Gambit Declined: Westphalian Variation           1927.0     2  \n",
       "Russian Game: Modern Attack |  Murrey Variation          1613.0     1  \n",
       "\n",
       "[1477 rows x 4 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">white_rating</th>\n",
       "      <th colspan=\"2\" halign=\"left\">black_rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>opening_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Van Geet Opening: Reversed Nimzowitsch</th>\n",
       "      <td>1216.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>1275.545455</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zukertort Opening: Reversed Mexican Defense</th>\n",
       "      <td>1240.294118</td>\n",
       "      <td>17</td>\n",
       "      <td>1429.411765</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Center Game</th>\n",
       "      <td>1293.307692</td>\n",
       "      <td>13</td>\n",
       "      <td>1351.384615</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ware Opening</th>\n",
       "      <td>1322.333333</td>\n",
       "      <td>24</td>\n",
       "      <td>1369.791667</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alekhine Defense: Mokele Mbembe</th>\n",
       "      <td>1332.250000</td>\n",
       "      <td>12</td>\n",
       "      <td>1217.250000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ruy Lopez: Morphy Defense |  Anderssen Variation</th>\n",
       "      <td>1885.047619</td>\n",
       "      <td>21</td>\n",
       "      <td>1799.666667</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slav Defense: Quiet Variation |  Pin Defense</th>\n",
       "      <td>1900.769231</td>\n",
       "      <td>13</td>\n",
       "      <td>1801.230769</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sicilian Defense: Grand Prix Attack</th>\n",
       "      <td>1913.466667</td>\n",
       "      <td>15</td>\n",
       "      <td>1872.733333</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ruy Lopez: Morphy Defense |  Classical Defense Deferred</th>\n",
       "      <td>1916.153846</td>\n",
       "      <td>13</td>\n",
       "      <td>1795.538462</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King's Indian Attack: Symmetrical Defense</th>\n",
       "      <td>1955.500000</td>\n",
       "      <td>12</td>\n",
       "      <td>1862.500000</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>375 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   white_rating        \\\n",
       "                                                           mean count   \n",
       "opening_name                                                            \n",
       "Van Geet Opening: Reversed Nimzowitsch              1216.000000    11   \n",
       "Zukertort Opening: Reversed Mexican Defense         1240.294118    17   \n",
       "Center Game                                         1293.307692    13   \n",
       "Ware Opening                                        1322.333333    24   \n",
       "Alekhine Defense: Mokele Mbembe                     1332.250000    12   \n",
       "...                                                         ...   ...   \n",
       "Ruy Lopez: Morphy Defense |  Anderssen Variation    1885.047619    21   \n",
       "Slav Defense: Quiet Variation |  Pin Defense        1900.769231    13   \n",
       "Sicilian Defense: Grand Prix Attack                 1913.466667    15   \n",
       "Ruy Lopez: Morphy Defense |  Classical Defense ...  1916.153846    13   \n",
       "King's Indian Attack: Symmetrical Defense           1955.500000    12   \n",
       "\n",
       "                                                   black_rating        \n",
       "                                                           mean count  \n",
       "opening_name                                                           \n",
       "Van Geet Opening: Reversed Nimzowitsch              1275.545455    11  \n",
       "Zukertort Opening: Reversed Mexican Defense         1429.411765    17  \n",
       "Center Game                                         1351.384615    13  \n",
       "Ware Opening                                        1369.791667    24  \n",
       "Alekhine Defense: Mokele Mbembe                     1217.250000    12  \n",
       "...                                                         ...   ...  \n",
       "Ruy Lopez: Morphy Defense |  Anderssen Variation    1799.666667    21  \n",
       "Slav Defense: Quiet Variation |  Pin Defense        1801.230769    13  \n",
       "Sicilian Defense: Grand Prix Attack                 1872.733333    15  \n",
       "Ruy Lopez: Morphy Defense |  Classical Defense ...  1795.538462    13  \n",
       "King's Indian Attack: Symmetrical Defense           1862.500000    12  \n",
       "\n",
       "[375 rows x 4 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats[stats[('white_rating','count')]>10].sort_values(by=[('white_rating','mean')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## String műveletek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2         True\n",
       "3        False\n",
       "4         True\n",
       "         ...  \n",
       "20053    False\n",
       "20054    False\n",
       "20055    False\n",
       "20056     True\n",
       "20057    False\n",
       "Name: moves, Length: 20058, dtype: bool"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess['moves'].str.startswith('e4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "chess['first_move']=chess['moves'].str.split(' ').str[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        d4\n",
       "1        d4\n",
       "2        e4\n",
       "3        d4\n",
       "4        e4\n",
       "         ..\n",
       "20053    d4\n",
       "20054    d4\n",
       "20055    d4\n",
       "20056    e4\n",
       "20057    d4\n",
       "Name: first_move, Length: 20058, dtype: object"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess['first_move'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Győzelmek száma első lépések szerint?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "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>winner</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first_move</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Na3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nc3</th>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nf3</th>\n",
       "      <td>725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nh3</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a3</th>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            winner\n",
       "first_move        \n",
       "Na3              4\n",
       "Nc3             99\n",
       "Nf3            725\n",
       "Nh3             15\n",
       "a3              27"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess[['first_move','winner']].groupby('first_move').agg('count').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ez nem jó!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Fehér győzelmek száma "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_move\n",
       "e4     6371\n",
       "d4     2258\n",
       "c4      383\n",
       "Nf3     373\n",
       "e3      142\n",
       "g3       85\n",
       "b3       82\n",
       "f4       68\n",
       "d3       50\n",
       "b4       48\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess.groupby('first_move')['winner'].apply(lambda ser: ser.str.contains(\"white\").sum()).nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first_move\n",
       "a3     0.629630\n",
       "Nh3    0.600000\n",
       "c3     0.553571\n",
       "b4     0.545455\n",
       "c4     0.534916\n",
       "Nf3    0.514483\n",
       "e4     0.505715\n",
       "h3     0.500000\n",
       "d4     0.499337\n",
       "b3     0.473988\n",
       "Name: winner, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chess.groupby('first_move')['winner'].apply(lambda ser: ser.str.contains(\"white\").sum()/ser.str.contains(\"white\").count()).nlargest(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Idő alapú adatok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "      <th>Date</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-04-01 00:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 01:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 02:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 03:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.41</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 04:00:00.000 +0200</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>04</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 19:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>26.016667</td>\n",
       "      <td>26.016667</td>\n",
       "      <td>0.43</td>\n",
       "      <td>10.9963</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1014.36</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 20:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>24.583333</td>\n",
       "      <td>24.583333</td>\n",
       "      <td>0.48</td>\n",
       "      <td>10.0947</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.5526</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.16</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 21:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>22.038889</td>\n",
       "      <td>22.038889</td>\n",
       "      <td>0.56</td>\n",
       "      <td>8.9838</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.66</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 22:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>21.522222</td>\n",
       "      <td>21.522222</td>\n",
       "      <td>0.60</td>\n",
       "      <td>10.5294</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.1000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.95</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-09 23:00:00.000 +0200</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>20.438889</td>\n",
       "      <td>20.438889</td>\n",
       "      <td>0.61</td>\n",
       "      <td>5.8765</td>\n",
       "      <td>39.0</td>\n",
       "      <td>15.5204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.16</td>\n",
       "      <td>Partly cloudy starting in the morning.</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96453 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                              \n",
       "2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
       "2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
       "2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
       "2006-04-01 03:00:00.000 +0200  Partly Cloudy        rain         8.288889   \n",
       "2006-04-01 04:00:00.000 +0200  Mostly Cloudy        rain         8.755556   \n",
       "...                                      ...         ...              ...   \n",
       "2016-09-09 19:00:00.000 +0200  Partly Cloudy        rain        26.016667   \n",
       "2016-09-09 20:00:00.000 +0200  Partly Cloudy        rain        24.583333   \n",
       "2016-09-09 21:00:00.000 +0200  Partly Cloudy        rain        22.038889   \n",
       "2016-09-09 22:00:00.000 +0200  Partly Cloudy        rain        21.522222   \n",
       "2016-09-09 23:00:00.000 +0200  Partly Cloudy        rain        20.438889   \n",
       "\n",
       "                               Apparent Temperature (C)  Humidity  \\\n",
       "Formatted Date                                                      \n",
       "2006-04-01 00:00:00.000 +0200                  7.388889      0.89   \n",
       "2006-04-01 01:00:00.000 +0200                  7.227778      0.86   \n",
       "2006-04-01 02:00:00.000 +0200                  9.377778      0.89   \n",
       "2006-04-01 03:00:00.000 +0200                  5.944444      0.83   \n",
       "2006-04-01 04:00:00.000 +0200                  6.977778      0.83   \n",
       "...                                                 ...       ...   \n",
       "2016-09-09 19:00:00.000 +0200                 26.016667      0.43   \n",
       "2016-09-09 20:00:00.000 +0200                 24.583333      0.48   \n",
       "2016-09-09 21:00:00.000 +0200                 22.038889      0.56   \n",
       "2016-09-09 22:00:00.000 +0200                 21.522222      0.60   \n",
       "2016-09-09 23:00:00.000 +0200                 20.438889      0.61   \n",
       "\n",
       "                               Wind Speed (km/h)  Wind Bearing (degrees)  \\\n",
       "Formatted Date                                                             \n",
       "2006-04-01 00:00:00.000 +0200            14.1197                   251.0   \n",
       "2006-04-01 01:00:00.000 +0200            14.2646                   259.0   \n",
       "2006-04-01 02:00:00.000 +0200             3.9284                   204.0   \n",
       "2006-04-01 03:00:00.000 +0200            14.1036                   269.0   \n",
       "2006-04-01 04:00:00.000 +0200            11.0446                   259.0   \n",
       "...                                          ...                     ...   \n",
       "2016-09-09 19:00:00.000 +0200            10.9963                    31.0   \n",
       "2016-09-09 20:00:00.000 +0200            10.0947                    20.0   \n",
       "2016-09-09 21:00:00.000 +0200             8.9838                    30.0   \n",
       "2016-09-09 22:00:00.000 +0200            10.5294                    20.0   \n",
       "2016-09-09 23:00:00.000 +0200             5.8765                    39.0   \n",
       "\n",
       "                               Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                               \n",
       "2006-04-01 00:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 01:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 02:00:00.000 +0200          14.9569         0.0   \n",
       "2006-04-01 03:00:00.000 +0200          15.8263         0.0   \n",
       "2006-04-01 04:00:00.000 +0200          15.8263         0.0   \n",
       "...                                        ...         ...   \n",
       "2016-09-09 19:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 20:00:00.000 +0200          15.5526         0.0   \n",
       "2016-09-09 21:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 22:00:00.000 +0200          16.1000         0.0   \n",
       "2016-09-09 23:00:00.000 +0200          15.5204         0.0   \n",
       "\n",
       "                               Pressure (millibars)  \\\n",
       "Formatted Date                                        \n",
       "2006-04-01 00:00:00.000 +0200               1015.13   \n",
       "2006-04-01 01:00:00.000 +0200               1015.63   \n",
       "2006-04-01 02:00:00.000 +0200               1015.94   \n",
       "2006-04-01 03:00:00.000 +0200               1016.41   \n",
       "2006-04-01 04:00:00.000 +0200               1016.51   \n",
       "...                                             ...   \n",
       "2016-09-09 19:00:00.000 +0200               1014.36   \n",
       "2016-09-09 20:00:00.000 +0200               1015.16   \n",
       "2016-09-09 21:00:00.000 +0200               1015.66   \n",
       "2016-09-09 22:00:00.000 +0200               1015.95   \n",
       "2016-09-09 23:00:00.000 +0200               1016.16   \n",
       "\n",
       "                                                        Daily Summary  \\\n",
       "Formatted Date                                                          \n",
       "2006-04-01 00:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 01:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 02:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 03:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "2006-04-01 04:00:00.000 +0200       Partly cloudy throughout the day.   \n",
       "...                                                               ...   \n",
       "2016-09-09 19:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 20:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 21:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 22:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "2016-09-09 23:00:00.000 +0200  Partly cloudy starting in the morning.   \n",
       "\n",
       "                                     Date Hour  \n",
       "Formatted Date                                  \n",
       "2006-04-01 00:00:00.000 +0200  2006-04-01   00  \n",
       "2006-04-01 01:00:00.000 +0200  2006-04-01   01  \n",
       "2006-04-01 02:00:00.000 +0200  2006-04-01   02  \n",
       "2006-04-01 03:00:00.000 +0200  2006-04-01   03  \n",
       "2006-04-01 04:00:00.000 +0200  2006-04-01   04  \n",
       "...                                   ...  ...  \n",
       "2016-09-09 19:00:00.000 +0200  2016-09-09   19  \n",
       "2016-09-09 20:00:00.000 +0200  2016-09-09   20  \n",
       "2016-09-09 21:00:00.000 +0200  2016-09-09   21  \n",
       "2016-09-09 22:00:00.000 +0200  2016-09-09   22  \n",
       "2016-09-09 23:00:00.000 +0200  2016-09-09   23  \n",
       "\n",
       "[96453 rows x 13 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Summary</th>\n",
       "      <th>Precip Type</th>\n",
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       "      <th>Formatted Date</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2006-04-01 00:00:00</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 01:00:00</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 02:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 03:00:00</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.41</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 04:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                    \n",
       "2006-04-01 00:00:00  Partly Cloudy        rain         9.472222   \n",
       "2006-04-01 01:00:00  Partly Cloudy        rain         9.355556   \n",
       "2006-04-01 02:00:00  Mostly Cloudy        rain         9.377778   \n",
       "2006-04-01 03:00:00  Partly Cloudy        rain         8.288889   \n",
       "2006-04-01 04:00:00  Mostly Cloudy        rain         8.755556   \n",
       "\n",
       "                     Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "Formatted Date                                                               \n",
       "2006-04-01 00:00:00                  7.388889      0.89            14.1197   \n",
       "2006-04-01 01:00:00                  7.227778      0.86            14.2646   \n",
       "2006-04-01 02:00:00                  9.377778      0.89             3.9284   \n",
       "2006-04-01 03:00:00                  5.944444      0.83            14.1036   \n",
       "2006-04-01 04:00:00                  6.977778      0.83            11.0446   \n",
       "\n",
       "                     Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                                             \n",
       "2006-04-01 00:00:00                   251.0          15.8263         0.0   \n",
       "2006-04-01 01:00:00                   259.0          15.8263         0.0   \n",
       "2006-04-01 02:00:00                   204.0          14.9569         0.0   \n",
       "2006-04-01 03:00:00                   269.0          15.8263         0.0   \n",
       "2006-04-01 04:00:00                   259.0          15.8263         0.0   \n",
       "\n",
       "                     Pressure (millibars)                      Daily Summary  \\\n",
       "Formatted Date                                                                 \n",
       "2006-04-01 00:00:00               1015.13  Partly cloudy throughout the day.   \n",
       "2006-04-01 01:00:00               1015.63  Partly cloudy throughout the day.   \n",
       "2006-04-01 02:00:00               1015.94  Partly cloudy throughout the day.   \n",
       "2006-04-01 03:00:00               1016.41  Partly cloudy throughout the day.   \n",
       "2006-04-01 04:00:00               1016.51  Partly cloudy throughout the day.   \n",
       "\n",
       "                           Date Hour  \n",
       "Formatted Date                        \n",
       "2006-04-01 00:00:00  2006-04-01   00  \n",
       "2006-04-01 01:00:00  2006-04-01   01  \n",
       "2006-04-01 02:00:00  2006-04-01   02  \n",
       "2006-04-01 03:00:00  2006-04-01   03  \n",
       "2006-04-01 04:00:00  2006-04-01   04  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras.index=idojaras.index.str[:19]\n",
    "idojaras.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Átalakítás a Pandas idő formátumává"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "idojaras.index = pd.to_datetime(idojaras.index, format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "      <th>Date</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-04-01 00:00:00</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.472222</td>\n",
       "      <td>7.388889</td>\n",
       "      <td>0.89</td>\n",
       "      <td>14.1197</td>\n",
       "      <td>251.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.13</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 01:00:00</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.355556</td>\n",
       "      <td>7.227778</td>\n",
       "      <td>0.86</td>\n",
       "      <td>14.2646</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.63</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 02:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>9.377778</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.9284</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.9569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.94</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 03:00:00</th>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.288889</td>\n",
       "      <td>5.944444</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14.1036</td>\n",
       "      <td>269.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.41</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-01 04:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>8.755556</td>\n",
       "      <td>6.977778</td>\n",
       "      <td>0.83</td>\n",
       "      <td>11.0446</td>\n",
       "      <td>259.0</td>\n",
       "      <td>15.8263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.51</td>\n",
       "      <td>Partly cloudy throughout the day.</td>\n",
       "      <td>2006-04-01</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                    \n",
       "2006-04-01 00:00:00  Partly Cloudy        rain         9.472222   \n",
       "2006-04-01 01:00:00  Partly Cloudy        rain         9.355556   \n",
       "2006-04-01 02:00:00  Mostly Cloudy        rain         9.377778   \n",
       "2006-04-01 03:00:00  Partly Cloudy        rain         8.288889   \n",
       "2006-04-01 04:00:00  Mostly Cloudy        rain         8.755556   \n",
       "\n",
       "                     Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "Formatted Date                                                               \n",
       "2006-04-01 00:00:00                  7.388889      0.89            14.1197   \n",
       "2006-04-01 01:00:00                  7.227778      0.86            14.2646   \n",
       "2006-04-01 02:00:00                  9.377778      0.89             3.9284   \n",
       "2006-04-01 03:00:00                  5.944444      0.83            14.1036   \n",
       "2006-04-01 04:00:00                  6.977778      0.83            11.0446   \n",
       "\n",
       "                     Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                                             \n",
       "2006-04-01 00:00:00                   251.0          15.8263         0.0   \n",
       "2006-04-01 01:00:00                   259.0          15.8263         0.0   \n",
       "2006-04-01 02:00:00                   204.0          14.9569         0.0   \n",
       "2006-04-01 03:00:00                   269.0          15.8263         0.0   \n",
       "2006-04-01 04:00:00                   259.0          15.8263         0.0   \n",
       "\n",
       "                     Pressure (millibars)                      Daily Summary  \\\n",
       "Formatted Date                                                                 \n",
       "2006-04-01 00:00:00               1015.13  Partly cloudy throughout the day.   \n",
       "2006-04-01 01:00:00               1015.63  Partly cloudy throughout the day.   \n",
       "2006-04-01 02:00:00               1015.94  Partly cloudy throughout the day.   \n",
       "2006-04-01 03:00:00               1016.41  Partly cloudy throughout the day.   \n",
       "2006-04-01 04:00:00               1016.51  Partly cloudy throughout the day.   \n",
       "\n",
       "                           Date Hour  \n",
       "Formatted Date                        \n",
       "2006-04-01 00:00:00  2006-04-01   00  \n",
       "2006-04-01 01:00:00  2006-04-01   01  \n",
       "2006-04-01 02:00:00  2006-04-01   02  \n",
       "2006-04-01 03:00:00  2006-04-01   03  \n",
       "2006-04-01 04:00:00  2006-04-01   04  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2006-04-01 00:00:00', '2006-04-01 01:00:00',\n",
       "               '2006-04-01 02:00:00', '2006-04-01 03:00:00',\n",
       "               '2006-04-01 04:00:00', '2006-04-01 05:00:00',\n",
       "               '2006-04-01 06:00:00', '2006-04-01 07:00:00',\n",
       "               '2006-04-01 08:00:00', '2006-04-01 09:00:00',\n",
       "               ...\n",
       "               '2016-09-09 14:00:00', '2016-09-09 15:00:00',\n",
       "               '2016-09-09 16:00:00', '2016-09-09 17:00:00',\n",
       "               '2016-09-09 18:00:00', '2016-09-09 19:00:00',\n",
       "               '2016-09-09 20:00:00', '2016-09-09 21:00:00',\n",
       "               '2016-09-09 22:00:00', '2016-09-09 23:00:00'],\n",
       "              dtype='datetime64[ns]', name='Formatted Date', length=96453, freq=None)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idojaras.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Vegyük csak a 2006, 2007, 2008 évek adatait. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "      <th>Date</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-01 01:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.161111</td>\n",
       "      <td>-3.238889</td>\n",
       "      <td>0.85</td>\n",
       "      <td>16.6152</td>\n",
       "      <td>139.0</td>\n",
       "      <td>9.9015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1016.15</td>\n",
       "      <td>Mostly cloudy throughout the day.</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01 02:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>-3.155556</td>\n",
       "      <td>0.82</td>\n",
       "      <td>20.2538</td>\n",
       "      <td>140.0</td>\n",
       "      <td>9.9015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.87</td>\n",
       "      <td>Mostly cloudy throughout the day.</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01 03:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.711111</td>\n",
       "      <td>-2.194444</td>\n",
       "      <td>0.82</td>\n",
       "      <td>14.4900</td>\n",
       "      <td>140.0</td>\n",
       "      <td>9.9015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1015.56</td>\n",
       "      <td>Mostly cloudy throughout the day.</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01 04:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.183333</td>\n",
       "      <td>-2.744444</td>\n",
       "      <td>0.86</td>\n",
       "      <td>13.9426</td>\n",
       "      <td>134.0</td>\n",
       "      <td>9.9015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1014.98</td>\n",
       "      <td>Mostly cloudy throughout the day.</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01 05:00:00</th>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.205556</td>\n",
       "      <td>-3.072222</td>\n",
       "      <td>0.85</td>\n",
       "      <td>15.9068</td>\n",
       "      <td>149.0</td>\n",
       "      <td>9.9820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1014.08</td>\n",
       "      <td>Mostly cloudy throughout the day.</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>05</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-30 20:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>snow</td>\n",
       "      <td>-6.038889</td>\n",
       "      <td>-6.038889</td>\n",
       "      <td>0.88</td>\n",
       "      <td>4.7334</td>\n",
       "      <td>212.0</td>\n",
       "      <td>4.0733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1042.91</td>\n",
       "      <td>Overcast throughout the day.</td>\n",
       "      <td>2008-12-30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-30 21:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>snow</td>\n",
       "      <td>-5.533333</td>\n",
       "      <td>-5.533333</td>\n",
       "      <td>0.82</td>\n",
       "      <td>3.1234</td>\n",
       "      <td>244.0</td>\n",
       "      <td>4.0733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1042.80</td>\n",
       "      <td>Overcast throughout the day.</td>\n",
       "      <td>2008-12-30</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-30 22:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>snow</td>\n",
       "      <td>-6.077778</td>\n",
       "      <td>-6.077778</td>\n",
       "      <td>0.85</td>\n",
       "      <td>3.3810</td>\n",
       "      <td>314.0</td>\n",
       "      <td>6.2951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1042.57</td>\n",
       "      <td>Overcast throughout the day.</td>\n",
       "      <td>2008-12-30</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-30 23:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>snow</td>\n",
       "      <td>-6.088889</td>\n",
       "      <td>-6.088889</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.6726</td>\n",
       "      <td>146.0</td>\n",
       "      <td>6.0697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1042.41</td>\n",
       "      <td>Overcast throughout the day.</td>\n",
       "      <td>2008-12-30</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31 00:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>snow</td>\n",
       "      <td>-6.088889</td>\n",
       "      <td>-6.088889</td>\n",
       "      <td>0.85</td>\n",
       "      <td>3.0751</td>\n",
       "      <td>277.0</td>\n",
       "      <td>6.0697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1042.21</td>\n",
       "      <td>Foggy starting overnight continuing until even...</td>\n",
       "      <td>2008-12-31</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26280 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                                    \n",
       "2006-01-01 01:00:00  Mostly Cloudy        rain         1.161111   \n",
       "2006-01-01 02:00:00  Mostly Cloudy        rain         1.666667   \n",
       "2006-01-01 03:00:00       Overcast        rain         1.711111   \n",
       "2006-01-01 04:00:00  Mostly Cloudy        rain         1.183333   \n",
       "2006-01-01 05:00:00  Mostly Cloudy        rain         1.205556   \n",
       "...                            ...         ...              ...   \n",
       "2008-12-30 20:00:00       Overcast        snow        -6.038889   \n",
       "2008-12-30 21:00:00       Overcast        snow        -5.533333   \n",
       "2008-12-30 22:00:00       Overcast        snow        -6.077778   \n",
       "2008-12-30 23:00:00       Overcast        snow        -6.088889   \n",
       "2008-12-31 00:00:00       Overcast        snow        -6.088889   \n",
       "\n",
       "                     Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "Formatted Date                                                               \n",
       "2006-01-01 01:00:00                 -3.238889      0.85            16.6152   \n",
       "2006-01-01 02:00:00                 -3.155556      0.82            20.2538   \n",
       "2006-01-01 03:00:00                 -2.194444      0.82            14.4900   \n",
       "2006-01-01 04:00:00                 -2.744444      0.86            13.9426   \n",
       "2006-01-01 05:00:00                 -3.072222      0.85            15.9068   \n",
       "...                                       ...       ...                ...   \n",
       "2008-12-30 20:00:00                 -6.038889      0.88             4.7334   \n",
       "2008-12-30 21:00:00                 -5.533333      0.82             3.1234   \n",
       "2008-12-30 22:00:00                 -6.077778      0.85             3.3810   \n",
       "2008-12-30 23:00:00                 -6.088889      0.84             2.6726   \n",
       "2008-12-31 00:00:00                 -6.088889      0.85             3.0751   \n",
       "\n",
       "                     Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                                             \n",
       "2006-01-01 01:00:00                   139.0           9.9015         0.0   \n",
       "2006-01-01 02:00:00                   140.0           9.9015         0.0   \n",
       "2006-01-01 03:00:00                   140.0           9.9015         0.0   \n",
       "2006-01-01 04:00:00                   134.0           9.9015         0.0   \n",
       "2006-01-01 05:00:00                   149.0           9.9820         0.0   \n",
       "...                                     ...              ...         ...   \n",
       "2008-12-30 20:00:00                   212.0           4.0733         0.0   \n",
       "2008-12-30 21:00:00                   244.0           4.0733         0.0   \n",
       "2008-12-30 22:00:00                   314.0           6.2951         0.0   \n",
       "2008-12-30 23:00:00                   146.0           6.0697         0.0   \n",
       "2008-12-31 00:00:00                   277.0           6.0697         0.0   \n",
       "\n",
       "                     Pressure (millibars)  \\\n",
       "Formatted Date                              \n",
       "2006-01-01 01:00:00               1016.15   \n",
       "2006-01-01 02:00:00               1015.87   \n",
       "2006-01-01 03:00:00               1015.56   \n",
       "2006-01-01 04:00:00               1014.98   \n",
       "2006-01-01 05:00:00               1014.08   \n",
       "...                                   ...   \n",
       "2008-12-30 20:00:00               1042.91   \n",
       "2008-12-30 21:00:00               1042.80   \n",
       "2008-12-30 22:00:00               1042.57   \n",
       "2008-12-30 23:00:00               1042.41   \n",
       "2008-12-31 00:00:00               1042.21   \n",
       "\n",
       "                                                         Daily Summary  \\\n",
       "Formatted Date                                                           \n",
       "2006-01-01 01:00:00                  Mostly cloudy throughout the day.   \n",
       "2006-01-01 02:00:00                  Mostly cloudy throughout the day.   \n",
       "2006-01-01 03:00:00                  Mostly cloudy throughout the day.   \n",
       "2006-01-01 04:00:00                  Mostly cloudy throughout the day.   \n",
       "2006-01-01 05:00:00                  Mostly cloudy throughout the day.   \n",
       "...                                                                ...   \n",
       "2008-12-30 20:00:00                       Overcast throughout the day.   \n",
       "2008-12-30 21:00:00                       Overcast throughout the day.   \n",
       "2008-12-30 22:00:00                       Overcast throughout the day.   \n",
       "2008-12-30 23:00:00                       Overcast throughout the day.   \n",
       "2008-12-31 00:00:00  Foggy starting overnight continuing until even...   \n",
       "\n",
       "                           Date Hour  \n",
       "Formatted Date                        \n",
       "2006-01-01 01:00:00  2006-01-01   01  \n",
       "2006-01-01 02:00:00  2006-01-01   02  \n",
       "2006-01-01 03:00:00  2006-01-01   03  \n",
       "2006-01-01 04:00:00  2006-01-01   04  \n",
       "2006-01-01 05:00:00  2006-01-01   05  \n",
       "...                         ...  ...  \n",
       "2008-12-30 20:00:00  2008-12-30   20  \n",
       "2008-12-30 21:00:00  2008-12-30   21  \n",
       "2008-12-30 22:00:00  2008-12-30   22  \n",
       "2008-12-30 23:00:00  2008-12-30   23  \n",
       "2008-12-31 00:00:00  2008-12-31   00  \n",
       "\n",
       "[26280 rows x 13 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ido_2016_2018=idojaras[(idojaras.index > '2006-01-01') & (idojaras.index <= '2008-12-31')].sort_index()\n",
    "ido_2016_2018 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Resample()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Formatted Date'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ido_2016_2018['Temperature (C)'].resample('M').apply(np.mean).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Három havonta vett minimum, maximum és átlag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-31</th>\n",
       "      <td>-14.088889</td>\n",
       "      <td>7.505556</td>\n",
       "      <td>-1.677314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-04-30</th>\n",
       "      <td>-13.927778</td>\n",
       "      <td>25.044444</td>\n",
       "      <td>5.816305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07-31</th>\n",
       "      <td>4.816667</td>\n",
       "      <td>34.005556</td>\n",
       "      <td>19.526449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-10-31</th>\n",
       "      <td>-5.994444</td>\n",
       "      <td>32.638889</td>\n",
       "      <td>16.826113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>-8.594444</td>\n",
       "      <td>18.883333</td>\n",
       "      <td>4.717391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-04-30</th>\n",
       "      <td>-4.183333</td>\n",
       "      <td>25.094444</td>\n",
       "      <td>8.633461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-07-31</th>\n",
       "      <td>1.361111</td>\n",
       "      <td>39.905556</td>\n",
       "      <td>21.060213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-10-31</th>\n",
       "      <td>-2.777778</td>\n",
       "      <td>37.127778</td>\n",
       "      <td>16.099004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-31</th>\n",
       "      <td>-11.127778</td>\n",
       "      <td>13.794444</td>\n",
       "      <td>1.265879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-30</th>\n",
       "      <td>-11.111111</td>\n",
       "      <td>23.927778</td>\n",
       "      <td>7.692553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-07-31</th>\n",
       "      <td>4.005556</td>\n",
       "      <td>35.027778</td>\n",
       "      <td>20.192354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-10-31</th>\n",
       "      <td>1.777778</td>\n",
       "      <td>37.755556</td>\n",
       "      <td>17.031314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-31</th>\n",
       "      <td>-7.805556</td>\n",
       "      <td>21.255556</td>\n",
       "      <td>5.237108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      min        max       mean\n",
       "Formatted Date                                 \n",
       "2006-01-31     -14.088889   7.505556  -1.677314\n",
       "2006-04-30     -13.927778  25.044444   5.816305\n",
       "2006-07-31       4.816667  34.005556  19.526449\n",
       "2006-10-31      -5.994444  32.638889  16.826113\n",
       "2007-01-31      -8.594444  18.883333   4.717391\n",
       "2007-04-30      -4.183333  25.094444   8.633461\n",
       "2007-07-31       1.361111  39.905556  21.060213\n",
       "2007-10-31      -2.777778  37.127778  16.099004\n",
       "2008-01-31     -11.127778  13.794444   1.265879\n",
       "2008-04-30     -11.111111  23.927778   7.692553\n",
       "2008-07-31       4.005556  35.027778  20.192354\n",
       "2008-10-31       1.777778  37.755556  17.031314\n",
       "2009-01-31      -7.805556  21.255556   5.237108"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ido_2016_2018.resample('3M').agg(['min','max', 'mean'])['Temperature (C)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "        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>Summary</th>\n",
       "      <th>Precip Type</th>\n",
       "      <th>Temperature (C)</th>\n",
       "      <th>Apparent Temperature (C)</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind Speed (km/h)</th>\n",
       "      <th>Wind Bearing (degrees)</th>\n",
       "      <th>Visibility (km)</th>\n",
       "      <th>Loud Cover</th>\n",
       "      <th>Pressure (millibars)</th>\n",
       "      <th>Daily Summary</th>\n",
       "      <th>Date</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formatted Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-01-05 00:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>-2.738889</td>\n",
       "      <td>0.92</td>\n",
       "      <td>17.3880</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.9015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1023.10</td>\n",
       "      <td>Foggy in the morning.</td>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05 01:00:00</th>\n",
       "      <td>Foggy</td>\n",
       "      <td>rain</td>\n",
       "      <td>2.633333</td>\n",
       "      <td>2.633333</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2.6887</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.4794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1020.86</td>\n",
       "      <td>Foggy in the morning.</td>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05 02:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.688889</td>\n",
       "      <td>-3.161111</td>\n",
       "      <td>0.92</td>\n",
       "      <td>20.5275</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.1985</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1022.27</td>\n",
       "      <td>Foggy in the morning.</td>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05 03:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.161111</td>\n",
       "      <td>-2.222222</td>\n",
       "      <td>0.95</td>\n",
       "      <td>11.3022</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.2021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1022.13</td>\n",
       "      <td>Foggy in the morning.</td>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-05 04:00:00</th>\n",
       "      <td>Overcast</td>\n",
       "      <td>rain</td>\n",
       "      <td>1.216667</td>\n",
       "      <td>-2.083333</td>\n",
       "      <td>0.96</td>\n",
       "      <td>10.9802</td>\n",
       "      <td>31.0</td>\n",
       "      <td>4.0572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1021.80</td>\n",
       "      <td>Foggy in the morning.</td>\n",
       "      <td>2006-01-05</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Summary Precip Type  Temperature (C)  \\\n",
       "Formatted Date                                               \n",
       "2006-01-05 00:00:00  Overcast        rain         1.666667   \n",
       "2006-01-05 01:00:00     Foggy        rain         2.633333   \n",
       "2006-01-05 02:00:00  Overcast        rain         1.688889   \n",
       "2006-01-05 03:00:00  Overcast        rain         1.161111   \n",
       "2006-01-05 04:00:00  Overcast        rain         1.216667   \n",
       "\n",
       "                     Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
       "Formatted Date                                                               \n",
       "2006-01-05 00:00:00                 -2.738889      0.92            17.3880   \n",
       "2006-01-05 01:00:00                  2.633333      0.96             2.6887   \n",
       "2006-01-05 02:00:00                 -3.161111      0.92            20.5275   \n",
       "2006-01-05 03:00:00                 -2.222222      0.95            11.3022   \n",
       "2006-01-05 04:00:00                 -2.083333      0.96            10.9802   \n",
       "\n",
       "                     Wind Bearing (degrees)  Visibility (km)  Loud Cover  \\\n",
       "Formatted Date                                                             \n",
       "2006-01-05 00:00:00                    30.0           9.9015         0.0   \n",
       "2006-01-05 01:00:00                   325.0           2.4794         0.0   \n",
       "2006-01-05 02:00:00                    30.0           6.1985         0.0   \n",
       "2006-01-05 03:00:00                    41.0           4.2021         0.0   \n",
       "2006-01-05 04:00:00                    31.0           4.0572         0.0   \n",
       "\n",
       "                     Pressure (millibars)          Daily Summary        Date  \\\n",
       "Formatted Date                                                                 \n",
       "2006-01-05 00:00:00               1023.10  Foggy in the morning.  2006-01-05   \n",
       "2006-01-05 01:00:00               1020.86  Foggy in the morning.  2006-01-05   \n",
       "2006-01-05 02:00:00               1022.27  Foggy in the morning.  2006-01-05   \n",
       "2006-01-05 03:00:00               1022.13  Foggy in the morning.  2006-01-05   \n",
       "2006-01-05 04:00:00               1021.80  Foggy in the morning.  2006-01-05   \n",
       "\n",
       "                    Hour  \n",
       "Formatted Date            \n",
       "2006-01-05 00:00:00   00  \n",
       "2006-01-05 01:00:00   01  \n",
       "2006-01-05 02:00:00   02  \n",
       "2006-01-05 03:00:00   03  \n",
       "2006-01-05 04:00:00   04  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_foggy=ido_2016_2018['Daily Summary'].str.contains('Fog')\n",
    "ido_2016_2018[is_foggy].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Formatted Date'>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_foggy=is_foggy.astype(float)\n",
    "is_foggy.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Formatted Date'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_foggy.astype(float).resample('M').apply(np.mean).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}