estatistica2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aula Estatística 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy.random import seed\n",
    "from numpy.random import randn\n",
    "from numpy import mean\n",
    "from numpy import std\n",
    "from numpy import median\n",
    "import pandas as pd\n",
    "#import chart_studio.plotly as py\n",
    "import plotly.figure_factory as ff\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import pandas as pd\n",
    "import warnings, sys, os, fnmatch\n",
    "from scipy.stats import kurtosis, skew, mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "moda =  ModeResult(mode=array([290.15]), count=array([98]))\n",
      "mediana =  290.11875\n"
     ]
    }
   ],
   "source": [
    "\n",
    "wet = pd.read_csv('temperature.csv')\n",
    "wet = wet.iloc[:,6].dropna()\n",
    "\n",
    "# Source:  Sokal, R.R. and F.J. Rohlf, 1968. Biometry, \n",
    "# Freeman Publishing Co., p 109.  Original data from Sokal, \n",
    "# R.R. and P.E. Hunter. 1955. A morphometric analysis of DDT-resistant \n",
    "# and non-resistant housefly strains Ann. Entomol. Soc. Amer. 48: 499-507.\n",
    "\n",
    "fly = pd.read_csv('housefly.csv')\n",
    "\n",
    "current_df = wet\n",
    "\n",
    "print('moda = ',mode(current_df))\n",
    "print('mediana = ',median(current_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45252.000000\n",
       "mean       290.215044\n",
       "std          5.889992\n",
       "min        265.783333\n",
       "25%        286.254750\n",
       "50%        290.118750\n",
       "75%        294.107542\n",
       "max        313.360000\n",
       "Name: San Diego, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribuição normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_df = pd.DataFrame(np.random.normal(0, 0.1, 100),columns = ['values'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f534a709a90>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "current_df.hist(bins=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Curtose e assimetria\n",
    "\n",
    "A curtose (kurtosis) está associada ao achatamento da distribuição.\n",
    "\n",
    "* Platicúrtica: < 0.\n",
    "* Mesocúrtica: = 0.\n",
    "* Leptocúrtica: > 0.\n",
    "\n",
    "Uma Distribuição é Simétrica quando seus valores de Média, Mediana e Moda coincidem. A comparação entre o valor da Média e o valor da Moda, dá, portanto, uma indicação da inclinação da distribuição (skewness).\n",
    "\n",
    "* Distribuição assimétrica à esquerda ou assimétrica negativa (left skewed ou negative skewed):média < mediana < moda.\n",
    "* Distribuição assimétrica à direita ou assimétrica positiva (right skewed ou positive skewed): média > mediana > moda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.23328203]\n",
      "[0.29990373]\n"
     ]
    }
   ],
   "source": [
    "print(kurtosis(current_df))\n",
    "print(skew(current_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gráfico quantil-quantil\n",
    "\n",
    "![](https://miro.medium.com/max/475/0*EfnZXzw3KDz-HN19.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.gofplots import qqplot\n",
    "\n",
    "qqplot_data = qqplot(current_df, line='s').gca().lines\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace({\n",
    "    'type': 'scatter',\n",
    "    'x': qqplot_data[0].get_xdata(),\n",
    "    'y': qqplot_data[0].get_ydata(),\n",
    "    'mode': 'markers',\n",
    "    'marker': {\n",
    "        'color': '#19d3f3'\n",
    "    }\n",
    "})\n",
    "\n",
    "fig.add_trace({\n",
    "    'type': 'scatter',\n",
    "    'x': qqplot_data[1].get_xdata(),\n",
    "    'y': qqplot_data[1].get_ydata(),\n",
    "    'mode': 'lines',\n",
    "    'line': {\n",
    "        'color': '#636efa'\n",
    "    }\n",
    "\n",
    "})\n",
    "\n",
    "\n",
    "fig['layout'].update({\n",
    "    'title': 'Quantile-Quantile Plot',\n",
    "    'xaxis': {\n",
    "        'title': 'Theoritical Quantities',\n",
    "        'zeroline': False\n",
    "    },\n",
    "    'yaxis': {\n",
    "        'title': 'Sample Quantities'\n",
    "    },\n",
    "    'showlegend': False,\n",
    "    'width': 800,\n",
    "    'height': 700,\n",
    "})\n",
    "\n",
    "\n",
    "iplot(fig, filename='normality-QQ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teste Shapiro-Wilk \n",
    "\n",
    "O teste de Shapiro-Wilk testa a hipótese nula que uma amostra retirada de uma população, tem distribuição normal. \n",
    "\n",
    "\n",
    "Valor-p (também chamado de nível descritivo ou probabilidade de significância, é a probabilidade de se obter uma estatística de teste igual ou mais extrema que aquela observada em uma amostra, sob a hipótese nula. Por exemplo, em testes de hipótese, pode-se rejeitar a hipótese nula a 5% caso o valor-p seja menor que 5%. Assim, uma outra interpretação para o valor-p, é que este é o menor nível de significância com que se rejeitaria a hipótese nula. Em termos gerais, um valor-p pequeno significa que a probabilidade de obter um valor da estatística de teste como o observado é muito improvável, levando assim à rejeição da hipótese nula.\n",
    "\n",
    "Em um teste clássico de hipóteses, são definidas duas hipóteses, a nula (H0) e a alternativa (HA). Em muitas aplicações da estatística, convenciona-se definir a hipótese alternativa como a hipótese formulada pelo pesquisador, enquanto a hipótese nula é o seu complemento. A princípio, a hipótese nula é considerada a verdadeira. Ao confrontarmos a hipótese nula com os achados de uma amostra aleatória tomada de uma população de interesse, verifica-se a sua plausibilidade em termos probabilísticos, o que nos leva a rejeitarmos ou não H0. Se não rejeitamos H0, tomamo-la como verdadeira; caso contrário, tomamos HA como verdadeira.\n",
    "\n",
    "No entanto, por utilizarmos nesta tomada de decisão uma amostra (uma parte da população) e não a população inteira, podemos cometer dois tipos de erro. Cometemos um erro tipo I quando rejeitamos H0 e H0 é verdadeira, e cometemos um erro tipo II quando não rejeitamos H0 e H0 é falsa. A tabela abaixo descreve estas situações."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=0.999, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jfialho/.conda/envs/ml_default/lib/python3.7/site-packages/scipy/stats/morestats.py:1660: UserWarning:\n",
      "\n",
      "p-value may not be accurate for N > 5000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import shapiro\n",
    "stat, p = shapiro(current_df)\n",
    "\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
    "# interpret\n",
    "if p > alpha:\n",
    "\tprint('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "\tprint('Sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Teste D'Agostino K2\n",
    "\n",
    "Testa a hipótese nula que uma amostra retirada de uma população, tem distribuição normal (Utiliza Curtose e Skew). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
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          [
           0,
           "#2a3f5f"
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         "hoverinfo": "none",
         "opacity": 0.75,
         "showscale": false,
         "type": "heatmap",
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          ],
          [
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        }
       ],
       "layout": {
        "annotations": [
         {
          "align": "left",
          "font": {
           "color": "#ffffff"
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          "showarrow": false,
          "text": "<b>Length of the sample data</b>",
          "x": -0.45,
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          "y": 0,
          "yref": "y"
         },
         {
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          "showarrow": false,
          "text": "<b>Test Statistic</b>",
          "x": 0.55,
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          "y": 0,
          "yref": "y"
         },
         {
          "align": "left",
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          "showarrow": false,
          "text": "<b>p-value</b>",
          "x": 1.55,
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          "showarrow": false,
          "text": "<b>Comments</b>",
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         {
          "align": "left",
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          "showarrow": false,
          "text": "45252",
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          "xanchor": "left",
          "xref": "x",
          "y": 1,
          "yref": "y"
         },
         {
          "align": "left",
          "font": {
           "color": "#000000"
          },
          "showarrow": false,
          "text": "23.67234711267577",
          "x": 0.55,
          "xanchor": "left",
          "xref": "x",
          "y": 1,
          "yref": "y"
         },
         {
          "align": "left",
          "font": {
           "color": "#000000"
          },
          "showarrow": false,
          "text": "7.237942649824475e-06",
          "x": 1.55,
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          "xref": "x",
          "y": 1,
          "yref": "y"
         },
         {
          "align": "left",
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          },
          "showarrow": false,
          "text": "Sample does not look Gaussian (reject H0)",
          "x": 2.55,
          "xanchor": "left",
          "xref": "x",
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        ],
        "height": 200,
        "margin": {
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        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
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            "error_y": {
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             "line": {
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[{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"colorscale\": {\"diverging\": [[0, 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\"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"dtick\": 1, \"gridwidth\": 2, \"showticklabels\": false, \"tick0\": -0.5, \"ticks\": \"\", \"zeroline\": false}, \"yaxis\": {\"autorange\": \"reversed\", \"dtick\": 1, \"gridwidth\": 2, \"showticklabels\": false, \"tick0\": 0.5, \"ticks\": \"\", \"zeroline\": false}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('9689637f-b960-446d-ac33-01d2fa4b4ee6');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import normaltest\n",
    "\n",
    "stat, p = normaltest(current_df)\n",
    "\n",
    "# interpret\n",
    "alpha = 0.05\n",
    "if p > alpha:\n",
    "    msg = 'Sample looks Gaussian (fail to reject H0)'\n",
    "else:\n",
    "    msg = 'Sample does not look Gaussian (reject H0)'\n",
    "\n",
    "result_mat = [\n",
    "    ['Length of the sample data', 'Test Statistic', 'p-value', 'Comments'],\n",
    "    [len(current_df), stat, p, msg]\n",
    "]\n",
    "\n",
    "normt_table = ff.create_table(result_mat)\n",
    "normt_table['data'][0].colorscale=[[0, '#2a3f5f'],[1, '#ffffff']]\n",
    "normt_table['layout']['height']=200\n",
    "normt_table['layout']['margin']['t']=50\n",
    "normt_table['layout']['margin']['b']=50\n",
    "\n",
    "iplot(normt_table, filename=\"D'Agostino-test-table\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlação\n",
    "\n",
    "É qualquer relação estatística (causal ou não causal) entre duas variáveis.\n",
    "\n",
    "\n",
    "A correlação é sempre um valor entre -1 e 1.\n",
    "\n",
    " \n",
    "\n",
    "* Quando é -1 diz-se que a correlação perfeita e negativa.\n",
    "* Quando é 1, é perfeita e positiva.\n",
    "* Quando é zero, não há correlação.  \n",
    "* Correlações baixas (por exemplo, 0,3) são correlações fracas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "pokemon = pd.read_csv('pokemon.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Number</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp_Atk</th>\n",
       "      <th>Sp_Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>Pr_Male</th>\n",
       "      <th>Height_m</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Catch_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>721.00000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>644.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "      <td>721.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>361.00000</td>\n",
       "      <td>417.945908</td>\n",
       "      <td>68.380028</td>\n",
       "      <td>75.013870</td>\n",
       "      <td>70.808599</td>\n",
       "      <td>68.737864</td>\n",
       "      <td>69.291262</td>\n",
       "      <td>65.714286</td>\n",
       "      <td>3.323162</td>\n",
       "      <td>0.553377</td>\n",
       "      <td>1.144979</td>\n",
       "      <td>56.773370</td>\n",
       "      <td>100.246879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>208.27906</td>\n",
       "      <td>109.663671</td>\n",
       "      <td>25.848272</td>\n",
       "      <td>28.984475</td>\n",
       "      <td>29.296558</td>\n",
       "      <td>28.788005</td>\n",
       "      <td>27.015860</td>\n",
       "      <td>27.277920</td>\n",
       "      <td>1.669873</td>\n",
       "      <td>0.199969</td>\n",
       "      <td>1.044369</td>\n",
       "      <td>89.095667</td>\n",
       "      <td>76.573513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>181.00000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>361.00000</td>\n",
       "      <td>424.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>541.00000</td>\n",
       "      <td>499.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>721.00000</td>\n",
       "      <td>720.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>255.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Number       Total          HP      Attack     Defense      Sp_Atk  \\\n",
       "count  721.00000  721.000000  721.000000  721.000000  721.000000  721.000000   \n",
       "mean   361.00000  417.945908   68.380028   75.013870   70.808599   68.737864   \n",
       "std    208.27906  109.663671   25.848272   28.984475   29.296558   28.788005   \n",
       "min      1.00000  180.000000    1.000000    5.000000    5.000000   10.000000   \n",
       "25%    181.00000  320.000000   50.000000   53.000000   50.000000   45.000000   \n",
       "50%    361.00000  424.000000   65.000000   74.000000   65.000000   65.000000   \n",
       "75%    541.00000  499.000000   80.000000   95.000000   85.000000   90.000000   \n",
       "max    721.00000  720.000000  255.000000  165.000000  230.000000  154.000000   \n",
       "\n",
       "           Sp_Def       Speed  Generation     Pr_Male    Height_m   Weight_kg  \\\n",
       "count  721.000000  721.000000  721.000000  644.000000  721.000000  721.000000   \n",
       "mean    69.291262   65.714286    3.323162    0.553377    1.144979   56.773370   \n",
       "std     27.015860   27.277920    1.669873    0.199969    1.044369   89.095667   \n",
       "min     20.000000    5.000000    1.000000    0.000000    0.100000    0.100000   \n",
       "25%     50.000000   45.000000    2.000000    0.500000    0.610000    9.400000   \n",
       "50%     65.000000   65.000000    3.000000    0.500000    0.990000   28.000000   \n",
       "75%     85.000000   85.000000    5.000000    0.500000    1.400000   61.000000   \n",
       "max    230.000000  160.000000    6.000000    1.000000   14.500000  950.000000   \n",
       "\n",
       "       Catch_Rate  \n",
       "count  721.000000  \n",
       "mean   100.246879  \n",
       "std     76.573513  \n",
       "min      3.000000  \n",
       "25%     45.000000  \n",
       "50%     65.000000  \n",
       "75%    180.000000  \n",
       "max    255.000000  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f534a5d3050>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokemon.hist(column=\"Weight_kg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.040863020692765845"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon['Pr_Male'].corr(pokemon[\"Height_m\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.matshow(pokemon.corr())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Number</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp_Atk</th>\n",
       "      <th>Sp_Def</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Generation</th>\n",
       "      <th>isLegendary</th>\n",
       "      <th>hasGender</th>\n",
       "      <th>Pr_Male</th>\n",
       "      <th>hasMegaEvolution</th>\n",
       "      <th>Height_m</th>\n",
       "      <th>Weight_kg</th>\n",
       "      <th>Catch_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Number</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.160370</td>\n",
       "      <td>0.107512</td>\n",
       "      <td>0.138104</td>\n",
       "      <td>0.121401</td>\n",
       "      <td>0.120161</td>\n",
       "      <td>0.111822</td>\n",
       "      <td>0.028156</td>\n",
       "      <td>0.983329</td>\n",
       "      <td>0.135022</td>\n",
       "      <td>-0.100328</td>\n",
       "      <td>-0.013414</td>\n",
       "      <td>-0.117981</td>\n",
       "      <td>-0.009710</td>\n",
       "      <td>0.087309</td>\n",
       "      <td>-0.074931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Total</td>\n",
       "      <td>0.160370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.642628</td>\n",
       "      <td>0.704164</td>\n",
       "      <td>0.605831</td>\n",
       "      <td>0.723737</td>\n",
       "      <td>0.706501</td>\n",
       "      <td>0.548890</td>\n",
       "      <td>0.092868</td>\n",
       "      <td>0.481837</td>\n",
       "      <td>-0.385978</td>\n",
       "      <td>0.113564</td>\n",
       "      <td>0.228503</td>\n",
       "      <td>0.526813</td>\n",
       "      <td>0.535966</td>\n",
       "      <td>-0.738280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HP</td>\n",
       "      <td>0.107512</td>\n",
       "      <td>0.642628</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.431680</td>\n",
       "      <td>0.228834</td>\n",
       "      <td>0.368640</td>\n",
       "      <td>0.376006</td>\n",
       "      <td>0.170031</td>\n",
       "      <td>0.071545</td>\n",
       "      <td>0.258926</td>\n",
       "      <td>-0.155031</td>\n",
       "      <td>-0.066704</td>\n",
       "      <td>0.093708</td>\n",
       "      <td>0.442872</td>\n",
       "      <td>0.431320</td>\n",
       "      <td>-0.478725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Attack</td>\n",
       "      <td>0.138104</td>\n",
       "      <td>0.704164</td>\n",
       "      <td>0.431680</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.433233</td>\n",
       "      <td>0.335205</td>\n",
       "      <td>0.207211</td>\n",
       "      <td>0.335013</td>\n",
       "      <td>0.093857</td>\n",
       "      <td>0.302786</td>\n",
       "      <td>-0.196892</td>\n",
       "      <td>0.213882</td>\n",
       "      <td>0.203840</td>\n",
       "      <td>0.408590</td>\n",
       "      <td>0.469395</td>\n",
       "      <td>-0.525106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Defense</td>\n",
       "      <td>0.121401</td>\n",
       "      <td>0.605831</td>\n",
       "      <td>0.228834</td>\n",
       "      <td>0.433233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.202519</td>\n",
       "      <td>0.483986</td>\n",
       "      <td>-0.008663</td>\n",
       "      <td>0.068409</td>\n",
       "      <td>0.274446</td>\n",
       "      <td>-0.269466</td>\n",
       "      <td>0.063899</td>\n",
       "      <td>0.122666</td>\n",
       "      <td>0.354205</td>\n",
       "      <td>0.476983</td>\n",
       "      <td>-0.436558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sp_Atk</td>\n",
       "      <td>0.120161</td>\n",
       "      <td>0.723737</td>\n",
       "      <td>0.368640</td>\n",
       "      <td>0.335205</td>\n",
       "      <td>0.202519</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.492861</td>\n",
       "      <td>0.443106</td>\n",
       "      <td>0.069689</td>\n",
       "      <td>0.409739</td>\n",
       "      <td>-0.336579</td>\n",
       "      <td>0.105893</td>\n",
       "      <td>0.175581</td>\n",
       "      <td>0.330579</td>\n",
       "      <td>0.285048</td>\n",
       "      <td>-0.539114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sp_Def</td>\n",
       "      <td>0.111822</td>\n",
       "      <td>0.706501</td>\n",
       "      <td>0.376006</td>\n",
       "      <td>0.207211</td>\n",
       "      <td>0.483986</td>\n",
       "      <td>0.492861</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.233487</td>\n",
       "      <td>0.055421</td>\n",
       "      <td>0.360215</td>\n",
       "      <td>-0.337265</td>\n",
       "      <td>0.017941</td>\n",
       "      <td>0.149796</td>\n",
       "      <td>0.313196</td>\n",
       "      <td>0.328645</td>\n",
       "      <td>-0.513014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Speed</td>\n",
       "      <td>0.028156</td>\n",
       "      <td>0.548890</td>\n",
       "      <td>0.170031</td>\n",
       "      <td>0.335013</td>\n",
       "      <td>-0.008663</td>\n",
       "      <td>0.443106</td>\n",
       "      <td>0.233487</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>0.286082</td>\n",
       "      <td>-0.216964</td>\n",
       "      <td>0.070098</td>\n",
       "      <td>0.147844</td>\n",
       "      <td>0.224617</td>\n",
       "      <td>0.108637</td>\n",
       "      <td>-0.410557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Generation</td>\n",
       "      <td>0.983329</td>\n",
       "      <td>0.092868</td>\n",
       "      <td>0.071545</td>\n",
       "      <td>0.093857</td>\n",
       "      <td>0.068409</td>\n",
       "      <td>0.069689</td>\n",
       "      <td>0.055421</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071875</td>\n",
       "      <td>-0.029916</td>\n",
       "      <td>0.010912</td>\n",
       "      <td>-0.125374</td>\n",
       "      <td>-0.051304</td>\n",
       "      <td>0.034003</td>\n",
       "      <td>-0.025227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>isLegendary</td>\n",
       "      <td>0.135022</td>\n",
       "      <td>0.481837</td>\n",
       "      <td>0.258926</td>\n",
       "      <td>0.302786</td>\n",
       "      <td>0.274446</td>\n",
       "      <td>0.409739</td>\n",
       "      <td>0.360215</td>\n",
       "      <td>0.286082</td>\n",
       "      <td>0.071875</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.644714</td>\n",
       "      <td>0.095428</td>\n",
       "      <td>0.047955</td>\n",
       "      <td>0.326323</td>\n",
       "      <td>0.425219</td>\n",
       "      <td>-0.319302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hasGender</td>\n",
       "      <td>-0.100328</td>\n",
       "      <td>-0.385978</td>\n",
       "      <td>-0.155031</td>\n",
       "      <td>-0.196892</td>\n",
       "      <td>-0.269466</td>\n",
       "      <td>-0.336579</td>\n",
       "      <td>-0.337265</td>\n",
       "      <td>-0.216964</td>\n",
       "      <td>-0.029916</td>\n",
       "      <td>-0.644714</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016769</td>\n",
       "      <td>-0.200026</td>\n",
       "      <td>-0.361465</td>\n",
       "      <td>0.272304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pr_Male</td>\n",
       "      <td>-0.013414</td>\n",
       "      <td>0.113564</td>\n",
       "      <td>-0.066704</td>\n",
       "      <td>0.213882</td>\n",
       "      <td>0.063899</td>\n",
       "      <td>0.105893</td>\n",
       "      <td>0.017941</td>\n",
       "      <td>0.070098</td>\n",
       "      <td>0.010912</td>\n",
       "      <td>0.095428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031731</td>\n",
       "      <td>0.040863</td>\n",
       "      <td>0.061196</td>\n",
       "      <td>-0.253645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>hasMegaEvolution</td>\n",
       "      <td>-0.117981</td>\n",
       "      <td>0.228503</td>\n",
       "      <td>0.093708</td>\n",
       "      <td>0.203840</td>\n",
       "      <td>0.122666</td>\n",
       "      <td>0.175581</td>\n",
       "      <td>0.149796</td>\n",
       "      <td>0.147844</td>\n",
       "      <td>-0.125374</td>\n",
       "      <td>0.047955</td>\n",
       "      <td>0.016769</td>\n",
       "      <td>0.031731</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.194621</td>\n",
       "      <td>0.129057</td>\n",
       "      <td>-0.173273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Height_m</td>\n",
       "      <td>-0.009710</td>\n",
       "      <td>0.526813</td>\n",
       "      <td>0.442872</td>\n",
       "      <td>0.408590</td>\n",
       "      <td>0.354205</td>\n",
       "      <td>0.330579</td>\n",
       "      <td>0.313196</td>\n",
       "      <td>0.224617</td>\n",
       "      <td>-0.051304</td>\n",
       "      <td>0.326323</td>\n",
       "      <td>-0.200026</td>\n",
       "      <td>0.040863</td>\n",
       "      <td>0.194621</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.661342</td>\n",
       "      <td>-0.382862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Weight_kg</td>\n",
       "      <td>0.087309</td>\n",
       "      <td>0.535966</td>\n",
       "      <td>0.431320</td>\n",
       "      <td>0.469395</td>\n",
       "      <td>0.476983</td>\n",
       "      <td>0.285048</td>\n",
       "      <td>0.328645</td>\n",
       "      <td>0.108637</td>\n",
       "      <td>0.034003</td>\n",
       "      <td>0.425219</td>\n",
       "      <td>-0.361465</td>\n",
       "      <td>0.061196</td>\n",
       "      <td>0.129057</td>\n",
       "      <td>0.661342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.367798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Catch_Rate</td>\n",
       "      <td>-0.074931</td>\n",
       "      <td>-0.738280</td>\n",
       "      <td>-0.478725</td>\n",
       "      <td>-0.525106</td>\n",
       "      <td>-0.436558</td>\n",
       "      <td>-0.539114</td>\n",
       "      <td>-0.513014</td>\n",
       "      <td>-0.410557</td>\n",
       "      <td>-0.025227</td>\n",
       "      <td>-0.319302</td>\n",
       "      <td>0.272304</td>\n",
       "      <td>-0.253645</td>\n",
       "      <td>-0.173273</td>\n",
       "      <td>-0.382862</td>\n",
       "      <td>-0.367798</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Number     Total        HP    Attack   Defense    Sp_Atk  \\\n",
       "Number            1.000000  0.160370  0.107512  0.138104  0.121401  0.120161   \n",
       "Total             0.160370  1.000000  0.642628  0.704164  0.605831  0.723737   \n",
       "HP                0.107512  0.642628  1.000000  0.431680  0.228834  0.368640   \n",
       "Attack            0.138104  0.704164  0.431680  1.000000  0.433233  0.335205   \n",
       "Defense           0.121401  0.605831  0.228834  0.433233  1.000000  0.202519   \n",
       "Sp_Atk            0.120161  0.723737  0.368640  0.335205  0.202519  1.000000   \n",
       "Sp_Def            0.111822  0.706501  0.376006  0.207211  0.483986  0.492861   \n",
       "Speed             0.028156  0.548890  0.170031  0.335013 -0.008663  0.443106   \n",
       "Generation        0.983329  0.092868  0.071545  0.093857  0.068409  0.069689   \n",
       "isLegendary       0.135022  0.481837  0.258926  0.302786  0.274446  0.409739   \n",
       "hasGender        -0.100328 -0.385978 -0.155031 -0.196892 -0.269466 -0.336579   \n",
       "Pr_Male          -0.013414  0.113564 -0.066704  0.213882  0.063899  0.105893   \n",
       "hasMegaEvolution -0.117981  0.228503  0.093708  0.203840  0.122666  0.175581   \n",
       "Height_m         -0.009710  0.526813  0.442872  0.408590  0.354205  0.330579   \n",
       "Weight_kg         0.087309  0.535966  0.431320  0.469395  0.476983  0.285048   \n",
       "Catch_Rate       -0.074931 -0.738280 -0.478725 -0.525106 -0.436558 -0.539114   \n",
       "\n",
       "                    Sp_Def     Speed  Generation  isLegendary  hasGender  \\\n",
       "Number            0.111822  0.028156    0.983329     0.135022  -0.100328   \n",
       "Total             0.706501  0.548890    0.092868     0.481837  -0.385978   \n",
       "HP                0.376006  0.170031    0.071545     0.258926  -0.155031   \n",
       "Attack            0.207211  0.335013    0.093857     0.302786  -0.196892   \n",
       "Defense           0.483986 -0.008663    0.068409     0.274446  -0.269466   \n",
       "Sp_Atk            0.492861  0.443106    0.069689     0.409739  -0.336579   \n",
       "Sp_Def            1.000000  0.233487    0.055421     0.360215  -0.337265   \n",
       "Speed             0.233487  1.000000    0.003920     0.286082  -0.216964   \n",
       "Generation        0.055421  0.003920    1.000000     0.071875  -0.029916   \n",
       "isLegendary       0.360215  0.286082    0.071875     1.000000  -0.644714   \n",
       "hasGender        -0.337265 -0.216964   -0.029916    -0.644714   1.000000   \n",
       "Pr_Male           0.017941  0.070098    0.010912     0.095428        NaN   \n",
       "hasMegaEvolution  0.149796  0.147844   -0.125374     0.047955   0.016769   \n",
       "Height_m          0.313196  0.224617   -0.051304     0.326323  -0.200026   \n",
       "Weight_kg         0.328645  0.108637    0.034003     0.425219  -0.361465   \n",
       "Catch_Rate       -0.513014 -0.410557   -0.025227    -0.319302   0.272304   \n",
       "\n",
       "                   Pr_Male  hasMegaEvolution  Height_m  Weight_kg  Catch_Rate  \n",
       "Number           -0.013414         -0.117981 -0.009710   0.087309   -0.074931  \n",
       "Total             0.113564          0.228503  0.526813   0.535966   -0.738280  \n",
       "HP               -0.066704          0.093708  0.442872   0.431320   -0.478725  \n",
       "Attack            0.213882          0.203840  0.408590   0.469395   -0.525106  \n",
       "Defense           0.063899          0.122666  0.354205   0.476983   -0.436558  \n",
       "Sp_Atk            0.105893          0.175581  0.330579   0.285048   -0.539114  \n",
       "Sp_Def            0.017941          0.149796  0.313196   0.328645   -0.513014  \n",
       "Speed             0.070098          0.147844  0.224617   0.108637   -0.410557  \n",
       "Generation        0.010912         -0.125374 -0.051304   0.034003   -0.025227  \n",
       "isLegendary       0.095428          0.047955  0.326323   0.425219   -0.319302  \n",
       "hasGender              NaN          0.016769 -0.200026  -0.361465    0.272304  \n",
       "Pr_Male           1.000000          0.031731  0.040863   0.061196   -0.253645  \n",
       "hasMegaEvolution  0.031731          1.000000  0.194621   0.129057   -0.173273  \n",
       "Height_m          0.040863          0.194621  1.000000   0.661342   -0.382862  \n",
       "Weight_kg         0.061196          0.129057  0.661342   1.000000   -0.367798  \n",
       "Catch_Rate       -0.253645         -0.173273 -0.382862  -0.367798    1.000000  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemon.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5346b73850>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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