Central_Limit_Theorem.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MLlab_week3_task1_411772.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPRVrNY1HFhCLDCDzuMouJ1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Heartbeat143/Central-Limit-Theorem/blob/master/Central_Limit_Theorem.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a8Vnv2MZDrDB",
        "colab_type": "text"
      },
      "source": [
        "# ***Central Limit Theorem***\n",
        "\n",
        "It is the sampling distribution of the sampling means approaches a **normal distribution** as the sample size gets larger, **no matter what the shape of the data distribution**. \n",
        "An essential component of the Central Limit Theorem is the average of sample means will be the population mean.\n",
        "Similarly, if you find the average of all of the standard deviations in your sample, you will find the actual standard deviation for your population.\n",
        "```\n",
        "    μ_sample = μ               # mean\n",
        "    σ_sample = σ / sqrt(n)     # standard deviation\n",
        "    σ_sample ^2 = σ^2 / n      # variance \n",
        "```\n",
        "Where, (n>= 30) and    \n",
        "  μ = Population mean,  \n",
        "  σ = Population standard deviation,  \n",
        "  μ_sample = Sample mean,  \n",
        "  σ_sample = Sample standard deviation,  \n",
        "  n = Sample size "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlKXJ4yaDxpl",
        "colab_type": "text"
      },
      "source": [
        "# Importing Libraries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtS2LJcUENfp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "7f23d30a-dd70-4ca1-9df6-03a9500e51ab"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sbn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6DRRPLQEX26",
        "colab_type": "text"
      },
      "source": [
        "# Generating Random sample data in Normal Distribustion form"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAV5yIGZElEm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "c2db107f-79d0-49cb-d83d-48e8c47b4ccc"
      },
      "source": [
        "sample_data = np.random.normal(size=100000)  \n",
        "plt.hist(sample_data)\n",
        "plt.show()  #to plot histogram without axises\n",
        "sbn.distplot(sample_data)\n",
        "# sbn.distplot(sample_data,kde=False)   #Note:- kde=False for remove Normal Dist line"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f087d1e4f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUSPC_OKunHG",
        "colab_type": "text"
      },
      "source": [
        "# Apply Central limit Theorem on data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x85xVUz_uf2H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def apply_CLT(sample_data,sample_size,total_samples):\n",
        "  sample_mean = []\n",
        "  for a in range(total_samples):  #iterating for total no of samples for choosing samples and their mean\n",
        "    sample = np.random.choice(sample_data, size = sample_size)    #Note:- replace=False for with-out replacement\n",
        "    mean = np.mean(sample)      #Finding mean of random chose samples using numpy\n",
        "    sample_mean.append(mean)     \n",
        "     \n",
        "  return sample_mean"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRVbDQmFwrTk",
        "colab_type": "text"
      },
      "source": [
        "# Normal Dist sample data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SiKxI5Uwpvd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d058787f-1b50-44a9-ec5c-51859d017a5c"
      },
      "source": [
        "sample_data = np.random.normal(size=100)\n",
        "sbn.distplot(sample_data)  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f087d106d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zUNtcGlxnMp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "5008f4a4-8071-457a-e6d3-213fe95ec1ff"
      },
      "source": [
        "#Appling CLT on sample data. Funtion = apply_CLT(sample_data,sample_size,total_samples)\n",
        "sbn.distplot( apply_CLT(sample_data,30,1000) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f087d097470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3stH26jyZVy",
        "colab_type": "text"
      },
      "source": [
        "# Uniform Dist sample data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shE32qs4yjtu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c8fc47f8-80ef-4a56-9642-81df5612bd12"
      },
      "source": [
        "sample_data= np.random.uniform(size=10000)\n",
        "sbn.distplot(sample_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f087cfa9fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXRcd3338fd3Vm2jfbRYki3vS7zEieMkDiULJHFCmhCgECCU7TRAgdOWtk9DaXloOJSHpfQpDymQUspaQkgpdcAhEEgI2HFiO/Eqb5IsW7JkaSRZ0miZ0Wjm9/wxI6M4ljWSR3PvzHxf5/h4lmvpI3nmo6vf/d3fFWMMSimlspfD6gBKKaXmlxa9UkplOS16pZTKclr0SimV5bTolVIqy7ms+sSVlZWmsbHRqk+vlFIZae/evb3GGP9s/o1lRd/Y2MiePXus+vRKKZWRROTUbP+NDt0opVSW06JXSqksp0WvlFJZToteKaWynBa9UkplOS16pZTKclr0SimV5bTolVIqy2nRK6VUlrPszFilpvrPF04ntd07rl04z0nSJxe/ZmUN3aNXSqksp0WvlFJZTodulMpQwVCE8ESMiaihtMBNnttpdSRlU1r0Stnc5Fj+4FiEAx0DtAZG6BwcIxiaOL+NAGWFHmpL8lhdW8zqmmLyPdMXv4775xYteqVsrjUwzLPHA7T0DGOAKp+XZf4iqorz8LgcOASCoQkCwTCn+0c53DmEQ2BDfSk3rvBTVZxn9ZegLKZFr5RNnTk3xpOHu2gNjODLc3HzqiqubCilssg77b8xxnBmYIyX2wfY09bPvvYB1tWXcOfaWorz3WlMr+xEi14pmwlPRHm6qZudLX0UeF28YV0tmxeX43bOPHdCRKgvK6C+rICbV1axo7mXHc29HDsb5NY11Vy3pAKHSBq+CmUnWvRK2cjpvhF+uKedc6MRrl1czu1X1Mz5IGuR18XtV9SwaVEZ2/Z38tMDXTR1DfHWqxtSnFrZnRa9UjYQM4bnjgd4+kg3JfluPvDaJSyqKEzJx64o8vKeLY3sPXWOJw508uVfn2BljY+bV1Wl5OMr+9OiV2lljKF7KEzvcJjR8SiRaIziPDf9I+OU5LtxOuw7rDBfZ7IOjUV4bG87rYER1teX8MYr61I+VVJE2NRYzqKKQh7dfZr3fXs3f/66FXz0lmU4bPw9V6mhRa9mbTaF1z0U4vmWPvacih8YbA2MMDoevej2LodQW5LHwvICVtcWs6ii0NbFnwpHzw7x+N4OItEYb9pYx9WLypB5HEP3+7x88Mal7O8Y4J+fPs7BM4N86W0bKM6z34FaXSIidbToVcp1D4U40DHA93adoqlrCIiPF29oKOFt1zSwxF9Elc9LkdeF0yEMjUX4+aGzdA+FODMQ4oWT/exo6aPA4+SqhWVcu7icikvMNMlE4YkoPz3Qyc6WPmpL8njbNQ1U+dIzDdLtdPBPf7SBDfWlfPqnTbzxKzv4+ruuZnm1Ly2fX6WfFr1KiXOj4xzoGGR/+wBnh0IIcM3icv5m6yr+YHklq2uLL7l33js8fv52eCLKie5hDnQMsLOll98197Ky2seNK/xp+Erm35GuIf7qR/s53DnE9Usq2Lq2JqkZNakkIrx7SyOra4v50++/xD0P7+CLf7SBO9fVpjWHSg8tejVn0ZihqWuIXa19nOwdAWBheQF3ra9lXV0JH7hx6Zw+rtflZG1dCWvrShgKRdjd1s/zLX088ttWXjp9jg/dtJRbVlWlZIgj2eGBVBifiPHwM808/EwzJflu3nXdIlbXFqft81/M5sXl/PSjr+FD39/Ln37/JT5441L++vaVWT9klmtmLHoR+SZwF9BjjFl7kecF+BfgTmAUeI8x5qVUB1X2MTQW4cW2fna39RMMTVBW4ObWNdVsqC+lvNBzfrtUlGhxnpvXrarmD5b52Xuqn5dOD/D+b+9hZbWPD920lLvW1+JK897wbMVihicOdPLFXxyjvX+MezfW8cm71vDkobNWRwOgpiSPRx+4joeeaOJrv2nhcOcgX75vI2VT/i9VZktmj/5bwFeA70zz/B3A8sSfa4GvJv5WWcQYw67Wfr67q42fHzqLMbC8uoh7N1awoto37yfheFwOrl9ayZfediVP7O/kq8+28Oc/3McXf3GM996wmDdeucB24/iDYxH+Z98Zvr/rNMe6g6yuLeY771vHa204BOV1OfnMvevYUF/K3/3kEHf9v9/x9Xddzdq6EqujqRSYseiNMc+JSOMlNrkH+I4xxgC7RKRURGqNMV0pyqjS5GJ74OFIlJfbB9jV2kdPMEy+28kNSyvZbNEBUrfTwZuuqueNV9bxq6M9fPXZZj790yY+u/0IN630c9PKKrYsrWBxZeGMQzuRaIyB0XGGwxMEQxMMhSLnp3xORA1Oh+B2OvC4HLidgsfpIN/tJN/jJM/tJN/txOOK/zYRjRmC4QkGRyN85dcn2NUa/40nPBFjbV0x//dtV3L3hgW2n8r41msaWFnj44Pf28ubv7qTv7ptJe97zeKkhnJ0lox9pWKMvg5on3K/I/HYq4peRB4AHgBYuDC3/rMz7U3QPRRiV2sfL7cPMD4Ro640nzdfVcf6+tK0Hzi8GIdDuHVNNbeuqebo2SF+/NIZntjfydNHeoD4LJ+F5QUsKM3D63bicToIhiL0j4yf/zM0ZfXHqQRwOYVYDKLGzCnfqhof77h2IW/aWM+6+szaK97QUMoTH30NH//xQT6z/QjbD3Xxj/eus/x4gpq7tB6MNcY8AjwCsGnTprm9g9S8CUeiHOocZO+pc7T1jeJyCOvqSrhuSQUN5QVWx5vWqppi/vbOYj5+xyra+kbZ2dLL8bNBTvWP0nFujPFojEg0RpHXTXmhm3VlpVQUeigr8NDWO0JRngtfngtfnptCrxOX4/c/yKIxQyQaYzwaY3wiRigSZWw8ylgk/md8IoaI4JD4D5fSfDcfuHGp7ce3k9nxuGmFn7vW1/KpbYe588u/5S1X1fOx21ZQW5KfhoQqlVJR9GeAqYtn1CceUxkgFjPsOtnH43s7eGJ/J5GoobLIc36NlEJv5kzMEhEWVxayuDL5pQNmKjynQ3A6nLM6U9XuJZ8sEeGeK+u4cYWff322hW/taOMn+85wx9pa3r1lEVctnN+Tu1TqpOJdvA34iIg8Svwg7KCOz9tb1+AYe0+d4zfHAjxzLEDvcBif18WVDWVcvbCUhvICfQOr80oLPPztnat513WL+I8dbfxoTzvb9neyoCSPW1ZXsWVpJVcsKCZmjK6MaVPJTK/8AXATUCkiHcD/BtwAxpivAduJT61sJj698r3zFTYXxWKGg2cG2dnSx562fnqHwwyORYgZKM53UZznxpcX/7sk301pgZtCrwuX04FThLFIlOHQBGeHQpzuH6G5Z5juoTAAxXkublxZxetXV3Hbmhr++2X9RUxNr6G8gE/+4Rr+8rYV/OxgF083dfNfe8/wvV3x34o8TgfF+fEhsMkD1Z7Eweypt70uBwUeJyUFbtbWldhy+YVsk8ysm7fP8LwBPpyyRDlu6lBCc88wTx7qomswBMTXKSnNd1Oc76axopBgKEIwNMHJ3hGGxn4/a+RiygrcLKwoZMvSSjbUl7BxYRlXLCi2/Rx0ZT+FXhdv3dTAWzc1EJ6IcrQrSFPXEE/s72QoNEFwLELfSJjxifhxjfgxkt8fktu2v/P8bRFYXlXEG9Yt4P7rFtpuimy2yJwB2BwyPhHjsT3tNHUNUVbg5k0b61hZ48M3Zc9nutk54YkoI+EoE7EYsRj87EAXHpfjVdPjDncOcbhzaF6/DpX9vC4nGxpK2dBQyqUmKMVM4qD2RIyta2sYHY/SEwyz73R86u4/P32ch59t5h2bF/LgHav0QucppkVvM6FIlG/vbON0/yi3X1HDDUsrZrXX7XU58bp+/ya51AWilUoXh8j51+bkOvura+HGFX7+jOU09wT5xm9P8q2dbexu6+dr919tceLsokVvI2PjUb654yRdg2Pct3kh6/SsxFfJtPMR1KtN93+4vr4Uj8vBY3vaue2fn+N9r1lMXalO5UwFLXobeeJAJ12DY9x/7SJWzXBySjoX41K5K92vs1U1xXzk5uV847etfG/XKT588zKKMmiKr13pd9AmDncOsq99gNetqpqx5NXM9Adh5iov9PDO6xbx9d+08IMXT/O+Gy69BMNs/q9z9Tc9LfrLkKoyGQlP8JN98XnJN63U63hmOv0hc/nqSvO5d2MdP9rbwS+butm6tsbqSBlN59bZwPaDXYTGo7zl6gZdB1yphI0Ly7h6YRk7mnvpHxmf+R+oaWnRW6x/ZJx97QNsWVpBTUl6LiWnVKZ4/ZpqRODpI91WR8loWvQWe+5EAIdDuGF5pdVRlLKdknw3W5ZWsr99gK7BMavjZCwtegsFQxFeOnWOqxaW6WngSk3jxhV+vG4HTx22xxW5MpEWvYV2tvQRjRleq3vzSk0r3+PkxhVVHO8e1r36OdKit0g4EmVXax9r60p0fQ+lZnDNojKcDmHvqXNWR8lIWvQWaeoaIjwRY8vSCqujKGV7BV4Xq2uL2dc+wEQ0ZnWcjKNFb5H9HQOUFrhZaOMrNyllJ5sWlTE6HuXI2aDVUTKOFr0FRsITNPcMs76uVC/woVSSllUVUZzn4iUdvpk1LXoLHOocJGZgQ4MuWqZUshwiXLWojOPdQQbHIlbHySha9BbY3z6I3+elplhPkFJqNq5eWIYB9rcPWB0lo2jRp9ngWIRTfSNsqC/RYRulZqmiyEttSR5HdZx+VrTo0+xgxwAG2FBfanUUpTLSymofp/tHGJvmspnq1bTo0+x49zBVPq/OnVdqjlbW+IgZaA4MWx0lY2jRp1EkGqOtb4TlVUVWR1EqY9WXFZDvdnJMh2+SpkWfRm29I0zEDMurfVZHUSpjOR3C8uoiTnQHiV3qiuTqPC36NDrRM4zTITQmLo6slJqbldU+guEJugZDVkfJCFr0adTcM8yiigI8Lv22K3U5Jn8r1uGb5GjjpEkwFOHsUIjlfh2fV+pyFXld1Jflc7xbiz4ZWvRp0twTnyGwTMfnlUqJpf4iOs6NMj6hi5zNRIs+TZp7hinwOKnVywUqlRKLKgqIGeg4N2p1FNvTok8DYwzNgWGW+otw6NmwSqXE5Mqvp/q16GeSVNGLyFYROSYizSLy4EWeXygiz4jIyyJyQETuTH3UzHVuNEIwNMHiSp1to1SqFHhcVPm8nOobsTqK7c1Y9CLiBB4G7gDWAG8XkTUXbPZ3wGPGmI3AfcC/pjpoJmtP7HHo2vNKpdaiigJO94/qfPoZJLNHvxloNsa0GmPGgUeBey7YxgDFidslQGfqIma+0/2jeJwOqnW1SqVSalF5IaFIjJ5g2OootpZM0dcB7VPudyQem+pTwP0i0gFsBz6aknRZ4nT/KHVl+TgdOj6vVCotqkiM0+vwzSWl6mDs24FvGWPqgTuB74rIqz62iDwgIntEZE8gEEjRp7a3SDRG1+CYDtsoNQ/KCz0Uel2c7tMDspeSTNGfARqm3K9PPDbV+4HHAIwxzwN5QOWFH8gY84gxZpMxZpPf759b4gzTcW6MmNHxeaXmg4iwqLxAZ97MIJmi3w0sF5HFIuIhfrB12wXbnAZeByAiq4kXfW7sss9g8kBsgxa9UvNiUUUB/SPjBEN6ecHpzFj0xpgJ4CPAU8AR4rNrDovIQyJyd2KzvwT+RET2Az8A3mOMHgaH+Ph8eaGHIq/L6ihKZaVFiZ2odt2rn1ZS7WOM2U78IOvUxz455XYTcENqo2U+Ywyn+0dZpuvPKzVvakryEaBzMMSaBSVWx7ElPTN2Hp0bjTAcntDxeaXmkcflwO/z0jkwZnUU29Kin0eTa3Do+LxS82tBab4W/SVo0c+jzoEQThGqi/X6sErNp9qSPIZCEwyHJ6yOYkta9POoa3CM6mIvLod+m5WaTwtK8wF0r34a2kDzxBhD58AYtSX5VkdRKustSLzPurToL0qLfp4EQxOMjEepLdX1bZSab/keJ2UFbjr1GrIXpUU/TzoH43sWC3SPXqm00AOy09OinyedA/E9ixq9opRSaVFbkk/fyDihSNTqKLajRT9PugbHqCj0kOd2Wh1FqZxQlxgm7dLhm1fRop8nXYMhakt12EapdKnVmTfT0qKfB6FIlP6RcRbosI1SaVOc56bI66JrUIv+Qlr082DyQKxOrVQqvWpK8uge0qtNXUiLfh50JQ7ELtCplUqlVbXPS08wpNeQvYAW/TzoGhyjyOvCl+e2OopSOaW6OI9I1HBuZNzqKLaiRT8Pzg6GqNXxeaXSrro4/r7T4ZtX0qJPsZgx9ATD519wSqn0qfLFFxDsCeoUy6m06FOsf2SciZjRFSuVsoDX7aS0wM3ZIS36qbToU6wn8QKr8ukevVJWqPbl0aNDN6+gRZ9iZxMvsCrdo1fKEtXFXgLDYaIxnXkzSYs+xXqCIcoK3HhduvSBUlaoKs4jGjP0jehe/SQt+hTrHgrpgVilLKQzb15Niz6FojFDb3Bci14pC/mLvAi/P16mtOhTqnc4TNSY81O8lFLp53E5KC/00K1Ff54WfQpNvrB0j14pa1UV59Ed1KGbSVr0KdQTDCOAX/folbJUdbGXvuEwE9GY1VFsQYs+hbqHQlQUeXA79duqlJWqfF5iBvp0zRtAiz6luod06QOl7MBfFH8fBnT4BtCiT5lINEbfcFjPiFXKBiqLPEB8goTSok+Z3uEwBj0jVik78LqdFOe5dI8+IamiF5GtInJMRJpF5MFptnmriDSJyGER+c/UxrS/yReUTq1Uyh4qffGlEBS4ZtpARJzAw8CtQAewW0S2GWOapmyzHPg4cIMx5pyIVM1XYLsKDMdn3FQWadErZQf+Ii/7OwYwxiAiVsexVDJ79JuBZmNMqzFmHHgUuOeCbf4EeNgYcw7AGNOT2pj2FwiGKS1w64wbpWzC7/MSisQYDk9YHcVyybRSHdA+5X5H4rGpVgArRGSHiOwSka0X+0Ai8oCI7BGRPYFAYG6JbSoQDOv8eaVsxJ/47VqHb1J3MNYFLAduAt4O/JuIlF64kTHmEWPMJmPMJr/fn6JPbb2YMfQOh8+/sJRS1qtM7HjpAdnkiv4M0DDlfn3isak6gG3GmIgx5iRwnHjx54TBsQiRqMGvUyuVso2SfDdup9CrRZ9U0e8GlovIYhHxAPcB2y7Y5ifE9+YRkUriQzmtKcxpa5N7DDp0o5R9OESoLNKZN5BE0RtjJoCPAE8BR4DHjDGHReQhEbk7sdlTQJ+INAHPAH9tjOmbr9B2o0WvlD1VFnnpHdZlEGacXglgjNkObL/gsU9OuW2AjyX+5JxAMEy+20mhR68qpZSd+H1eDp0ZJBKN5fSMuNz9ylMoMByfcZPrc3WVsht/kReDLm6mRZ8CPcGwnhGrlA35deYNoEV/2UbHJxgJT+j4vFI2NHmmeq4vbqZFf5kmp27pHHql7MfjclCS79Y9eqsDZLrJqVu6R6+UPfmLvLpHb3WATNcTDON0CGWFHqujKKUuotLnJRAME58cmJu06C9TIBimssiDQ2fcKGVL/iIP4YkYwVDuLm6mRX+ZAkFd40YpO5tcmiSXz5DVor8ME9EY50bHdXxeKRubvKxgLh+Q1aK/DH0j48QMupiZUjZWnO/G43Tk9AFZLfrLoGvcKGV/8cXNPLpHr+Zmcsxv8ldDpZQ9Vfpye4qlFv1lCATDlOS78bp0MTOl7Mxf5GVgNEIoErU6iiW06C9DQNe4USoj+H3xxc1O9o5YHcUSWvRzZIyJz6HXolfK9ibXvGkJDFucxBpa9HN0dijEeDSmc+iVygCTRd8a0D16NQstPfEXjM64Ucr+PC4Hpflu3aNXs9PcEwTQMXqlMoTf59WiV7PTEhghz+2gyJvU1RiVUharLPJyMjCSk4ubadHPUUtgGH+RXj5QqUzh93kZGY/SPZR78+m16OeoJTCs4/NKZZBcnnmjRT8HQ6EI3UNhXeNGqQwyuWPWqkWvkjE5RUunViqVOYrzXBR4nLTk4BRLLfo5aOmJ7xHo0I1SmUNEWOIv1KEblZyWwDAuh1Culw9UKqMs9Rfl5ElTWvRz0BIYprGyEKdDZ9wolUmWVBbROTjG2HhuLW6mRT8HzT3DLPUXWh1DKTVLS/yFGJN7i5tp0c9SJBrjVN8oS/1FVkdRSs3S5Pu2tTe3xum16GfpdP8oEzGjRa9UBlpcGf9NPNfG6ZMqehHZKiLHRKRZRB68xHZvFhEjIptSF9FeJmfcLKvSolcq0+R7nNSV5ufczJsZi15EnMDDwB3AGuDtIrLmItv5gD8DXkh1SDtpTrxAlugYvVIZaYm/UPfoL2Iz0GyMaTXGjAOPAvdcZLtPA58DQinMZzstPSNUF3vx5bmtjqKUmoP4FMvhnFrcLJmirwPap9zvSDx2nohcBTQYY352qQ8kIg+IyB4R2RMIBGYd1g5aAsM6Pq9UBlvqL2RkPEpPMHcWN7vsg7Ei4gC+BPzlTNsaYx4xxmwyxmzy+/2X+6nTzhhDS2BYx+eVymBLEjtqzT25M06fTNGfARqm3K9PPDbJB6wFnhWRNuA6YFs2HpANBMMEQxO6R69UBpvcUdOif6XdwHIRWSwiHuA+YNvkk8aYQWNMpTGm0RjTCOwC7jbG7JmXxBaaPBCrRa9U5qryefHlubTopzLGTAAfAZ4CjgCPGWMOi8hDInL3fAe0k2adWqlUxhMRllcVcSJxOdBckNR18Iwx24HtFzz2yWm2venyY9nT8e4gvjwX1cW6aqVSmWxZVRG/PpqZE0LmQs+MnYXj3cOsqPbp5QOVynDLq3z0Doc5NzJudZS00KJPkjGGE91BVlTrsI1SmW5Z4n3cnCNnyGrRJ6l3eJxzoxGWV/msjqKUukzLcmyKpRZ9kk50xw/crKjWolcq09WV5pPvdnKiW4teTXH8fNHr0I1Smc7hEJbl0MwbLfoknegZpjjPpdeJVSpLLKsqOr8abbbTok/SCZ1xo1RWWVZVROdgiGAoYnWUeadFnwRjDMd7gizX8XmlssbyxImPLTmwZLEWfRICw2EGRiM6Pq9UFsmlNW+06JMweWReZ9wolT0WlhfgcTpy4oCsFn0SJmfcLNc9eqWyhsvpYIm/kONntegV8aUPSgvc+It0xo1S2WRVjY9jWvQK4nv0K6p0xo1S2WZ1bTGdgyEGR7N75o0W/QxiMcORriHWLCi2OopSKsVW1cbf10fODlmcZH5p0c/gdP8oo+NRVtfqgVilss3qmvj7+miXFn1Oa0q8ANbUllicRCmVan6fl/JCD0ezfJxei34GTZ1DOB2iM26UykIiwqoaH0e06HPbka4hlvoLyXM7rY6ilJoHq2qKOX42SDRmrI4yb7ToZ9DUNcSaWj0Qq1S2WlXrYywS5XT/qNVR5o0W/SWcGxmnazDEai16pbLW6pr4+zubD8hq0V/CkckDsTq1Uqmstby6CIf8/v2ejbToL2Fyxo3u0SuVvfLcThZXFmb1AVkt+kto6hqiyuelUpc+UCqrra4t5mgWnzSlRX8JR7qCujevVA5YXVtMe/8YQ1l6ERIt+mmMT8Ro7gnq+LxSOWBtXfyEyEMdgxYnmR9a9NM4enaISNRwhRa9UllvQ3286Pdr0eeWfe0DAFzZUGpxEqXUfCst8LCoooD9ifd9ttGin8a+0wNUFnmpK823OopSKg3W15dyoEOLPqfsax/gyoZSXYNeqRyxob6EzsEQPcGQ1VFSLqmiF5GtInJMRJpF5MGLPP8xEWkSkQMi8isRWZT6qOkzOBqhtXeEjQt12EapXLEhMUx7oD37xulnLHoRcQIPA3cAa4C3i8iaCzZ7GdhkjFkPPA58PtVB02lfh47PK5VrrlhQjEPIyuGbZPboNwPNxphWY8w48Chwz9QNjDHPGGMmVwTaBdSnNmZ67Ts9gAisr9c16JXKFQUeFyuqfVk58yaZoq8D2qfc70g8Np33A09e7AkReUBE9ojInkAgkHzKNNvXfo5l/iJ8eW6royil0mhDfSn7OwYwJruWLE7pwVgRuR/YBHzhYs8bYx4xxmwyxmzy+/2p/NQpY4w5fyBWKZVb1jeUMDAaob1/zOooKZVM0Z8BGqbcr0889goi8nrgE8DdxphwauKl3+n+Uc6NRrhSD8QqlXM21Mff9/uybJw+maLfDSwXkcUi4gHuA7ZN3UBENgJfJ17yPamPmT56opRSuWtljY8Cj5O9bf1WR0mpGYveGDMBfAR4CjgCPGaMOSwiD4nI3YnNvgAUAT8SkX0ism2aD2d7u9v6KfQ4WVntszqKUirN3E4HmxrLeb61z+ooKeVKZiNjzHZg+wWPfXLK7denOJdldrb0sXlxOS6nnkumVC66bkk5n//5MXqHw1mzRLm22RRnB0O0BkbYsrTS6ihKKYtct6QCgBdPZs/wjRb9FDtbegHYsqzC4iRKKausqyuh0OPk+ZbsGb7Rop9iR3MfZQXu8xcLVkrlnslx+l1ZNE6vRZ9gjOH5ll6uX1qBw6ELmSmVy65fWsGJnmECwYydKf4KWvQJbX2jdA6GuF7H55XKeZPj9C+czI69ei36hB3N8fH5G5bq+LxSuW7tgmKKvK6sGb7Rok94vqWPmuI8FlcWWh1FKWUxl9PBNY1l/O5Eb1ase6NFD0SiMXa09LJlWYVeaEQpBcAtq6tp6xuluWfY6iiXTYseeKG1n4HRCLdfUWN1FKWUTdy2phqApw6ftTjJ5dOiB7Yf6qLA4+TGFfZcUVMplX7VxXlsXFjKz7XoM180Znjq0FluWVVFnttpdRyllI1svaKGQ2eG6Dg3OvPGNpbzRf/iyX76Rsa5c12t1VGUUjYzOZz7i8PdFie5PDlf9E8e6iLP7eCmlTpso5R6pcbKQlZW+zJ+nD6niz4WMzx56Cw3r6yiwJPUQp5KqRxz+xXV7G7rp284c8+Szemif7Gtn0AwzB06bKOUmsYb1i8gZuDHL73qwnoZI6eL/jvPt1GS7+bW1dVWR1FK2dTKGh/XNJbxvRdOEYtl5slTOVv0ZwbGeOpwN/dtbiDfo7NtlFLTe9f1jXqD6ssAAAi3SURBVJzqG+U3JwJWR5mTnC367zzfBsAfX99oZQylVAbYekUNlUVevvv8KaujzElOFv3o+ASPvtjO7VdUU1eab3UcpZTNeVwO3rG5gWeO9XC6L/Pm1Odk0f/3y2cYHIvwni2LrY6ilMoQ77h2EQ4R/mPnSaujzFrOFf3o+AQP/7qZdXUlXNNYZnUcpVSGqCnJ400b6/jerlO09Y5YHWdWcq7o//WZFjoHQ/z9XWt0pUql1Kz89e0r8TgdfGb7EaujzEpOFf3J3hEeea6VezfWsXlxudVxlFIZpqo4jw/fsoxfNnWfv1hRJsiZojfG8A9PHMbjcvDxO1ZZHUcplaHed8NiGsrz+YcnDhOKRK2Ok5ScKfqv/aaVZ48F+ItbV1BVnGd1HKVUhspzO3no7rUc7x7mwf86kBFXoMqJot9+sIvP/fwof7hhAe/d0mh1HKVUhrt5VRV/ddsKfrKvk6/9ptXqODPK+pW8drb08hc/3MfVi8r4wlvW43DoAVil1OX78M3LONY9zOefOkpFkYe3bmqwOtK0srbojTH8229b+dzPj9FYUcAj77paLyyilEoZEeELb1lP/0iY//X4AZo6h/jEG1bjdtpvoCQri76pc4gv/uIYvz7awx1ra/j8W9bjy3NbHUsplWXy3E6+/d7NfPbJo/z7707ycvsAf7N1JVuWVlod7RWSKnoR2Qr8C+AEvmGM+T8XPO8FvgNcDfQBbzPGtKU26qUNjkX47YkAP3m5k6ePdOPzuvj7u9bwvhsadb68UmreuJwO/v6uNWxoKOUff3aEd/zbC1y7uJw3XVXHLauq8fu8VkecuehFxAk8DNwKdAC7RWSbMaZpymbvB84ZY5aJyH3A54C3zUfg1sAwB88MMjgWoX9knLbeEZoDwxzpChKNGcoLPfzF61fwnhsaKcnXvXilVHrcvWEBt62p5gcvnuYbvz3J3/zXQUQOssxfxIoaH0srCykr9FBe6GFjQxkLKwrSli2ZPfrNQLMxphVARB4F7gGmFv09wKcStx8HviIiYuZh3tEvm7r57JNHz99fUJLHsmofH3itn1tWVbFxYRlOPeCqlLJAntvJe29YzHu2NNLUNcSvjvRwoGOQgx2DbD/YxWQjfubetbyzYlHaciVT9HVA+5T7HcC1021jjJkQkUGgAnjFqWMi8gDwQOLusIgcAyov3G42TgHPz/UfX77Lym4DmZxfs1sjk7PzTpvkv/9zcP/s/9lk9ln/hEjrwVhjzCPAI1MfE5E9xphN6cyRKpmcHTI7v2a3RiZnh8zOfznZk5kHdAaYOkG0PvHYRbcRERdQQvygrFJKKYslU/S7geUislhEPMB9wLYLttkGvDtx+y3Ar+djfF4ppdTszTh0kxhz/wjwFPHpld80xhwWkYeAPcaYbcC/A98VkWagn/gPg2Q9MvMmtpXJ2SGz82t2a2Rydsjs/HPOLrrjrZRS2c1+5+oqpZRKKS16pZTKcmkvehEpF5FfisiJxN+vunCriFwpIs+LyGEROSAi83KWbbJEZKuIHBORZhF58CLPe0Xkh4nnXxCRxvSnvLgksn9MRJoS3+dfiUj6zuJIwkz5p2z3ZhExImKbqXPJZBeRtya+/4dF5D/TnXE6SbxuForIMyLycuK1c6cVOS9GRL4pIj0icmia50VEvpz42g6IyFXpzjidJLK/M5H5oIjsFJENSX1gY0xa/wCfBx5M3H4Q+NxFtlkBLE/cXgB0AaXpzpr4/E6gBVgCeID9wJoLtvlT4GuJ2/cBP7Qi6xyz3wwUJG5/yC7Zk82f2M4HPAfsAjZZnXsW3/vlwMtAWeJ+ldW5Z5H9EeBDidtrgDarc0/J9lrgKuDQNM/fCTwJCHAd8ILVmWeRfcuU18sdyWa3YujmHuDbidvfBt544QbGmOPGmBOJ251AD+BPW8JXOr8EhDFmHJhcAmKqqV/T48DrxB4rqc2Y3RjzjDFmNHF3F/HzJOwime89wKeJr68USme4GSST/U+Ah40x5wCMMT1pzjidZLIboDhxuwToTGO+SzLGPEd89t907gG+Y+J2AaUiUpuedJc2U3ZjzM7J1wuzeL9aUfTVxpiuxO2zQPWlNhaRzcT3KlrmO9g0LrYERN102xhjJoDJJSCslkz2qd5PfE/HLmbMn/i1u8EY87N0BktCMt/7FcAKEdkhIrsSq8TaQTLZPwXcLyIdwHbgo+mJlhKzfV/YVdLv13lZAkFEngZqLvLUJ6beMcYYEZl2fmfip+x3gXcbY2KpTammEpH7gU3AjVZnSZaIOIAvAe+xOMpcuYgP39xEfM/sORFZZ4wZsDRVct4OfMsY808icj3x82jW6vs0PUTkZuJF/5pktp+XojfGvH6650SkW0RqjTFdiSK/6K+rIlIM/Az4ROLXK6vMZgmIDpstAZFMdkTk9cR/CN9ojAmnKVsyZsrvA9YCzyZGymqAbSJytzFmT9pSXlwy3/sO4mOsEeCkiBwnXvy70xNxWslkfz+wFcAY87yI5BFfdMsuw0+XktT7wq5EZD3wDeAOY0xSPWPF0M3U5RLeDfzPhRskllr4b+LjaI+nMdvFZPISEDNmF5GNwNeBu200RjzpkvmNMYPGmEpjTKMxppH4mKUdSh6Se938hPjePCJSSXwoxw5Xmk4m+2ngdQAishrIAwJpTTl324A/Tsy+uQ4YnDKcbGsishD4MfAuY8zxpP+hBUeVK4BfASeAp4HyxOObiF+9CuIreEaAfVP+XJnurFMy3wkcJ36c4BOJxx4iXioQf5H/CGgGXgSWWJV1DtmfBrqnfJ+3WZ15Nvkv2PZZbDLrJsnvvRAfemoCDgL3WZ15FtnXADuIz8jZB9xmdeYp2X9AfKZehPhvTe8HPgh8cMr3/eHE13bQZq+ZmbJ/Azg35f26J5mPq0sgKKVUltMzY5VSKstp0SulVJbToldKqSynRa+UUllOi14ppbKcFr1SSmU5LXqllMpy/x9z9KtTBqpVjQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydbOFNBMwgh1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "8a58cf33-5600-4e14-ef7d-e4a068338a59"
      },
      "source": [
        "#Appling CLT on sample data. Funtion = apply_CLT(sample_data,sample_size,total_samples)\n",
        "sbn.distplot( apply_CLT(sample_data,30,100) )  #(30 0 and 100 0 0 0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f087cf5e6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd3xc1YH28d/RjHpvlmx1uYB7xRYuJJSEFkxngZjQnWRJ3Wx2N2+yb7JJ3pRNJRtIQoCYUEKAdQiEUAMEbOMiWza4YqtasmVbXVYv5/1DMnGIjUfWjO6dmef7+ejj0TQ9vpIeH997z7nGWouIiLhXhNMBRETkg6moRURcTkUtIuJyKmoREZdTUYuIuJw3EG+akZFhCwsLA/HWIiIhafPmzQ3W2swTPRaQoi4sLKS0tDQQby0iEpKMMdUne0y7PkREXE5FLSLicipqERGXU1GLiLicilpExOVU1CIiLqeiFhFxORW1iIjLqahFRFwuIDMTRcbSYxtqAvK+Ny7KD8j7ioyURtQiIi6nohYRcTkVtYiIy6moRURcTkUtIuJyKmoREZdTUYuIuJyKWkTE5VTUIiIup6IWEXE5FbWIiMupqEVEXE5FLSLicipqERGXU1GLiLicT0VtjPmiMWaHMWa7MeZ3xpiYQAcTEZEhpyxqY0wO8DlggbV2BuABrg90MBERGeLrrg8vEGuM8QJxwIHARRIRkeOdsqittXXAD4Ea4CDQaq196f3PM8asNMaUGmNKjxw54v+kIiJhypddH6nA5UARMAGIN8aseP/zrLX3WWsXWGsXZGZm+j+piEiY8mXXxwVApbX2iLW2D1gNLA5sLBEROcaXoq4BSowxccYYA5wP7ApsLBEROcaXfdQbgKeALcA7w6+5L8C5RERkmNeXJ1lrvw58PcBZRETkBDQzUUTE5VTUIiIup6IWEXE5FbWIiMv5dDBRxK1aO/vYUtNMTWMnB1q78EYYYqO8ZCdFMzMnhaykaIbOKhUJXipqCUo9/QP8dl01//PqXtq6+4n2RpCTGou10NTRw+6Dbby25whZSdF8ZGoWU8cnqbAlaKmoJehUNnRw+6pNVDR08KEpmUwbn0ROaiwRxxXx0Z5+tte18lZFI49sqKEoI57lsyeQlaQVeiX4aB+1BJUtNc1cde9aWrr6eOi2hTx020Ly0uL+rqQBEqK9lBSn87nzJrN89gQOtXVz7+v7KK1qwlrrUHqR06MRtQSNdfsauHXVJrKTY3jo1oUUZsSf8jWeCENJcTrTJyTxROl+VpfVUdHQwZVzc4j0aJwiwUE/qRIUKhs6+PSjW8hPi2P1pxf7VNLHS4yJ5NYlRVwwdRxb97fw4JpKOnr6A5RWxL9U1OJ6rV193P7QJiIMPHDzWaQnRJ/W+0QYw3lnZnHDwnzqWrr45V/Laero9XNaEf9TUYurWWv50hNbqWns5Bcr5pOfHjfq95yZk8ztS4vo7B3gvjfKOdze7YekIoGjohZXW72ljld2HeY/Lj6TkuJ0v71vQXo8dy4rZtDCr9+s5GBrl9/eW8TfVNTiWofbuvmvZ3ewoCCVW5cU+f39s5NjuHNZMR4D979ZSW1zp9+/hog/qKjFlay1fPXp7fT0D/Lf18zCExGYySqZidGsPGciMZERPLCmkurGjoB8HZHRUFGLK/1l12Fe3nmIf/nIFIozEwL6tdLio1h5zkQSY7z8Zm0VFQ1HA/r1REZKRS2u0zcwyHee30VxZjy3LfX/Lo8TSY6N5M5lxSTHRfLQuioqjqisxT1U1OI6j2+soeJIB1+5eOqYTkpJjInkjqVFpMZF8dBbVbxV3jhmX1vkg6ioxVXauvv4ySt7KSlO44Kp48b86yfGRHLHsmJS46K4ddVG1u1rGPMMIu+nohZXue+vFTR19PK1S6c5ttpdQrSXO5YVU5AWz20PbWKtylocpqIW12jp7GXVuiounTWeGTnJjmZJiPby2J2LKEyP57ZVKmtxlopaXOM3a6s42tPPZ8+b5HQUANITonn0jkUUZQyV9Zq9KmtxhlbPE1do6+7jN2sr+ei0LM7MTnI6DgCPbagB4Op5uTywppJbfrORm0oKmJyVOKr3vXFRvj/iSRjRiFpc4bfrqmjr7udz5092Oso/iI/2csfSIjITo3l4fTV7D7U7HUnCjIpaHNfVO8ADayo578xxju+bPpm4aC+3L/lbWb+rspYxpKIWx60uq6W5s49PnlPsdJQPdKysxyVG84jKWsaQilocNThoeXBNJTNzkllYlOZ0nFOKi/Zy29IixiUNjaz31KusJfBU1OKov757hPIjHdy+tChorhIeF+XltiVFZCVF88iGaso13VwCTEUtjnpgTSXZSTFcMnO801FG5FhZZyRE8cj6aupatJ61BI6KWhyzu76NNfsa+MTiAqK8wfejGBfl5ZbFRcRGeVi1tpKGoz1OR5IQFXy/HRIyfvtWNdHeCG5cGLznFSfHRnLb4iIs8Nu3qujqHXA6koQgFbU4or27j6fL6rhs9gRS4qKcjjMqGYnRfHxRAc0dfTy2sZqBQet0JAkxKmpxxNNldXT2DnBTSYHTUfyiKCOeK+bmUH6kg+feOeB0HAkxmkIuY85ay8Prq5mZk8zsvBSn4/jN/IJUDrV1s2ZfA/lp8cwJob+bOEsjahlzm6qaeffQUVaUBO++6ZO5cHo2BWlxPL21jiPtOrgo/qGiljH36IZqEmO8LJ+d43QUv/NEGK5fmI83wvC7jTX0DQw6HUlCgIpaxlRrZx/Pb6/nyrk5xEZ5nI4TEMmxkVw7P4/6tm5e2lHvdBwJASpqGVPPbKujt3+Q6xbkOR0loM7ITmRRURrryhupauhwOo4EOZ+K2hiTYox5yhiz2xizyxhzdqCDSWh6cnMtU8cnMX2CO9acDqSLZmSTEhfJ/26ppbdfu0Dk9Pk6or4beMFaeyYwG9gVuEgSqnbXt/F2bSvXzs8NmnU9RiPa6+Hqebk0dvTy8k7tApHTd8qiNsYkA+cADwBYa3uttS2BDiah58nSWiI9hivmht5BxJMpzkxgYeHQLpCDrVoPRE6PL+dRFwFHgN8YY2YDm4HPW2v/bsebMWYlsBIgPz/0TruS0entH+TxjTVMyUrkhe3hNbr86PQsth9o5ZltB1i5zN1rbos7+bLrwwvMA35hrZ0LdAD/8f4nWWvvs9YusNYuyMzM9HNMCXav7j5MR+8ACwpSnY4y5uKivFw0PZvqxk627td/RmXkfCnqWqDWWrth+POnGCpuEZ89tXk/iTFeJo0b3YVhg9W8glTyUmN5fns97d19TseRIHPKorbW1gP7jTFnDN91PrAzoKkkpBxu7+a1PUeYl5+KJyL0DyKeSIQxXDZ7Akd7+vn1m5VOx5Eg4+tZH58FHjXGvA3MAb4TuEgSav6wpY6BQcv8/PDb7XG83NQ4ZuYkc/+bFRxu73Y6jgQRn4raWrt1eP/zLGvtFdba5kAHk9BgreWJ0v0sKEglIzHa6TiO++i0LHr7B/nZX/Y6HUWCiGYmSkCV7W+h/EgH1y7IdTqKK6QnRHPjonx+t3E/FbrWovhIRS0B9dTmWmIjPVw6a4LTUVzjs+dNJtobwd0aVYuPVNQSMD39Azz39kEunJ5FQrSWPj8mMzGam84u4NltB3QFc/GJiloC5rXdR2jt6gurmYi+WrmsmGivh3te3ed0FAkCKmoJmKfL6shIiGbppAyno7hOesLQqPrprXVUanU9OQUVtQREa2cfr+4+zPLZE/B69GN2IncuKybKG8HPNaqWU9BvkATEc+8cpHdgkCu12+OkMhOjuXFhAX/cWkddixZskpNTUUtAPF1Wx6RxCczICf11p0fj9mVFADyg2YryAVTU4nf7mzrZWNXElXNzwmLd6dHISYll+ewJPL6phpbOXqfjiEupqMXv/ri1DoDL5+jcaV+s/FAxnb0DPLK+2uko4lIqavEray1/KKtjYVEaualxTscJCmdmJ/HhMzJZta6K7r4Bp+OIC6moxa+217VRfqRDBxFHaOU5xTQc7eXpsjqno4gLqajFr1aX1RLlieCSmeOdjhJUzi5O58zsRFatq8Ja63QccRkVtfhN/8Agz247wPlTx5EcG+l0nKBijOHWJYXsrm9nQ2WT03HEZVTU4jdr9jXQcLRXU8ZP0+VzckiJi2TV2iqno4jLqKjFb/5QVkdKXCTnnjHO6ShBKSbSww0L83lpZz21zZ1OxxEXUVGLXxzt6efFHfVcOnM8UV79WJ2uFSUFADysU/XkOPqNEr94aUc93X2DXDVPuz1GIycllgunZ/P4xv109epUPRmioha/+ENZHXlpscwL8+si+sMtiwtp7ep7b+KQiIpaRu1QWzdr9zVw5RxNGfeHhUVpTB2fpFP15D0qahm1Z7cdYNCisz38xBjDrYuHTtVbX6FT9URFLX6weksds/NSKM5McDpKyFg+ZwKpcZGsWqdV9QR0ITv5B49tqPH5uQdbu9h5sI3LZk8Y0evCma/baVZuCi/tOMQ9r+0jNS7qlM+/cVH+aKOJS2lELaOytaYFjzHMykl2OkrIWVSUBsBGzVQMeypqOW0Dg5at+1s4IzuReF1l3O9S4qI4c3wSm6qa6BsYdDqOOEhFLaet/MhR2nv6mZuf4nSUkFVSnEZn7wDb61qdjiIOUlHLaSuraSY20sMZWYlORwlZEzMTyEiIYn1Fo9NRxEEqajkt3X0D7DzYxqzcZF1lPIAijKGkOJ39zV3UNesCuOFKv2FyWnYcaKVvwGom4hiYl59KlCdCo+owpqKW07KlpoWMhChyU2OdjhLyYiI9zMlLYVttC509/U7HEQeoqGXEmjt7qWzoYG5+qqaMj5GS4nT6By2ba5qdjiIOUFHLiG3d3wLAnDyd7TFWspNjKEyPY0NlE4Na/yPsqKhlRKy1lNU0U5QR79NsOfGfkuJ0mjp62Xuo3ekoMsZU1DIitc1dNBztZa5G02Nu2oQkEqO9WqgpDKmoZUQ2VzcT6THM0JTxMeeNiOCsojTePdROU0ev03FkDKmoxWc9/QNsq21hxoRkYiI9TscJS2cVpmEMbNCpemFFRS0+e6e2lZ7+Qc4qTHM6SthKjo1k2vgkSqubtf5HGFFRi89Kq5vJTIymID3O6ShhraQ4na6+Ad6ubXE6iowRn4vaGOMxxpQZY/4UyEDiTofauqlp6uSsAp077bSijHjGJUazvqJJl+oKEyMZUX8e2BWoIOJum6qa8BjDXE0Zd5wZXv+jrqWL/Vr/Iyz4VNTGmFzgUuD+wMYRN+obGKSspoVpE5K07rRLzM1PISYygjX7GpyOImPA1xH1T4F/A0569MIYs9IYU2qMKT1y5Ihfwok77DjQRlffgA4iuki018PCwnR21LXqVL0wcMqiNsZ8DDhsrd38Qc+z1t5nrV1grV2QmZnpt4DivNKqJlLjIinOjHc6ihzn7InpGAPryjWqDnW+jKiXAMuNMVXA48B5xphHAppKXKPxaA8VDR2cVZhGhA4iukpybCSzclMorW6mq3fA6TgSQKcsamvtV6y1udbaQuB64FVr7YqAJxNX2FTVTISBeQU6iOhGSydl0Ns/yKYqTSsPZTqPWk5qYNCypaaZM7KTSIqJdDqOnMCElFiKM+N5q6JRE2BC2IiK2lr7urX2Y4EKI+6y82AbR3v6OUujaVdbNimD1q4+nnv7oNNRJEA0opaTequ8kdS4SKZk6+K1bjY5K5HMhGjuX1OhCTAhSkUtJ3SwtYuqxg5KitN1ENHlIoxh6aQMtte1aQnUEKWilhN6q7yRSI9hQYHOnQ4Gc/JTSI+P4v43K5yOIgGgopZ/0NHTz9b9LczNSyU2SsuZBoNITwQ3nV3AX3YfZk+9rgATalTU8g9Kq5roH7ScPTHd6SgyAjefXUh8lId7X9/ndBTxMxW1/J2e/gHeqmhkYmY8WUkxTseREUiNj2LF2QU8u+0AlQ0dTscRP1JRy9/5Y9kB2rr7OWeylgEIRncsLSbSE8G9r2lUHUpU1PKewUHLr94oZ3xyDJPGJTgdR05DZmI0NyzM5w9ldexv6nQ6jviJilre88quQ5Qf6eCcyZm6OEAQ++SHiokwRvuqQ4iKWt7zqzcqyE2N1RXGg9z45FhuWJjHk6W1VDdqX3UoUFELMHTe9ObqZu5cVownQqPpYHfXuZPwRBju/step6OIH6ioBWstP3n5XbKSovmns/KcjiN+MC4phpsXF/J0WR37Dh91Oo6Mkq6rJKwrb2RjVRP/tXw6MZGa4BKsHttQ83efZyRE4/VE8MXfb+WGhfmn/b43Ljr914p/aEQd5o6NprOTYjSaDjEJ0V6WTMzgnbpWapt1BkgwU1GHuTf3NlBa3cxd507UaDoEnTM5g/goD89vr9fKekFMRR3GBgYt33t+NzkpsVyn0XRIio70cP7ULCobOrQGSBBTUYex1Vtq2XmwjX+76AyivRpNh6qzCtPISIji+R31DAxqVB2MVNRhqrO3nx++tIfZeSksnz3B6TgSQJ4Iw4XTsznS3qNrKwYpFXWYuv/NSg619fCfl07VLMQwMG18EsUZ8by88xCdvf1Ox5ERUlGHodrmTn7xejkXz8hmQaEuDBAOjDF8bNYEuvsGeGXXIafjyAipqMOMtZav/3EHxsDXPjbN6TgyhrKTY1hUnMaGiiYOtnY5HUdGQEUdZl7cUc9fdh/mixdMIScl1uk4MsYumJpFTKSHZ7YeYFCn6wUNFXUYae/u4xvP7GTq+CRuXVLodBxxQFyUl4tnZFPd1ElpVbPTccRHKuow8q0/7eRwezffuXIGXo++9eFqfkEqRRnxvLDjIG3dfU7HER/otzVMvLijnidKa/nUhyYyNz/V6TjiIGMMV87JoX/A8tzbB52OIz5QUYeBw+3dfGX1O8zISeILF0xxOo64QEZiNB8+Yxzv1LWyu77N6ThyCirqEDcwaPnSE9vo6OnnJ9fNIcqrb7kMOWdKBuMSo3lm6wF6+gecjiMfQL+1Ie7uV97lzb0N/N/LpjE5K9HpOOIi3ogIrpybQ0tXH6/s1LnVbqaiDmGv7DzEz17dx7Xzc7lxFOsRS+gqSI9nYVEa68obtRSqi6moQ9S+w+188YmtzMhJ4ltXzNA0cTmpC6dlkxjj5anNtfQNDDodR05ARR2CDrd3c/ODm4j2evjlivlaZ1o+UGyUh6vm5XK4vYeXtQvElXQprjHw/ksk+cuJLpHU2dvP7atKaero5fefLCE3NS4gX1tCy5SsRBYWprF2XwNTxydRlBHvdCQ5jkbUIaSnf4BPPryZHQda+Z8b5jIrN8XpSBJELp6ZTWp8FE9t3k9Pn84CcRMVdYjoGxjkrkfLeHNvA9+7ehYXTMtyOpIEmWivh6vn5dLS2ceft9c7HUeOo6IOAX0Dg3zh91t5Zdchvnn5dK5boMtqyekpyohn6aQMNlU16dJdLqKiDnLdfQN86uHNPPf2Qf7PJWfyibMLnY4kQe6CaVmMS4xmdVktnT26yIAbqKiDWHt3Hzc/uJFX9xzm21fMYOU5E52OJCEg0hPBtQvy6OwZYHVZna5e7gIq6iDV0dPPx+/fwObqZn76T3NYUVLgdCQJITkpsXx0ehY7D7bx2MbAnLUkvjtlURtj8owxrxljdhpjdhhjPj8WweTkWrv6+PWbFeypb+dXN83n8jk5TkeSELRkUgaTxyXwrT/tZO8h7a92ki8j6n7gS9baaUAJcJcxRtdwckjj0R7ue6Oc1q4+HrptIedP1dkdEhgRxnDN/Fzio7x89ndldOuUPcecsqittQettVuGb7cDuwAN4RxQ39bNfW9U0NM/yO1LiygpTnc6koS4xJhIfnjtbHbXt/PdP+9yOk7YGtHMRGNMITAX2HCCx1YCKwHy87UAkL/tb+pk1boqIj2GO5cWk5UUE7AZjyLHO/fMcdy2pIgH11aybHKmztF3gM8HE40xCcD/Al+w1v7DSuPW2vustQustQsyMzP9mTHslR85ygNrK4mN8rDynIlkJcU4HUnCzL9ffAbTxifx5ae2cait2+k4YcenojbGRDJU0o9aa1cHNpIcb9fBNh5aV0VqXCQrzykmLT7K6UgShqK9Hn52w1y6+wb5lye2MjioU/bGki9nfRjgAWCXtfbHgY8kx2yva+XRDdVkJ8dw57JikmIinY4kYWzSuAS+sXwaa/c18qs3KpyOE1Z8GVEvAW4CzjPGbB3+uCTAucLetv0tPL6phtzUOG5bUkRclBY6FOddtyCPS2eO50cv7WHr/han44QNX876WGOtNdbaWdbaOcMffx6LcOFqS3UzT5TupyA9nluXFGo9aXENYwzfuWom2ckx3PXoFlo6e52OFBY0M9FlNlY28b9bapk4LoGbzy4k2quSFndJjo3knhvncbi9m399cpummI8BFbWLbKpq4umtdUzJSuSmkgJdMVxca3ZeCl+9ZCqv7DrMr9/U/upAUxO4xLbaFp4uq2NKVgIfX5RPpEffGnG3mxcXcsnMbL7/wh5Kq5qcjhPS1AYusOtgG08O75O+cWEBXpW0BAFjDN+7ehZ5qbF85rEyGo/2OB0pZKkRHLbv8FF+t7GGCSmxfOJs7e6Q4JIUE8k9H59HU2cvX/i9zq8OFLWCg2oaO3hkfTXpCVHcslhnd0hwmj4hmW9cNp039zbw01fedTpOSFJRO6S+rZtVb1WRGOPVedIS9G5YmMd1C3L52av7eEHXW/Q7FbUDWjp7WbW2kihPBLctLSJRMw4lyBlj+OblM5idm8yXntjKvsNav9qfVNRjrKt3gFXrqujpH+TmxYWkxmntDgkNMZEefnnT/KHFw367mbbuPqcjhQwV9RjqGxjk4fXVNB7tZUVJAeOTY52OJOJX45NjuefGedQ0dfIvOrjoNyrqMTJoLU+W7qeqsYNr5ucyMTPB6UgiAbGoOJ3//Ng0Xtl1mLv/stfpOCFBR7DGgLWWP79zkO0H2rh4Rjaz81KcjiQSUJ84u4C3a1u5+y97mTgugeWzJzgdKahpRD0G1uxrYF15I4snprN0UobTcUQCbmjxphmcVZjKvz65TTMXR0kj6gB7ZtsBnt9ez4ycZC6ZOZ6h5b1FgsdoLvl24bRsKo508IkHN/LpD00kPSH6vcduXKRL9vlKI+oAequ8kX99YhuF6XFcOz+XCJW0hJm4aC+3LC4EYNW6Kjp7+p0NFKRU1AGyu76NlQ+Xkp8ex4qSAi2yJGErPSGaFYsKaOnq45EN1fQPDDodKeioPQLgYGsXtzy4idhID6tuPUuzDiXsFWbEc828XKoaO3lqSy2DWsN6RNQgftbW3cctD27iaE8/v/9kCbmpcU5HEnGF2XkptHT18eKOeqK9EXx8Ub6O2fhII2o/6ukf4JO/3Uz5kaP8YsU8pk9IdjqSiKt8aEomH56SyaaqZr75p526OoyPNKL2k8FBy5effJu3Khr58XWzWTY50+lIIq70kWlZ9A4M8pu1VcRFefjyhWc6Hcn1VNR+YK3la3/czjPbDvDlC8/gqnm5TkcScS1jDJfOHE9Behz3vFZOXJSXu86d5HQsV1NRj5K1lm/9aRePbajh0x+eyD9/eKLTkURczxjDt6+YSVfvAD94cQ9RngjuPKfY6ViupaIepR+99C4Prq3klsWF/NuFZ+jgiIiPPBGGH147m74By//78y66+gb47HmT9Dt0AirqUfj5q3v5+Wv7uGFhHl+/bJp+wERGyOuJ4O7r5xAT6eHHL7/L0Z5+vnLxmfpdeh8V9Wm6/80KfvjSu1w5N4dvXzFTP1gip8nrieAH18wiIdrDfW9UcKS9h+9fPUvXDz2OinqErLX8/NV9/Ojld7lkZjY/uGYWngiVtMhoREQYvrF8OuOSYvjBi3s43N7NvR+fT3Ksrn4EOo96RKy1fO/53fzo5Xe5am4OP7t+Ll5NDRfxC2MMd507iR9dO5sNFU1cee9ayo8cdTqWK6hlfNTTP8CXntjGr96o4KaSAn547WyVtEgAXD0/l0fvWERLZx9X3LOW13YfdjqS49Q0Pmjt7OPmBzeyuqyOL31kCt+8fDoR2t0hEjCLitN55jNLyEuN49ZVm/je87vpC+PFnFTUp7DrYBvL71nDluoW7r5+Dp89f7IOHIqMgdzUOFb/82JuWJjPL/9azvX3raemsdPpWI5QUX+AP5TVcuW9a+nqHeB3Kxdx+ZwcpyOJhJWYSA/fvWomP7thLu/Wt3PR3W/wyPrqsFsjREV9Aq1dfXzh8TK++PttzMpN4U+fW8r8gjSnY4mEreWzJ/DiF89hfkEqX3t6O9fft569h9qdjjVmVNTv89ruw1z00zd49u2DfPGCKTx2xyLGJcY4HUsk7E1IieW3ty3ku1fNZHd9Oxff/Sbf/fMuWrv6nI4WcDqPelhtcyfffHYnL+08xKRxCaxeMV9XCxdxGWMMNyzM56PTsvje87v51RsV/L50P585dxIrSgqIifQ4HTEgwr6oG472cO9r5TyyvhpPhOHfLzqT25cWaVaUiIulJ0Tzg2tnc/PiQr7/wm6+/dwufvnXcm5fWsyKknwSY0JrokzYFnVVQwe/WVvJk5tr6e4b4Jr5uXz+ginkpMQ6HU1EfDQjJ5mHb1/E+opG7nltH99/YTc/f3UvV8zNYUVJAVPHJzkd0S/Cqqi7egd4aWc9T22uZc2+BrwRhstmT+CucycxMTPB6XgicppKitMpKU7nndpWHnqriqc21/Lohhqmjk/i8jkTuHhGNgXp8U7HPG0mEKe5LFiwwJaWlvr9fUfKWkttcxfryht4Zddh1uxtoKtvgJyUWK6en8uKRfmMSwr8gcLHNtQE/GuIBJsbF+UH7L2bO3p5emsdz2w7QFlNCwCTxiXwoSmZzC9IZX5BKllj8Ls/EsaYzdbaBSd6zKcRtTHmIuBuwAPcb639nh/z+UVP/wB1zV1UN3ayq76NHQfaKKtu5kBrNwATkmO4Zn4ul84az8LCNM0sFAlhqfFR3LqkiFuXFLG/qZNXdh3i1d2HeXh9NQ+sqQQgJyWWeQWpTJ+QRGF6PEUZ8RSkx7nygOQpi9oY4wHuAT4C1AKbjDHPWGt3+jtMXUsXPX0D9A1YevsH6R0YoMF3tBkAAAWTSURBVLff0jswSG//IB09/bR09tLS1UdLZx/Nnb0caOlif1MXh9q7Of4/B3lpscwtSOVTRWksKkpnSlaCZhSKhKG8tLj3Sru3f5AdB1rZUtPClppmSquaeHbbgb97/oTkGLKSY0iPjyYjIYr0hCjS4qNJjPYSG+UhPtpDXJSX2EgPXo8h0hOBJ8LgjTBEeSMYn+z/41y+jKgXAvustRUAxpjHgcsBvxf1+T96ne4+3+bzJ8V4SY6LZHxSLEsmZZCXFkteahx5aXGckZ2o5RFF5B9EeSOYm5/K3PxUbqcIGJrgVt3YQWVDB1UNnVQ3dnC4vYfa5k621bbQ1NHLwKBvu4gzEqIo/dpH/J7bl6LOAfYf93ktsOj9TzLGrARWDn961BizZ/TxRiQDaBjjrzkawZYXgi+z8gbWqPJ+3I9BfBTw7VsNmP887ZcXnOwBv531Ya29D7jPX+83UsaY0pPtiHejYMsLwZdZeQNLeceOL7M66oC84z7PHb5PRETGgC9FvQmYbIwpMsZEAdcDzwQ2loiIHHPKXR/W2n5jzGeAFxk6Pe9Ba+2OgCcbOcd2u5ymYMsLwZdZeQNLecdIQCa8iIiI/2jlIRERl1NRi4i4XFAUtTHmImPMHmPMPmPMf5zg8U8ZY94xxmw1xqwxxkwbvr/QGNM1fP9WY8wv3ZD3uOddbYyxxpgFx933leHX7THGXOjmvG7dvsaYW4wxR47Ldcdxj91sjNk7/HFzEOQdOO7+MTuI78vPhDHmOmPMTmPMDmPMY8fd77ptfIq8jmzjEbHWuvqDoQOY5UAxEAVsA6a97zlJx91eDrwwfLsQ2O62vMPPSwTeANYDC4bvmzb8/GigaPh9PC7O68rtC9wC/PwEr00DKob/TB2+nerWvMOPHR3L7TuCzJOBsmPbDxjn8m18wrxObeORfgTDiPq9KezW2l7g2BT291hr2477NB5w8gjpKfMO+xbwfaD7uPsuBx631vZYayuBfcPv59a8TvA174lcCLxsrW2y1jYDLwMXBSjnMaPJ6xRfMt8J3DO8HbHWHh6+363b+GR5g0IwFPWJprD/w+XAjTF3GWPKgf8GPnfcQ0XGmDJjzF+NMcsCGxXwIa8xZh6QZ619bqSvDYDR5AUXbt9hVxtj3jbGPGWMOTZhy5Xbd9iJ8gLEGGNKjTHrjTFXBDTp3/iSeQowxRizdjjbRSN4rb+NJi84s41HJGQuHGCtvQe4xxhzI/A14GbgIJBvrW00xswHnjbGTH/fCHxMGWMigB8z9N9d1ztFXtdt32HPAr+z1vYYYz4JPASc53CmD/JBeQustXXGmGLgVWPMO9bacseS/o2Xod0JH2ZotvIbxpiZjib6YCfMa61twb3b+D3BMKIe6RT2x4ErAIZ3ITQO397M0H6sKQHKecyp8iYCM4DXjTFVQAnwzPABOiem6592XpduX6y1jdbanuFP7wfm+/raABhNXqy1dcN/VgCvA3MDGXaYL9upFnjGWts3vJvuXYaK0JXbmJPndWobj4zTO8lP9cHQv4QVDB1cO3agYPr7njP5uNuXAaXDtzMZPhjH0IGGOiDN6bzve/7r/O3g3HT+/mBiBYE/mDiavK7cvsD4425fCawfvp0GVDJ0kCt1+Lab86YC0cO3M4C9nOBAr0OZLwIeOi7bfiDdxdv4ZHkd2cYj/js6HcDHb8QlDP0LWA58dfi+bwLLh2/fDewAtgKvHfsmAVcfd/8W4DI35H3fc98rvuHPvzr8uj3AxW7O69btC3x3ONe24Z+HM4977W0MHaTdB9zq5rzAYuCd4fvfAW4fi7w+ZjYM7RLbOZztepdv4xPmdXIbj+RDU8hFRFwuGPZRi4iENRW1iIjLqahFRFxORS0i4nIqahERl1NRi4i4nIpaRMTl/j9CGwMd+GokrgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0RHmgIEREDu",
        "colab_type": "text"
      },
      "source": [
        "# Conclusion:- Sampling means from any distribution produces a normal sampling distribution\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5qpRkDV5tl0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0e5747c4-4328-4f62-8cf4-9f8928e6c5c4"
      },
      "source": [
        "#Trying for Multinomial Dist\n",
        "k=100\n",
        "mul_data = np.random.multinomial(100,[1/k]*k)   #sum of pvals (probability_values) = 1 i.e [1/5,1/5,1/5,1/5,1/5] for k = 5\n",
        "sbn.distplot(mul_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc04772f860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyAIznkD6TdQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "95e8392d-0dd3-43fa-ce9e-9bcc8b22a400"
      },
      "source": [
        "sbn.distplot( apply_CLT(mul_data,300,100) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc047715a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}