Central_Limit_Theorem.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MLlab_week3_task1_411772.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/tn-220/Python-IV-V/blob/main/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"
      },
      "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"
      },
      "source": [
        "# Importing Libraries "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtS2LJcUENfp"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sbn"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y6DRRPLQEX26"
      },
      "source": [
        "# Generating Random sample data in Normal Distribustion form"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAV5yIGZElEm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "001764bf-9d01-4214-e5e8-7a162acbc3c7"
      },
      "source": [
        "sample_data = np.random.normal(size=100000)  \n",
        "plt.hist(sample_data)\n",
        "plt.show()  #to plot histogram without axises\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sbn.distplot(sample_data,kde=False)   #Note:- kde=False for remove Normal Dist line"
      ],
      "metadata": {
        "id": "NH5BCO7LMNX8",
        "outputId": "44b80253-6626-4de9-8497-232c9d9040fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f850864ec50>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUSPC_OKunHG"
      },
      "source": [
        "# Apply Central limit Theorem on data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x85xVUz_uf2H"
      },
      "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": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRVbDQmFwrTk"
      },
      "source": [
        "# Normal Dist sample data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SiKxI5Uwpvd",
        "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": {
          "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"
      },
      "source": [
        "# Uniform Dist sample data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shE32qs4yjtu",
        "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": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydbOFNBMwgh1",
        "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": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0RHmgIEREDu"
      },
      "source": [
        "# Conclusion:- Sampling means from any distribution produces a normal sampling distribution\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5qpRkDV5tl0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "ca90bd85-b9fc-43d2-e9d5-937e1adc6e1f"
      },
      "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": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f85085a8510>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EyAIznkD6TdQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "b9da5b80-6e44-47a1-e499-902cf38b783a"
      },
      "source": [
        "sbn.distplot( apply_CLT(mul_data,300,100) )"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f85084b6f50>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}