Smart_dist_Package_Analysis.ipynb

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      "cell_type": "markdown",
      "metadata": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75L5AcCZd6tf"
      },
      "source": [
        "# **Smart_dist by [Alao David I.](https://github.com/invest41)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SvsP01rM0iP"
      },
      "source": [
        "\n",
        "**Statistical Distribution Package**\n",
        "\n",
        "> **Smart-dist** is a package that contains code for processing Statistical Distribution as well as a Crypto Analysis  \n",
        "  \n",
        "<br/><br/>\n",
        "\n",
        "\n",
        "|Crypto Chart||\n",
        "|--|--|\n",
        "|![473D53FD-6C4D-4A46-89D6-B810F8015DB2](https://user-images.githubusercontent.com/70070334/136704293-e3d8290c-dff6-4158-ae94-6ded81b1bf9e.jpeg)|![75270875-19BA-405E-81B5-E5B9B66DB100](https://user-images.githubusercontent.com/70070334/136704295-30e050d4-7364-4686-bc30-9be49d3da4eb.jpeg)|\n",
        "\n",
        "  \n",
        "<br/><br/>\n",
        "\n",
        "**Standardized Statistical Representation**<br/>\n",
        "\n",
        "|Gaussian Distribution| Probability Density Function | Binomial Distribution |\n",
        "|--|--|--|\n",
        "|![gauss_dist](https://user-images.githubusercontent.com/70070334/136705128-f450b393-27e2-4dfa-8308-579efa113dac.jpeg)|![prob_dist](https://upload.wikimedia.org/wikipedia/commons/thumb/1/1a/Boxplot_vs_PDF.svg/350px-Boxplot_vs_PDF.svg.png)| ![bino_dist](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Binomial_distribution_pmf.svg/300px-Binomial_distribution_pmf.svg.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75qnL-ynNBRi"
      },
      "source": [
        "**This is a notebook containing basic analysis of my python library - smart_dist**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PWKBr4mchXC"
      },
      "source": [
        "### **Accessing the Package**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8G6G0uCDLfPN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c460b07f-86cb-48e5-bf45-d1b948eed5cf"
      },
      "source": [
        "!pip install smart_dist -q -U plotly -q -U kaleido -q"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 25.3 MB 1.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 79.9 MB 100 kB/s \n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 28.1 MB/s \n",
            "\u001b[?25h  Building wheel for smart-dist (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Et06h52KLnQ4"
      },
      "source": [
        "import smart_dist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HmZgZu3IccKc",
        "outputId": "5de074b7-7d44-40a4-9322-5f6d3d0aade6"
      },
      "source": [
        "#Get an idea of the Package Content\n",
        "help(smart_dist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on package smart_dist:\n",
            "\n",
            "NAME\n",
            "    smart_dist\n",
            "\n",
            "PACKAGE CONTENTS\n",
            "    Binomialdistribution\n",
            "    Coinanalysis\n",
            "    Gaussiandistribution\n",
            "    Generaldistribution\n",
            "\n",
            "FILE\n",
            "    /usr/local/lib/python3.7/dist-packages/smart_dist/__init__.py\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ed98QeyQWf6m",
        "outputId": "7ff222f0-4a0c-4904-c5d5-648e5acdb83d"
      },
      "source": [
        "#Package directory\n",
        "dir(smart_dist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Binomial',\n",
              " 'Binomialdistribution',\n",
              " 'Coinanalysis',\n",
              " 'Gaussian',\n",
              " 'Gaussiandistribution',\n",
              " 'Generaldistribution',\n",
              " '__builtins__',\n",
              " '__cached__',\n",
              " '__doc__',\n",
              " '__file__',\n",
              " '__loader__',\n",
              " '__name__',\n",
              " '__package__',\n",
              " '__path__',\n",
              " '__spec__',\n",
              " 'crypto']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9uWZUAHjiYt"
      },
      "source": [
        "#### **Accessing Metadata**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "NEQsemoDXB5a",
        "outputId": "b5356bfa-2bf8-44f3-a27e-88b9810c1375"
      },
      "source": [
        "#Confirming name of Package\n",
        "smart_dist.__name__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'smart_dist'"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mPhjSvcfHih"
      },
      "source": [
        "!pip install importlib_metadata -q\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "SMAe7djRiV62",
        "outputId": "b8265779-2729-450e-a74b-6505b925a49b"
      },
      "source": [
        "from importlib_metadata import metadata as met\n",
        "metadata = dict(met(smart_dist.__name__))\n",
        "pd.DataFrame(metadata, ['Data'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>Metadata-Version</th>\n",
              "      <th>Name</th>\n",
              "      <th>Version</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Home-page</th>\n",
              "      <th>Author</th>\n",
              "      <th>Author-email</th>\n",
              "      <th>License</th>\n",
              "      <th>Platform</th>\n",
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              "      <th>Description</th>\n",
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              "      <th>Data</th>\n",
              "      <td>2.1</td>\n",
              "      <td>smart-dist</td>\n",
              "      <td>1.0.2</td>\n",
              "      <td>Binomial and Gaussian distributions along with...</td>\n",
              "      <td>https://github.com/invest41/smart-dist</td>\n",
              "      <td>Alao David I.</td>\n",
              "      <td>alaodavid41@gmail.com</td>\n",
              "      <td>MIT</td>\n",
              "      <td>UNKNOWN</td>\n",
              "      <td>text/markdown</td>\n",
              "      <td>LICENSE</td>\n",
              "      <td>pandas</td>\n",
              "      <td>### Statistical Distribution Package\\n\\n&gt; **Sm...</td>\n",
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            "text/plain": [
              "     Metadata-Version  ...                                        Description\n",
              "Data              2.1  ...  ### Statistical Distribution Package\\n\\n> **Sm...\n",
              "\n",
              "[1 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
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    {
      "cell_type": "code",
      "metadata": {
        "id": "eQiGSy9YizZa"
      },
      "source": [
        "fl = open('README.md', 'w')\n",
        "fl.write(metadata['Description'])\n",
        "fl.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X48p0aJbwJXG"
      },
      "source": [
        "### **Coin Analysis Module**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_eqzKKDLtd7"
      },
      "source": [
        "from smart_dist import Coinanalysis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAQ9Iwayc8Ke",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "247a9d7d-aea7-48ce-e29d-51e276b7dc2e"
      },
      "source": [
        "#Documentation on the Coinanalysis Module\n",
        "help(Coinanalysis)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on module smart_dist.Coinanalysis in smart_dist:\n",
            "\n",
            "NAME\n",
            "    smart_dist.Coinanalysis\n",
            "\n",
            "CLASSES\n",
            "    builtins.object\n",
            "        crypto\n",
            "    \n",
            "    class crypto(builtins.object)\n",
            "     |  Object that functions on processes in crypto processing\n",
            "     |  \n",
            "     |  Methods defined here:\n",
            "     |  \n",
            "     |  __init__(self)\n",
            "     |      Initialize self.  See help(type(self)) for accurate signature.\n",
            "     |  \n",
            "     |  get_crypto_visuals(self, coin, period='5d', interval='15m', MA=False, days=[7, 25, 99], boll=False, boll_sma=25, save_fig=False, img_format='png')\n",
            "     |      Utilizing Plotly and yfinance to make a Stock Market Coin-USD timeseries plot using Candlesticks\n",
            "     |      \n",
            "     |      \n",
            "     |      \n",
            "     |      Parameters\n",
            "     |        -----------------\n",
            "     |      \n",
            "     |          coin: str\n",
            "     |                \n",
            "     |                    - Symbol of Crypto coin i.e 'BTC', 'ETH', 'DOGE', 'BNB', 'SXP'\n",
            "     |                    - Must be currently listed on https://finance.yahoo.com/cryptocurrencies/\n",
            "     |      \n",
            "     |      \n",
            "     |          period: str, Default \"5d\"\n",
            "     |                  \n",
            "     |                    - Answers the question - How far back from realtime should data be sourced?\n",
            "     |                    - Valid periods: '1d','5d','1mo','3mo','6mo','1y','2y','5y','10y','ytd','max'\n",
            "     |      \n",
            "     |      \n",
            "     |          interval: str, Default \"15m\"\n",
            "     |      \n",
            "     |                    - References intra-period intervals of data\n",
            "     |                    - Valid intervals: '1m','2m','5m','15m','30m','60m','90m','1h','1d','5d','1wk','1mo','3mo'\n",
            "     |                    - Intraday data cannot extend last 60 days\n",
            "     |      \n",
            "     |          \n",
            "     |          MA: bool, Default False\n",
            "     |               \n",
            "     |                    - References the plotting of the Moving Average (MA) data\n",
            "     |          \n",
            "     |      \n",
            "     |          \n",
            "     |          days: list, tuple, 1D array, Pandas Series, Set, Default = [7, 25, 99]\n",
            "     |      \n",
            "     |                    - Must be an iterable containing integer values only\n",
            "     |                    - The integers represent, day subsets for the Moving Average data\n",
            "     |                \n",
            "     |      \n",
            "     |          boll: bool, Default False\n",
            "     |      \n",
            "     |                    - References the plotting of the bollinger band\n",
            "     |                    - Important: • When both MA and boll are True, MA takes preference\n",
            "     |                                 • The above algorithm is in place to avoid a rowdy chart\n",
            "     |      \n",
            "     |          boll_sma:   Integer, Default 25             \n",
            "     |                      \n",
            "     |                    - Indicates the SMA value used as a reference point for plotting the bollinger band\n",
            "     |          \n",
            "     |          \n",
            "     |          save_fig:   bool, Default False\n",
            "     |                      \n",
            "     |                    - Saves the plot to the current working directory\n",
            "     |          \n",
            "     |      \n",
            "     |          img_format: str, Default \"png\"\n",
            "     |                    - The desired image format:'png', 'jpg' or 'jpeg', 'webp', 'svg', and 'pdf'\n",
            "     |          \n",
            "     |      \n",
            "     |      \n",
            "     |      \n",
            "     |      Returns\n",
            "     |        -----------------\n",
            "     |           None\n",
            "     |           \n",
            "     |      \n",
            "     |      Examples of valid use\n",
            "     |        -----------------\n",
            "     |        >>> get_crypto_visuals(\"BTC\")\n",
            "     |        >>> get_crypto_visuals(\"BTC\", MA=True)\n",
            "     |        >>> get_crypto_visuals(\"BTC\", period=\"10d\", interval=\"5m\", MA=True, days=[10,30,100], save_fig=True, img_format='jpeg')\n",
            "     |        >>> get_crypto_visuals(\"BTC\", period=\"10d\", interval=\"5m\", MA=False, days=[10,30,100], boll=True)\n",
            "     |  \n",
            "     |  ----------------------------------------------------------------------\n",
            "     |  Data descriptors defined here:\n",
            "     |  \n",
            "     |  __dict__\n",
            "     |      dictionary for instance variables (if defined)\n",
            "     |  \n",
            "     |  __weakref__\n",
            "     |      list of weak references to the object (if defined)\n",
            "\n",
            "FILE\n",
            "    /usr/local/lib/python3.7/dist-packages/smart_dist/Coinanalysis.py\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXPKyYZI5QgJ"
      },
      "source": [
        "#Access NumPy through the Coinanalysis module\n",
        "from smart_dist.Coinanalysis import np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCTLaFEe5knw"
      },
      "source": [
        "#Access Pandas through the Coinanalysis module\n",
        "from smart_dist.Coinanalysis import pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPdSkN0p6IdC"
      },
      "source": [
        "#Access matplotlib.pyplot through the Coinanalysis module\n",
        "from smart_dist.Coinanalysis import plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-spPOPif6V5B"
      },
      "source": [
        "#Access Seaborn through the Coinanalysis module\n",
        "from smart_dist.Coinanalysis import sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNEc4hTc6whx"
      },
      "source": [
        "#Access yfinance api from the Coinanalysis module\n",
        "from smart_dist.Coinanalysis import yf"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0qpUOHGL7KW"
      },
      "source": [
        "#crypto module for visualizing crypto data\n",
        "from smart_dist.Coinanalysis import crypto"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAhkYu_aMKiu",
        "outputId": "4c14c3b1-97fb-4e35-98b8-8d7db58455e7"
      },
      "source": [
        "coin = crypto()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Crypto Working Space Activated...\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "BoQ9fcy_tbId",
        "outputId": "43fae8d9-859b-4e52-fef9-68aea46eaf8d"
      },
      "source": [
        "coin.get_crypto_visuals('BTC')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "zBLQwwpvVuAb",
        "outputId": "1545defe-4188-4d94-bf61-78bfb0e484eb"
      },
      "source": [
        "coin.get_crypto_visuals(\"BTC\", boll=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "Kpnp3VwhMroE",
        "outputId": "91982308-570d-41ed-b303-a810bad8cdc7"
      },
      "source": [
        "coin.get_crypto_visuals('ADA',\n",
        "                     boll = True,\n",
        "                     period = '10d',\n",
        "                     interval = '15m')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
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($)\"}},\"title\":{\"text\":\"ADA-USD Price Analysis\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "vOtp810pV4RW",
        "outputId": "545db5a8-9e7a-4bc8-a1b9-79d431641432"
      },
      "source": [
        "coin.get_crypto_visuals(\"BTC\",\n",
        "                   period=\"5d\",\n",
        "                   interval=\"15m\",\n",
        "                   MA=True,\n",
        "                   days=[10,30,100],\n",
        "                   save_fig = True)\n",
        "\n",
        "#Download the image\n",
        "from google.colab import files\n",
        "files.download('./fig_timeplot_btc-usd.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
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              "    async function download(id, filename, size) {\n",
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              "<IPython.core.display.Javascript object>"
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          "metadata": {}
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          "data": {
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              "download(\"download_d59b77bf-83bd-44fc-8c4f-5d1f2fe14f86\", \"fig_timeplot_btc-usd.png\", 67984)"
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    {
      "cell_type": "code",
      "source": [
        "#Visualising via Matplotlib\n",
        "plt.figure(figsize = (20,10))\n",
        "plt.grid(False), plt.axis('off')\n",
        "plt.imshow(plt.imread(\"./fig_timeplot_btc-usd.png\"))"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "8gfXVDvoeWI8",
        "outputId": "cf8c3048-f10f-4ccb-fe68-d25146b3517f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f4747ed1250>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
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Urb/xTb26AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "Y04dKswdWLxU",
        "outputId": "6263ab82-f3d1-422d-a19d-0417caa3247b"
      },
      "source": [
        "coin.get_crypto_visuals(\"ETH\",\n",
        "                   period=\"3mo\",\n",
        "                   interval=\"1d\",\n",
        "                   boll=True,\n",
        "                   boll_sma=26\n",
        "                   )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      "source": [
        "### **Gaussian Distribution**"
      ]
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          "base_uri": "https://localhost:8080/"
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        "outputId": "6a213bc4-5041-4e67-b93b-6044f619d13d"
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      "source": [
        "help(smart_dist.Gaussiandistribution)"
      ],
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          "text": [
            "Help on module smart_dist.Gaussiandistribution in smart_dist:\n",
            "\n",
            "NAME\n",
            "    smart_dist.Gaussiandistribution\n",
            "\n",
            "CLASSES\n",
            "    smart_dist.Generaldistribution.Distribution(builtins.object)\n",
            "        Gaussian\n",
            "    \n",
            "    class Gaussian(smart_dist.Generaldistribution.Distribution)\n",
            "     |  Gaussian(mu=0, sigma=1)\n",
            "     |  \n",
            "     |  Gaussian distribution class for calculating and \n",
            "     |  visualizing a Gaussian distribution.\n",
            "     |  \n",
            "     |  Attributes:\n",
            "     |          mean (float) representing the mean value of the distribution\n",
            "     |          stdev (float) representing the standard deviation of the distribution\n",
            "     |          data_list (list of floats) a list of floats extracted from the data file\n",
            "     |  \n",
            "     |  Method resolution order:\n",
            "     |      Gaussian\n",
            "     |      smart_dist.Generaldistribution.Distribution\n",
            "     |      builtins.object\n",
            "     |  \n",
            "     |  Methods defined here:\n",
            "     |  \n",
            "     |  __add__(self, other)\n",
            "     |      Function to add together two Gaussian distributions\n",
            "     |      \n",
            "     |      Args:\n",
            "     |              other (Gaussian): Gaussian instance\n",
            "     |              \n",
            "     |      Returns:\n",
            "     |              Gaussian: Gaussian distribution\n",
            "     |  \n",
            "     |  __init__(self, mu=0, sigma=1)\n",
            "     |      Generic distribution class for calculating and \n",
            "     |      visualizing a probability distribution.\n",
            "     |      \n",
            "     |      Attributes:\n",
            "     |              mean (float) representing the mean value of the distribution\n",
            "     |              stdev (float) representing the standard deviation of the distribution\n",
            "     |              data_list (list of floats) a list of floats extracted from the data file\n",
            "     |  \n",
            "     |  __repr__(self)\n",
            "     |      Function to output the characteristics of the Gaussian instance\n",
            "     |      \n",
            "     |      Args:\n",
            "     |              None\n",
            "     |      \n",
            "     |      Returns:\n",
            "     |              string: characteristics of the Gaussian\n",
            "     |  \n",
            "     |  calculate_mean(self)\n",
            "     |      Function to calculate the mean of the data set.\n",
            "     |      \n",
            "     |      Args: \n",
            "     |              None\n",
            "     |      \n",
            "     |      Returns: \n",
            "     |              float: mean of the data set\n",
            "     |  \n",
            "     |  calculate_stdev(self, sample=True)\n",
            "     |      Function to calculate the standard deviation of the data set.\n",
            "     |      \n",
            "     |      Args: \n",
            "     |              sample (bool): whether the data represents a sample or population\n",
            "     |      \n",
            "     |      Returns: \n",
            "     |              float: standard deviation of the data set\n",
            "     |  \n",
            "     |  pdf(self, x)\n",
            "     |      Probability density function calculator for the gaussian distribution.\n",
            "     |      \n",
            "     |      Args:\n",
            "     |              x (float): point for calculating the probability density function\n",
            "     |              \n",
            "     |      \n",
            "     |      Returns:\n",
            "     |              float: probability density function output\n",
            "     |  \n",
            "     |  plot_histogram(self)\n",
            "     |      Function to output a histogram of the instance variable data using \n",
            "     |      matplotlib pyplot library.\n",
            "     |      \n",
            "     |      Args:\n",
            "     |              None\n",
            "     |              \n",
            "     |      Returns:\n",
            "     |              None\n",
            "     |  \n",
            "     |  plot_histogram_pdf(self, n_spaces=50)\n",
            "     |      Function to plot the normalized histogram of the data and a plot of the \n",
            "     |      probability density function along the same range\n",
            "     |      \n",
            "     |      Args:\n",
            "     |              n_spaces (int): number of data points \n",
            "     |      \n",
            "     |      Returns:\n",
            "     |              list: x values for the pdf plot\n",
            "     |              list: y values for the pdf plot\n",
            "     |  \n",
            "     |  ----------------------------------------------------------------------\n",
            "     |  Methods inherited from smart_dist.Generaldistribution.Distribution:\n",
            "     |  \n",
            "     |  read_data_file(self, file_name)\n",
            "     |      Function to read in data from a txt file. The txt file should have\n",
            "     |      one number (float) per line. The numbers are stored in the data attribute.\n",
            "     |                      \n",
            "     |      Args:\n",
            "     |              file_name (string): name of a file to read from\n",
            "     |      \n",
            "     |      Returns:\n",
            "     |              None\n",
            "     |  \n",
            "     |  ----------------------------------------------------------------------\n",
            "     |  Data descriptors inherited from smart_dist.Generaldistribution.Distribution:\n",
            "     |  \n",
            "     |  __dict__\n",
            "     |      dictionary for instance variables (if defined)\n",
            "     |  \n",
            "     |  __weakref__\n",
            "     |      list of weak references to the object (if defined)\n",
            "\n",
            "FILE\n",
            "    /usr/local/lib/python3.7/dist-packages/smart_dist/Gaussiandistribution.py\n",
            "\n",
            "\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "GYaCO0Mdxe4V"
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      "source": [
        "#Import Gaussian Class from Gaussiandistribution Module from the smart_dist Package\n",
        "from smart_dist.Gaussiandistribution import Gaussian"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WDduHuYZxnL9",
        "outputId": "d4d235d6-1857-437e-a626-6d68f1be00be"
      },
      "source": [
        "#Initialise a Gaussian Instance\n",
        "Gauss = Gaussian()\n",
        "Gauss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 0, standard deviation 1"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4KJ6PfIzGRR"
      },
      "source": [
        "#Creating a sample file for gaussian distribution\n",
        "\n",
        "fw = open('gau_test.txt', 'w')\n",
        "\n",
        "prob_array = np.asanyarray([i for i in range(2,14, 2)] + [r for r in range(10, 0, -2)])\n",
        "prob = prob_array/sum(prob_array)\n",
        "\n",
        "gau_flens = [fw.write(f'{v}\\n') for v in np.random.choice(range(0,11), 20, p=prob)]\n",
        "fw.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dVuTfF77aJu"
      },
      "source": [
        "#### **Creating Gaussian Distribution from file Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jxb60Sh7PGk",
        "outputId": "5adfc18a-f2d0-484c-f0e4-faa0b681f5cd"
      },
      "source": [
        "Gauss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 0, standard deviation 1"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHIgiNW-xrVL",
        "outputId": "3052458e-be33-4711-d11d-c4c10f4c8001"
      },
      "source": [
        "Gauss.read_data_file('gau_test.txt')\n",
        "sorted(Gauss.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 3, 4, 4, 4, 4, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYXfJay56r8M",
        "outputId": "cf626b97-62ae-441a-fc84-7700f9c057b2"
      },
      "source": [
        "Gauss.calculate_mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.1"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waWxsUjd6xdg",
        "outputId": "001da86a-8da7-47c0-95a5-727f35816a1a"
      },
      "source": [
        "Gauss.calculate_stdev()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5110283068684702"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTjiYdJ061Uk",
        "outputId": "b65700ca-d0a4-4b92-cd0e-c17b03248f88"
      },
      "source": [
        "Gauss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 5.1, standard deviation 2.5110283068684702"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cfmU_It9Kc6"
      },
      "source": [
        "#### **Finding probability of a number occuring**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QyPbolX9XZ6",
        "outputId": "e12f043b-bc75-4a0e-cbd3-05cb8eee2d07"
      },
      "source": [
        "#For reference\n",
        "sorted(Gauss.data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 1, 2, 3, 3, 4, 4, 4, 4, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2_ZBC3I8OK-",
        "outputId": "359c7ca5-eba5-420b-c148-dff253b27ea9"
      },
      "source": [
        "#Finding probablity of a number occuring \n",
        "\n",
        "nums = [i for i in range(1, 20, 1)]\n",
        "\n",
        "num_prob = [ (num, round(Gauss.pdf(num) * 100, 2)) for num in nums ]\n",
        "\n",
        "\n",
        "for num, prob in sorted(num_prob, key = lambda x: x[1], reverse = True):\n",
        "    print(f'{num} -> {prob}%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5 -> 15.88%\n",
            "6 -> 14.9%\n",
            "4 -> 14.43%\n",
            "7 -> 11.93%\n",
            "3 -> 11.2%\n",
            "8 -> 8.16%\n",
            "2 -> 7.41%\n",
            "9 -> 4.76%\n",
            "1 -> 4.19%\n",
            "10 -> 2.37%\n",
            "11 -> 1.01%\n",
            "12 -> 0.36%\n",
            "13 -> 0.11%\n",
            "14 -> 0.03%\n",
            "15 -> 0.01%\n",
            "16 -> 0.0%\n",
            "17 -> 0.0%\n",
            "18 -> 0.0%\n",
            "19 -> 0.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQdZlnMZ9Teg"
      },
      "source": [
        "#### **Plotting distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "RPiOBKIb4CUF",
        "outputId": "cf0f469a-b016-4813-9dc9-b90ea647912a"
      },
      "source": [
        "Gauss.plot_histogram()"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "we3z6fIYyyKg",
        "outputId": "dfd04bd3-acdc-4102-edf6-708698bbd2dd"
      },
      "source": [
        "Gauss.plot_histogram_pdf(n_spaces=len(prob_array))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0.0,\n",
              "  0.8181818181818182,\n",
              "  1.6363636363636365,\n",
              "  2.4545454545454546,\n",
              "  3.272727272727273,\n",
              "  4.090909090909091,\n",
              "  4.909090909090909,\n",
              "  5.7272727272727275,\n",
              "  6.545454545454546,\n",
              "  7.363636363636364,\n",
              "  8.181818181818182],\n",
              " [0.020197559659619453,\n",
              "  0.037123947232409395,\n",
              "  0.061362199802058884,\n",
              "  0.09120932062070307,\n",
              "  0.12191834200232034,\n",
              "  0.1465515451464569,\n",
              "  0.15841754625049506,\n",
              "  0.15399537910282132,\n",
              "  0.1346181515886666,\n",
              "  0.1058256838348439,\n",
              "  0.07481179422883551])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ct4UFRRQ6cBx"
      },
      "source": [
        "#### **Adding Gaussian Distributions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdbqAeM9501H",
        "outputId": "37cb35d7-6d5e-4b52-f79a-fbc7be2fcdc0"
      },
      "source": [
        "gau1 = Gaussian(3, 5)\n",
        "gau1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 3, standard deviation 5"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ujfYG-P6FmG",
        "outputId": "6b6502dd-0b1a-40fc-b739-a2fecdc95ede"
      },
      "source": [
        "gau2 = Gaussian(7, 10)\n",
        "gau2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 7, standard deviation 10"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b6AQcjM86Ktn",
        "outputId": "75da0e87-76d0-4bc9-8170-755cfebb066d"
      },
      "source": [
        "gau3 = gau1 + gau2\n",
        "gau3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 10, standard deviation 11.180339887498949"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g11FKeIGv7Ai"
      },
      "source": [
        "### **Binomial Distribution Module**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UtDsrm1ReUw",
        "outputId": "6d1d87d6-90f0-49a8-b9dd-1c402b589efd"
      },
      "source": [
        "help(smart_dist.Binomialdistribution)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on module smart_dist.Binomialdistribution in smart_dist:\n",
            "\n",
            "NAME\n",
            "    smart_dist.Binomialdistribution\n",
            "\n",
            "CLASSES\n",
            "    smart_dist.Generaldistribution.Distribution(builtins.object)\n",
            "        Binomial\n",
            "    \n",
            "    class Binomial(smart_dist.Generaldistribution.Distribution)\n",
            "     |  Binomial(prob=0.5, size=20)\n",
            "     |  \n",
            "     |  Binomial distribution class for calculating and \n",
            "     |  visualizing a Binomial distribution.\n",
            "     |  \n",
            "     |  Attributes:\n",
            "     |      mean (float) representing the mean value of the distribution\n",
            "     |      stdev (float) representing the standard deviation of the distribution\n",
            "     |      data_list (list of floats) a list of floats to be extracted from the data file\n",
            "     |      p (float) representing the probability of an event occurring\n",
            "     |      n (int) number of trials\n",
            "     |  \n",
            "     |  Method resolution order:\n",
            "     |      Binomial\n",
            "     |      smart_dist.Generaldistribution.Distribution\n",
            "     |      builtins.object\n",
            "     |  \n",
            "     |  Methods defined here:\n",
            "     |  \n",
            "     |  __add__(self, other)\n",
            "     |      Function to add together two Binomial distributions with equal p\n",
            "     |      \n",
            "     |      Args:\n",
            "     |          other (Binomial): Binomial instance\n",
            "     |          \n",
            "     |      Returns:\n",
            "     |          Binomial: Binomial distribution\n",
            "     |  \n",
            "     |  __init__(self, prob=0.5, size=20)\n",
            "     |      Generic distribution class for calculating and \n",
            "     |      visualizing a probability distribution.\n",
            "     |      \n",
            "     |      Attributes:\n",
            "     |              mean (float) representing the mean value of the distribution\n",
            "     |              stdev (float) representing the standard deviation of the distribution\n",
            "     |              data_list (list of floats) a list of floats extracted from the data file\n",
            "     |  \n",
            "     |  __repr__(self)\n",
            "     |      Function to output the characteristics of the Binomial instance\n",
            "     |      \n",
            "     |      Args:\n",
            "     |          None\n",
            "     |      \n",
            "     |      Returns:\n",
            "     |          string: characteristics of the Gaussian\n",
            "     |  \n",
            "     |  calculate_mean(self)\n",
            "     |      Function to calculate the mean from p and n\n",
            "     |      \n",
            "     |      Args: \n",
            "     |          None\n",
            "     |      \n",
            "     |      Returns: \n",
            "     |          float: mean of the data set\n",
            "     |  \n",
            "     |  calculate_stdev(self)\n",
            "     |      Function to calculate the standard deviation from p and n.\n",
            "     |      \n",
            "     |      Args: \n",
            "     |          None\n",
            "     |      \n",
            "     |      Returns: \n",
            "     |          float: standard deviation of the data set\n",
            "     |  \n",
            "     |  pdf(self, k)\n",
            "     |      Probability density function calculator for the binomial distribution.\n",
            "     |      \n",
            "     |      Args:\n",
            "     |          x (float): point for calculating the probability density function\n",
            "     |          \n",
            "     |      \n",
            "     |      Returns:\n",
            "     |          float: probability density function output\n",
            "     |  \n",
            "     |  plot_bar(self)\n",
            "     |      Function to output a histogram of the instance variable data using \n",
            "     |      matplotlib pyplot library.\n",
            "     |      \n",
            "     |      Args:\n",
            "     |          None\n",
            "     |          \n",
            "     |      Returns:\n",
            "     |          None\n",
            "     |  \n",
            "     |  plot_bar_pdf(self)\n",
            "     |      Function to plot the pdf of the binomial distribution\n",
            "     |      \n",
            "     |      Args:\n",
            "     |          None\n",
            "     |      \n",
            "     |      Returns:\n",
            "     |          list: x values for the pdf plot\n",
            "     |          list: y values for the pdf plot\n",
            "     |  \n",
            "     |  replace_stats_with_data(self)\n",
            "     |      Function to calculate p and n from the data set\n",
            "     |      \n",
            "     |      Args: \n",
            "     |          None\n",
            "     |      \n",
            "     |      Returns: \n",
            "     |          float: the p value\n",
            "     |          float: the n value\n",
            "     |  \n",
            "     |  ----------------------------------------------------------------------\n",
            "     |  Methods inherited from smart_dist.Generaldistribution.Distribution:\n",
            "     |  \n",
            "     |  read_data_file(self, file_name)\n",
            "     |      Function to read in data from a txt file. The txt file should have\n",
            "     |      one number (float) per line. The numbers are stored in the data attribute.\n",
            "     |                      \n",
            "     |      Args:\n",
            "     |              file_name (string): name of a file to read from\n",
            "     |      \n",
            "     |      Returns:\n",
            "     |              None\n",
            "     |  \n",
            "     |  ----------------------------------------------------------------------\n",
            "     |  Data descriptors inherited from smart_dist.Generaldistribution.Distribution:\n",
            "     |  \n",
            "     |  __dict__\n",
            "     |      dictionary for instance variables (if defined)\n",
            "     |  \n",
            "     |  __weakref__\n",
            "     |      list of weak references to the object (if defined)\n",
            "\n",
            "FILE\n",
            "    /usr/local/lib/python3.7/dist-packages/smart_dist/Binomialdistribution.py\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Td63vgWhqWJi"
      },
      "source": [
        "from smart_dist.Binomialdistribution import Binomial"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aiehA2azqiuN",
        "outputId": "de027130-c6f9-4e7f-8af6-7729b1a1a2e8"
      },
      "source": [
        "bino = Binomial()\n",
        "bino"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 10.0, standard deviation 2.23606797749979, p 0.5, n 20"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQAjoJ_Gqzmk"
      },
      "source": [
        "fw = open('bin_test.txt', 'w')\n",
        "\n",
        "#outputrandom binaries\n",
        "f_lens = [ fw.write(f'{i}\\n') for i in np.random.randint(0,2,10) ] \n",
        "fw.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8krWvgvPtKT"
      },
      "source": [
        "#### **Creating Binomial Distribution from file Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glmHrbmYFn30",
        "outputId": "7eb85926-efe7-4c8d-ff35-08a33a58fbe9"
      },
      "source": [
        "bino"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 10.0, standard deviation 2.23606797749979, p 0.5, n 20"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_bla0BrSsT8z"
      },
      "source": [
        "bino.read_data_file('bin_test.txt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAvQaZAKDppv",
        "outputId": "f2f8f625-1e25-4e1d-a8d8-5ad450c54d5f"
      },
      "source": [
        "bino.data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 1, 0, 1, 0, 1, 0, 0, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hCzQx2DuGX64",
        "outputId": "ddb3cfd9-b67a-417d-87c0-39158dfcaea7"
      },
      "source": [
        "bino.replace_stats_with_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.4, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4mQ7gErWOpUJ",
        "outputId": "f1236118-2039-4e96-dee3-2138cec5e252"
      },
      "source": [
        "bino"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 4.0, standard deviation 1.5491933384829668, p 0.4, n 10"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B58khnN9TRCx"
      },
      "source": [
        "#### **Finding probablity of a number occuring**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2m1RV2H9TfgA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbd8247f-809e-4d8e-9d8f-ca9e2b3b4738"
      },
      "source": [
        "#For reference\n",
        "bino.data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0, 0, 1, 0, 1, 0, 1, 0, 0, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3Ki2R6zTSmV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc5b9fd0-a885-44e3-9f42-e382d20e18e7"
      },
      "source": [
        "#Finding probablity of a number occuring \n",
        "\n",
        "nums = [i for i in range(0, len(bino.data)+1)]\n",
        "\n",
        "num_prob = [ (num, round(bino.pdf(num) * 100, 2)) for num in nums ]\n",
        "\n",
        "\n",
        "for num, prob in sorted(num_prob, key = lambda x: x[1], reverse = True):\n",
        "    print(f'{num} -> {prob}%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 -> 25.08%\n",
            "3 -> 21.5%\n",
            "5 -> 20.07%\n",
            "2 -> 12.09%\n",
            "6 -> 11.15%\n",
            "7 -> 4.25%\n",
            "1 -> 4.03%\n",
            "8 -> 1.06%\n",
            "0 -> 0.6%\n",
            "9 -> 0.16%\n",
            "10 -> 0.01%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o28zaeCOUZ2T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3de9646e-d4c1-45a0-8753-35a50763e4b8"
      },
      "source": [
        "bino"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 4.0, standard deviation 1.5491933384829668, p 0.4, n 10"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQAGLPbVCY52"
      },
      "source": [
        "#### **Plotting distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nG8mVwv_-wF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6299a3d3-ef65-4cae-efe2-4c616ed45143"
      },
      "source": [
        "bino.plot_bar()"
      ],
      "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": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW84w8uWsxkV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "cd8892f6-a199-4ac5-af79-9f86ddfc889a"
      },
      "source": [
        "bino.plot_bar_pdf()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAayElEQVR4nO3de7RdZX3u8e9jAlJFEUhOjwIhUPES6wUbodbWWkWEA8JRUbFe0OLg2Iq11Y6O2FqwaFvUoz3aqpUhCAcVFLQ0Cl4QRD2KmoAgBU0NMUKCyk0EBZHA7/wx55aVxczeayd77pVsvp8x1thrzcu7fnMnYz17vu9c70xVIUnSsAeMuwBJ0tbJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyINS7JP+W5O9mqK1FSX6eZF77+qIkr56Jttv2PpvkqJlqbxrv+7YkNyb58Wy/t7QpBoS2SJK1Se5IcluSW5J8Pclrkvz6/1ZVvaaq3jpiWwdMtk1VXVNVO1bV3TNQ+1uSfGSo/YOr6rQtbXuadSwC3ggsqar/voltHpbkA0l+nOT2JFckedU03uMZSdbNVM26f5g/7gI0Jzy3qr6YZCfgD4H3APsDI3+AjSLJ/KraMJNtbiUWATdV1fVdK5NsD3wRuB54KrAOeBZwWpKdq+rds1ap7l+qyoePzX4Aa4EDhpbtB9wD/Hb7+lTgbe3zBcBngFuAm4Gv0pzJnt7ucwfwc+CvgcVAAUcD1wBfGVg2v23vIuCfgG8BtwL/AezSrnsGsK6rXuAg4FfAXe37XT7Q3qvb5w8A3gz8kObD+f8CO7XrJuo4qq3tRuBvJ/k97dTuf0Pb3pvb9g9oj/meto5TO/Y9un3/Bw8tf3G7z0Pb1wU8cmD9qcDbgAcPvcfPgUcA84C/Aa4GbgMuAfZo9/09YAXws/bn7w20e1Hb7tfbtj4N7Ap8tP03WAEsHtj+McD57b/3KuBFA+v+B3BV+/7rgb8a9/9pH/c+7GLSjKuqb9H8lfsHHavf2K5bCPwmzQdUVdXLaT5on1tNF9I7Bvb5Q+CxwHM28ZavAP4EeDiwAXjvCDV+DvhH4OPt+z2xY7NXto8/AvYGdgT+dWib3wceTfMX/XFJHruJt/wXmpDYuz2eVwCvqqovAgcD17V1vLJj32cDn62qXwwt/ySwA81ZxSa1+w2+x45VdR3wBuAlNB/SD6X5Hd6eZBfgXJrf467Au4Fzk+w60OyRwMuB3YDfAi4GPgzsAnwXOB4gyYNpwuFjwH9r93t/kiVtOycD/6uqHgL8NnDhZMei2WVAqC/X0XxYDLuL5oN8z6q6q6q+Wu2fkpN4S1X9oqru2MT606vqP9sPwr8DXjQxiL2FXgq8u6rWVNXPgTcBRyYZ7Jr9+6q6o6ouBy4H7hM0bS1HAm+qqtuqai3wLpoP2FEsAH40vLCa7rYb2/Wb49XAm6tqVTUur6qbgEOA71fV6VW1oarOAL4HPHdg3w9X1dVV9TPgs8DVVfXFtqazgH3b7Q4F1lbVh9u2vk0TbC9s198FLEny0Kr6aVVdupnHoh4YEOrLbjRdCsPeCawGvpBkTZJlI7R17TTW/xDYjs3/0Bz0iLa9wbbn05z5TBi86uh2mrOMYQvamobb2m3EOm6kCdWNtEG1oF2/Ofag6V4aNnzccN96fzLw/I6O1xO/hz2B/dsLGG5JcgtN8E4Mxr+A5gzmh0m+nGTSsyHNLgNCMy7JU2g+TP7f8Lr2L+g3VtXewGHAG5I8a2L1Jpqc6gxjj4Hni2j+Kr0R+AXwoIG65tF0bY3a7nU0H3CDbW9g4w/DUdzY1jTc1voR9/8icHDbXTPoBcCdwDfa17czcLzc+yEM3cd6LU330LDh455uvcPv8eWqetjAY8eq+lOAqlpRVYfTdD+dA3xiM95DPTEgNGOSPDTJocCZwEeq6oqObQ5N8sgkoRkAvZtm8BSaD969N+OtX5ZkSZIHAScAZ1dzGex/ATskOSTJdjQDww8c2O8nwOLBS3KHnAH8ZZK9kuzIvWMW07qSqq3lE8A/JHlIkj1p+v8/Mvmev3Y6zbjNWUkWJ9kuyXNoxgje0nbzAFwG/HGSeUkOohnrGDzWXdsrzSZ8CHhrkn3SeEI7znAe8Kgkf5xkfpIXA0toLi6Yrs+0bb28rXu7JE9J8tgk2yd5aZKdquoumgHue6ZoT7PIgNBM+HSS22j+WvxbmkHNTV3iug/NX8Q/pxnYfH9Vfald90/Am9uuiL+axvufTnPFzo9pBm3/HKD94Pwzmg/C9TRnFIPfBTir/XlTkq6+71Patr8C/AD4JfC6adQ16HXt+6+hObP6WNv+lKrqTpqrna4FvknzQfpumqum3jmw6etpxgkmunHOGWjjezSBt6b9/T6ibeMTwBfaNk8GfqMdhziU5oKCm2iuKDu0qqbdlVVVtwEH0ozBXEfzb/R27g3qlwNrk9wKvKatW1uJTD0+KEm6P/IMQpLUyYCQJHUyICRJnQwISVKnOTNZ34IFC2rx4sXjLkOStimXXHLJjVW1sGvdnAmIxYsXs3LlynGXIUnblCTD35r/NbuYJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVKnXgMiyUFJViVZ3XVjmCRvSHJVku8kuaCdBnli3d1JLmsfy/usU5J0X719D6K9Ocv7aO6nuw5YkWR5VV01sNm3gaVVdXuSPwXeQXMjdoA7qupJfdUnSZpcn2cQ+wGr2/v5/ormJjKHD25QVV+qqtvbl98Adu+xHknSNPT5Terd2PheweuA/SfZ/miam59P2CHJSppbPJ5YVecM75DkGOAYgEWLFm1xwdp2LV52bm9trz3xkN7alrZmW8VUG0leBixl41sk7llV65PsDVyY5Iqq2ugG61V1EnASwNKlS73zkSTNoD67mNaz8c3kd6fjpudJDqC5TeVh7a0VAaiq9e3PNcBFwL491ipJGtJnQKwA9mlv+L49zT1pN7oaKcm+wAdpwuH6geU7J3lg+3wB8DRgcHBbktSz3rqYqmpDkmOBzwPzgFOq6sokJwArq2o58E5gR+CsJADXVNVhwGOBDya5hybEThy6+kmS1LNexyCq6jzgvKFlxw08P2AT+30deHyftUmSJuc3qSVJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUaf64C9DctHjZub21vfbEQ3prW9K9PIOQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdeo1IJIclGRVktVJlnWsf0OSq5J8J8kFSfYcWHdUku+3j6P6rFOSdF+9BUSSecD7gIOBJcBLkiwZ2uzbwNKqegJwNvCOdt9dgOOB/YH9gOOT7NxXrZKk++pzLqb9gNVVtQYgyZnA4cBVExtU1ZcGtv8G8LL2+XOA86vq5nbf84GDgDN6rFealr7mm3KuKW0t+uxi2g24duD1unbZphwNfHY6+yY5JsnKJCtvuOGGLSxXkjRoqxikTvIyYCnwzunsV1UnVdXSqlq6cOHCfoqTpPupPgNiPbDHwOvd22UbSXIA8LfAYVV153T2lST1p8+AWAHsk2SvJNsDRwLLBzdIsi/wQZpwuH5g1eeBA5Ps3A5OH9gukyTNkt4GqatqQ5JjaT7Y5wGnVNWVSU4AVlbVcpoupR2Bs5IAXFNVh1XVzUneShMyACdMDFhLkmZHr3eUq6rzgPOGlh038PyASfY9BTilv+okSZPZKgapJUlbHwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktRppIBI8twkhokk3Y+M+qH/YuD7Sd6R5DF9FiRJ2jqMFBBV9TJgX+Bq4NQkFyc5JslDeq1OkjQ2I3cbVdWtwNnAmcDDgecBlyZ5XU+1SZLGaNQxiMOT/DtwEbAdsF9VHQw8EXhjf+VJksZl/ojbPR/456r6yuDCqro9ydEzX5YkadxG7WL68XA4JHk7QFVdsKmdkhyUZFWS1UmWdax/epJLk2xIcsTQuruTXNY+lo9YpyRphowaEM/uWHbwZDskmQe8r91uCfCSJEuGNrsGeCXwsY4m7qiqJ7WPw0asU5I0QybtYkryp8CfAb+V5DsDqx4CfG2KtvcDVlfVmratM4HDgasmNqiqte26e6ZduSSpV1ONQXwM+CzwT8BgF9FtVXXzFPvuBlw78HodsP80atshyUpgA3BiVZ0zvEGSY4BjABYtWjSNpiVJU5kqIKqq1iZ57fCKJLuMEBJbYs+qWp9kb+DCJFdU1dVDxZ0EnASwdOnS6rEWSbrfGeUM4lDgEqCADKwrYO9J9l0P7DHwevd22Uiqan37c02Si7j3i3qSpFkwaUBU1aHtz702o+0VwD5J9qIJhiOBPx5lxyQ7A7dX1Z1JFgBPA96xGTVIkjbTVIPUT55sfVVdOsm6DUmOBT4PzANOqaork5wArKyq5UmeAvw7sDPw3CR/X1WPAx4LfLAdvH4AzRjEVZt4K0lSD6bqYnrXJOsKeOZkO1fVecB5Q8uOG3i+gqbraXi/rwOPn6I2SVKPpupi+qPZKkSStHWZqovpmVV1YZLnd62vqk/1U5Ykadym6mL6Q+BC4Lkd6wowICRpjpqqi+n49uerZqccSdLWYtTpvndN8t52Yr1Lkrwnya59FydJGp9RJ+s7E7gBeAFwRPv8430VJUkav1HvB/HwqnrrwOu3JXlxHwVJkrYOo55BfCHJkUke0D5eRPMFOEnSHDXVZa63ce8cTH8BfKRd9QDg58Bf9VqdJGlsprqK6SGzVYgkaesy6hjExAR6+wA7TCwbvg2pJGnuGCkgkrwaeD3NvEmXAb8LXMwUczFJkrZdow5Svx54CvDDdn6mfYFbeqtKkjR2owbEL6vqlwBJHlhV3wMe3V9ZkqRxG3UMYl2ShwHnAOcn+Snww/7KkiSN20gBUVXPa5++JcmXgJ2Az/VWlSRp7KZzFdOTgd+n+V7E16rqV71VJUkau1En6zsOOA3YFVgAfDjJm/ssTJI0XqOeQbwUeOLAQPWJNJe7vq2vwjSzFi87t7e21554SG9tSxqfUa9iuo6BL8gBDwTWz3w5kqStxVRzMf0LzZjDz4Ark5zfvn428K3+y5MkjctUXUwr25+XAP8+sPyiXqqRJG01ppqs77SJ50m2Bx7VvlxVVXf1WZgkabxGnYvpGTRXMa2lmfp7jyRHOVmfJM1do17F9C7gwKpaBZDkUcAZwO/0VZgkabxGvYppu4lwAKiq/wK266ckSdLWYNQziEuSfIh77yj3Uu4dwJY0C/r6LovfY9GmjBoQrwFeC/x5+/qrwPt7qUiStFWYMiCSzAMur6rHAO/uvyRJ0tZgyjGIqrobWJVk0SzUI0naSozaxbQzzTepvwX8YmJhVR3WS1WSpLEbNSD+rtcqJElbnUm7mJLskOQvgBcCj6G5D8SXJx5TNZ7koCSrkqxOsqxj/dOTXJpkQ5IjhtYdleT77eOoaR6XJGkLTTUGcRqwFLgCOJjmC3MjaQe339futwR4SZIlQ5tdA7wS+NjQvrsAxwP7A/sBxyfZedT3liRtuam6mJZU1eMBkpzM9GZw3Q9YXVVr2v3PBA4HrprYoKrWtuvuGdr3OcD5VXVzu/584CCab29LkmbBVGcQv56Qr6o2TLPt3YBrB16va5fN2L5JjkmyMsnKG264YZrlSZImM1VAPDHJre3jNuAJE8+T3DobBU6mqk6qqqVVtXThwoXjLkeS5pSppvuetwVtrwf2GHi9O6PfhW498IyhfS/aglokSdM06mR9m2MFsE+Svdp7SRwJLB9x388DBybZuR2cPrBdJkmaJb0FRDtmcSzNB/t3gU9U1ZVJTkhyGECSpyRZR3MZ7QeTXNnuezPwVpqQWQGcMDFgLUmaHaN+UW6zVNV5wHlDy44beL6Cpvuoa99TgFP6rE+StGl9djFJkrZhBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI6GRCSpE4GhCSpkwEhSepkQEiSOhkQkqROBoQkqZMBIUnqZEBIkjoZEJKkTgaEJKmTASFJ6mRASJI69RoQSQ5KsirJ6iTLOtY/MMnH2/XfTLK4Xb44yR1JLmsf/9ZnnZKk+5rfV8NJ5gHvA54NrANWJFleVVcNbHY08NOqemSSI4G3Ay9u111dVU/qqz5J0uT6PIPYD1hdVWuq6lfAmcDhQ9scDpzWPj8beFaS9FiTJGlEfQbEbsC1A6/Xtcs6t6mqDcDPgF3bdXsl+XaSLyf5gx7rlCR16K2LaQv9CFhUVTcl+R3gnCSPq6pbBzdKcgxwDMCiRYvGUKYkzV19nkGsB/YYeL17u6xzmyTzgZ2Am6rqzqq6CaCqLgGuBh41/AZVdVJVLa2qpQsXLuzhECTp/qvPgFgB7JNkryTbA0cCy4e2WQ4c1T4/AriwqirJwnaQmyR7A/sAa3qsVZI0pLcupqrakORY4PPAPOCUqroyyQnAyqpaDpwMnJ5kNXAzTYgAPB04IcldwD3Aa6rq5r5qlSTdV69jEFV1HnDe0LLjBp7/Enhhx36fBD7ZZ22SpMn5TWpJUqet9SomSWO2eNm5vbS79sRDemlXM88zCElSJ88gxsS/ziRt7TyDkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVInA0KS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmdDAhJUicDQpLUyYCQJHUyICRJnQwISVKn+eMuQJIAFi87t7e21554SG9tz2WeQUiSOhkQkqROBoQkqZNjEK2++j/t+5S0rfIMQpLUqdeASHJQklVJVidZ1rH+gUk+3q7/ZpLFA+ve1C5fleQ5fdYpSbqv3gIiyTzgfcDBwBLgJUmWDG12NPDTqnok8M/A29t9lwBHAo8DDgLe37YnSZolfY5B7Aesrqo1AEnOBA4HrhrY5nDgLe3zs4F/TZJ2+ZlVdSfwgySr2/Yu7rFeSfcjfu9iaqmqfhpOjgAOqqpXt69fDuxfVccObPOf7Tbr2tdXA/vThMY3quoj7fKTgc9W1dlD73EMcEz78tHAql4O5r4WADfO0nuNw1w/Ppj7x+jxbftm6xj3rKqFXSu26auYquok4KTZft8kK6tq6Wy/72yZ68cHc/8YPb5t39ZwjH0OUq8H9hh4vXu7rHObJPOBnYCbRtxXktSjPgNiBbBPkr2SbE8z6Lx8aJvlwFHt8yOAC6vp81oOHNle5bQXsA/wrR5rlSQN6a2Lqao2JDkW+DwwDzilqq5McgKwsqqWAycDp7eD0DfThAjtdp+gGdDeALy2qu7uq9bNMOvdWrNsrh8fzP1j9Pi2fWM/xt4GqSVJ2za/SS1J6mRASJI6GRDTMNXUIdu6JHsk+VKSq5JcmeT1466pD0nmJfl2ks+Mu5Y+JHlYkrOTfC/Jd5M8ddw1zaQkf9n+//zPJGck2WHcNW2pJKckub79btjEsl2SnJ/k++3PnWe7LgNiRCNOHbKt2wC8saqWAL8LvHYOHiPA64HvjruIHr0H+FxVPQZ4InPoWJPsBvw5sLSqfpvmApgjx1vVjDiVZlqhQcuAC6pqH+CC9vWsMiBG9+upQ6rqV8DE1CFzRlX9qKoubZ/fRvPBstt4q5pZSXYHDgE+NO5a+pBkJ+DpNFcIUlW/qqpbxlvVjJsP/Eb73akHAdeNuZ4tVlVfobmSc9DhwGnt89OA/zmrRWFATMduwLUDr9cxxz48B7Uz6+4LfHO8lcy4/wP8NXDPuAvpyV7ADcCH2260DyV58LiLmilVtR7438A1wI+An1XVF8ZbVW9+s6p+1D7/MfCbs12AAaH7SLIj8EngL6rq1nHXM1OSHApcX1WXjLuWHs0Hngx8oKr2BX7BGLom+tL2wx9OE4SPAB6c5GXjrap/7ReIZ/07CQbE6O4X038k2Y4mHD5aVZ8adz0z7GnAYUnW0nQRPjPJR8Zb0oxbB6yrqokzv7NpAmOuOAD4QVXdUFV3AZ8Cfm/MNfXlJ0keDtD+vH62CzAgRjfK1CHbtHaq9ZOB71bVu8ddz0yrqjdV1e5VtZjm3+/CqppTf31W1Y+Ba5M8ul30LDaeYn9bdw3wu0ke1P5/fRZzaBB+yOBUREcB/zHbBWzTs7nOpk1NHTLmsmba04CXA1ckuaxd9jdVdd4Ya9L0vQ74aPuHzBrgVWOuZ8ZU1TeTnA1cSnPV3bfZCqak2FJJzgCeASxIsg44HjgR+ESSo4EfAi+a9bqcakOS1MUuJklSJwNCktTJgJAkdTIgJEmdDAhJUicDQhqSZPck/9HOonl1kve0l4xOts/fzFZ90mwxIKQB7ZevPgWc086i+ShgR+AfptjVgNCcY0BIG3sm8Muq+jBAey/0vwT+JMmfJfnXiQ2TfCbJM5KcSDO76GVJPtque0WS7yS5PMnp7bLFSS5sl1+QZFG7/NQkH0jyjSRr2jZPae/lcOrA+x2Y5OIklyY5q50zS+qNASFt7HHARpP5tRMWXsMmZh6oqmXAHVX1pKp6aZLHAW8GnllVT6S5/wTAvwCnVdUTgI8C7x1oZmfgqTRhtBz457aWxyd5UpIFbZsHVNWTgZXAG2bigKVNcaoNaeY9Ezirqm4EqKqJef6fCjy/fX468I6BfT5dVZXkCuAnVXUFQJIrgcU0k0MuAb7W9IKxPXBxz8eh+zkDQtrYVcARgwuSPBRYBNzCxmfdM3mryzvbn/cMPJ94PR+4Gzi/ql4yg+8pTcouJmljFwAPSvIK+PWtZt9Fc0vINcCTkjwgyR40dxmccFc7VTrAhcALk+zatrFLu/zr3Ht7zJcCX51GXd8AnpbkkW2bD07yqOkenDQdBoQ0oL0xy/NoPuC/D/wX8Euaq5S+BvyA5izjvTQzik44CfhOko+2s/z+A/DlJJcDE1Onvw54VZLv0Mya+3pGVFU3AK8Ezmj3vxh4zOYepzQKZ3OVJHXyDEKS1MmAkCR1MiAkSZ0MCElSJwNCktTJgJAkdTIgJEmd/j9HqSatOda5SAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
              " [0.006046617599999997,\n",
              "  0.04031078399999999,\n",
              "  0.12093235199999997,\n",
              "  0.21499084799999998,\n",
              "  0.250822656,\n",
              "  0.2006581248,\n",
              "  0.11147673600000005,\n",
              "  0.04246732800000001,\n",
              "  0.010616832000000005,\n",
              "  0.0015728640000000009,\n",
              "  0.00010485760000000006])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgTP973rCmhN"
      },
      "source": [
        "#### **Adding Binomial Distributions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NzblnJSnCv1f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5916c13c-4ee2-4ca2-8c72-903f22bddbc8"
      },
      "source": [
        "bino1 = Binomial(0.4, 10)\n",
        "bino1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 4.0, standard deviation 1.5491933384829668, p 0.4, n 10"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N36F9itjC0ZZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9387d09-7d51-49f7-9d06-9b1e20ba8bb2"
      },
      "source": [
        "bino2 = Binomial(0.4, 15)\n",
        "bino2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 6.0, standard deviation 1.8973665961010275, p 0.4, n 15"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSRfnnWyDGGp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22c933f2-c079-495c-f30d-189200a7d2b5"
      },
      "source": [
        "bino3 = bino1 + bino2\n",
        "bino3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mean 10.0, standard deviation 2.449489742783178, p 0.4, n 25"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "75TFGznzb2m7"
      },
      "source": [
        "\n",
        "### **Let's Connect**\n",
        "<p>\n",
        "  Author: Alao David I. <br/>\n",
        "  Date: 19-10-2021\n",
        "  <!--ul>\n",
        "    <li>Author: Alao David I.</li>\n",
        "    <li>Date: 12th of Aug. 2021</li>\n",
        "  </ul-->\n",
        " \n",
        "  <a href=\"https://bit.ly/3kkaHhz\">\n",
        "    <img align=\"right\" height=100 width=100 src=\"https://cvs-disease.herokuapp.com/static/images/profile.259def32692b.jpeg\" />\n",
        "  </a>\n",
        "</p>\n",
        "\n",
        "<table>\n",
        "<tbody>\n",
        "<tr>\n",
        "<td><a href=\"https://invest41.github.io/AlaoDavid.github.io/\" target=\"_blank\">Project Portfolio</a></td>\n",
        "<td><a href=\"https://github.com/invest41\" target=\"_blank\">GitHub</a></td>\n",
        "<td><a href=\"https://mobile.twitter.com/Wilder_Maxim\" target=\"_blank\">Twitter</a></td>\n",
        "<td><a href=\"https://www.kaggle.com/welcomehere\" target=\"_blank\">Kaggle</a></td>\n",
        "<td><a href=\"https://www.linkedin.com/in/david-alao-72362113b/\" target=\"_blank\">Linkedin</a></td>\n",
        "<td><a href=\"https://public.tableau.com/app/profile/alao.david\" target=\"_blank\">Tableau</a></td>\n",
        "</tbody>\n",
        "</table>"
      ]
    }
  ]
}