Sol_Prob_Distributions.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Sol Prob Distributions.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pX8MjxH6JyzY"
      },
      "source": [
        "# Binomial Distribution\n",
        "Fashion Trends Online (FTO) is an e-commerece company that sells woman apparel. It is observed that 10% of their customers return the items purchased by them for many reasons (such as size, color, and material mismatch). On a specific day, 20 customers purchased items from FTO. Calculate:\n",
        "1. Probability that exactly 5 customers will return the items.\n",
        "2. Probability that a maximum of 5 customers will return the items.\n",
        "3. Probability that more than 5 customers will return the items purchased by them.\n",
        "4. Average number of customers who are likely to return the items and the variance and the standard deviation of the number of returns.\n",
        "5. To visualize how thw PMF varies with increasing number of successful trials."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trKhIVYogb1o"
      },
      "source": [
        "from scipy import stats\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "import numpy as np\n",
        "import warnings"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk9UROgILIY3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d81ff709-0d10-4581-8505-99daf0bc94d1"
      },
      "source": [
        "# Probability that exactly 5 customers will return the items.\n",
        "stats.binom.pmf(5, 20, 0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03192136111995428"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fKysnze_Lblt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "68e51d03-b7ea-4afa-9a56-7c8576d0cc7d"
      },
      "source": [
        "# Probability that a maximum of 5 customers will return the items.\n",
        "stats.binom.cdf(5, 20, 0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.988746865835491"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmYwMyrSLr53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cdb634c9-fb01-4f46-dc03-750fbcabaad9"
      },
      "source": [
        "# Probability that more than 5 customers will return the items purchased by them.\n",
        "1 - stats.binom.cdf(5, 20, 0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.011253134164509015"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fcSGO7fLyqE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3bb2c7c3-adff-4b18-cd48-1f913ecdcc56"
      },
      "source": [
        "# Average number of customers who are likely to return the items and the variance and the standard deviation of the number of returns.\n",
        "mean, var = stats.binom.stats(20, 0.1)\n",
        "print(\"Average: \", mean, \"Variance: \", var)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average:  2.0 Variance:  1.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "83IfGAEpMMJq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "0cc46c1c-8159-4172-a181-f8f3e93a6e54"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sn\n",
        "import matplotlib.pyplot as plt\n",
        "# To visualize how thw PMF varies with increasing number of successful trials.\n",
        "# range(0,21) returns all values from 0 to 20 (excluding 21)\n",
        "pmf_df = pd.DataFrame({'success' : range(0,21), 'pmf' : list(stats.binom.pmf(range(0,21), 20, 0.1))})\n",
        "# Create a bar plot with number of success as x and pmf as y\n",
        "\n",
        "sn.barplot(x = pmf_df.success, y = pmf_df.pmf)\n",
        "plt.ylabel('pmf')\n",
        "plt.xlabel('Numbers of items returned')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Numbers of items returned')"
            ]
          },
          "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": "markdown",
      "metadata": {
        "id": "6XclJx-EPh_N"
      },
      "source": [
        "# Poisson Distribution\n",
        "\n",
        "In many situations, we may be interested in calculating the number of events that may occur over a period of time or space. \n",
        "\n",
        "The number of calls arriving at a call center follows a Poisson distribution at 10 calls per hour.\n",
        "1. Calculate the probability that the number of calls will be maximum 5.\n",
        "2. Calculate the probability that the number of calls over a 3-hour period will exceed 30.\n",
        "3. To visualize the Poisson distribution for the average calls per hour as 10, we can plot PMF for all possible number of calls the call center can receive ranging from 0 to 30. We will create a DataFrame which will contain the number of calls ranging from 0 to 30 in one column named 'success' and the corresponding PMFs in another column named 'pmf'. The plotting is done using barplot in seaborn library."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNcbJ6NaQrGT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9752a0d3-509e-47e0-cf8f-064bca33314b"
      },
      "source": [
        "# Calculate the probability that the number of calls will be maximum 5.\n",
        "stats.poisson.cdf(5, 10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06708596287903189"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ChlWKH6Q-Yw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9028d9e3-0ff5-4a7e-dc66-28dde14a6c3f"
      },
      "source": [
        "# Calculate the probability that the number of calls over a 3-hour period will exceed 30.\n",
        "1 - stats.poisson.cdf(30, 30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.45164848742208863"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PiH0p5k3RjXi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "edce31eb-c679-4dad-d4f5-d7c6b9d1f9d7"
      },
      "source": [
        "# To visualize the Poisson distribution for the average calls per hour as 10.\n",
        "# range(0,30) returns all values from 0 to 30 (excluding 30) \n",
        "pmf_df = pd.DataFrame( { 'success': range(0,30), 'pmf': list(stats.poisson.pmf( range(0,30),\n",
        "10) ) } )\n",
        "\n",
        "# Create a bar plot with number of calls as x and pmf as y\n",
        "sn.barplot( x = pmf_df.success, y = pmf_df.pmf );\n",
        "plt.xlabel('Number of Calls Received');"
      ],
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2YVlgS1fZtgG"
      },
      "source": [
        "# Exponential Distribution\n",
        "\n",
        "The time-to-failure of an avionic system follows an exponential distribution with a mean time between failures (MTBF) of 1000 hours. Calculate\n",
        "\n",
        "1. The probability that the system will fail before 1000 hours.\n",
        "2. The probability that it will not fail up to 2000 hours.\n",
        "3. The time by which 10% of the system will fail (i.e. calculate P10 life)\n",
        "4. To visualize the exponential distribution by plotting the PDF function against different time-to-failure hours. Create a list of time-to-failure ranging from 100 to 5000 and then calculate and plot the PDF against those."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LnBNsJIkabr5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b2672b3-c008-478d-d917-55f01194969e"
      },
      "source": [
        "# The probability that the system will fail before 1000 hours.\n",
        "stats.expon.cdf(1000, loc= 1/1000, scale = 1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6321201909489326"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HN8aPg54edFr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a84fcc3b-fe3d-48a8-c225-6a7f60e7eefe"
      },
      "source": [
        "# The probability that it will not fail up to 2000 hours.\n",
        "1 - stats.expon.cdf(2000, loc= 1/1000, scale = 1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13533541857196352"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObPLoYJQet51",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "190de135-9643-484a-b24c-4f46cd6e9bd1"
      },
      "source": [
        "# The time by which 10% of the system will fail (i.e. calculate P10 life). o/p appears in hours.\n",
        "stats.expon.ppf(.1, loc = 1/1000, scale =1000)      // ppf is percent point function and is inverse of CDF"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "105.36151565782632"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZXdbQ6Qfzcz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "c73353c9-8c59-427c-b26d-516396861c23"
      },
      "source": [
        ";"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtRUAKsahBin"
      },
      "source": [
        "# Normal Distribution\n",
        "\n",
        "Daily returns of stocks traded in BSE for BEML and GLAXO stocks is given. Firstly, Load and prepare the data. Anser the following:\n",
        "1. What is the expected daily rate of return of these stocks?\n",
        "2. Which stocks have higher risk or volatility as far as daily returns are concerned?\n",
        "3. What is the expected range of return for 95% confidence interval?\n",
        "4. Which stock has higher probability of making a daily return of 2% or more?\n",
        "5. Which stock has higher probability of making a loss (risk) of 2% or more? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfheyRJBl_7P",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "744dab6d-b2d0-4dbb-ae79-3c3bafa91ea3"
      },
      "source": [
        "# Setting precision level to 4 to show only upto 4 decimal points\n",
        "pd.option_context('display.precision', 2)\n",
        "\n",
        "from google.colab import files\n",
        "import pandas as pd\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "beml_df = pd.read_csv( 'BEML.csv' )\n",
        "beml_df[0:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1bd41420-d7f6-4878-af44-0601b06c64b2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1bd41420-d7f6-4878-af44-0601b06c64b2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving BEML.csv to BEML.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Last</th>\n",
              "      <th>Close</th>\n",
              "      <th>Total Trade Quantity</th>\n",
              "      <th>Turnover (Lacs)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-01-04</td>\n",
              "      <td>1121.0</td>\n",
              "      <td>1151.00</td>\n",
              "      <td>1121.00</td>\n",
              "      <td>1134.0</td>\n",
              "      <td>1135.60</td>\n",
              "      <td>101651.0</td>\n",
              "      <td>1157.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-05</td>\n",
              "      <td>1146.8</td>\n",
              "      <td>1149.00</td>\n",
              "      <td>1128.75</td>\n",
              "      <td>1135.0</td>\n",
              "      <td>1134.60</td>\n",
              "      <td>59504.0</td>\n",
              "      <td>676.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-01-06</td>\n",
              "      <td>1140.0</td>\n",
              "      <td>1164.25</td>\n",
              "      <td>1130.05</td>\n",
              "      <td>1137.0</td>\n",
              "      <td>1139.60</td>\n",
              "      <td>128908.0</td>\n",
              "      <td>1482.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-01-07</td>\n",
              "      <td>1142.0</td>\n",
              "      <td>1159.40</td>\n",
              "      <td>1119.20</td>\n",
              "      <td>1141.0</td>\n",
              "      <td>1144.15</td>\n",
              "      <td>117871.0</td>\n",
              "      <td>1352.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-01-08</td>\n",
              "      <td>1156.0</td>\n",
              "      <td>1172.00</td>\n",
              "      <td>1140.00</td>\n",
              "      <td>1141.2</td>\n",
              "      <td>1144.05</td>\n",
              "      <td>170063.0</td>\n",
              "      <td>1971.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date    Open     High  ...    Close  Total Trade Quantity  Turnover (Lacs)\n",
              "0  2010-01-04  1121.0  1151.00  ...  1135.60              101651.0          1157.18\n",
              "1  2010-01-05  1146.8  1149.00  ...  1134.60               59504.0           676.47\n",
              "2  2010-01-06  1140.0  1164.25  ...  1139.60              128908.0          1482.84\n",
              "3  2010-01-07  1142.0  1159.40  ...  1144.15              117871.0          1352.98\n",
              "4  2010-01-08  1156.0  1172.00  ...  1144.05              170063.0          1971.42\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ut0Yvme2mw4O",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "101988a0-b7c6-4b48-8db9-c5d7db20c151"
      },
      "source": [
        "from google.colab import files\n",
        "import pandas as pd\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "glaxo_df = pd.read_csv( 'GLAXO.csv' )\n",
        "glaxo_df[0:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e70ee06e-ffd8-4e33-a233-c4d142f66198\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e70ee06e-ffd8-4e33-a233-c4d142f66198\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving GLAXO.csv to GLAXO.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Last</th>\n",
              "      <th>Close</th>\n",
              "      <th>Total Trade Quantity</th>\n",
              "      <th>Turnover (Lacs)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-01-04</td>\n",
              "      <td>1613.00</td>\n",
              "      <td>1629.10</td>\n",
              "      <td>1602.00</td>\n",
              "      <td>1629.0</td>\n",
              "      <td>1625.65</td>\n",
              "      <td>9365.0</td>\n",
              "      <td>151.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-05</td>\n",
              "      <td>1639.95</td>\n",
              "      <td>1639.95</td>\n",
              "      <td>1611.05</td>\n",
              "      <td>1620.0</td>\n",
              "      <td>1616.80</td>\n",
              "      <td>38148.0</td>\n",
              "      <td>622.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-01-06</td>\n",
              "      <td>1618.00</td>\n",
              "      <td>1644.00</td>\n",
              "      <td>1617.00</td>\n",
              "      <td>1639.0</td>\n",
              "      <td>1638.50</td>\n",
              "      <td>36519.0</td>\n",
              "      <td>595.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-01-07</td>\n",
              "      <td>1645.00</td>\n",
              "      <td>1654.00</td>\n",
              "      <td>1636.00</td>\n",
              "      <td>1648.0</td>\n",
              "      <td>1648.70</td>\n",
              "      <td>12809.0</td>\n",
              "      <td>211.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-01-08</td>\n",
              "      <td>1650.00</td>\n",
              "      <td>1650.00</td>\n",
              "      <td>1626.55</td>\n",
              "      <td>1640.0</td>\n",
              "      <td>1639.80</td>\n",
              "      <td>28035.0</td>\n",
              "      <td>459.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date     Open     High  ...    Close  Total Trade Quantity  Turnover (Lacs)\n",
              "0  2010-01-04  1613.00  1629.10  ...  1625.65                9365.0           151.74\n",
              "1  2010-01-05  1639.95  1639.95  ...  1616.80               38148.0           622.58\n",
              "2  2010-01-06  1618.00  1644.00  ...  1638.50               36519.0           595.09\n",
              "3  2010-01-07  1645.00  1654.00  ...  1648.70               12809.0           211.00\n",
              "4  2010-01-08  1650.00  1650.00  ...  1639.80               28035.0           459.11\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ISRuYx-FnDg1"
      },
      "source": [
        "# Since our analysis will involve only daily close prices, so we will select Date and Close columns from the DataFrames.\n",
        "\n",
        "beml_df = beml_df[['Date', 'Close']]\n",
        "glaxo_df = glaxo_df[['Date', 'Close']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "at4A4LcNncEW"
      },
      "source": [
        "# Sort rows by time in ascending order\n",
        "glaxo_df = glaxo_df.set_index(pd.DatetimeIndex(glaxo_df['Date']) )\n",
        "beml_df = beml_df.set_index(pd.DatetimeIndex(beml_df['Date']) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84l4l-j8nnYb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "af530a24-cfa3-4de3-cdf8-113beae60193"
      },
      "source": [
        "# Display first five records after the above step.\n",
        "glaxo_df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>2010-01-04</td>\n",
              "      <td>1625.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>2010-01-05</td>\n",
              "      <td>1616.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>2010-01-06</td>\n",
              "      <td>1638.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>2010-01-07</td>\n",
              "      <td>1648.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>2010-01-08</td>\n",
              "      <td>1639.80</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Date    Close\n",
              "Date                           \n",
              "2010-01-04  2010-01-04  1625.65\n",
              "2010-01-05  2010-01-05  1616.80\n",
              "2010-01-06  2010-01-06  1638.50\n",
              "2010-01-07  2010-01-07  1648.70\n",
              "2010-01-08  2010-01-08  1639.80"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7PkzYw7n0mg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "224a8bcd-2356-4b5c-edec-061654696a7f"
      },
      "source": [
        "# plot the trend of close prices of GLAXO stock using plot() method.\n",
        "\n",
        "plt.plot( glaxo_df.Close );\n",
        "plt.xlabel( 'Time' );\n",
        "plt.ylabel( 'Close Price' );"
      ],
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xW_MuOY0oD0c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "a0fd1cdf-b3dd-4927-ba52-a62235d52b24"
      },
      "source": [
        "# plot the trend of close prices of BEML stock using plot() method.\n",
        "plt.plot( beml_df.Close );\n",
        "plt.xlabel( 'Time' );\n",
        "plt.ylabel( 'Close' );"
      ],
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yQMJJ7eokGr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "11d5792a-9e45-4462-9df6-693776f3ea80"
      },
      "source": [
        "# pct_change() in Pandas will give the percentage change in a column value shifted by a period.\n",
        "glaxo_df['gain'] = glaxo_df.Close.pct_change( periods = 1 )\n",
        "beml_df['gain'] = beml_df.Close.pct_change( periods = 1 )\n",
        "glaxo_df.head( 5 )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Close</th>\n",
              "      <th>gain</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>2010-01-04</td>\n",
              "      <td>1625.65</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>2010-01-05</td>\n",
              "      <td>1616.80</td>\n",
              "      <td>-0.005444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>2010-01-06</td>\n",
              "      <td>1638.50</td>\n",
              "      <td>0.013422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>2010-01-07</td>\n",
              "      <td>1648.70</td>\n",
              "      <td>0.006225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>2010-01-08</td>\n",
              "      <td>1639.80</td>\n",
              "      <td>-0.005398</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Date    Close      gain\n",
              "Date                                     \n",
              "2010-01-04  2010-01-04  1625.65       NaN\n",
              "2010-01-05  2010-01-05  1616.80 -0.005444\n",
              "2010-01-06  2010-01-06  1638.50  0.013422\n",
              "2010-01-07  2010-01-07  1648.70  0.006225\n",
              "2010-01-08  2010-01-08  1639.80 -0.005398"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wQDngE0o4vu"
      },
      "source": [
        "# Drop NAN using dropna()\n",
        "glaxo_df = glaxo_df.dropna()\n",
        "beml_df = beml_df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Etfv8pSZpC7V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "6c7d68ab-d803-42df-fa6d-35fccbc3a965"
      },
      "source": [
        "# Now plot again against time\n",
        "\n",
        "plt.figure( figsize = ( 8, 6 ));\n",
        "plt.plot( glaxo_df.index, glaxo_df.gain );\n",
        "plt.xlabel( 'Time' );\n",
        "plt.ylabel( 'gain' );"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOh5mSd9pjXD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "a2a64b50-2b33-48fd-8e63-7a374d58a9fc"
      },
      "source": [
        "# Distribution plot of daily gain of BEML and GLAXO stocks\n",
        "sn.distplot( glaxo_df.gain, label = 'Glaxo' );\n",
        "sn.distplot( beml_df.gain, label = 'BEML' );\n",
        "plt.xlabel( 'gain' );\n",
        "plt.ylabel( 'Density' );\n",
        "plt.legend();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: 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",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: 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"
          ],
          "name": "stderr"
        },
        {
          "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": "A3GPiLhEr6fN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fd2cfc34-9ebf-4698-8a4a-11213e750a9c"
      },
      "source": [
        "# Mean and Variance\n",
        "\n",
        "print( \"Daily gain of Glaxo\")\n",
        "print(\"---------------------\")\n",
        "print( \"Mean: \", round(glaxo_df.gain.mean(), 4) )\n",
        "print( \"Standard Deviation: \", round(glaxo_df.gain.std(), 4) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Daily gain of Glaxo\n",
            "---------------------\n",
            "Mean:  0.0004\n",
            "Standard Deviation:  0.0134\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtplF3EbshDw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1ee88ea-7c33-423c-ab4a-ec409785f54b"
      },
      "source": [
        "print( \"Daily gain of BEML\")\n",
        "print(\"---------------------\")\n",
        "print( \"Mean: \", round(beml_df.gain.mean(), 4) )\n",
        "print( \"Standard Deviation: \", round(beml_df.gain.std(), 4) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Daily gain of BEML\n",
            "---------------------\n",
            "Mean:  0.0003\n",
            "Standard Deviation:  0.0264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8Wa6AYEsppI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c15a8463-ce43-4b9d-a303-e2b961ed7dda"
      },
      "source": [
        "# The describe() method of DataFrame returns the detailed statistical summary of a variable.\n",
        "beml_df.gain.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1738.000000\n",
              "mean        0.000271\n",
              "std         0.026431\n",
              "min        -0.133940\n",
              "25%        -0.013736\n",
              "50%        -0.001541\n",
              "75%         0.011985\n",
              "max         0.198329\n",
              "Name: gain, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdRYQ7AStavL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b69069e-8485-4d09-b6ba-54c11efd3dc5"
      },
      "source": [
        "# Confidence Interval GLAXO\n",
        "from scipy import stats\n",
        "glaxo_df_ci = stats.norm.interval(0.95,\n",
        "loc = glaxo_df.gain.mean(),\n",
        "scale = glaxo_df.gain.std())\n",
        "print( \"Gain at 95% confidence interval is:\", np.round(glaxo_df_ci, 4) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gain at 95% confidence interval is: [-0.0258  0.0266]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pP1z5jbmuEfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5db8f10-63f9-47a8-adb5-49f309da86a7"
      },
      "source": [
        "# Confidence Interval BEML\n",
        "beml_df_ci = stats.norm.interval( 0.95,\n",
        "loc=beml_df.gain.mean(),\n",
        "scale=beml_df.gain.std())\n",
        "print( \"Gain at 95% confidence interval is:\", np.round(beml_df_ci, 4) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gain at 95% confidence interval is: [-0.0515  0.0521]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBX2VK0EuTfe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ca328174-0c7a-4f62-adca-a7ea6890c41d"
      },
      "source": [
        "# Cumulative Probability Distribution\n",
        "\n",
        "print( \"Probability of making 2% loss or higher in Glaxo: \")\n",
        "stats.norm.cdf( -0.02,\n",
        "loc=glaxo_df.gain.mean(),\n",
        "scale=glaxo_df.gain.std())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Probability of making 2% gain or higher in Glaxo: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06352488667177397"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sad78g_wu-rC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6ff99be-1b74-4fad-9bd9-b179c8f4c113"
      },
      "source": [
        "# Cumulative Probability Distribution\n",
        "print( \"Probability of making 2% loss or higher in BEML: \")\n",
        "stats.norm.cdf( -0.02,\n",
        "loc=beml_df.gain.mean(),\n",
        "scale=beml_df.gain.std())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Probability of making 2% loss or higher in BEML: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.22155987503755292"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLusQJX1vLku",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6826d35-9c9d-44dd-b447-d951e140cd39"
      },
      "source": [
        "# Probability of making 2% gain or higher\n",
        "\n",
        "print( \"Probability of making 2% gain or higher in Glaxo: \",\n",
        "1 - stats.norm.cdf( 0.02,\n",
        "loc=glaxo_df.gain.mean(),\n",
        "scale=glaxo_df.gain.std()) )\n",
        "print( \"Probability of making 2% gain or higher in BEML: \",\n",
        "1 - stats.norm.cdf( 0.02,\n",
        "loc=beml_df.gain.mean(),\n",
        "scale=beml_df.gain.std()) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Probability of making 2% gain or higher in Glaxo:  0.07104511457618568\n",
            "Probability of making 2% gain or higher in BEML:  0.22769829484075343\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}