2.2-ProbabilityDistribution.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2.2-ProbabilityDistribution.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Ktxu4flqeot",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "771a63c1-8ed7-4180-a5fe-e2b5181a2191"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzM8gqYgqslO",
        "colab_type": "text"
      },
      "source": [
        "** 1. Bernoulli Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mf1QpuprqnZc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "aa3eea8f-5021-419a-bcd9-d24f91be9cfd"
      },
      "source": [
        "from scipy.stats import bernoulli\n",
        "data_bern = bernoulli.rvs(size=10000,p=0.5)\n",
        "\n",
        "ax= sns.distplot(data_bern,\n",
        "                 kde=False,\n",
        "                 color=\"skyblue\",\n",
        "                 hist_kws={\"linewidth\": 15,'alpha':1})\n",
        "ax.set(xlabel='Bernoulli Distribution', ylabel='Frequency')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Bernoulli Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "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": "aBP5yTkzq1Qd",
        "colab_type": "text"
      },
      "source": [
        "**2. Binomial Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g-O0RoGoqvAN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "618e7444-e6aa-4d7a-e4f4-47eab7797814"
      },
      "source": [
        "from scipy.stats import binom\n",
        "data_binom = binom.rvs(n=10,p=0.8,size=10000)\n",
        "\n",
        "ax = sns.distplot(data_binom,\n",
        "                  kde=False,\n",
        "                  color='skyblue',\n",
        "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
        "ax.set(xlabel='Binomial Distribution', ylabel='Frequency')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Binomial Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "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": "9K2ubqHLq9le",
        "colab_type": "text"
      },
      "source": [
        "**3. Poison Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iyh3rQYiq02R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0c5cdf46-0e09-4b2a-b985-e1a3db4165ec"
      },
      "source": [
        "from scipy.stats import poisson\n",
        "data_poisson = poisson.rvs(mu=3, size=10000)\n",
        "\n",
        "ax = sns.distplot(data_poisson,\n",
        "                  kde=False,\n",
        "                  color='skyblue',\n",
        "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
        "ax.set(xlabel='Poisson Distribution', ylabel='Frequency')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Poisson Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAUg0lEQVR4nO3df7DddX3n8ecLohWwXaFJWQxgsE1lsSLBCOyqu1RafmgLWBW1ainrmnYGWrXdqZFxhLVjJztrtVBdtogpYBFE/EG2zapIS3V3qhAI8lOHDIIkAgli+T0g9L1/nO81p+Hmfk6Se+45997nY+bO+X7f3x/nfW7gvu73x/18U1VIkjSV3UbdgCRp/BkWkqQmw0KS1GRYSJKaDAtJUtOCUTcwDAsXLqwlS5aMug1JmlWuv/76B6pq0WTL5mRYLFmyhHXr1o26DUmaVZLcvb1lnoaSJDUZFpKkJsNCktRkWEiSmgwLSVKTYSFJajIsJElNhoUkqcmwkCQ1zcm/4NborFr/wKT1lcsWznAnkqaTRxaSpCbDQpLU5GmoeWR7p4jA00SSpuaRhSSpybCQJDUZFpKkJsNCktRkWEiSmgwLSVKTYSFJajIsJElNhoUkqcmwkCQ1GRaSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNQ0tLJIckOQfktyW5NYk7+nq+yS5Kskd3eveXT1Jzk2yIclNSQ7v29ep3fp3JDl1WD1LkiY3zCOLp4E/rqpDgKOA05McAqwErq6qpcDV3TzACcDS7msFcB70wgU4CzgSOAI4ayJgJEkzY2hhUVX3VtUN3fQjwO3AYuAk4KJutYuAk7vpk4CLq+dbwAuS7AccB1xVVQ9W1Y+Bq4Djh9W3JOnZZuSaRZIlwDLg28C+VXVvt+g+YN9uejFwT99mG7va9uqSpBky9LBI8nzgC8B7q+rh/mVVVUBN0/usSLIuybotW7ZMxy4lSZ2hhkWS59ALikuq6otd+f7u9BLd6+auvgk4oG/z/bva9ur/SlWdX1XLq2r5okWLpveDSNI8N8y7oQJ8Gri9qj7Wt2gNMHFH06nAlX313+nuijoKeKg7XfVV4Ngke3cXto/tapKkGbJgiPt+FfBO4OYkN3a1M4FVwOVJ3gXcDZzSLVsLvA7YADwOnAZQVQ8m+VPgum69D1fVg0PsW5K0jaGFRVX9XyDbWXzMJOsXcPp29rUaWD193UmSdoR/wS1JajIsJElNhoUkqcmwkCQ1GRaSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqMiwkSU2GhSSpybCQJDUZFpKkJsNCktRkWEiSmgwLSVKTYSFJajIsJElNhoUkqWnBqBuQpsOq9Q9MWl+5bOEMdyLNTR5ZSJKaDAtJUpNhIUlqMiwkSU2GhSSpybCQJDUZFpKkJsNCktRkWEiSmoYWFklWJ9mc5Ja+2tlJNiW5sft6Xd+yDyTZkOR7SY7rqx/f1TYkWTmsfiVJ2zfM4T4uBD4BXLxN/eNV9dH+QpJDgLcCLwVeCHw9yS93iz8J/DqwEbguyZqqum2IfQ+dQ1NImm2GFhZV9Y0kSwZc/STgsqp6Evh+kg3AEd2yDVV1J0CSy7p1Z3VYSNJsM4prFmckuak7TbV3V1sM3NO3zsautr36syRZkWRdknVbtmwZRt+SNG/NdFicB/wicBhwL/Dn07Xjqjq/qpZX1fJFixZN124lSczwEOVVdf/EdJJPAX/bzW4CDuhbdf+uxhR1SdIMmdEjiyT79c2+AZi4U2oN8NYkP5PkIGApcC1wHbA0yUFJnkvvIviamexZkjTgkUWSl1XVzTuy4ySXAkcDC5NsBM4Cjk5yGFDAXcDvAVTVrUkup3fh+mng9Kp6ptvPGcBXgd2B1VV16470IUnadYOehvqfSX6G3u2wl1TVQ60Nquptk5Q/PcX6HwE+Mkl9LbB2wD4lSUMw0GmoqnoN8HZ61w+uT/LZJL8+1M4kSWNj4GsWVXUH8EHg/cB/As5N8t0kvzWs5iRJ42GgsEhyaJKPA7cDrwV+s6r+XTf98SH2J0kaA4Nes/hL4ALgzKp6YqJYVT9M8sGhdCZJGhuDhsXrgSf67lDaDXheVT1eVZ8ZWneSpLEw6DWLrwN79M3v2dUkSfPAoGHxvKp6dGKmm95zOC1JksbNoGHxWJLDJ2aSvAJ4Yor1JUlzyKDXLN4LfD7JD4EA/xZ4y9C6kiSNlYHCoqquS3Iw8JKu9L2q+snw2pIkjZMdGXX2lcCSbpvDk1BV2z4FT5I0Bw06kOBn6D2H4kbgma5cPPuRqZKkOWjQI4vlwCFVVcNsRpI0nga9G+oWehe1JUnz0KBHFguB25JcCzw5UayqE4fSlSRprAwaFmcPswlJ0ngb9NbZf0zyImBpVX09yZ70nlwnSZoHBh2i/N3AFcBfdaXFwJeH1ZQkabwMeoH7dOBVwMPw0wch/cKwmpIkjZdBw+LJqnpqYibJAnp/ZyFJmgcGDYt/THImsEf37O3PA/97eG1JksbJoGGxEtgC3Az8HrCW3vO4JUnzwKB3Q/0L8KnuS5I0zww6NtT3meQaRVW9eNo7kiSNnR0ZG2rC84A3A/tMfzuSpHE00DWLqvpR39emqvoL4PVD7k2SNCYGPQ11eN/sbvSONHbkWRiSpFls0B/4f943/TRwF3DKtHcjSRpLg94N9avDbkSSNL4GPQ31R1Mtr6qPTU87kqRxtCN3Q70SWNPN/yZwLXDHMJqSJI2XQcNif+DwqnoEIMnZwN9V1TuG1ZgkaXwMOtzHvsBTffNPdTVJ0jww6JHFxcC1Sb7UzZ8MXDScliRJ42bQu6E+kuT/AK/pSqdV1frhtSVJGieDnoYC2BN4uKrOATYmOWhIPUmSxsygj1U9C3g/8IGu9BzgbxrbrE6yOcktfbV9klyV5I7ude+uniTnJtmQ5Kb+vxhPcmq3/h1JTt3RDyhJ2nWDHlm8ATgReAygqn4I/GxjmwuB47eprQSurqqlwNXdPMAJwNLuawVwHvTCBTgLOBI4AjhrImAkSTNn0LB4qqqKbpjyJHu1NqiqbwAPblM+ia0Xxi+id6F8on5x9XwLeEGS/YDjgKuq6sGq+jFwFc8OIEnSkA16N9TlSf6K3g/xdwP/mZ17ENK+VXVvN30fW2+/XQzc07fexq62vfqzJFlB76iEAw88cCdak6a2av0Dk9ZXLls4w51IM68ZFkkCfA44GHgYeAnwoaq6alfeuKoqybMeqLQL+zsfOB9g+fLl07ZfSdIAYdH9UF9bVS+jdxpoV9yfZL+qurc7zbS5q28CDuhbb/+utgk4epv6NbvYgyRpBw16zeKGJK+chvdbA0zc0XQqcGVf/Xe6u6KOAh7qTld9FTg2yd7dhe1ju5okaQYNes3iSOAdSe6id0dU6B10HLq9DZJcSu+oYGGSjfTualpF7/rHu4C72fpMjLXA64ANwOPAafTe4MEkfwpc16334ara9qK5JGnIpgyLJAdW1Q/o3ZW0Q6rqbdtZdMwk6xZw+nb2sxpYvaPvL0maPq0jiy/TG2327iRfqKo3zkRTkqTx0rpmkb7pFw+zEUnS+GqFRW1nWpI0j7ROQ708ycP0jjD26KZh6wXunxtqd5KksTBlWFTV7jPViCRpfO3IEOWSpHnKsJAkNRkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqMiwkSU2GhSSpybCQJDUZFpKkJsNCktRkWEiSmgwLSVKTYSFJajIsJElNhoUkqcmwkCQ1GRaSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqWjCKN01yF/AI8AzwdFUtT7IP8DlgCXAXcEpV/ThJgHOA1wGPA79bVTcMs79V6x+YtL5y2cJhvq0kja1RHln8alUdVlXLu/mVwNVVtRS4upsHOAFY2n2tAM6b8U4laZ4bp9NQJwEXddMXASf31S+unm8BL0iy3ygalKT5alRhUcDXklyfZEVX27eq7u2m7wP27aYXA/f0bbuxq0mSZshIrlkAr66qTUl+AbgqyXf7F1ZVJakd2WEXOisADjzwwOnrVJI0miOLqtrUvW4GvgQcAdw/cXqpe93crb4JOKBv8/272rb7PL+qllfV8kWLFg2zfUmad2b8yCLJXsBuVfVIN30s8GFgDXAqsKp7vbLbZA1wRpLLgCOBh/pOV0lznnfnaRyM4jTUvsCXenfEsgD4bFV9Jcl1wOVJ3gXcDZzSrb+W3m2zG+jdOnvazLcsSfPbjIdFVd0JvHyS+o+AYyapF3D6DLQmSdqOcbp1VpI0pgwLSVKTYSFJajIsJElNhoUkqcmwkCQ1GRaSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqQmw0KS1GRYSJKaDAtJUpNhIUlqMiwkSU2GhSSpybCQJDUZFpKkpgWjbkDSaK1a/8Ck9ZXLFs5wJxpnHllIkpoMC0lSk2EhSWoyLCRJTYaFJKnJsJAkNRkWkqQmw0KS1GRYSJKaDAtJUpPDfUgaGYcamT08spAkNRkWkqSmWXMaKsnxwDnA7sAFVbVqxC1JGnOe5po+s+LIIsnuwCeBE4BDgLclOWS0XUnS/DFbjiyOADZU1Z0ASS4DTgJuG2lXkrQdc+2oJlU16h6akrwJOL6q/ks3/07gyKo6o2+dFcCKbvYlwPd24S0XApP/S89+frbZay5/Pj/beHhRVS2abMFsObJoqqrzgfOnY19J1lXV8unY17jxs81ec/nz+dnG36y4ZgFsAg7om9+/q0mSZsBsCYvrgKVJDkryXOCtwJoR9yRJ88asOA1VVU8nOQP4Kr1bZ1dX1a1DfMtpOZ01pvxss9dc/nx+tjE3Ky5wS5JGa7achpIkjZBhIUlqMiz6JDk+yfeSbEiyctT9TKckByT5hyS3Jbk1yXtG3dN0S7J7kvVJ/nbUvUynJC9IckWS7ya5Pcm/H3VP0ynJ+7r/Jm9JcmmS5426p52VZHWSzUlu6avtk+SqJHd0r3uPssedZVh05sGQIk8Df1xVhwBHAafPsc8H8B7g9lE3MQTnAF+pqoOBlzOHPmOSxcAfAsur6lfo3cDy1tF2tUsuBI7fprYSuLqqlgJXd/OzjmGx1U+HFKmqp4CJIUXmhKq6t6pu6KYfofcDZ/Fou5o+SfYHXg9cMOpeplOSfwP8R+DTAFX1VFX982i7mnYLgD2SLAD2BH444n52WlV9A3hwm/JJwEXd9EXAyTPa1DQxLLZaDNzTN7+ROfTDtF+SJcAy4Nuj7WRa/QXwJ8C/jLqRaXYQsAX46+4U2wVJ9hp1U9OlqjYBHwV+ANwLPFRVXxttV9Nu36q6t5u+D9h3lM3sLMNinknyfOALwHur6uFR9zMdkvwGsLmqrh91L0OwADgcOK+qlgGPMUtPY0ymO39/Er1QfCGwV5J3jLar4ane3yrMyr9XMCy2mvNDiiR5Dr2guKSqvjjqfqbRq4ATk9xF7/Tha5P8zWhbmjYbgY1VNXEUeAW98Jgrfg34flVtqaqfAF8E/sOIe5pu9yfZD6B73TzifnaKYbHVnB5SJEnonfe+vao+Nup+plNVfaCq9q+qJfT+3f6+qubEb6dVdR9wT5KXdKVjmFtD8/8AOCrJnt1/o8cwhy7gd9YAp3bTpwJXjrCXnTYrhvuYCSMYUmSmvQp4J3Bzkhu72plVtXaEPWkwfwBc0v0Scydw2oj7mTZV9e0kVwA30Ltjbz2zeHiMJJcCRwMLk2wEzgJWAZcneRdwN3DK6DrceQ73IUlq8jSUJKnJsJAkNRkWkqQmw0KS1GRYSJKaDAvNKkmeSXJjN0Lp55PsOcW6J8706MFJzk6yqevxjiRf7B+wsRuuY7sDOCb53SQvnGL5h5P8Wjd9V5KFO9DbkiS/3Te/PMm5g26v+c1bZzWrJHm0qp7fTV8CXD9Of2SY5Gzg0ar6aDf/Fnqjxr6sqrYMsP01wH+tqnWTLNu9qp7pm7+L3mitDwzY29Hdvn9jkPWlfh5ZaDb7JvBL3fMCvpzkpiTfSnIo/PS39E9002/ujka+k+QbXe2lSa7tjgJuSrK0q/9Rt+4tSd7b1ZZ0z5L4VPfsha8l2aPVYFV9Dvga8Nvdfq7pfqPfPcmF3Xvc3D3T4U3Acnp/gHdjkj26o4f/nuQG4M3dNm/qe4s/6ba/Nskvde/xr9ZJ8mg3uQp4Tbfv9yU5Ot2zP6b4Hp6d3jMarklyZ5I/3Ml/K81yhoVmpW446xOAm4H/BqyvqkOBM4GLJ9nkQ8BxVfVy4MSu9vvAOVV1GL0f0huTvILeX0gfSe+5H+9Osqxbfynwyap6KfDPwBsHbPcG4OBtaocBi6vqV6rqZcBfV9UVwDrg7VV1WFU90a37o6o6vKoum2TfD3Xbf4LeyLtTWQl8s9v3x7dZNtX38GDgOHrD+J/VjTGmecaw0GyzRzdcyTp64wp9Gng18BmAqvp74OeT/Nw22/0/4MIk76Y3nAvAPwFnJnk/8KLuh/OrgS9V1WNV9Si9ge1e063//aqaGCrlemDJgD1nktqdwIuT/GWS44GpRgD+3BTLLu173ZUn6E31Pfy7qnqyO921mVk6xLZ2jWGh2eaJ7jfjw6rqD7oHVTVV1e8DH6Q3svD1SX6+qj5L7yjjCWBtktc2dvNk3/QzDD622jK2GRyvqn5M76l319A7wpnqoU2PTbGsJpl+mu7/7SS7Ac8dsM/t2dnPrTnEsNBc8E3g7fDTi7gPbPusjiS/WFXfrqoP0XuY0AFJXgzcWVXn0hsJ9NBuXyd3o6DuBbyhq+2UJG8EjmXrEcBEfSGwW1V9gV6ITQw7/gjwszvwFm/pe/2nbvou4BXd9InAxGmjqfbd/B5qfvM3BM0FZwOrk9wEPM7W4aD7/Y/uAnboPQf5O8D7gXcm+Qm9J5j9WVU9mORC4Npuuwuqan16Txcc1PvSe4DPXsAtwGsnuRNqMb2n3038wvaB7vVC4H8leYLBTivt3X3uJ4G3dbVPAVcm+Q7wFbYemdwEPNPVL6Q3wuuEs2l/DzWPeeusJKnJ01CSpCbDQpLUZFhIkpoMC0lSk2EhSWoyLCRJTYaFJKnp/wPLQLW0namMtQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cv40pVbNrEwM",
        "colab_type": "text"
      },
      "source": [
        "**4. Exponential Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERQmOggbrBHO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "c3bbf048-e66b-43a9-e606-45f275e2eebe"
      },
      "source": [
        "from scipy.stats import expon\n",
        "data_expon = expon.rvs(scale=1,loc=0,size=1000)\n",
        "\n",
        "ax = sns.distplot(data_expon,\n",
        "                  kde=True,\n",
        "                  color='skyblue',\n",
        "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
        "ax.set(xlabel='Exponential Distribution', ylabel='Frequency')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Exponential Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "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": "HM04gLMcrYzY",
        "colab_type": "text"
      },
      "source": [
        "**5. Uniform Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtYjzoR5raaw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d01d8ba9-75a2-4111-8e96-090727dbe616"
      },
      "source": [
        "from scipy.stats import uniform\n",
        "n = 10000\n",
        "start = 10\n",
        "width = 20\n",
        "data_uniform = uniform.rvs(size=n, loc = start, scale=width)\n",
        "\n",
        "ax = sns.distplot(data_uniform,\n",
        "                  bins=100,\n",
        "                  kde=True,\n",
        "                  color='skyblue',\n",
        "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
        "ax.set(xlabel='Uniform Distribution ', ylabel='Frequency')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Uniform Distribution ')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOik0o2KrPtX",
        "colab_type": "text"
      },
      "source": [
        "**6. Normal Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KEAG_RN5rJlK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "0db2866b-e540-427f-84dc-a1c474f9931c"
      },
      "source": [
        "from scipy.stats import norm\n",
        "# generate random numbers from N(0,1)\n",
        "data_normal = norm.rvs(size=10000,loc=0,scale=1)\n",
        "\n",
        "ax = sns.distplot(data_normal,\n",
        "                  kde=True,\n",
        "                  color='skyblue',\n",
        "                  hist_kws={\"linewidth\": 15,'alpha':1})\n",
        "ax.set(xlabel='Normal Distribution', ylabel='Frequency')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Normal Distribution')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBV9P5XgrNZB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}