From 47213c2b222ad7ac2a535d14e845b328478cc9c0 Mon Sep 17 00:00:00 2001 From: leticiademarchiferreira <127995157+leticiademarchiferreira@users.noreply.github.com> Date: Mon, 15 May 2023 23:54:56 +0200 Subject: [PATCH] C:\Users\Leticia Demarchi\Documents\Ironhack Lisbon April\Labs\5 Week\lab-probability-distributions\your-code --- your-code/continuous.ipynb | 268 ++++++++++++++++++++++++++++++++----- your-code/discrete.ipynb | 187 ++++++++++++++++++++++---- 2 files changed, 400 insertions(+), 55 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..d5628b9 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -12,6 +13,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -24,6 +26,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -35,9 +38,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.07133206 2.81215984 2.15503942 2.20259191 2.62521224 2.67064225\n", + " 2.91088173 2.37057642 2.53237171 2.83794532]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -48,6 +60,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -73,14 +86,60 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def generate_uniform_random_numbers(bottom, ceiling, count):\n", + " return np.random.uniform(bottom, ceiling, count)\n", + "\n", + "\n", + "set1_bottom = 10\n", + "set1_ceiling = 15\n", + "set1_count = 100\n", + "\n", + "set1_random_numbers = generate_uniform_random_numbers(set1_bottom, set1_ceiling, set1_count)\n", + "\n", + "set2_bottom = 10\n", + "set2_ceiling = 60\n", + "set2_count = 1000\n", + "\n", + "set2_random_numbers = generate_uniform_random_numbers(set2_bottom, set2_ceiling, set2_count)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(set1_random_numbers, bins=10)\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.title('Uniform Distribution (bottom=10, ceiling=15, count=100)')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(set2_random_numbers, bins=10)\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.title('Uniform Distribution (bottom=10, ceiling=60, count=1000)')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -89,14 +148,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here: sim \n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -114,14 +174,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def generate_normal_random_numbers(mean, std_dev, count):\n", + " return np.random.normal(mean, std_dev, count)\n", + "\n", + "set1_mean = 10\n", + "set1_std_dev = 1\n", + "set1_count = 1000\n", + "\n", + "set1_random_numbers = generate_normal_random_numbers(set1_mean, set1_std_dev, set1_count)\n", + "\n", + "set2_mean = 10\n", + "set2_std_dev = 50\n", + "set2_count = 1000\n", + "\n", + "set2_random_numbers = generate_normal_random_numbers(set2_mean, set2_std_dev, set2_count)\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(set1_random_numbers, bins=30)\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.title('Normal Distribution (mean=10, std_dev=1, count=1000)')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(set2_random_numbers, bins=30)\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Count')\n", + "plt.title('Normal Distribution (mean=10, std_dev=50, count=1000)')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -130,14 +232,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here: sim \n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -147,6 +250,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -158,14 +262,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.read_csv('vehicles.csv')\n", + "\n", + "plt.hist(df['Fuel Barrels/Year'], bins=30)\n", + "plt.xlabel('Fuel Barrels/Year')\n", + "plt.ylabel('Count')\n", + "plt.title('Histogram of Fuel Barrels/Year')\n", + "\n", + "plt.show()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -174,14 +300,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(df['CO2 Emission Grams/Mile'], bins=30)\n", + "plt.xlabel('CO2 Emission Grams/Mile')\n", + "plt.ylabel('Count')\n", + "plt.title('Histogram of CO2 Emission Grams/Mile')\n", + "\n", + "plt.show()\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -190,14 +333,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(df['Combined MPG'], bins=30)\n", + "plt.xlabel('Combined MPG')\n", + "plt.ylabel('Count')\n", + "plt.title('Histogram of Combined MPG')\n", + "\n", + "plt.show()\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -206,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -214,6 +374,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -221,6 +382,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -237,14 +399,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def generate_exponential(mean, size):\n", + " return np.random.exponential(scale=mean, size=size)\n", + "\n", + "sequence1 = generate_exponential(1, 1000)\n", + "sequence2 = generate_exponential(100, 1000)\n", + "\n", + "plt.hist(sequence1, bins=100, alpha=0.5, label='Mean = 1')\n", + "plt.hist(sequence2, bins=100, alpha=0.5, label='Mean = 100')\n", + "plt.xlabel('Values')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Exponential Distribution')\n", + "plt.legend()\n", + "\n", + "plt.show()\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -253,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -261,6 +448,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -273,15 +461,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability that a customer will spend less than fifteen minutes in the bank: 1.0\n" + ] + } + ], "source": [ "# your answer here\n", - "# Hint: This is same as saying P(x<15)\n" + "# Hint: This is same as saying P(x<15)\n", + "import scipy.stats as stats\n", + "\n", + "mean = 10\n", + "\n", + "scale = 1 / mean\n", + "\n", + "probability = stats.expon.cdf(15, scale=scale)\n", + "\n", + "print(\"The probability that a customer will spend less than fifteen minutes in the bank:\", probability)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -290,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -314,7 +520,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..713d342 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -18,6 +19,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -33,9 +35,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of picking an apple: 0.6\n", + "Probability of picking an orange: 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", @@ -43,10 +54,19 @@ "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "total_fruits = 100\n", + "apples = 60\n", + "oranges = 40\n", + "\n", + "probability_apple = apples / total_fruits\n", + "print(\"Probability of picking an apple:\", probability_apple)\n", + "\n", + "probability_orange = oranges / total_fruits\n", + "print(\"Probability of picking an orange:\", probability_orange)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -61,14 +81,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that the first 5 fruits are all apples: 0.07775999999999998\n", + "Probability that the first 5 fruits are all apples and the next 15 fruits are all oranges: 8.349416423424006e-08\n" + ] + } + ], "source": [ - "# your code here\n" + "probability_5_apples = probability_apple ** 5\n", + "print(\"Probability that the first 5 fruits are all apples:\", probability_5_apples)\n", + "\n", + "probability_5_apples_15_oranges = probability_apple ** 5 * probability_orange ** 15\n", + "print(\"Probability that the first 5 fruits are all apples and the next 15 fruits are all oranges:\", probability_5_apples_15_oranges)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -83,14 +117,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of getting 5 apples and 15 oranges: 0.0012944935222876579\n" + ] + } + ], "source": [ - "# your code here\n" + "import math\n", + "\n", + "n = 20 \n", + "k = 5 \n", + "p = probability_apple \n", + "\n", + "combinations = math.comb(n, k)\n", + "\n", + "probability_5_apples = combinations * (p ** k) * ((1 - p) ** (n - k))\n", + "\n", + "print(\"Probability of getting 5 apples and 15 oranges:\", probability_5_apples)\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -101,14 +154,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability that less than 5 fruits picked are apples: 0.00031703112116863\n" + ] + } + ], "source": [ - "# your code here\n" + "import math\n", + "\n", + "n = 20 \n", + "p = probability_apple \n", + "\n", + "probability_less_than_5_apples = 0\n", + "\n", + "for i in range(0, 5):\n", + " combinations = math.comb(n, i)\n", + " probability = combinations * (p ** i) * ((1 - p) ** (n - i))\n", + " probability_less_than_5_apples += probability\n", + "\n", + "print(\"Probability that less than 5 fruits picked are apples:\", probability_less_than_5_apples)\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -119,15 +193,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n = 20 \n", + "p = probability_apple \n", + "\n", + "x = np.arange(0, n+1) \n", + "\n", + "pmf = [math.comb(n, k) * (p ** k) * ((1 - p) ** (n - k)) for k in x]\n", + "\n", + "plt.bar(x, pmf)\n", + "plt.xlabel('Number of Apples')\n", + "plt.ylabel('Probability')\n", + "plt.title('PDF of Binomial Distribution')\n", + "plt.show()\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -151,14 +250,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of scoring 5 goals in a match: 0.0537750255819468\n" + ] + } + ], "source": [ - "# your code here\n" + "lambda_val = 2.3 \n", + "k = 5 \n", + "\n", + "probability_5_goals = math.exp(-lambda_val) * (lambda_val**k) / math.factorial(k)\n", + "\n", + "print(\"Probability of scoring 5 goals in a match:\", probability_5_goals)" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -167,13 +280,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "\n", + "lambda_val = 2.3 \n", + "x = np.arange(0, 11) \n", + "pmf = [math.exp(-lambda_val) * (lambda_val**k) / math.factorial(k) for k in x]\n", + "\n", + "plt.bar(x, pmf)\n", + "plt.xlabel('Number of Goals')\n", + "plt.ylabel('Probability')\n", + "plt.title('Poisson Distribution: Number of Goals')\n", + "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -192,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.11.2" } }, "nbformat": 4,