diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..c75d4d8 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,9 +35,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.71255359 2.29484226 2.3061185 2.98560217 2.16481461 2.00452989\n", + " 2.20532162 2.37842544 2.56037458 2.83296153]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -73,11 +82,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "import matplotlib.pyplot as plt \n", + "\n", + "def uniforms (a,b,count):\n", + " x = uniform.rvs(size=count)\n", + " randoms = a + (b-a) * x\n", + " return randoms \n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "num_uniforms1 = uniforms(10,15,100)\n", + "num_uniforms2 = uniforms(10,60,1000)\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12,8))\n", + "axes[0].hist(num_uniforms1, color = 'r')\n", + "axes[1].hist(num_uniforms2, color = 'b')\n", + "max_y = max(axes[0].get_ylim()[1], axes[1].get_ylim()[1])\n", + "axes[0].set_ylim(0, max_y)\n", + "axes[1].set_ylim(0, max_y)\n", + "plt.show()" ] }, { @@ -93,7 +138,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here: Because the number of randoms requested is different with two different intervals. \n" ] }, { @@ -114,11 +159,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def generate_normal (mean, std, size):\n", + " return np.random.normal(mean, std, size)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "data1 = generate_normal(10, 1, 1000)\n", + "data2 = generate_normal(10, 50, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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MakeModelYearEngine DisplacementCylindersTransmissionDrivetrainVehicle ClassFuel TypeFuel Barrels/YearCity MPGHighway MPGCombined MPGCO2 Emission Grams/MileFuel Cost/Year
0AM GeneralDJ Po Vehicle 2WD19842.54.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular19.388824181717522.7647061950
1AM GeneralFJ8c Post Office19844.26.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
2AM GeneralPost Office DJ5 2WD19852.54.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular20.600625161716555.4375002100
3AM GeneralPost Office DJ8 2WD19854.26.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
4ASC IncorporatedGNX19873.86.0Automatic 4-spdRear-Wheel DriveMidsize CarsPremium20.600625142116555.4375002550
\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement \\\n", + "0 AM General DJ Po Vehicle 2WD 1984 2.5 \n", + "1 AM General FJ8c Post Office 1984 4.2 \n", + "2 AM General Post Office DJ5 2WD 1985 2.5 \n", + "3 AM General Post Office DJ8 2WD 1985 4.2 \n", + "4 ASC Incorporated GNX 1987 3.8 \n", + "\n", + " Cylinders Transmission Drivetrain Vehicle Class \\\n", + "0 4.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \n", + "1 6.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \n", + "2 4.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "3 6.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "4 6.0 Automatic 4-spd Rear-Wheel Drive Midsize Cars \n", + "\n", + " Fuel Type Fuel Barrels/Year City MPG Highway MPG Combined MPG \\\n", + "0 Regular 19.388824 18 17 17 \n", + "1 Regular 25.354615 13 13 13 \n", + "2 Regular 20.600625 16 17 16 \n", + "3 Regular 25.354615 13 13 13 \n", + "4 Premium 20.600625 14 21 16 \n", + "\n", + " CO2 Emission Grams/Mile Fuel Cost/Year \n", + "0 522.764706 1950 \n", + "1 683.615385 2550 \n", + "2 555.437500 2100 \n", + "3 683.615385 2550 \n", + "4 555.437500 2550 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "data = pd.read_csv(r\"C:\\Users\\nunoc\\Ambiente de Trabalho\\Iron Hack\\Week 5\\Labs\\lab-probability-distributions\\your-code\\vehicles.csv\")\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "data['Fuel Barrels/Year'].hist()" ] }, { @@ -174,11 +460,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "data['CO2 Emission Grams/Mile'].hist()\n" ] }, { @@ -190,11 +497,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "data['Combined MPG'].hist()" ] }, { @@ -206,11 +534,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\nunoc\\anaconda3\\lib\\site-packages\\scipy\\stats\\_morestats.py:1816: UserWarning: p-value may not be accurate for N > 5000.\n", + " warnings.warn(\"p-value may not be accurate for N > 5000.\")\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Fuel Barrels/Year': {'shapiro_p': 0.0, 'normal_p': 0.0},\n", + " 'CO2 Emission Grams/Mile': {'shapiro_p': 0.0, 'normal_p': 0.0},\n", + " 'Combined MPG': {'shapiro_p': 0.0, 'normal_p': 0.0}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# you answer here:\n" + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from scipy.stats import normaltest, shapiro\n", + "\n", + "selected_columns = ['Fuel Barrels/Year', 'CO2 Emission Grams/Mile', 'Combined MPG']\n", + "\n", + "normality_results = {}\n", + "\n", + "for column in selected_columns: \n", + " shapiro_stat, shapiro_p = shapiro(data[column])\n", + " normal_stat, normal_p = normaltest(data[column])\n", + " normality_results[column] = {'shapiro_p': shapiro_p, 'normal_p': normal_p}\n", + "\n", + "normality_results" ] }, { @@ -237,11 +601,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "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 exponential_sequence(mean, size):\n", + " return np.random.exponential(scale=mean, size=size)\n", + "\n", + "\n", + "sequence_mean_1 = exponential_sequence(mean=1, size=1000)\n", + "sequence_mean_100 = exponential_sequence(mean=100, size=1000)\n", + "\n", + "# Plot histograms\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.hist(sequence_mean_1, bins=100, color='blue', alpha=0.7)\n", + "plt.title('Exponential Distribution (Mean = 1)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.hist(sequence_mean_100, bins=100, color='orange', alpha=0.7)\n", + "plt.title('Exponential Distribution (Mean = 100)')\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { @@ -256,9 +657,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# your answer here:\n" - ] + "source": [] }, { "cell_type": "markdown", @@ -273,10 +672,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7768698398515702\n" + ] + } + ], "source": [ + "from scipy.stats import expon\n", + "\n", + "lambda_inv = 10\n", + "\n", + "expon_dist = expon(scale = lambda_inv)\n", + "\n", + "print(expon_dist.cdf(15)) \n", + "\n", + "\n", "# your answer here\n", "# Hint: This is same as saying P(x<15)\n" ] @@ -290,11 +706,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.2231301601484298" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "1-expon_dist.cdf(15)" ] } ], @@ -314,7 +741,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.11" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..403eb5a 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,17 +33,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "\"\"\"\n", - "Calculate:\n", - "p = probability that the fruit is an apple \n", - "q = probability that the fruit is an orange\n", - "\"\"\"\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", "\n", - "# your code here\n" + "from scipy.stats import bernoulli\n", + "from scipy.stats import binom\n", + "from scipy.stats import geom\n", + "from scipy.stats import poisson\n", + "\n", + "from scipy.stats import uniform\n", + "from scipy.stats import expon\n", + "from scipy.stats import norm" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6\n", + "0.4\n" + ] + } + ], + "source": [ + "p = 0.6 #apple\n", + "q = 0.4 \n", + "\n", + "bernoulli_apple = bernoulli(p)\n", + "bernoulli_orange = bernoulli(q)\n", + "\n", + "print(bernoulli_apple.pmf(1))\n", + "print(bernoulli_orange.pmf(1))\n", + "\n" ] }, { @@ -61,11 +92,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability 5 apples is: 0.07775999999999998\n", + "Probability 5 apples q5 oranges is: 8.349416423424006e-08\n" + ] + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "prob_5_apples = p**5\n", + "\n", + "prob_5_apples_15_oranges = p**5 * (q**15)\n", + "\n", + "print('Probability 5 apples is: ', prob_5_apples)\n", + "print('Probability 5 apples q5 oranges is: ', prob_5_apples_15_oranges)" ] }, { @@ -83,11 +129,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0012944935222876583\n" + ] + } + ], "source": [ - "# your code here\n" + "n = 20 \n", + "p = 0.6 \n", + "\n", + "binomial_dist = binom(n,p)\n", + "\n", + "print(binomial_dist.pmf(5))\n" ] }, { @@ -101,11 +160,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.00031703112116863004\n" + ] + } + ], "source": [ - "# your code here\n" + "print(binomial_dist.cdf(4))" ] }, { @@ -119,12 +186,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + 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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" + "X = np.arange(0,20)\n", + "\n", + "plt.plot(X, binomial_dist.pmf(X), \"o\")\n", + "plt.show()\n" ] }, { @@ -151,11 +231,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Probability of scoring 5 goals: 0.0537750255819468\n" + ] + } + ], "source": [ - "# your code here\n" + "import math\n", + "\n", + "average_goals = 2.3\n", + "goals = 5\n", + "\n", + "# Calculate the probability using the Poisson PMF formula\n", + "probability_5_goals = (math.exp(-average_goals) * average_goals**goals) / math.factorial(goals)\n", + "\n", + "# Print the result\n", + "print(\"Probability of scoring 5 goals:\", probability_5_goals)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.053775025581946814\n" + ] + } + ], + "source": [ + "mu = 2.3\n", + "poisson_dist = poisson(mu)\n", + "\n", + "print(poisson_dist.pmf(5))" ] }, { @@ -167,12 +286,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "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" + "X = np.arange(0, 10)\n", + "plt.plot(X, poisson_dist.pmf(X), \"o\")\n", + "plt.title('Poisson Distribution')\n", + "plt.ylabel('Probability')\n", + "plt.xlabel('Number of Tries')\n", + "plt.show()\n" ] } ], @@ -192,7 +326,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.11" } }, "nbformat": 4,