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pull request estadistica descriptiva #3

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561aa90
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jaimed411 Jun 10, 2023
d755b42
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jaimed411 Jun 12, 2023
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240 changes: 186 additions & 54 deletions notebook/problems.ipynb
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "ac622319",
"metadata": {},
Expand All @@ -9,6 +10,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "aa8993e4",
"metadata": {},
Expand All @@ -17,6 +19,7 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "5e0ab0d5",
"metadata": {},
Expand All @@ -30,90 +33,227 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 5,
"id": "34720ab6",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.76405235 0.40015721 0.97873798 2.2408932 1.86755799 -0.97727788\n",
" 0.95008842 -0.15135721 -0.10321885 0.4105985 0.14404357 1.45427351\n",
" 0.76103773 0.12167502 0.44386323 0.33367433 1.49407907 -0.20515826\n",
" 0.3130677 -0.85409574 -2.55298982 0.6536186 0.8644362 -0.74216502\n",
" 2.26975462 -1.45436567 0.04575852 -0.18718385 1.53277921 1.46935877\n",
" 0.15494743 0.37816252 -0.88778575 -1.98079647 -0.34791215 0.15634897\n",
" 1.23029068 1.20237985 -0.38732682 -0.30230275 -1.04855297 -1.42001794\n",
" -1.70627019 1.9507754 -0.50965218 -0.4380743 -1.25279536 0.77749036\n",
" -1.61389785 -0.21274028 -0.89546656 0.3869025 -0.51080514 -1.18063218\n",
" -0.02818223 0.42833187 0.06651722 0.3024719 -0.63432209 -0.36274117\n",
" -0.67246045 -0.35955316 -0.81314628 -1.7262826 0.17742614 -0.40178094\n",
" -1.63019835 0.46278226 -0.90729836 0.0519454 0.72909056 0.12898291\n",
" 1.13940068 -1.23482582 0.40234164 -0.68481009 -0.87079715 -0.57884966\n",
" -0.31155253 0.05616534 -1.16514984 0.90082649 0.46566244 -1.53624369\n",
" 1.48825219 1.89588918 1.17877957 -0.17992484 -1.07075262 1.05445173\n",
" -0.40317695 1.22244507 0.20827498 0.97663904 0.3563664 0.70657317\n",
" 0.01050002 1.78587049 0.12691209 0.40198936 1.8831507 -1.34775906\n",
" -1.270485 0.96939671 -1.17312341 1.94362119 -0.41361898 -0.74745481\n",
" 1.92294203 1.48051479 1.86755896 0.90604466 -0.86122569 1.91006495\n",
" -0.26800337 0.8024564 0.94725197 -0.15501009 0.61407937 0.92220667\n",
" 0.37642553 -1.09940079 0.29823817 1.3263859 -0.69456786 -0.14963454\n",
" -0.43515355 1.84926373 0.67229476 0.40746184 -0.76991607 0.53924919\n",
" -0.67433266 0.03183056 -0.63584608 0.67643329 0.57659082 -0.20829876\n",
" 0.39600671 -1.09306151 -1.49125759 0.4393917 0.1666735 0.63503144\n",
" 2.38314477 0.94447949 -0.91282223 1.11701629 -1.31590741 -0.4615846\n",
" -0.06824161 1.71334272 -0.74475482 -0.82643854 -0.09845252 -0.66347829\n",
" 1.12663592 -1.07993151 -1.14746865 -0.43782004 -0.49803245 1.92953205\n",
" 0.94942081 0.08755124 -1.22543552 0.84436298 -1.00021535 -1.5447711\n",
" 1.18802979 0.31694261 0.92085882 0.31872765 0.85683061 -0.65102559\n",
" -1.03424284 0.68159452 -0.80340966 -0.68954978 -0.4555325 0.01747916\n",
" -0.35399391 -1.37495129 -0.6436184 -2.22340315 0.62523145 -1.60205766\n",
" -1.10438334 0.05216508 -0.739563 1.5430146 -1.29285691 0.26705087\n",
" -0.03928282 -1.1680935 0.52327666 -0.17154633 0.77179055 0.82350415\n",
" 2.16323595 1.33652795]\n"
]
}
],
"source": [
"#import libraries\n",
"import numpy as np\n",
"\n",
"\n",
"# Set seed in order to get similar results\n",
"np.random.seed(0)\n",
"\n",
"\n",
"# create the data\n",
"data = np.random.normal(0, 1, 20)\n",
"\n",
"\n",
"#print results"
"#print results\n",
"print(data)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"id": "49c55822",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0.07091049314116117"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Use numpy to get the mean of your data"
"#Use numpy to get the mean of your data\n",
"np.mean(data)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 7,
"id": "03529459",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"1.0433044724105929"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get the variance of your data"
"#get the variance of your data\n",
"data.var()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 8,
"id": "e53f30c5",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"1.0214227686959954"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Standard deviation\n"
"# Standard deviation\n",
"np.std(data)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 9,
"id": "9bce852f",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mode: ModeResult(mode=array([-2.77259276]), count=array([1]))\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_668/2492951849.py:9: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
" mode_result = stats.mode(data)\n"
]
}
],
"source": [
"#import libraries and print the mode\n",
"from scipy import stats\n",
"import numpy as np\n",
"\n",
"\n",
"# Mode for continuous array\n",
"data = np.random.normal(0, 1, 200)\n",
"\n",
"mode_result = stats.mode(data)\n",
"\n",
"# Mode for continuous array\n"
"\n",
"print(\"Mode:\", mode_result)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 10,
"id": "c682cb6e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mediana: -0.14680835842106837\n"
]
}
],
"source": [
"# Median\n"
"# Median\n",
"mediana = np.median(data)\n",
"\n",
"# Imprimir la mediana\n",
"print(\"Mediana:\", mediana)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 11,
"id": "39c3fabd",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentil de 25 (Q1): -0.6900993503585511\n",
"Percentil de 50 (Median): -0.14680835842106837\n",
"Percentil de 75 (Q3): 0.5347408373388216\n",
"Rango intercuartil (IQR): 1.2248401876973727\n"
]
}
],
"source": [
"# Print the Quantiles\n",
"# Print the percentiles\n",
"percentil = np.percentile(data, [25, 50, 75])\n",
"\n",
"# Print the quantiles\n",
"print(\"Percentil de 25 (Q1):\", percentil[0])\n",
"print(\"Percentil de 50 (Median):\", percentil[1])\n",
"print(\"Percentil de 75 (Q3):\", percentil[2])\n",
"print(\"Rango intercuartil (IQR):\", percentil[2] - percentil[0])\n",
"\n",
"# This match with np.median, why?\n"
"\n",
"# This match with np.median, why?\n",
"#np.median y np.percentile son parecidos porque ambos separan el data set en dos mitades."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "46c70c3d",
"metadata": {},
Expand All @@ -128,58 +268,50 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 14,
"id": "d590308e",
"metadata": {},
"outputs": [
{
"ename": "IndentationError",
"evalue": "expected an indented block (4006267275.py, line 20)",
"output_type": "error",
"traceback": [
"\u001b[1;36m Input \u001b[1;32mIn [8]\u001b[1;36m\u001b[0m\n\u001b[1;33m def avg_calc(ls):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
"name": "stdout",
"output_type": "stream",
"text": [
"Datos: [4, 2, 5, 8, 6]\n",
"Desviación Estándar: 2.0\n"
]
}
],
"source": [
"import math\n",
"import sys\n",
"import numpy as np\n",
"\n",
"# Define the standard deviation function\n",
"\n",
"def sd_calc(data):\n",
" #code here\n",
"# Función para calcular la desviación estándar\n",
"def desv_estandar(data):\n",
" # Calcular la media utilizando la función np.mean()\n",
" media = np.mean(data)\n",
" \n",
" # Calcular la suma de los cuadrados de las diferencias con la media\n",
" suma_cuadrados = sum((x - media) ** 2 for x in data)\n",
" \n",
" # Calcular la varianza dividiendo la suma de los cuadrados entre el número de elementos\n",
" varianza = suma_cuadrados / len(data)\n",
" \n",
"\n",
" # calculate stan. dev.\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"# Define the average function\n",
"\n",
"def avg_calc(ls):\n",
" #code here\n",
" \n",
" \n",
" \n",
"\n",
" # calculate average\n",
" \n",
" \n",
" # Calcular la desviación estándar tomando la raíz cuadrada de la varianza\n",
" std_deviation = math.sqrt(varianza)\n",
" \n",
" return std_deviation\n",
"\n",
"# Datos\n",
"data = [4, 2, 5, 8, 6]\n",
"\n",
"#print the data\n",
"\n",
"#print the standard deviation of the data"
"# Imprimir los datos y la desviación estándar\n",
"print(\"Datos:\", data)\n",
"print(\"Desviación Estándar:\", desv_estandar(data))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "04b71791",
"metadata": {},
Expand Down Expand Up @@ -209,7 +341,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
"version": "3.11.3"
}
},
"nbformat": 4,
Expand Down