{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1+1" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_1*3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Título\n", "==\n", "\n", "__negrita__\n", "\n", "**negrita**\n", "*cursiva*\n", "\n", "__ejemplo__\n", "\n", "*texto en cursiva*\n", "\n", "\n", "- item 1\n", "- item 2\n", "\n", "\n", "> Comentario especial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como trabajar con NUMPY\n", "==" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 2 3 4]\n", " [ 5 6 7 8]\n", " [ 9 10 11 12]]\n", "\n" ] } ], "source": [ "import numpy as np \n", "A = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9,10, 11, 12]])\n", "print(A) \n", "print(type(A)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como trabajar con PANDAS\n", "==" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idNombreSexoSueldo
01Juanm10000
12Ramónm20000
23Annaf10500
34Julietaf13000
45Pablom30000
56Pedrom10500
67Gabyf11000
78Ceciliaf27000
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" ], "text/plain": [ " id Nombre Sexo Sueldo\n", "0 1 Juan m 10000\n", "1 2 Ramón m 20000\n", "2 3 Anna f 10500\n", "3 4 Julieta f 13000\n", "4 5 Pablo m 30000\n", "5 6 Pedro m 10500\n", "6 7 Gaby f 11000\n", "7 8 Cecilia f 27000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd \n", "dat_csv = pd.read_csv('empleados.csv', encoding = \"ISO-8859-1\") \n", "dat_csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Como trabajar con MATPLOTLIB\n", "==" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt \n", "x = list(range(-4 , 6)) \n", "y = [i**2 for i in x] \n", "plt.plot(x,y) \n", "plt.show " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**texto**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**texto**" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 4 }