{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Data for plotting\n", "t = np.arange(0.0, 2.0, 0.01)\n", "s = 1 + np.sin(2 * np.pi * t)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(t, s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.set(xlabel='time (s)', ylabel='voltage (mV)', title='Adding labels')\n", "ax.plot(t, s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.set(xlabel='time (s)', ylabel='voltage (mV)', title='Adding labels')\n", "ax.grid()\n", "ax.plot(t, s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig.savefig(\"test.png\") # to save image as a file\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x1 = np.linspace(0.0, 5.0)\n", "x2 = np.linspace(0.0, 2.0)\n", "\n", "y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)\n", "y2 = np.cos(2 * np.pi * x2)\n", "\n", "plt.subplot(2, 1, 1)\n", "plt.plot(x1, y1, 'o-')\n", "plt.title('A tale of 2 subplots')\n", "plt.ylabel('Damped oscillation')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(x2, y2, '.-')\n", "plt.xlabel('time (s)')\n", "plt.ylabel('Undamped')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.random.seed(19680801)\n", "\n", "# example data\n", "mu = 100 # mean of distribution\n", "sigma = 15 # standard deviation of distribution\n", "x = mu + sigma * np.random.randn(437)\n", "\n", "num_bins = 50\n", "\n", "fig, ax = plt.subplots()\n", "\n", "# the histogram of the data\n", "n, bins, patches = ax.hist(x, num_bins, density=1)\n", "\n", "# add a 'best fit' line\n", "y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *\n", " np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n", "ax.plot(bins, y, '--')\n", "ax.set_xlabel('Smarts')\n", "ax.set_ylabel('Probability density')\n", "ax.set_title(r'Histogram of IQ: $\\mu=100$, $\\sigma=15$')\n", "\n", "# Tweak spacing to prevent clipping of ylabel\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.path as mpath\n", "import matplotlib.patches as mpatches\n", "# import matplotlib.pyplot as plt\n", "\n", "fig, ax = plt.subplots()\n", "\n", "Path = mpath.Path\n", "path_data = [\n", " (Path.MOVETO, (1.58, -2.57)),\n", " (Path.CURVE4, (0.35, -1.1)),\n", " (Path.CURVE4, (-1.75, 2.0)),\n", " (Path.CURVE4, (0.375, 2.0)),\n", " (Path.LINETO, (0.85, 1.15)),\n", " (Path.CURVE4, (2.2, 3.2)),\n", " (Path.CURVE4, (3, 0.05)),\n", " (Path.CURVE4, (2.0, -0.5)),\n", " (Path.CLOSEPOLY, (1.58, -2.57)),\n", " ]\n", "codes, verts = zip(*path_data)\n", "path = mpath.Path(verts, codes)\n", "patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)\n", "ax.add_patch(patch)\n", "\n", "# plot control points and connecting lines\n", "x, y = zip(*path.vertices)\n", "line, = ax.plot(x, y, 'go-')\n", "\n", "ax.grid()\n", "ax.axis('equal')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from matplotlib.ticker import MaxNLocator\n", "from collections import namedtuple\n", "\n", "\n", "n_groups = 5\n", "\n", "means_men = (20, 35, 30, 35, 27)\n", "std_men = (2, 3, 4, 1, 2)\n", "\n", "means_women = (25, 32, 34, 20, 25)\n", "std_women = (3, 5, 2, 3, 3)\n", "\n", "fig, ax = plt.subplots()\n", "\n", "index = np.arange(n_groups)\n", "bar_width = 0.35\n", "\n", "opacity = 0.4\n", "error_config = {'ecolor': '0.3'}\n", "\n", "rects1 = ax.bar(index, means_men, bar_width,\n", " alpha=opacity, color='b',\n", " yerr=std_men, error_kw=error_config,\n", " label='Men')\n", "\n", "rects2 = ax.bar(index + bar_width, means_women, bar_width,\n", " alpha=opacity, color='r',\n", " yerr=std_women, error_kw=error_config,\n", " label='Women')\n", "\n", "ax.set_xlabel('Group')\n", "ax.set_ylabel('Scores')\n", "ax.set_title('Scores by group and gender')\n", "ax.set_xticks(index + bar_width / 2)\n", "ax.set_xticklabels(('A', 'B', 'C', 'D', 'E'))\n", "ax.legend()\n", "\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n", "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n", "sizes = [15, 30, 45, 10]\n", "explode = (0, 0.1, 0, 0) # only \"explode\" the 2nd slice (i.e. 'Hogs')\n", "\n", "fig1, ax1 = plt.subplots()\n", "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n", " shadow=True, startangle=90)\n", "ax1.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.\n", "\n", "plt.show()" ] }, { "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }