{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Second type of mathematical plot\n", "\n", "# importing all our libraries\n", "\n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# reading our data and converting it into a numpy array\n", "\n", "A = pd.read_csv('data_1d.csv', header=None).as_matrix()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 95.72416241, 197.17963609],\n", " [ 35.75761893, 67.59066954],\n", " [ 28.81684742, 60.85413282],\n", " [ 99.95848131, 196.90739698],\n", " [ 66.80974831, 125.31112852],\n", " [ 58.21569264, 115.78578459],\n", " [ 53.82107634, 110.76277271],\n", " [ 81.29608217, 157.98528569],\n", " [ 80.64869706, 159.61941373],\n", " [ 78.25281369, 149.00386554],\n", " [ 16.86348369, 31.46759088],\n", " [ 90.79914756, 184.18521966],\n", " [ 53.31273701, 103.22061016],\n", " [ 71.61878902, 143.27181836],\n", " [ 18.41059178, 46.73641801],\n", " [ 57.12434676, 107.1237942 ],\n", " [ 81.13468392, 168.30937401],\n", " [ 82.01525542, 166.82994267],\n", " [ 36.92490825, 70.50145559],\n", " [ 44.58712604, 96.86937025],\n", " [ 26.66235446, 50.37323584],\n", " [ 74.06505712, 145.51917071],\n", " [ 17.74057673, 46.55187461],\n", " [ 6.81974382, 13.24378631],\n", " [ 26.31736009, 62.5006665 ],\n", " [ 82.80411905, 159.91664958],\n", " [ 39.44653696, 77.98420415],\n", " [ 76.72812059, 147.26918314],\n", " [ 42.21585614, 83.6452954 ],\n", " [ 94.35857584, 191.95607269],\n", " [ 42.14178513, 91.73273416],\n", " [ 3.21124861, 8.25141677],\n", " [ 6.8856734 , 16.09638942],\n", " [ 13.87577216, 33.35444214],\n", " [ 63.29740364, 131.50574553],\n", " [ 60.49251936, 128.69884306],\n", " [ 79.60728523, 160.3103805 ],\n", " [ 83.44436306, 160.60324574],\n", " [ 54.89207011, 104.40029316],\n", " [ 62.04594417, 126.96945605],\n", " [ 66.65375794, 124.51916761],\n", " [ 61.31981316, 125.21741443],\n", " [ 47.31955299, 98.71948438],\n", " [ 81.1509876 , 166.43245548],\n", " [ 91.02151367, 179.23687261],\n", " [ 16.84346201, 33.77723374],\n", " [ 76.50643657, 161.37296513],\n", " [ 71.86570468, 138.575401 ],\n", " [ 9.84808948, 17.90332828],\n", " [ 35.78971241, 74.10859171],\n", " [ 35.888692 , 74.82132297],\n", " [ 90.82944871, 179.24154705],\n", " [ 7.26207284, 6.43437238],\n", " [ 35.35928817, 78.08635679],\n", " [ 79.07925376, 154.59178976],\n", " [ 21.55345833, 50.0307539 ],\n", " [ 79.47642778, 165.44196619],\n", " [ 48.76796664, 110.89728797],\n", " [ 54.79386201, 114.69188511],\n", " [ 87.50352552, 186.74420631],\n", " [ 38.94913047, 70.69722298],\n", " [ 9.39561282, 19.17903005],\n", " [ 11.12118682, 27.02295992],\n", " [ 46.13555826, 87.29244922],\n", " [ 85.69757358, 167.92461412],\n", " [ 50.1102964 , 96.02054844],\n", " [ 11.07366673, 24.94632465],\n", " [ 22.22813085, 54.00414588],\n", " [ 67.5539548 , 133.16525443],\n", " [ 12.78718566, 28.00622322],\n", " [ 46.96549084, 100.60558947],\n", " [ 4.84854065, 13.8572726 ],\n", " [ 40.53733193, 82.59066294],\n", " [ 26.33046127, 50.23180391],\n", " [ 38.88472635, 79.48177242],\n", " [ 93.83826653, 191.32431784],\n", " [ 60.08165997, 120.49511544],\n", " [ 50.11396522, 111.35232289],\n", " [ 17.71868528, 44.93760649],\n", " [ 85.07303114, 172.13320308],\n", " [ 41.32150973, 84.889236 ],\n", " [ 35.45154709, 69.91899124],\n", " [ 74.02102554, 149.77864633],\n", " [ 90.5197211 , 187.59390974],\n", " [ 34.54059704, 66.32685895],\n", " [ 94.56166213, 189.14496411],\n", " [ 52.95626785, 103.44683588],\n", " [ 80.10643641, 158.52943007],\n", " [ 49.82275758, 98.69883963],\n", " [ 61.78172531, 122.64739506],\n", " [ 60.28610836, 124.76334632],\n", " [ 77.32069019, 155.67749312],\n", " [ 15.64635875, 35.41060825],\n", " [ 71.54061834, 151.32345871],\n", " [ 62.45239811, 129.43395879],\n", " [ 79.05983237, 163.09690261],\n", " [ 72.19769344, 142.89852264],\n", " [ 71.41775342, 148.09420854],\n", " [ 31.00882008, 63.79687625],\n", " [ 40.32528222, 86.72368533]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A # checking out our data " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "x = A[:,0] # making x as all the elements in col 0\n", "y = A[:,1] # making y as all the elements in col 1" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "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": [ "# scatter plots are very useful in classification problems \n", "\n", "plt.scatter(x,y) # plotting the scatter plot" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"x-axis\")\n", "plt.ylabel(\"y-axis\")\n", "plt.title(\"scatter-plot\")\n", "plt.scatter(x,y)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_line= np.linspace(0,100,100)\n", "y_line = 2*x_line+1\n", "plt.xlabel(\"x-axis\")\n", "plt.ylabel(\"y-axis\")\n", "plt.title(\"plotting two curves on the same plot\")\n", "plt.plot(x_line, y_line, color=\"red\")\n", "plt.scatter(x,y)\n", "plt.show()\n", "# we can see the line chart passing throgh the points " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }