{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualisierungen mit matplotlib\n", "\n", "\n", "In diesem Module lernen Sie einfache Diagramme wie Liniendiagramme oder Balkendiagramme mit Python und der Bibliothek Matplotlib erstellen. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Liniendiagramme\n", "\n", "Um die matplotlib zu Nutzen müssen Sie das package pyplot importieren. Dies geschieht mit der Anweisung `import matplotlib.pyplot as plt`. Dabei wird `plt` als ALias für pyplot verwendet. Ein Diagramm wird mit der `plot`-Methode erstellt. Die Methode `plt.show()` zeigt das Diagramm an. Die matplotlib bietet auch die Möglichkeit Diagramme abzuspeichern, dies geschieht mit `plt.savefig(filename)`. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "import matplotlib.pyplot as plt\n", "\n", "plt.plot([1,2,4,8,16]) # plottet angegebene 5 Zahlen mit Standardabstand\n", "plt.show() # Zeichnet den Plot\n", "\n", "# TODO: Speichern Sie das Diagramm ab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ein weiteres Diagramm\n", "Im folgenden Beispiel werden zwei kleinere Datensätze visualisiert. Mit `xlabel(...)` und `ylabel(...)` werden die Achsenbeschriftungen erstellt. Die Methode `legend(...)` zeigt die Namen der Datenreihen an." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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44wA8+eSTdO7cmW7dunHJJW7C0j179nDVVVfRs2dPunfvznvvHT2LxtatWxk8eDDdu3fnuuuuI3iF+8cee4yuXbvStWtXnnjiCQBWrVpFp06duOGGG+jRowf33Xcfs2fP5vrrr+d3v/tdJHeRMSYce7bCm2OgbjIMewKieO7Xek7H6N7Ji/lxfUaJbrNzs1r85VddirROmzZtyM7OZtOmTfnWWbRoEevWreOHH34AYMeOHYA7BLhy5UoqV658qOz+++9n4MCBjB8/nh07dtCzZ0/OOOOMI7Z37733cuqpp3L33XfzwQcfMHbsWAAWLFjAc889x9y5c1FVevXqRf/+/albty5Lly7lueee49lnnwXgk08+4ZFHHiElJaVIz9cYU8JycuCd62DvFrh6JlSpFdVwrOdUjrRp04YVK1Zw88038+GHH1KrlnvzdevWjcsvv5yXX36ZChXc95Xp06fz0EMPceKJJzJgwAAyMzNZs2bNEdv77LPPuOKKKwD45S9/Sd26dQGYPXs25557LtWrV6dGjRqcd955fP755wC0atWK3r17l9ZTNsaEa86TkDoDhjwATU+IdjTWczpWRe3hRMqKFStITEykUaNGVKhQ4YjBEaHfC9WtW5dvv/2WadOm8cwzzzBx4kTGjx/PBx98wGeffcakSZO47777WLx4MarKW2+9RceOHY9oZ+PGjccUZ/Xq1Y9pfWNMBKyZCx/9FToPh5OvjnY0gPWcyoTNmzdz/fXXc9NNNyEiJCcns2jRInJycli7di1ff/01AFu2bCEnJ4fzzz+f++67j2+++eZQndNPP52HH36YHTt2sHv3boYMGcJTTz116DzSwoULj2r3tNNO49VXXwVg6tSpbN++HYB+/frx7rvvsnfvXvbs2cM777xDv379SmlvGGOKZO82eHM01GkBZz8Z1fNMQdZzilP79u3jxBNPJCsriwoVKjBixAhuv/12APr27Uvr1q05/vjj6dq1Kz169ABg3bp1jB49+lCv6sEHHyQ7O5srrriCnTt3oqr8+te/pk6dOvz5z3/mtttuo1u3buTk5NC6dWvef//9I2L4y1/+wqWXXkqPHj3o378/LVu2BKBHjx6MGjWKnj17AnD11VfTvXt3Vq1aVUp7xxgTFlV49/9gz2YYMx2q1I52RIdIcISVOVpKSormnmxwyZIldOrUKUoRlR+2n42JsDlPwfQ/wZkPQ6/rSnTTIrJAVYs90skO6xljTHm0dh7MvAc6/Qp6XhvtaI5iyckYY8qbvdvgzaugVjM4++mYOc8UZOecjDGmPFGF926EXekwZhpUrRPtiPJkyckYY8qTr/4FS6fAkAeh+UnRjiZfETusJyLjRWSTiPwQKKsnIjNEZLn/W9eXi4g8KSKpIvKdiPQIrDPS118uIiMD5SeJyPd+nSfFz7FQnDaMMaZcWLcAZtwNHX8Jvf8v2tEUKJLnnJ4HhuYquxP4SFXbAx/5xwBnAu397VrgX+ASDfAXoBfQE/hLKNn4OtcE1htanDaMMaZc2LcD3hgFNZvCObF5nikoYslJVT8DtuUqPgd4wd9/ARgeKH9Rna+AOiLSFBgCzFDVbaq6HZgBDPXLaqnqV+rGwr+Ya1tFaSMu1ahR49D9KVOm0KFDB1avXg1Anz59jvgLcM899/DII4+UbpDGmNigCpNugoz1cOFzUK1etCMqVGmP1musqun+/gagsb/fHFgbqJfmywoqT8ujvDhtHEVErhWR+SIyf/PmzWE+tej46KOPuOWWW5g6dSqtWrUCYM6cOUf8NcaUc1+PhSWT4Yx7ICk+LrIctaHkvscT0V8AF7cNVR2rqimqmtKwYcMIRFYyPvvsM6655href/992rZtC7hpKQYOHEi3bt0YNGjQURdrBRgwYAB33HEHPXv2pEOHDocuymqMKYPWL3Q/tO0wFE65qfD6MaK0R+ttFJGmqpruD6mF5ndYB7QI1EvyZeuAAbnKZ/nypDzqF6eNYzP1Ttjw/TFv5ghNjoczHyqwyv79+xk+fDizZs3iuOOOO1R+8803M3LkSEaOHMn48eO55ZZbePfdd49a/+DBg3z99ddMmTKFe++9t0RnyzXGxIjMne48U/VGMPxfMX+eKai0e06TgNCIu5HAe4HyK/2Iut7ATn9obhowWETq+oEQg4FpflmGiPT2o/SuzLWtorQRlypWrEifPn0YN27cEeVffvkll112GQAjRoxg9uzZea5/3nnnAXDSSSfZNe+MKYtUYdItsGMtXDA+Ls4zBUWs5yQir+F6PQ1EJA036u4hYKKIjAFWAxf56lOAs4BUYC8wGkBVt4nIfcA8X++vqhoaZHEDbkRgVWCqv1HUNo5ZIT2cSElISGDixIkMGjSIBx54gD/84Q9FWr9y5coAJCYmcvDgwUiEaIyJpvnj4Md34Yx7oWWvaEdTZBFLTqp6aT6LBuVRV4Eb89nOeGB8HuXzga55lG8tahvxqlq1anzwwQf069ePxo0bM2bMGPr06cOECRMYMWIEr7zyik1VYUx5lP4tfPgHaPcL6HNLtKMpFrtCRJyrV68eH374IaeddhoNGzbkqaeeYvTo0fzjH/+gYcOGPPfcc9EO0RhTmjIz3HmmavXh3P9AQnxeQtWmzCiETZkRPbafjSkiVXhrDCx+F0a9D636FL5OhNiUGcYYY5wFz8MPb8HAP0Y1MZUES07GGFMWbPgBPrwT2g6Cvr+OdjTHzJJTMdnh0Miy/WtMEezfBW+MhCp14vo8U1D8P4MoqFKlClu3brUP0AhRVbZu3UqVKlWiHYoxsU8V3r8dtq2AC8ZBjdi9qk1R2Gi9YkhKSiItLY1Yv+5ePKtSpQpJSUmFVzSmvFv4Enw/EU7/IySfGu1oSowlp2KoWLEirVu3jnYYxpjybuOPMOX30GYA9PtNtKMpUXZYzxhj4tGBPe73TFVqwXn/hYTEaEdUoqznZIwx8eiD38KWZXDle1CjUbSjKXHWczLGmHiz8BX49lXofwe06R/taCLCkpMxxsSTTT/BlN9Ccj/o//toRxMxlpyMMSZeHNjrzjNVqg7njytz55mC7JyTMcbEi6m/g80/wYh3oGbjaEcTUdZzMsaYePDtBFj4Mpz2W2h7erSjiThLTsYYE+s2L3NXgWjVF/rfGe1oSoUlJ2OMiWVZ+9x5popV3XmmxPJxNqZ8PEtjjIlXU++ATYvhiregVtNoR1NqrOdkjDGx6vs34ZsX4NTbod0Z0Y6mVFlyMsaYWLQlFSbfCi1PcRd1LWcsORljTKzJynTnmRIrlavzTEHl7xkbY0ysm3YXbPweLnsDajePdjRRYT0nY4yJJT+8DfPHQ99bocPgaEcTNZacjDEmVmz9GSbdAi16wcA/RzuaqLLkZIwxsSB0nikh0Z9nqhjtiKLKzjkZY0wsmP4n2PAdXDoB6rSIdjRRZz0nY4yJtsXvwrz/wik3Qcczox1NTLDkZIwx0bRtJUy6GZqnwBn3RDuamBGV5CQivxaRxSLyg4i8JiJVRKS1iMwVkVQReV1EKvm6lf3jVL88ObCdu3z5UhEZEigf6stSReTOQHmebRhjTFQc3A9vjgYRuGB8uT/PFFTqyUlEmgO3ACmq2hVIBC4B/g48rqrtgO3AGL/KGGC7L3/c10NEOvv1ugBDgWdFJFFEEoFngDOBzsClvi4FtGGMMaUrM8ONzFu/EM55Fuq2inZEMSVah/UqAFVFpAJQDUgHBgJv+uUvAMP9/XP8Y/zyQSIivnyCqu5X1ZVAKtDT31JVdYWqHgAmAOf4dfJrwxhjSkdODix6DZ5Oge9ed1NgdBoW7ahiTqmP1lPVdSLyCLAG2AdMBxYAO1T1oK+WBoR+Ft0cWOvXPSgiO4H6vvyrwKaD66zNVd7Lr5NfG0cQkWuBawFatmxZvCdqjDG5rV8IU34PaV+7c0yXvgbNT4p2VDEpGof16uJ6Pa2BZkB13GG5mKGqY1U1RVVTGjZsGO1wjDHxbs8Wdwhv7OmwfaU7jDdmhiWmAkTjd05nACtVdTOAiLwN9AXqiEgF37NJAtb5+uuAFkCaPwxYG9gaKA8JrpNX+dYC2jDGmJKXfRDmj4NP7ocDe+CUG6H/76FK7WhHFvOicc5pDdBbRKr580CDgB+BT4ALfJ2RwHv+/iT/GL/8Y1VVX36JH83XGmgPfA3MA9r7kXmVcIMmJvl18mvDGGNK1srP4T/9YOrvoVl3+L85MOR+S0xhisY5p7ki8ibwDXAQWAiMBT4AJojI33zZOL/KOOAlEUkFtuGSDaq6WEQm4hLbQeBGVc0GEJGbgGm4kYDjVXWx39Yd+bRhjDElY2eau9rD4negTku4+GU4bpgbLm7CJq5DYfKTkpKi8+fPj3YYxphYl5UJc56Czx8F1M1e2/cWqFg12pFFhYgsUNWU4q5v19YzxphjoQpLp8CHd8GO1dDpbHf4ro6N9D0WlpyMMaa4tiyHqXfAzx9Bw+PgyvegzYBoR1UmWHIyxpiiysyAzx6Gr/4FFavBkAeh5zV2+aESZMnJGGPCpequ6jDjbti9EbpfAYPugRr2e8iSZsnJGGPCNe9/MOW37sezl7wGSfYj2kix5GSMMeH64W1o3BXGzIQEm3EokgpMTiLSo6DlqvpNyYZjjDExau82WDsXTv21JaZSUFjP6dEClinuKt/GGFP2/fwxaDZ0iKlLgZZZBSYnVT29tAIxxpiYtmwaVKsPzQs8oGRKSNjnnESkD5AcXEdVX4xATMYYE1tysiF1JrQfDAmJ0Y6mXAgrOYnIS0BbYBGQ7YsVsORkjCn70ubDvm3QYXC0Iyk3wu05pQCd1S7EZ4wpj5ZPA0mEtoOiHUm5Ee6Qkx+AJpEMxBhjYtay6dCyN1StE+1Iyo3ChpJPxh2+qwn8KCJfA/tDy1X17MiGZ4wxUbZzHWz8Hs64N9qRlCuFHdZ7pFSiMMaYWLV8uvtrQ8hLVWFDyT8trUCMMSYmLZvmpr9o2DHakZQrYZ1zEpHeIjJPRHaLyAERyRaRjEgHZ4wxUZWVCSs/hfZDbCbbUhbugIingUuB5UBV4GpfZowxZdeq2ZC1FzoMiXYk5U7YP8JV1VQRSVTVbOA5EZkTwbiMMSb6lk+DClUh+dRoR1LuhJuc9opIJWCRiDwMpAPVIxeWMcZEmao739SmP1SsGu1oyp1wD+uN8HVvAvYALYDzIxWUMcZE3ZZlsGO1u2SRKXXhJqfTgIqqmqGq96rq7cBxEYzLGGOia9k099fON0VFuMnpKeBzEekUKPtrBOIxxpjYsHy6m1iwdlK0IymXwk1OK4GrgDdF5EJfZuMqjTFl074dsHqOHdKLonAHRKiqfiMi/YHXRKQXYNeNN8aUTYcmFrRDetESbs8pHUBVtwBDcNfb6xqpoIwxJqqWT4eqdSHp5GhHEhWrt+7hnkmLWbphV9RiKLTnJCKJwI+hx6qaA/zO34wxpmzJyYHlM6DdGeVqYkFV5euV2xg3eyUzlmwkUYTjmtSkY5OaUYmn0J6T/9FtD5GSu3aHiNQRkTdF5CcRWSIip4hIPRGZISLL/d+6vq6IyJMikioi34lIj8B2Rvr6y0VkZKD8JBH53q/zZCj2/NowxphD1n8De7e4SxaVAwcO5vDOwjR+9fRsLh77FV+v2sYNA9ryxZ0DuaRny6jFFe45p0XAeyLyBu53TgCo6tvFbPefwIeqeoH/cW814A/AR6r6kIjcCdwJ3AGcCbT3t17Av4BeIlIP+AtuIkQFFojIJFXd7utcA8wFpgBDgal+m3m1YYwxzrJpIAnQrmxPLLh9zwFe/XoNL365io0Z+2nbsDr3n9uV87onUbVS9HuM4SanesBWYGCgTIEiJycRqY373dQoAFU9ABwQkXOAAb7aC8AsXOI4B3jRz8L7le91NfV1Z6jqNr/dGcBQEZkF1FLVr3z5i8BwXHLKrw1jjHGWT4MWvaBavWhHEhGpm3Yz/ouVvP1NGplZOfRr34CHzu9G//YNSUiInUHYYSUnVR1dgm22Bjbjrs93ArAAuBVorKrpvs4GoLG/3xxYG1g/zZcVVJ6WRzkFtHEEEbkWuBagZcvodWuNMaUsIx3Sv4VBf4l2JCVKVZmduoVxs1cya+lmKlVIYPiJzSxmMIEAAB6qSURBVLjq1NYc16RWtMPLU1jJSUQ64A6VNVbVriLSDThbVf9WzDZ7ADer6lwR+Sfu8NohqqoiosXYdtgKakNVxwJjAVJSUiIahzEmhhyaWLBsnG/KzMrmvUXrGD97FUs37qJBjUrcdkZ7rujdigY1Kkc7vAKFe1jvv7jRef8BUNXvRORVoDjJKQ1IU9W5/vGbuOS0UUSaqmq6P2y3yS9fh7uWX0iSL1vH4UN0ofJZvjwpj/oU0IYxxrjkVCsJGnWOdiTHZPOu/bz01Wpe+Wo1W/cc4LgmNXn4gm6cfUIzqlSM/vmkcISbnKqp6te5BuwdLE6DqrpBRNaKSEdVXQoMwg1V/xEYCTzk/77nV5kE3CQiE3ADInb65DINeCAw4m4wcJeqbhORDBHpjRsQcSXu8kuhbeXVhjGmvDu4H37+BE64OC4nFszOUZakZ/DCnFW8t2g9B7JzGHhcI8ac2po+betTggOuS0W4yWmLiLTFDYJARC7A/zC3mG4GXvEj9VYAo3HD2ieKyBhgNXCRrzsFOAtIBfb6uvgkdB8wz9f7a2hwBHAD8DxuYsSp/gYuKeXVhjGmvFv9BWTtiYsh5BmZWfyUvosl6Rn8tCGDH9N3sXRDBplZOVSpmMBFJycxum9r2jasEe1Qi03cILhCKom0wZ2D6QNsx11r7wpVXRXR6GJASkqKzp8/P9phGGMibeqdsOA5+P1KqFQt2tEAkJOjrN2+lyXpLgEtSc9gSXoGadv3HapTp1pFOjWpRaemtejUtCa/6NyYOtUqRTFqR0QWqGpKcdcPd7TeCuAMEakOJKhq9K5pYYwxkbB8GrQ+LWqJae+Bg/y04XACWpK+i6UbdrF7vzuDIgKtG1TnhBZ1uLRnSzo1rUmnprVoUqtK3B2yC0e4o/Uq4yYXTAYqhHaEqtq0GcaY+LclFbatgN43RLwpVWX9zkyWrPdJaINLRKu27iF0IKtm5Qoc17Qm5/Vo7ntEtejYuGZM/Di2tIR7zuk9YCfuN0n7IxeOMcZEwfLITCyYmZXN8o27/WG5jEO9oozMw+PJWtWvRqcmtRh+YvNDvaGkulXLZG+oKMJNTkmqOjSikRhjTLQs+xAadoI6xf/R/c59WXyzZvuhQ3JL0jNYuWUP2TmuO1StUiIdm9Rk2AnN6NS0Fp2b1qRjk1rUqBzux3D5Eu5emSMix6vq9xGNxhhjSltmhptY8JQbi7+JrGzO+ufnrNvhBio0r1OVTk1rcmbXJocOy7WqVy2mLg8U68JNTqcCo0RkJe6wnuAustAtYpEZY0xpWPEJ5Bw8piHkk79dz7od+3j4/G4M6dKE2tUqlmCA5VO4yenMiEZhjDHRsmw6VKntLvZaDKrK+C9W0bFxTS5MSSr354pKSlgz4arqatxlh7JwP8QN3YwxJn7l5LhLFrUdBInFO/fz1YptLEnP4KpTky0xlaBwh5LfjJs7aSOQ44sVsMN6xpj4lb4I9myCDsUf7zX+i5XUq16Jc05sXnhlE7ZwvyrcCnRU1a2RDMYYY0rV8umAuCnZi2H11j3MXLKRm05vFzcXVI0XYR3Ww82btDOSgRhjTKlbNg2STobq9Yu1+vNzVlEhQbiid6sSDswU2HMSkdv93RXALBH5gMCPcFX1sQjGZowxkbNrI6z/Bgb+qXirZ2bxxvw0hnVrRuNaVUo4OFPYYb2a/u8af6vkb8YYE99SZ7i/xRxCPnF+Grv3H+Sqvq1LMCgTUmByUtV7g49FpIYv3x3JoIwxJuKWTYOazaDJ8UVeNTtHeX7OSk5OrsvxSbUjEJwJ65yTiHQVkYXAYmCxiCwQkS6RDc0YYyLk4AE3sWD7XxRrYsGZSzaydts+6zVFULgDIsYCt6tqK1VtBfwGN3W7McbEnzVfwoFdxR5CPn72SprXqcovOjcu4cBMSLjJqbqqfhJ6oKqzgOoRicgYYyJt+XRIrAxt+hd51R/W7WTuym2M6pNMhcRwP0JNUYX7O6cVIvJn4CX/+ArcCD5jjIk/y6ZB8qlQqejfsZ/7YhXVKiVy0cktIhCYCQk37V8FNATe9rcGvswYY+LL1p9h6/Jizd20aVcmk79dz4UnJVG7ql3cNZLC7TnVU9VbIhqJMcaUhuXT3d/2g4u86itfreFAdg6jbCBExIWbnMaLSBIwD/gc+MzmdjLGxKVl06BBB6hXtASTmZXNK3NXM+i4RrRuYKfcIy3cq5L3BzoBTwF1gA9EZFskAzPGmBK3fzes/qJYvabJ365ny+4DXHWq9ZpKQ7hXJT8V6OdvdYD3cT0oY4yJHytmQfaBIp9vCs7Z1Kdt8a7DZ4om3MN6s4AFwIPAFFU9ELGIjDEmUpZPg8q1oOUpRVotNGfT388/3uZsKiXhJqcGQF/gNOAWEckBvlTVP0csMmOMKUmqsHwGtB0IiUUbaWdzNpW+cM857cD9rmklkA60xSUqY4yJDxu+g13pRT6kF5qz6bKeLW3OplIU7jmnFcBPwGzgX8BoO7RnjIkry0ITC/6iSKs9P2cViSKMOMXmbCpN4f4It52qnqWqD6jq7JJITCKSKCILReR9/7i1iMwVkVQReV1EKvnyyv5xql+eHNjGXb58qYgMCZQP9WWpInJnoDzPNowx5cCyD6F5D6jRMOxVDs/Z1NTmbCpl4San+iLyBxEZKyLjQ7djbPtWYEng8d+Bx1W1HbAdGOPLxwDbffnjvh4i0hm4BOgCDAWe9QkvEXgGOBPoDFzq6xbUhjGmLNuzBdYtKPLcTYfmbLLh46Uu3OT0HlAbmAl8ELgVi/9B7y+B//nHAgwE3vRVXgCG+/vn+Mf45YN8/XOACaq6X1VXAqlAT39LVdUVvoc3ATinkDaMMWXZ8hmAQofwf98UmrMppVVduiXViVxsJk/hjtarpqp3lGC7TwC/5/BMu/WBHap60D9OA0LDYpoDawFU9aCI7PT1mwNfBbYZXGdtrvJehbRhjCnLlk+DGk2gyQlhrxKas+muMztFMDCTn3B7Tu+LyFkl0aCIDAM2qeqCktheJIjItSIyX0Tmb968OdrhGGOORXYWpH7sJhZMCH+Ki+e+cHM2DbY5m6Ii3FfqVlyC2iciGSKyS0QyitlmX+BsEVmFO+Q2EPgnUEdEQj25JGCdv78OaAHgl9cGtgbLc62TX/nWAto4gqqOVdUUVU1p2DD8k6fGmBi0di7s31mkIeSL1+/kqxXbGNmnlc3ZFCXh/s6ppqomqGpVVa3lH9cqToOqepeqJqlqMm5Aw8eqejnwCXCBrzYSd54LYJJ/jF/+saqqL7/Ej+ZrDbQHvsZdnLa9H5lXybcxya+TXxvGmLJq2TRIqAhtBoS9SmjOpotTWkYsLFOwAs85ichxqvqTiPTIa7mqflOCsdwBTBCRvwELgXG+fBzwkoikAttwyQZVXSwiE4EfgYPAjaqa7eO+CZgGJALjVXVxIW0YY8qq5dMhuS9Urll4XWDzrv1MWrSeS3q2oHY1m7MpWgobEPEb4Brg0TyWKe6QXLH56d5n+fsrcCPtctfJBC7MZ/37gfvzKJ8CTMmjPM82jDFl1NIPYfNP0GNk4XW9V+audnM29UmOXFymUAUmJ1W9xv89vXTCMcaYErJ8BkwcAU1PgO5XhLXK/oPZvPzVagYe14g2DWtEOEBTkALPOYnI7wP3L8y17IFIBWWMMcckdSZMuBwaHgcj3oUq4Z0in/xtupuzyWa6jbrCBkRcErh/V65lQ0s4FmOMOXY/f+wSU4MOcOV7UK1eWKupKuNnr6RD4xr0bWdzNkVbYclJ8rmf12NjjImuFbPgtUuhfrsiJSaAuSu38WN6Blf1bW1zNsWAwpKT5nM/r8fGGBM9Kz+DVy+Bem1cYqpetN7P+NkrqVutIsO724VjYkFho/VO8D+2FaBq4Ie3Atgleo0xsWHVbHjlIqjbCq6cBNUbFGn1NVv3MmPJRm4c0M7mbIoRhY3Ws1fJGBPbVs+BVy6EOi1h5OQiTYkRYnM2xR67LocxJn6t/hJevgBqJ/nE1KjIm9iVmcXE+WttzqYYY8nJGBOf1syFVy6AWk1dYqpZvAu0vmFzNsUkS07GmPizdh68fD7UaAwj34eaTYq1GTdn0yqbsykGWXIyxsSXtAXw8nlu0MOo913PqZg+WrKRNdv2Wq8pBllyMsbEj3XfwEvnut8vjXofajU7ps2NtzmbYpYlJ2NMfFi/EF4aDlXruEN5tZOOaXM2Z1Nss1fEGBP70r+FF4dD5dqux1SnReHrFOK5L1ZRtaLN2RSrLDkZY2Lbhu/hxXPcfEyjJrvfMx2j0JxNF5yUZHM2xShLTsaY2LXhB3jhbKhY3Q0Xr5tcIps9NGdT35LZnil5lpyMMbFp44/w4tlQoYrrMdUrmRF1oTmbTu/YkLY2Z1PMsuRkjIk9m5bAC7+CxEruHFO9NiW26UNzNtnw8ZhmyckYE1s2/eQSU0IFNyqvftsS23Rozqb2jWpwaruiXRzWlC5LTsaY2LF5mUtMkuDOMTVoV6KbPzRn06k2Z1Oss+RkjIkNW5bDC8Pc/ZGToWGHEm8iNGfTuTZnU8yz5GSMib6tP8PzwyAn2yemjiXeRGjOpst6tbQ5m+JAYZMNGmNMZB1KTFnuHFOj4yLSzKE5m3onR2T7pmRZz8kYEz3bVrhzTAczXY+pceeINBOas+mX3ZrSpLbN2RQPrOdkjImO7avg+V9B1l6fmLpErKnQnE2j+9rw8XhhPSdjTOnbvtolpgO74cpJ0OT4iDWVnaO88OUqTmpVlxNb2JxN8cKSkzGmdO1Y40bl7d8JV74HTbtFtLmPf9rE6q17ucp6TXHFkpMxpvTsTHODH/bthBHvQrMTI97k+NkraVa7CkO62JxN8aTUk5OItBCRT0TkRxFZLCK3+vJ6IjJDRJb7v3V9uYjIkyKSKiLfiUiPwLZG+vrLRWRkoPwkEfner/Ok+F/b5deGMaYU7FznE9N2uPIdaN6j8HWOwbKNu7jupfl8uWIrI/sk25xNcSYar9ZB4Deq2hnoDdwoIp2BO4GPVLU98JF/DHAm0N7frgX+BS7RAH8BegE9gb8Eks2/gGsC6w315fm1YYyJpIz17lDeni0w4h1oflLEmlq1ZQ+3TVjIkCc+Y07qVn59Rge7jl4cKvXReqqaDqT7+7tEZAnQHDgHGOCrvQDMAu7w5S+qqgJfiUgdEWnq685Q1W0AIjIDGCois4BaqvqVL38RGA5MLaANY0ykZKS74eK7N7nElJQSkWbSd+7jyY9SmTh/LRUThetOa8t1p7WhbvVKEWnPRFZUh5KLSDLQHZgLNPaJC2ADEDpA3BxYG1gtzZcVVJ6WRzkFtJE7rmtxvTRatrRZMo0ptl0bXGLKSIcRb0OLniXexJbd+3n2k595ee5qVJUrerXkxtPb0aiW/Z4pnkUtOYlIDeAt4DZVzQhehFFVVUQ0ku0X1IaqjgXGAqSkpEQ0DmPKrN2bfGJaD1e8CS17l+jmd+7NYuznP/PcF6vIzMrm/B5J3DKoPS3qVSvRdkx0RCU5iUhFXGJ6RVXf9sUbRaSpqqb7w3abfPk6oEVg9SRfto7Dh+hC5bN8eVIe9QtqwxhTknZvdolpZxpc/ia06lNim96z/yDPz1nFfz79mYzMgwzr1pRf/6KDTRxYxkRjtJ4A44AlqvpYYNEkIDTibiTwXqD8Sj9qrzew0x+amwYMFpG6fiDEYGCaX5YhIr19W1fm2lZebRhjSsqeLS4xbV8Nl02E5L4lstnMrGzGz15J/398wj+mLaVn63pMuaUfT1/WwxJTGRSNnlNfYATwvYgs8mV/AB4CJorIGGA1cJFfNgU4C0gF9gKjAVR1m4jcB8zz9f4aGhwB3AA8D1TFDYSY6svza8MYUxL2bIUXzobtK11iat3vmDeZlZ3DmwvSePKj5aTvzKRP2/qMvbIjPVraL0HKMnGD4Ex+UlJSdP78+dEOw5jYt3eb6zFtTYVLJ0Db049pczk5yuTv1vP4jGWs2rqXE1vU4XdDOtLXZrCNCyKyQFWLPTTTLvxqjDl2e7fBi2e7CQMvfe2YEpOqMv3HjTw2fRlLN+7iuCY1+d+VKQzq1Mhmry1HLDkZY47Nvu3w0nDYvBQueQ3aDSrWZlSV2albeGTaUr5N20mbBtV56tLu/PL4piQkWFIqbyw5GWOKb98OeHE4bFoCF78C7c8o1mbmr9rGP6YtZe7KbTSvU5WHz+/GeT2a2yWHyjFLTsaY4sncCS+dCxsXw8UvQ4fBRd7ED+t28uj0pXyydDMNalTm3rO7cEnPFlSuYNOol3eWnIwxRZeZAS+dBxu+h4tehI5DC18nIHXTLh6bsYwp32+gdtWK3DH0OEb2aUW1SvaRZBx7Jxhjimb/Lnj5fEhfBBe+AMedFfaqa7ft5YmZy3lnYRpVKyZyy6D2XN2vNbWqVIxgwCYeWXIyxoRv/y54+QJYtwAufB46DQtrtY0ZmTz18XJen7eWBBHGnNqa6/u3pX6NypGN18QtS07GmPDs3w2vXAhp8+CC8dD57EJX2bbnAP/+9GdemLOK7Bzlkp4tuOn09jSpbRdlNQWz5GSMKdyBPfDqRbD2azj/f9BleIHVMzKz+N/nKxk/eyV7DxxkePfm3DaoAy3r20VZTXgsORljCnZgL7x6Maz5Es77L3Q9L9+q+w5k88KXq/j3pz+zY28WZ3Ztwu2/6ED7xjVLL15TJlhyMsbk78BeeO1iWP0FnDsWjr8gz2r7D2Yz4eu1PP1JKpt37WdAx4b8dnBHujavXcoBm7LCkpMxJm9Z+2DCpbDyczj3P9DtwqOqHMzO4e2F6/jnzOWs27GPnq3r8ezlPTg5uV4UAjZliSUnY8zRsjJhwmWw4lMY/iyccPERi3NylA++T+fxmctYsXkP3ZJq8+B5x9OvfQO7/p0pEZacjDFHysqE1y+Hnz+Bc56GEy87tEhV+finTTwyfRlL0jPo0LgG/77iJIZ0aWxJyZQoS07GmMMO7oeJIyB1Jpz9FHS/4tCiOalb+Mf0pSxcs4NW9avxxMUn8qsTmpFoF2U1EWDJyRjjHNwPE6+E5dNh2BPQ40oAFq7ZziPTl/JF6laa1q7Cg+cdzwUnJVHRLspqIsiSkzEGDh6AN0bBsg/hl49BymiWpGfw6PSlzFyyifrVK/HnYZ25vFdLqlS0i7KayLPkZEx5l50Fb46GpVPgrEdYkXwxj7+2kMnfrqdmlQr8bkhHRvVJpnpl+7gwpcfebcaUZ6HE9NP7bO//Nx5c05O33v2MyhUSuPH0tlzbry21q9lFWU3ps+RkTHmVnQVvjYElk5na/FZundkOWM+Vp7TihgHtaFjTLspqoseSkzHlUfZBDkwcQ6Wl7/FQzgj+u7I3F6U05+aB7WlWp2q0ozPGkpMxx0QVcrJBcwK34OO8lgfrqPub7zbyWx6qo4cf51kn1y0nm8ysg6TNm0y7rZ/wwMHL2NR1DB+d0YHkBtWjvTeNOcSSU1kz+wk4mOk+tNDDH16h++jhD71Dy4tSN7ScQpaXVFuEEUteyylkeeCDP/gBr7k+4HPy+pAP1IlDVYA2KrxV/2rOv+geOjaxi7Ka2GPJqaz5/FHYnxEoEBA5/FcS8r6PfywUsjyvbVHI8vzWL0rdhFxl4bZFwcslASTxyMcJiYH28rkdVUcC28mrTu62ctcJPsfcdfKKIfw6B3Jgyg8bee7L1WzadZCTW9fnmkFdOL9dcuTeh8YcI0tOZc0dqzjyw9+UV9k5yrsL1/HER8tYu20fKa2SefzSjvRuUz/aoRlTKEtOZU2C/UCyvFNVPvxhA4/NWMbyTbvp0qwWz43uyoAODe36dyZuWHIypoxQVWYt28yj05fyw7oM2jWqwbOX92BolyYk2PXvTJwpd8lJRIYC/wQSgf+p6kNRDsmYYzZ3xVYemb6Ueau2k1S3Ko9eeALDuze3i7KauFWukpOIJALPAL8A0oB5IjJJVX+MbmTFp6putLIqOQrK4cfBcg7dV5TDyzVYnvux335OAfUObyvU3uF6BB7nV89t63DsOYceH1mPXM8xVO+I557fvsjRQHsaiPFwW8F4QvFn5xS0PLC9nMK3l51z5LZzAs87nFhycnLXP3w/O0fZlXmQRjUrc9/wrlyc0oJKFeyirCa+lavkBPQEUlV1BYCITADOAUo8OU2ct5axn6846gO44A/kI+uF84FsikYEEkRIEBD/1z2WI5a5x4fvJybktTyPdROOXvdQWwlQQRKOasttO/xYEhKOjj25fjUu6WkXZTVlR3lLTs2BtYHHaUCvSDRUp1pFOjau6QfNBT5kOPxYcn3wBD/sIPjBdrieu597W4fLcz8+eluH/x7xQc2RH7DBx8F6cPSH81HP0W8vQSAhIfdzlqPjSjhcP996CYfjzDf+hMP75XBckBjYJzYgwJj4UN6SU1hE5FrgWoCWLVsWaxuDuzRhcJcmJRmWMcaUG+XtwPQ6oEXgcZIvO4KqjlXVFFVNadiwYakFZ4wxxilvyWke0F5EWotIJeASYFKUYzLGGJNLuTqsp6oHReQmYBpuKPl4VV0c5bCMMcbkUq6SE4CqTgGmRDsOY4wx+Stvh/WMMcbEAUtOxhhjYo4lJ2OMMTHHkpMxxpiYI6p2DZyCiMhmYHW04yiiBsCWaAcRZbYPHNsPju2H0t8HrVS12D8UteRUBonIfFVNiXYc0WT7wLH94Nh+iL99YIf1jDHGxBxLTsYYY2KOJaeyaWy0A4gBtg8c2w+O7Yc42wd2zskYY0zMsZ6TMcaYmGPJyRhjTMyx5BQHRGS8iGwSkR9yld8sIj+JyGIReThQfpeIpIrIUhEZEigf6stSReTO0nwOJSGv/SAir4vIIn9bJSKLAsvK0344UUS+8vthvoj09OUiIk/65/qdiPQIrDNSRJb728hoPJfiymcfnCAiX4rI9yIyWURqBZaVufeCiLQQkU9E5Ef/GXCrL68nIjP86zpDROr68vh6L6iq3WL8BpwG9AB+CJSdDswEKvvHjfzfzsC3QGWgNfAzbnqQRH+/DVDJ1+kc7ed2rPsh1/JHgbvL434ApgNn+vtnAbMC96cCAvQG5vryesAK/7euv1832s/tGPfBPKC/v38VcF9Zfi8ATYEe/n5NYJl/rg8Dd/ryO4G/x+N7wXpOcUBVPwO25Sr+P+AhVd3v62zy5ecAE1R1v6quBFKBnv6WqqorVPUAMMHXjRv57AfAfSsELgJe80XlbT8oEOop1AbW+/vnAC+q8xVQR0SaAkOAGaq6TVW3AzOAoZGPvmTksw86AJ/5+zOA8/39MvleUNV0Vf3G398FLAGa457DC77aC8Bwfz+u3guWnOJXB6CfiMwVkU9F5GRf3hxYG6iX5svyKy8r+gEbVXW5f1ze9sNtwD9EZC3wCHCXLy9P+2Exh5PLhUALf7/M7wMRSQa6A3OBxqqa7hdtABr7+3G1Hyw5xa8KuG54b+B3wETfeyivLuVwr6k8+j/g16raAvg1MC7K8UTDVcANIrIAd5jrQJTjKRUiUgN4C7hNVTOCy9Qdt4vL3wtZcopfacDbvov+NZCDu7DjOg5/YwRI8mX5lcc9EakAnAe8Higub/thJPC2v/8G7pAVlKP9oKo/qepgVT0J90XlZ7+ozO4DEamIS0yvqGro9d/oD9fh/4YO+cfVfrDkFL/exQ2KQEQ64E7obgEmAZeISGURaQ20B77GnSxuLyKtRaQScImvWxacAfykqmmBsvK2H9YD/f39gUDo8OYk4Eo/Uqs3sNMf8pkGDBaRun4012BfFrdEpJH/mwD8Cfi3X1Qm3wv+SMk4YImqPhZYNAn3ZQX/971Aefy8F6I9IsNuhd9w3wLTgSxcj2kMLhm9DPwAfAMMDNT/I+5b41L8CC5ffhZuRM/PwB+j/bxKYj/48ueB6/OoX272A3AqsAA34mwucJKvK8Az/rl+D6QEtnMVbnBAKjA62s+rBPbBrf51XQY8hL8CTll9L/jXXIHvgEX+dhZQH/gI9wVlJlAvHt8LdvkiY4wxMccO6xljjIk5lpyMMcbEHEtOxhhjYo4lJ2OMMTHHkpMxxpiYY8nJmBglIrsLWJYsua5Sb0xZYsnJmDLMXz3DmLhjycmYGCYiNUTkIxH5xs9TFLxqdqKI/NfP5TNdRKr6dWaJyAMi8inuh6nGxB1LTsbEtkzgXFXtgbtc1aOBC/y2B55R1S7ADg5PEQFQR1X7q+qjpRuuMSXDuvzGxDYBHhCR03AX923O4SkQVqpqaObfBUByYL3gRXCNiTuWnIyJbZcDDXHXyssSkVVAFb9sf6BeNlA18HhP6YRnTGTYYT1jYlttYJNPTKcDraIdkDGlwXpOxsQgP8puP/AKMFlE5uOuOv1TVAMzppTYVcmNiUEicgLwX1XtWWhlY8ogO6xnTIwRketx8xX9KdqxGBMt1nMyxhgTc6znZIwxJuZYcjLGGBNzLDkZY4yJOZacjDHGxBxLTsYYY2LO/wMOEOnyHrt+mgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data_d = {\n", " 1555: 3_500,\n", " 1703: 7_000,\n", " 1791: 9_541,\n", " 1900: 213_711,\n", " 1940: 542_800,\n", " 1990: 575_794,\n", " 2019: 621_877\n", "} # Quelle: https://de.wikipedia.org/wiki/Einwohnerentwicklung_von_D%C3%BCsseldorf\n", "data_k = {\n", " 1600: 40_000,\n", " 1714: 42_015,\n", " 1810: 45_029,\n", " 1900 : 372_229,\n", " 1930: 740_082,\n", " 1960: 803_616,\n", " 2019: 1_087_863\n", "} # Quelle: https://de.wikipedia.org/wiki/Einwohnerentwicklung_von_K%C3%B6ln\n", "plt.plot(list(data_d.keys()), list(data_d.values()), label='Düsseldorf')\n", "plt.plot(list(data_k.keys()), list(data_k.values()), label='Köln')\n", "plt.ylabel(\"Einwohnerzahl\")\n", "plt.xlabel(\"Jahr\")\n", "plt.title(\"Entwicklung der Einwohnerzahlen von Düsseldorf und Köln\")\n", "plt.legend() \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Aufgabe\n", "Gegeben sind folgende Daten in der Variable data. Stellen Sie die Felder x und y in einem Liniendiagramm da." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = {\n", " 1714: 2.983, \n", " 1784: 3.531, \n", " 1820:5.936, \n", " 1875:36.580, \n", " 1905:110.317,\n", " 1922: 260.169,\n", " 1935: 439.627,\n", " 1971:448.791,\n", " 1989: 532.152, \n", " 2018:498.590} # Quelle: https://de.wikipedia.org/wiki/Einwohnerentwicklung_von_Duisburg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Überprüfung\n", "Das Diagramm soll in etwa wiefolgt aussehen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Eine ausführliche Dokumentation der plot-Methode findet sich hier: https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.plot.html" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Normalerweise werden die Wertebereiche der X- und Y-Achse automatsich bestimmt. \n", "# Wenn man mehrere Diagramme verwendet kann es übersichtlicher sein, diese Werte manuell zu setzen\n", "\n", "plt.plot(range(5))\n", "plt.plot([4,4,5,6,7])\n", "\n", "plt.xlim(0, 5)\n", "plt.ylim(0, 10)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Hier zwei Diagramme nebeneinander\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(18,10))\n", "\n", "ax2 = plt.subplot(221)\n", "ax2.plot(list(data_d.keys()), list(data_d.values()))\n", "ax2.set_title('Entwicklung der Einwohnerzahlen von Düsseldorf')\n", "\n", "ax3 = plt.subplot(222)\n", "ax3.plot(list(data_k.keys()), list(data_k.values()))\n", "ax3.set_title('Entwicklung der Einwohnerzahlen von Köln')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Das Problem bei dieser Darstellung ist, dass es so aussieht als ob Köln und Düsseldorf 2019 die gleiche Einwohnerzahl haben. Dies liegt daran, dass die Y-Achsen unterschiedliche Auflösungen haben. Durch `set_ylim()` kann man den Wertebereich der Y-Achse bestimmen. Hier haben Metode beim subplot die Prefix `set_`." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "plt.figure(figsize=(18,10))\n", "\n", "\n", "ax2 = plt.subplot(221)\n", "ax2.plot(list(data_d.keys()), list(data_d.values()))\n", "ax2.set_title('Entwicklung der Einwohnerzahlen von Düsseldorf')\n", "ax2.set_ylim(top=1_100_000)\n", "ax2.set_xlabel(\"Jahr\")\n", "\n", "\n", "ax3 = plt.subplot(222)\n", "ax3.plot(list(data_k.keys()), list(data_k.values()))\n", "ax3.set_title('Entwicklung der Einwohnerzahlen von Köln')\n", "ax3.set_ylim(top=1_100_000)\n", "ax3.set_xlabel(\"Jahr\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Balkendiagramme\n", "\n", "Mit der Methode `bar` kann man ein Balkendiagramm erzeugen." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "einwohner_2018_l = ('Düsseldorf', 'Köln', 'Duisburg', 'Essen', 'Dortmund')\n", "einwohner_2018_z = [619_294, 1_085_664, 498_590, 583_109, 587_010]\n", "y_pos = [0, 1, 2, 3, 4]\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_xticks(range(len(einwohner_2018_l)))\n", "ax.set_xticklabels(einwohner_2018_l, rotation='vertical')\n", "\n", "plt.bar(y_pos, einwohner_2018_z, align='center')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mit dem Parameter `color` können Farben für die einzelnen Balken angegeben werden. Mit dem Parameter `alpha` kann man die Farben weniger aufdringlich gestalten." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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jgEOA45McMmxVkrSyTGWAAIcBW6vqtqp6ALgYOHbgmiRpRUlVDV3DbkvyU8DGqvqFtvyzwPOr6nU7bXcScFJb/H7glokWuvsOBL4wdBED8bOvXCv580/DZ/+eqlo934q9Jl3JJFXVucC5Q9fx7Uqyuao2DF3HEPzsK/Ozw8r+/NP+2af1FNadwEFjy2tbmyRpQqY1QK4D1ic5OMk+wHHAZQPXJEkrylSewqqqB5O8DrgCWAWcX1U3DVzWnjA1p9uWgZ995VrJn3+qP/tUXkSXJA1vWk9hSZIGZoBIkroYIJKkLgbIQJJc1X6/eehaJKnHVI7CmhFPTfLDwMuSXAxkfGVVfXKYsiYnyRHAbwPfw+jvYoCqqqcNWdckJDltnub7gS1Vdf2k6xlCkv8ErGPs/6GqumiwgpZZkrcBC45aqqpfmWA5e4QBMpzfAk5nNAnyj3l4gBTwoiGKmrDzgFOBLcBDA9cyaRvaz1+35Zcwmt/0S0n+sqr+YLDKJiDJnwL/AbieHX/2BcxsgACb2+8jGN0E9r1t+RWM/g1MHYfxDihJgN+sqjOHrmUISa6tqucPXccQklwB/Jeq+te2/HjgL4GfZHQUMtN3l07yaeCQWoH/ASW5Gjiqqr7RlvcGPlRVPzpsZbvPayADav94VvJdhK9O8odJfjDJc+Z+hi5qQr4b+PrY8jeAdVX1bzu1z6p/Ar5z6CIG8l3AE8aWH9/apo6nsIZ3TZLnVdV1QxcygLmjj/Gbya2U03d/AVyb5NK2/FLgPUkeB9w8XFkTcyBwc5JPMBaYVfWy4UqamDcBn2pHIgFeyOha4NTxFNbAktwMfB9wO/AVdlxIfuaghWnZJdnA6Hw4wN9X1ebFtp8lbQDJt6iqv510LUNI8p3s+AJ1bVV9fsh6ehkgA0vyPfO1V9Xtk65lUhYYgfRNVfWWSdUyhPZEzRuq6hlD1zKk9nd/fVX9vySPBVZV1ZeHrmsSkqxhx+hDAKrqo8NV1MdTWAOrqtuTHAr8UGv6WFXdMGRNE/CEXW8yu6rqoSQ3JPnuqvrc0PUMIclrGD3s7cmMRmOtAf43cOSQdU1Cm/v108BNwL+35gKmLkA8AhlYklOA1wDva00/AZxbVW8briottyQfBp4HfILRqUtgxVwDIMn1jB5NfW1VPbu13VhVPzBsZcsvyS3AM6tq6gdLeAQyvBMZPY73K/DNbyf/AMx8gCRZy+hzzl0H+BhwSlVtG66qifmdoQsY2Ner6oHRSHZIsheLTLKbMbcBezMDo+0MkOGFh0+ie4idZqXPmiR/V1UvAC5gNBrpFW3Vf21tPzZUbZOyUi4WL+Jvk7wB2DfJjwGvZcekyln3VeD6djuj8RFoUzcT3VNYA2sXlDcB/7c1vRx4d1X9yXBVTUaS66vqWbtqm0VJvsyOb9z7MPpG+pWqeuJwVU1OkkcxOvo+itEXpiuAd62EiYVJNs3XXlUXTrqWpTJAHgHa5LkXtMWPVdWnhqxnuc0dgbRvYBcA72mrjgdOqKqZv5A6rt2R4FjgsKp6w9D1TFqSJwNrq+ofh65Fu8cAGUj7R7Ogqrp3UrUMpQ3jfBvwg4y+jX8c+OWqumPQwgaS5JqqOnzoOiYhyUeAlzE6jX49sB3426padIj3LEjyGea53jONNxH1GshwtjD6SxRGt7W4r73eD/gccPBwpS2vJAdV1R1trsvLdlr348DMB0iSnxxbfBSj2fgr6dvck6rqS0l+Abigqs5IslKOQMbvvPAYRtcA9x+oliXxXlgDqaqD2zeOK4CXVtWBVXUA8OPsGNI7q65Msm7nxiQnAGdPvJphvHTs52jgy6ys+6LtleSpwCuB9w9dzCRV1T1jP3e2651TedrWI5DhPa+qfmluoao+kOSNQxY0AacBH0rykqq6FSDJ64GfAea9xcWsqaoThq5hYGcy+vL0d1V1XZKnAbcOXNNE7HTD0Lmjz6mcXOs1kIG123p/DPiz1vQq4IVVdfRwVS2/JEcC72Q06uwXGE0qe0lV3TdoYRPS/sM8Gzic0amrfwBOrarbBi1My67dRHHOg8BngT+qqluGqaifATKwdjH9DEZ35ITR7Qx+Z4VcRP8hRsOXPw68sqq+NnBJE5PkGuDt7BiBdhyjAQQr4vkoSf4A+F3g34APAocCv1pVf7bojnpEMUA0cWNzIAI8mtGzMOYmUNZKmAsx38O0VtgorOur6llJfoLRUeipwNVVdejApS27JPsBr+ZbH+c7dRMJvQYykCR/zeLPR57ZeyJV1VSe790TxoZvX53kdOBiRn8Pfhr4m8EKm7y5/3teArynqu6du63JCnA5cA1wIztupjiVPAIZyELPQ5jjrS5m09gcgPn+t6xpnAvQI8mbGB15/Buj61/7Ae9fCafwknyyqmbiyZsGyCNAkn2B757Gi2hSr3Y0dn+7vf3jgCdM64OVdkeSU4F/ZTR8efxeWFN33dN5IANL8lJGM3E/2JafleSyYavSckvyiiRPaK9/I8n7kjx76LqWW5JfG1s8sqoeAmh3o566awCdHgD+kNHIuy3tZyqfRmmADO+3GR3CfxGgqq5nhmeh65t+s6q+nOQFjCYSXsjogUqz7rix16/fad3GSRYyoNOA762qdW1C8cHTeurSABneN6rq/p3aPK84++Zu4f8S4JyqupTRXXlnXRZ4Pd/yrLqJ0S3dp56jsIZ3U5KfAVYlWc/oMP7jA9ek5XdnkncyevbJm5M8mpXxha4WeD3f8qx6iNHzQK7G54FoKZI8FvifPPy5CG9cSZPqVqL2574RuLGqbm33hfqBqvrQwKUtqyQPMXqEb4B92fFNPMBjqmrvoWqblAWeB1JVddHEi1kiA+QRJMkq4HFV9aWha9HySPLEdhfaeW/nP40jcbR7kpxSVWfvqm0arIRD5ke0JH+R5IltGONNwC1J/sfQdWnZ/EX7PTfyZgtTPhJHu22+I5Cfm3QRe4JHIAMbu6XDq4DnAr8ObKmqZw5cmqQ9KMnxjO44/UOM7nk35wnAv0/jkzi9iD68vZPszWhW7v+qqm8kMdVnXJIXztdeVR+dr10z4ePAXcCBwB+PtX8ZmMqHaRkgw3sno9s53wB8tD3m1Wsgs2/8NOVjGM0F2gK8aJhytNyq6vYk24CvzMqtijyF9QiUZK+qenDoOjQ5SQ4C/qCqjh+6Fi2vdqeJn51n/tfU8QhkYElOAS5gdBj7LuDZwOnATA/n1LfYBjxj6CI0EV8DbkxyJaMhzcB0zgMxQIb381V1dpKjgdXACYwCxQCZYUnexo6Jc48CnsXoNKZm398wI7fuN0CGN3f7hhcDF1TVDVlBD0ZYwcaH7D7I6JkYfz9UMZqcqrowyT7A97WmW6rqG0PW1MtrIANLcgGwhtENFA8FVgEfqarnDlqYll2S1QBVtX3oWjQ5SX6E0c0zP8voC+RBwKZpHIFngAwsydzpi9uq6otJDgDWVNVUDuvT4trR5RnA6xidugqjI5C3VdWZQ9amyUiyBfiZuef/JPk+RkegU/el0Znow3sB8HjgmW1uwNMZPZ1Ns+lU4AjgeVX15KraH3g+cER70JBm397jD4+rqn8GpvIeYB6BDCTJi6rqw+3Z6HO+OR+gqpwPMIOSfAr4sar6wk7tq4EPVdXMP1RqpUtyPqNnof9Za3oVsKqqfn64qvp4EX04Pwx8uKpeOt44Nx9gmJI0AXvvHB4wug7S7kig2fffgJPZ8QTGjwHvGK6cfgbIcK5boN35ALPtgc51mhFV9fUkfwr86bQPoDBAhvN04P3OB1hxDk0y361qwugUpmbUPAMo5p6PMrUDKLwGMrCdHi7zIPBZ5wNIsyfJacAxwElV9ZnW9jTgHOCDVXXWkPX1MEAeAZwPIM2+WRxA4TDegWTkt5N8AbgF+Ock25P81tC1SVoWCw6gYEqH8Rogw3E+gLSyzNwACk9hDWQWD2clLaxdMP/KfKuAx1TV1B2FOAprOM4HkFaQqlo1dA17mqewhjNzh7OSVhZPYQ1kFg9nJa0sBogkqYunsCRJXQwQSVIXA0SS1MUAkSR1MUAkSV3+P6oYuFwi93LUAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "einwohner_2018_l = ('Düsseldorf', 'Köln', 'Duisburg', 'Essen', 'Dortmund')\n", "einwohner_2018_z = [619_294, 1_085_664, 498_590, 583_109, 587_010]\n", "y_pos = [0, 1, 2, 3, 4]\n", "farben = ['#000000', '#dbb243', '#2e42d3', '#e54fe3', '#f23434']\n", "\n", "fig, ax = plt.subplots()\n", "ax.set_xticks(range(len(einwohner_2018_l)))\n", "ax.set_xticklabels(einwohner_2018_l, rotation='vertical')\n", "\n", "plt.bar(y_pos, einwohner_2018_z, align='center', alpha=0.6, color=farben)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exkurs Daten aus Pandas ploten" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "# TODO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lektion: matplotlib\n", "\n", "Version: 2020-10-27" ] } ], "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": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 2 }