{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Μαθηματικά με Python - Modules\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Πρέπει να φτιάξω κώδικα για ό,τι χρειάζομαι;\n", "\n", "Οχι δε χρειάζεται. Υπάρχει έτοιμος κώδικας σε πακέτα ή αρθρώματα (βιβλιοθήκες) που μπορείς να εισάγεις και να χρησιμοποιήσεις στα scripts. \n", "\n", "### Τι είναι ένα άρθρωμα (module) και πώς το χρησιμοποιώ;\n", "Με απλά λόγια, ένα άρθρωμα είναι ένα αρχείο με κώδικα Python. Μπορεί να περιέχει μεταβλητές, συναρτήσεις, ακόμα και κώδικα προς εκτέλεση.\n", "\n", "Μπορεί να χρησημοποιηθεί αφού το εισάγουμε στο script με τη δήλωση `import`.\n", "\n", "Για παράδειγμα, μπορούμε να εισάγουμε το math ώστε να εκτελέσουμε πιο πολύπλοκους υπολογισμούς." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.0\n" ] } ], "source": [ "import math # φορτώσαμε το module math\n", "\n", "print(math.sqrt(16)) # τώρα μπορούμε να το χρησιμοποιήσουμε για να υπολογίσουμε την τετραγωνική ρίζα ενός αριθμού" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Μπορούμε ακόμα να επιλέξουμε τι θα φορτώσουμε χρησιμοποιώντας τη δήλωση `from`.\n", "Έτσι, δε χρειάζεται να χρησιμοποιούμε τη σύνταξη `module.`.\n", "\n", "Το ανωτέρω κελί μπορεί να γίνει αλλιώς:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4.0\n" ] } ], "source": [ "from math import sqrt # φορτώνεται μόνο η συνάρτηση sqrt\n", "\n", "print(sqrt(16)) # χωρίς 'math.'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Μπορώ να δω τι περιέχει ένα modeule;\n", "Ναι, χρησιμοποιώντας τη συνάρτηση `dir()` **αφού φορτωθεί**.\n", "Ωστόσο, είναι καλύτερο να ανατρέξουμε στην [**τεκμηρίωση**](https://docs.python.org/3/library/math.html)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['__doc__',\n", " '__loader__',\n", " '__name__',\n", " '__package__',\n", " '__spec__',\n", " 'acos',\n", " 'acosh',\n", " 'asin',\n", " 'asinh',\n", " 'atan',\n", " 'atan2',\n", " 'atanh',\n", " 'ceil',\n", " 'comb',\n", " 'copysign',\n", " 'cos',\n", " 'cosh',\n", " 'degrees',\n", " 'dist',\n", " 'e',\n", " 'erf',\n", " 'erfc',\n", " 'exp',\n", " 'expm1',\n", " 'fabs',\n", " 'factorial',\n", " 'floor',\n", " 'fmod',\n", " 'frexp',\n", " 'fsum',\n", " 'gamma',\n", " 'gcd',\n", " 'hypot',\n", " 'inf',\n", " 'isclose',\n", " 'isfinite',\n", " 'isinf',\n", " 'isnan',\n", " 'isqrt',\n", " 'ldexp',\n", " 'lgamma',\n", " 'log',\n", " 'log10',\n", " 'log1p',\n", " 'log2',\n", " 'modf',\n", " 'nan',\n", " 'perm',\n", " 'pi',\n", " 'pow',\n", " 'prod',\n", " 'radians',\n", " 'remainder',\n", " 'sin',\n", " 'sinh',\n", " 'sqrt',\n", " 'tan',\n", " 'tanh',\n", " 'tau',\n", " 'trunc']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(math)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Σχεδιάζοντας !\n", "\n", "Για να σχεδιάσουμε, θα φορτώσουμε το πακέτο [**matplotlib**](https://matplotlib.org).\n", "Συνήθως χρησιμοποιούμε τη συλλογή pyplot μετονομάζωντας τη σε plt για ευκολία χρησιμοποιώντας τη λέξη `as`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt # now plt is the pyplot collection of the matplotlib package" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Τώρα είμαστε έτοιμοι να σχεδιάσουμε γραφικές παραστάσεις!!\n", "\n", "1. Θα χρησιμοποιήσουμε τη συνάρτηση `plot()`\n", "2. Η συνάρτηση plot δέχεται μια λίστα με τιμές για τα x και μια λίστα με τιμές για τα y\n", "3. Μπορούμε να σχεδιάσουμε πολλές γραμμές ταυτόχρονα καλώντας την plot πάνω από μία φορές, οι οποίες εμφανίζονται με την `plt.show()` " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_values = [10, 20, 30]\n", "y_values = [5, 8, 10]\n", "\n", "plt.plot(x_values, y_values)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x_values2 = [5, 10, 15]\n", "y_values2 = [1, 3, 4]\n", "\n", "x_values3 = [8, 12, 23]\n", "y_values3 = [5, 5, 5]\n", "\n", "plt.plot(x_values, y_values)\n", "plt.plot(x_values2, y_values2)\n", "plt.plot(x_values3, y_values3)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ποιες τιμές αναπαριστά κάθε γραμμή;\n", "Όταν απεικονίζουμε πολλαπλές τιμές, καλό είναι να συμπεριλαμβάνουμε ένα υπόμνημα.\n", "\n", "Κάθε φορά που καλούμε την `plot()` μπορούμε να προσθέσουμε ένα όρισμα `label=` για να ονομάσουμε τη γραμμή.\n", "Το κείμενο εσωκλείεται σε μονά `'` ή διπλά quotes `\"`.\n", "\n", "Για να εμφανιστεί το υπόμνημα καλούμε την `plt.legend()` πριν την `plt.show()`\n" ] }, { "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.plot(x_values, y_values, label='line 1')\n", "plt.plot(x_values2, y_values2, label=\"line 2\")\n", "plt.plot(x_values3, y_values3, label='line 3')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Δοκίμασε το!\n", "Χρησιμοποίησε το παρακάτω κελί για να αναπαραστήσεις μια δική σου συνάρτηση:\n", "\n", "Μετά, κάνε click στις καρτέλες `Kernel` > `Restart & Run All`" ] }, { "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }