{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.lines import Line2D\n", "from datetime import timedelta\n", "from datetime import date\n", "import math\n", "# get the number of days between min and max dates\n", "# then calculate the number of periods by dividing the number of \n", "# days by the number of datapoints and finally calculate \n", "# new dates with equal distance between them\n", "\n", "def get_new_dates(df):\n", " dates = []\n", " for study_type in df.Type.unique():\n", " days_between = (max(df[df.Type==study_type]['Date']) - min(df[df.Type==study_type]['Date'])).days\n", " period = math.ceil(days_between/(len(df[df.Type==study_type])-1))\n", " list_dates = [min(df[df.Type==study_type]['Date'])] \n", " for i in range(len(df[df.Type==study_type])-1):\n", " list_dates.append(list_dates[-1] + timedelta(days=period))\n", " dates.extend(list_dates)\n", " return dates\n", "\n", "def credits_cumsum(df):\n", " cumsum = []\n", " for study_type in df.Type.unique():\n", " local_cumsum = df[df.Type==study_type]['Credits'].cumsum().values\n", " cumsum.extend(local_cumsum)\n", " return cumsum\n", "\n", "def moving_avg_grade(df, min_periods=1):\n", " new_average = []\n", " for study_type in df.Type.unique():\n", " local_average = df[df.Type==study_type]['Grade'].expanding(min_periods=min_periods).mean().values\n", " new_average.extend(local_average)\n", " return new_average\n", "\n", "def moving_avg_avg(df, min_periods=1):\n", " new_average = []\n", " for study_type in df.Type.unique():\n", " local_average = df[df.Type==study_type]['Average'].expanding(min_periods=min_periods).mean().values\n", " new_average.extend(local_average)\n", " return new_average\n", "\n", "def print_general_stats(df):\n", " print('European Credits:\\n')\n", " print(\"Psychology Bachelor: \\t\\t{}\".format(sum(df[df.Type=='BA'].Credits)))\n", " print(\"I/O Psychology Master: \\t\\t{}\".format(sum(df[df.Type=='IO'].Credits)))\n", " print(\"Clinical Psychology Premaster:\\t{}\".format(sum(df[df.Type=='PM-CL'].Credits)))\n", " print(\"Clinical Psychology Master: \\t{}\".format(sum(df[df.Type=='CL'].Credits)))\n", " print(\"Data Science Premaster: \\t{}\".format(sum(df[df.Type=='PM-DS'].Credits)))\n", " print(\"Data Science Master: \\t\\t{}\".format(sum(df[df.Type=='DS'].Credits)))\n", " print(\"Total number of credits: \\t{}\".format(sum(df.Credits)))\n", " print('Unweighted average:\\n')\n", " print(\"Psychology Bachelor: \\t\\t{}\".format(round(np.mean(df[(df.Type=='BA') & (df.Grade != 'P')].Grade), 2)))\n", " print(\"I/O Psychology Master: \\t\\t{}\".format(round(np.mean(df[df.Type=='IO'].Grade), 2)))\n", " print(\"Clinical Psychology Premaster:\\t{}\".format(round(np.mean(df[df.Type=='PM-CL'].Grade), 2)))\n", " print(\"Clinical Psychology Master: \\t{}\".format(round(np.mean(df[df.Type=='CL'].Grade), 2)))\n", " print(\"Data Science Premaster: \\t{}\".format(round(np.mean(df[df.Type=='PM-DS'].Grade), 2)))\n", " print(\"Data Science Master: \\t\\t{}\".format(round(np.mean(df[df.Type=='DS'].Grade), 2)))\n", " print(\"Total number of credits (including bachelor): \\t{}\".format(round(np.mean(df[(df.Grade != 'P')].Grade), 2)))\n", " print(\"Total number of credits (excluding bachelor): \\t{}\".format(round(np.mean(df[(df.Grade != 'P') &\n", " (df.Type != 'BA')].Grade), 2)))\n", " \n", "def plot_histogram(df):\n", " df = df.sort_values('Date')\n", " to_plot = df.loc[df.Grade!='P', :].copy()\n", " to_plot['new_date'] = get_new_dates(to_plot)\n", " to_plot['mov_grade'] = moving_avg_grade(to_plot)\n", " to_plot['mov_avg'] = moving_avg_avg(to_plot)\n", "\n", " fig, ax = plt.subplots()\n", " colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] * 10\n", "\n", " for i, study_type in enumerate(df.Type.unique()):\n", " plt.bar(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].new_date.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Grade.values.astype(float), 40,\n", " color=colors[i], label=study_type)\n", "\n", " plt.ylim(ymin=5.8, ymax=10.1)\n", " fig.set_size_inches(15, 7)\n", " plt.legend()\n", " plt.show()\n", " # plt.savefig('test.png', dpi=300)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_excel('grades_date_inaccurate.xlsx')\n", "df.Date = pd.to_datetime(df.Date)\n", "df.Course = df.apply(lambda row: row.Course.split('(')[0], 1)\n", "df = df.sort_values('Date')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### General Stats" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "European Credits:\n", "\n", "Psychology Bachelor: \t\t180\n", "I/O Psychology Master: \t\t60\n", "Clinical Psychology Premaster:\t36\n", "Clinical Psychology Master: \t60\n", "Data Science Premaster: \t30\n", "Data Science Master: \t\t78\n", "Total number of credits: \t444\n", "Unweighted average:\n", "\n", "Psychology Bachelor: \t\t6.91\n", "I/O Psychology Master: \t\t8.29\n", "Clinical Psychology Premaster:\t8.08\n", "Clinical Psychology Master: \t8.5\n", "Data Science Premaster: \t9.2\n", "Data Science Master: \t\t8.38\n", "Total number of credits (including bachelor): \t7.77\n", "Total number of credits (excluding bachelor): \t8.45\n" ] } ], "source": [ "print_general_stats(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Largest difference" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Largest difference:\n", " Course Date Grade Credits Type Average difference\n", "37 Clinical Psychology 2015-10-01 9 6 CL 6.5 2.5\n", "Smallest difference:\n", " Course Date Grade Credits Type Average difference\n", "38 Work Group Psychology 2013-12-16 8.5 6 IO 8.28 0.22\n" ] } ], "source": [ "df['difference'] = df.apply(lambda row: row.Grade-row.Average if type(row.Grade)!=str else None, 1)\n", "print('Largest difference:')\n", "print(df[df.difference == df.difference.max()])\n", "\n", "print('Smallest difference:')\n", "print(df[df.difference == df.difference.min()])\n", "\n", "to_plot_diff = pd.DataFrame(columns=[\"Course\", \"Grade\", \"Average\", \"Difference\"])\n", "small = list(df[df.difference == df.difference.min()][['Course', 'Grade', 'Average']].values[0])\n", "big = list(df[df.difference == df.difference.max()][['Course', 'Grade', 'Average']].values[0])\n", "to_plot_diff.loc[len(to_plot_diff)] = small + ['Small']\n", "to_plot_diff.loc[len(to_plot_diff)] = big + ['Big']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", " \n", "# data to plot\n", "n_groups = 2\n", "grades = to_plot_diff.Grade.values\n", "average = to_plot_diff.Average.values\n", " \n", "# create plot\n", "fig, ax = plt.subplots()\n", "index = np.arange(n_groups)\n", "bar_width = 0.35\n", "opacity = 1\n", " \n", "rects1 = plt.barh(index, grades, bar_width,alpha=opacity,color='#1976d2',\n", " label='My Grade',edgecolor = \"black\", linewidth=4)\n", "rects2 = plt.barh(index + bar_width, average, bar_width,alpha=opacity,color='#00bcd4',\n", " label=\"Students' Average\",edgecolor = \"black\", linewidth=4)\n", "\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['bottom'].set_visible(False)\n", "ax.spines['left'].set_visible(False)\n", "ax.tick_params(axis=u'both', which=u'both',length=0)\n", "plt.xticks([])\n", "\n", "for i, v in enumerate(grades):\n", " ax.text(v-1.5, i-0.08, str(v), color='white', fontweight='bold', fontsize=22)\n", "for i, v in enumerate(average):\n", " ax.text(v-1.5, i+0.28, str(round(v, 1)), color='white', fontweight='bold', fontsize=22)\n", "\n", "# plt.title('Smallest and largest difference between \\n') # my grade and the average\n", "# plt.text(0.3,9.65, 'my grade', fontsize=12,color='#1976d2')\n", "# plt.text(0.6,9.65, 'and', fontsize=12)\n", "# plt.text(0.74,9.65, 'the average', fontsize=12,color='#00bcd4')\n", "plt.yticks(index + bar_width/2, to_plot_diff.Course.values)\n", "ax.set_yticklabels(['Clinical\\nPsychology', 'Work Group\\nPsychology'])\n", "# plt.legend()\\\n", "plt.tight_layout()\n", "# plt.savefig('bar_difference.png',dpi=600, transparent=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualization" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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AANBRAh0AAEBHCXQAAAAdJdABAAB0lEAHAADQUQIdAABAR/UV6Kpqj6r6XFVd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\noKMEOgAAgI4S6AAAADrq/wcmOirtF4NlawAAAABJRU5ErkJggg==\n", 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CmhDizCQBTQghhBBNaneF4kBl7YEqLlCjZ8RvlyMWk8ZlCWY6hni/RCk+FNLK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\nGSi3BXa7nYcf+zPfb9yG39AJ+FzSy/PdtW8Dtq3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jQo8gha4BKpYLYLLrMWPGEPTyv8hZ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\nz2utnR28m+IkE8zzkHqXAbHAy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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = df.sort_values('Date')\n", "to_plot = df.loc[df.Grade!='P', :].copy()\n", "to_plot['new_date'] = get_new_dates(to_plot)\n", "to_plot['mov_grade'] = moving_avg_grade(to_plot, min_periods=2)\n", "to_plot['mov_avg'] = moving_avg_avg(to_plot, min_periods=2)\n", "to_plot['cumsum_credits'] = credits_cumsum(to_plot)\n", "\n", "fig, ax = plt.subplots(figsize=(15, 5))\n", "colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] * 10\n", "colors = ['#f44336','#607d8b','#4caf50','#00C4B4','#ffc107','#9c27b0']\n", "colors = ['#03a9f4', '#29b6f6', '#4fc3f7', '#81d4fa', '#b3e5fc', '#e1f5fe']\n", "colors.reverse()\n", "colors = ['#29b6f6', '#29b6f6', '#29b6f6', '#29b6f6', '#29b6f6', '#29b6f6']\n", "\n", "for i, study_type in enumerate(to_plot.Type.unique()):\n", " plt.scatter(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].new_date.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Grade.values.astype(float), \n", " color=colors[i], alpha=.85, label=study_type, linewidth=1, edgecolor='black',\n", " s=to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Credits.values*20)\n", " plt.plot(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].new_date.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].mov_grade.values.astype(float), \n", " color=colors[i], alpha=0.5, label=study_type, linewidth=6)\n", " plt.plot(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].new_date.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].mov_avg.values.astype(float), \n", " '--', color=colors[i], alpha=0.5, label=study_type, linewidth=4)\n", " \n", "plt.ylim(ymin=5.8, ymax=10.1)\n", "\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "\n", "# # Add names of education\n", "# plt.text(date(2012,1,1), 10.2, 'Bachelor\\nPsychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2014,1,18), 9.2, 'Master\\n IO Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2015,1,25), 8.8, 'Pre-master\\nClinical Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2016,2,10), 9.8, 'Master\\nClinical Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2017,3,10), 10.2, 'Pre-master\\nData Science', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2018,6,1), 9.2, 'Master\\nData Science', fontsize=12, color='black',\n", "# horizontalalignment='center', fontweight='bold')\n", "\n", "# Add averages of my own grades\n", "plt.text(date(2013,8,30), 6.67, \n", " str(round(to_plot.loc[to_plot.Type=='BA', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#f44336', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2014,8,25), 8, \n", " str(round(to_plot.loc[to_plot.Type=='IO', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#607d8b', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2015,8,20), 7.78, \n", " str(round(to_plot.loc[to_plot.Type=='PM-CL', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#4caf50', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2016,9,20), 8.2, \n", " str(round(to_plot.loc[to_plot.Type=='CL', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#00C4B4', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2017,9,10), 8.95, \n", " str(round(to_plot.loc[to_plot.Type=='PM-DS', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#ffc107', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2019,3,15), 8.1, \n", " str(round(to_plot.loc[to_plot.Type=='DS', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#9c27b0', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "\n", "# add averages of average\n", "plt.text(date(2014,8,1), 7.1, \n", " str(round(to_plot.loc[to_plot.Type=='IO', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#607d8b', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2015,8,15), 6.78, \n", " str(round(to_plot.loc[to_plot.Type=='PM-CL', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#4caf50', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2016,8,15), 6.9, \n", " str(round(to_plot.loc[to_plot.Type=='CL', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#00C4B4', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2017,8,15), 7.6, \n", " str(round(to_plot.loc[to_plot.Type=='PM-DS', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#ffc107', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2019,2,5), 6.85, \n", " str(round(to_plot.loc[to_plot.Type=='DS', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=10, color='#9c27b0', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "\n", "# color axes\n", "# ax.spines['bottom'].set_color('#dddddd')\n", "# ax.spines['left'].set_color('#dddddd')\n", "ax.tick_params(axis='x', colors='black')\n", "ax.tick_params(axis=u'both', which=u'both',length=0)\n", "ax.set_xticks([date(2011+(i),1,1) for i in np.arange(0, 10, 2)])\n", "ax.set_yticks(np.arange(6, 11, 1))\n", "\n", "# Create custom legend\n", "cmap = plt.cm.coolwarm\n", "\n", "custom_lines = [Line2D([0], [0], color='black', markersize=12,marker='o',markerfacecolor='#dddddd',\n", " markeredgewidth=1.5),\n", " Line2D([0], [0], color='#dddddd', lw=4),\n", " Line2D([0], [0], color='#dddddd', lw=6,linestyle='--')]\n", "# ax.legend(custom_lines, ['My Grade', 'My Average', \"Students' Average\"],\n", "# fontsize=16)|\n", "\n", "plt.tick_params(axis='both', which='major', labelsize=14)\n", "plt.tick_params(axis='both', which='minor', labelsize=14)\n", "plt.show()\n", "# plt.savefig('grades_viz_sizes_v2.png', dpi=600, transparent=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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CiCuOJGhCCCGEiCqPIxe/x15nO6+riIqi71GDgShEJYQQjYMkaEIIIYSImoDP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\nhl5JVIyT0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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = df.sort_values('Date')\n", "to_plot = df.loc[df.Grade!='P', :].copy()\n", "to_plot['new_date'] = get_new_dates(to_plot)\n", "to_plot['mov_grade'] = moving_avg_grade(to_plot, min_periods=2)\n", "to_plot['mov_avg'] = moving_avg_avg(to_plot, min_periods=2)\n", "to_plot['cumsum_credits'] = credits_cumsum(to_plot)\n", "# df.loc[:, 'Date'] = df['Date'].dt.strftime(\"%Y%m%d\").astype(int)\n", "\n", "fig, ax = plt.subplots(figsize=(15, 5))\n", "colors = ['#63ace5', '#83d0c9', '#d0e1f9', '#e6d4a2', '#93a8b0', '#dacdbe']\n", "# colors = ['#dacdbe', '#63ace5', '#d0e1f9', '#e6d4a2', '#93a8b0', '#83d0c9']\n", "\n", "\n", "# colors = ['#63ace5', '#83d0c9', '#e6d4a2', '#d0e1f9', '#93a8b0', '#dacdbe']\n", "\n", "# colors = ['#63ace5', '#83d0c9', '#e6d4a2', '#d0e1f9', '#f0908a', '#dacdbe']\n", "\n", "\n", "#4a707a\n", "\n", "for i, study_type in enumerate(to_plot.Type.unique()):\n", " plt.scatter(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].new_date.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Grade.values.astype(float), \n", " color=colors[i], alpha=.9, label=study_type, linewidth=1, edgecolor='black',\n", " s=to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Credits.values*20)\n", " plt.plot(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].new_date.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].mov_grade.values.astype(float), \n", " color=colors[i], alpha=0.7, label=study_type, linewidth=6, zorder=0)\n", " plt.plot(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].new_date.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].mov_avg.values.astype(float), \n", " '--', color=colors[i], alpha=0.7, label=study_type, linewidth=4, zorder=0)\n", " \n", "plt.ylim(ymin=5.8, ymax=10.1)\n", "\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "\n", "# # Add names of education\n", "# plt.text(date(2012,1,1), 10.2, 'Bachelor\\nPsychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2014,1,18), 9.2, 'Master\\n IO Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2015,1,25), 8.8, 'Pre-master\\nClinical Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2016,2,10), 9.8, 'Master\\nClinical Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2017,3,10), 10.2, 'Pre-master\\nData Science', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2018,6,1), 9.2, 'Master\\nData Science', fontsize=12, color='black',\n", "# horizontalalignment='center', fontweight='bold')\n", "\n", "# Add averages of my own grades\n", "plt.text(date(2013,8,30), 6.67, \n", " str(round(to_plot.loc[to_plot.Type=='BA', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2014,8,25), 8, \n", " str(round(to_plot.loc[to_plot.Type=='IO', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2015,8,20), 7.78, \n", " str(round(to_plot.loc[to_plot.Type=='PM-CL', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2016,9,20), 8.2, \n", " str(round(to_plot.loc[to_plot.Type=='CL', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2017,9,10), 8.95, \n", " str(round(to_plot.loc[to_plot.Type=='PM-DS', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2019,3,15), 8.1, \n", " str(round(to_plot.loc[to_plot.Type=='DS', 'Grade'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "\n", "# add averages of average\n", "plt.text(date(2014,8,1), 7.1, \n", " str(round(to_plot.loc[to_plot.Type=='IO', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2015,8,15), 6.78, \n", " str(round(to_plot.loc[to_plot.Type=='PM-CL', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2016,8,15), 6.9, \n", " str(round(to_plot.loc[to_plot.Type=='CL', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2017,8,15), 7.6, \n", " str(round(to_plot.loc[to_plot.Type=='PM-DS', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "plt.text(date(2019,2,5), 6.85, \n", " str(round(to_plot.loc[to_plot.Type=='DS', 'Average'].mean(), 1))+'\\navg', \n", " fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "\n", "# color axes\n", "# ax.spines['bottom'].set_color('#dddddd')\n", "# ax.spines['left'].set_color('#dddddd')\n", "ax.tick_params(axis='x', colors='black')\n", "ax.tick_params(axis=u'both', which=u'both',length=0)\n", "ax.set_xticks([date(2011+(i),1,1) for i in np.arange(0, 10, 2)])\n", "ax.set_yticks(np.arange(6, 11, 1))\n", "\n", "# Create custom legend\n", "cmap = plt.cm.coolwarm\n", "\n", "custom_lines = [Line2D([0], [0], color='black', markersize=12,marker='o',markerfacecolor='#dddddd',\n", " markeredgewidth=1.5),\n", " Line2D([0], [0], color='#dddddd', lw=4),\n", " Line2D([0], [0], color='#dddddd', lw=6,linestyle='--')]\n", "# ax.legend(custom_lines, ['My Grade', 'My Average', \"Students' Average\"],\n", "# fontsize=16)|\n", "\n", "plt.tick_params(axis='both', which='major', labelsize=14)\n", "plt.tick_params(axis='both', which='minor', labelsize=14)\n", "plt.show()\n", "# plt.savefig('grades_viz_sizes_temp.png', dpi=600, transparent=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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Y3nMz3J5Ca2Sr7Fuqvn8FBPpG9yDaXxgipAX3W8Vfc/gMGlELqRacAcC1hI5P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\nAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n", "from matplotlib.cbook import get_sample_data\n", "def imscatter(x, y, image, ax=None, zoom=1):\n", " if ax is None:\n", " ax = plt.gca()\n", " try:\n", " image = plt.imread(image)\n", " except TypeError:\n", " # Likely already an array...\n", " pass\n", " im = OffsetImage(image, zoom=zoom)\n", " x, y = np.atleast_1d(x, y)\n", " artists = []\n", " for x0, y0 in zip(x, y):\n", " ab = AnnotationBbox(im, (x0, y0), xycoords='data', frameon=False)\n", " artists.append(ax.add_artist(ab))\n", " ax.update_datalim(np.column_stack([x, y]))\n", " ax.autoscale()\n", " return artists\n", "\n", "df = df.sort_values('Date')\n", "to_plot = df.loc[df.Grade!='P', :].copy()\n", "to_plot['new_date'] = get_new_dates(to_plot)\n", "to_plot['mov_grade'] = moving_avg_grade(to_plot, min_periods=1)\n", "to_plot['mov_avg'] = moving_avg_avg(to_plot, min_periods=1)\n", "to_plot['cumsum_credits'] = credits_cumsum(to_plot)\n", "to_plot['date_int'] = to_plot[['new_date']].diff()\n", "to_plot['date_int'] = to_plot[['date_int']].dropna().apply(lambda row: \n", " row.date_int.days, 1)\n", "to_plot['date_int'] = to_plot.date_int.fillna(0).values\n", "to_plot['date_int'] = to_plot['date_int'].cumsum()\n", "\n", "fig, ax = plt.subplots(figsize=(15, 5))\n", "colors = ['#63ace5', '#83d0c9', '#d0e1f9', '#e6d4a2', '#93a8b0', '#dacdbe']\n", "# colors = ['#dacdbe', '#63ace5', '#d0e1f9', '#e6d4a2', '#93a8b0', '#83d0c9']\n", "# colors = ['#63ace5', '#83d0c9', '#e6d4a2', '#d0e1f9', '#93a8b0', '#dacdbe']\n", "# colors = ['#63ace5', '#83d0c9', '#e6d4a2', '#d0e1f9', '#f0908a', '#dacdbe']\n", "\n", "\n", "for i, study_type in enumerate(to_plot.Type.unique()):\n", " plt.scatter(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].date_int.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Grade.values.astype(float), \n", " color=colors[i], alpha=.9, label=study_type, linewidth=0, edgecolor='black',\n", " s=to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Credits.values*20)\n", " plt.plot(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].date_int.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].mov_grade.values.astype(float), \n", " color=colors[i], alpha=0.7, label=study_type, linewidth=6, zorder=0)\n", " plt.plot(to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].date_int.values, \n", " to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].mov_avg.values.astype(float), \n", " '--', color=colors[i], alpha=1, label=study_type, linewidth=4, zorder=0)\n", " x = to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].date_int.values\n", " y = to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Grade.values.astype(float)\n", " sizes = to_plot[(to_plot.Grade!='P')&(to_plot.Type==study_type)].Credits.values\n", " size_dict = {2:0.01, 5:0.011, 6:0.015, 12:0.02, 18:0.025, 24:0.03}\n", " for x_i, y_i, size in zip(x, y, sizes):\n", " imscatter(x_i, y_i, 'circle_3.png', zoom=size_dict[size], ax=ax)\n", "\n", "\n", "\n", " \n", "plt.ylim(ymin=5.8, ymax=10.1)\n", "\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "\n", "# # Add names of education\n", "# plt.text(date(2012,1,1), 10.2, 'Bachelor\\nPsychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2014,1,18), 9.2, 'Master\\n IO Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2015,1,25), 8.8, 'Pre-master\\nClinical Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2016,2,10), 9.8, 'Master\\nClinical Psychology', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2017,3,10), 10.2, 'Pre-master\\nData Science', fontsize=12, color='black', \n", "# horizontalalignment='center', fontweight='bold')\n", "# plt.text(date(2018,6,1), 9.2, 'Master\\nData Science', fontsize=12, color='black',\n", "# horizontalalignment='center', fontweight='bold')\n", "\n", "# # Add averages of my own grades\n", "# plt.text(date(2013,8,30), 6.67, \n", "# str(round(to_plot.loc[to_plot.Type=='BA', 'Grade'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2014,8,25), 8, \n", "# str(round(to_plot.loc[to_plot.Type=='IO', 'Grade'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2015,8,20), 7.78, \n", "# str(round(to_plot.loc[to_plot.Type=='PM-CL', 'Grade'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2016,9,20), 8.2, \n", "# str(round(to_plot.loc[to_plot.Type=='CL', 'Grade'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2017,9,10), 8.95, \n", "# str(round(to_plot.loc[to_plot.Type=='PM-DS', 'Grade'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2019,3,15), 8.1, \n", "# str(round(to_plot.loc[to_plot.Type=='DS', 'Grade'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "\n", "# # add averages of average\n", "# plt.text(date(2014,8,1), 7.1, \n", "# str(round(to_plot.loc[to_plot.Type=='IO', 'Average'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2015,8,15), 6.78, \n", "# str(round(to_plot.loc[to_plot.Type=='PM-CL', 'Average'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2016,8,15), 6.9, \n", "# str(round(to_plot.loc[to_plot.Type=='CL', 'Average'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2017,8,15), 7.6, \n", "# str(round(to_plot.loc[to_plot.Type=='PM-DS', 'Average'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "# plt.text(date(2019,2,5), 6.85, \n", "# str(round(to_plot.loc[to_plot.Type=='DS', 'Average'].mean(), 1))+'\\navg', \n", "# fontsize=8, color='grey', horizontalalignment='center', alpha=0.8, fontweight='bold')\n", "\n", "# color axes\n", "# ax.spines['bottom'].set_color('#dddddd')\n", "# ax.spines['left'].set_color('#dddddd')\n", "ax.tick_params(axis='x', colors='black')\n", "ax.tick_params(axis=u'both', which=u'both',length=0)\n", "# ax.set_xticks([date(2011+(i),1,1) for i in np.arange(0, 10, 2)])\n", "ax.set_yticks(np.arange(6, 11, 1))\n", "\n", "# Create custom legend\n", "cmap = plt.cm.coolwarm\n", "\n", "custom_lines = [Line2D([0], [0], color='black', markersize=12,marker='o',markerfacecolor='#dddddd',\n", " markeredgewidth=1.5),\n", " Line2D([0], [0], color='#dddddd', lw=4),\n", " Line2D([0], [0], color='#dddddd', lw=6,linestyle='--')]\n", "# ax.legend(custom_lines, ['My Grade', 'My Average', \"Students' Average\"],\n", "# fontsize=16)|\n", "\n", "plt.tick_params(axis='both', which='major', labelsize=14)\n", "plt.tick_params(axis='both', which='minor', labelsize=14)\n", "plt.show()\n", "# plt.savefig('circles_plot_full.png', dpi=600, transparent=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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LzXzNiLBWnakITmWtay0RiRH069cv0FFEkPB4PCxevJjvFn+H3WJn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\nJcNpefo5hISGcWDzKn5b8DHJdUP572svExkZ6ZPxxLv09HRGjB5Dbt0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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.dates as mdates\n", "fig, ax = plt.subplots()\n", "colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] * 10\n", "colors = ['#56b4e9', 'purple', 'red', 'green', 'yellow', 'orange']\n", "\n", "\n", "for i, study_type in enumerate(df.Type.unique()):\n", " plt.scatter(df[(df.Grade!='P')&(df.Type==study_type)].Date.values, \n", " df[(df.Grade!='P')&(df.Type==study_type)].Grade.values.astype(float), \n", " color=colors[i], alpha=0.8, label=study_type, linewidth=1, \n", " s=df[(df.Grade!='P')&(df.Type==study_type)].Credits.values**2.5, edgecolor='black')\n", "\n", "plt.ylim(ymin=5.8, ymax=10.1)\n", "fig.set_size_inches(15, 5)\n", "# plt.legend()\n", "\n", "# # # Set number of xticks\n", "ax.xaxis.set_major_locator(mdates.DayLocator(interval=300)) #to get a tick every 60 days\n", "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n", "\n", "plt.show()\n", "# plt.savefig('test.png', dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Compared to all other students **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "15% of students achieved the same or a higher result." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.ticker import MaxNLocator\n", "from collections import namedtuple\n", "\n", "\n", "avg_df = avg_df.sort_values('Avg', ascending=False)\n", "\n", "# to_plot = avg_df.loc[avg_df.Education=='MS-DS']\n", "to_plot = avg_df\n", "\n", "n_groups = len(to_plot)\n", "\n", "my_scores = to_plot.My.values\n", "avg_scores = to_plot.fillna(0).Avg.values\n", "\n", "\n", "fig, ax = plt.subplots(figsize=(10, 10))\n", "\n", "index = np.arange(n_groups)\n", "bar_width = 0.4\n", "opacity = .8\n", "\n", "rects1 = ax.barh(index, my_scores, bar_width,\n", " alpha=opacity, color='#003f5c',\n", " label='My Grade')\n", "\n", "rects2 = ax.barh(index + bar_width, avg_scores, bar_width,\n", " alpha=opacity, color='#ffa600',\n", " label='The Average Grade')\n", "\n", "\n", "ax.set_title('My grades during three different masters versus the average', fontsize=14, fontweight='bold')\n", "plt.text(3.5,25.5,'[DS] = Data Science \\n[CP] = Clinical Psychology \\n[IOP] = I/O Psychology')\n", "ax.set_yticks(index + bar_width / 2)\n", "ax.set_yticklabels(to_plot.Course.values)\n", "ax.legend(loc='upper left',frameon=False)\n", "\n", "for i, v in enumerate(my_scores):\n", " ax.text(v-0.5, i, str(v), color='white', fontweight='bold', va='center', fontsize=10)\n", "\n", "for i, v in enumerate(avg_scores):\n", " if v == 0:\n", " ax.text(v + .05, i+0.4, str(v), color='white', fontweight='bold', va='center')\n", " else:\n", " ax.text(v - .6, i+0.4, str(v), color='black', fontweight='bold', va='center')\n", "\n", "ax.spines['top'].set_visible(False)\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['bottom'].set_visible(False)\n", "ax.spines['left'].set_visible(False)\n", "ax.get_xaxis().set_ticks([])\n", "\n", "fig.tight_layout()\n", "plt.show()\n", "# plt.savefig('scores.png', dpi=300)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "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": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 }