diff --git a/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb new file mode 100644 index 0000000..93529b6 --- /dev/null +++ b/hello_python_fact/HiPy Competition - Registrations for 1st Nov Event FACT.ipynb @@ -0,0 +1,583 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Adam Ruby - HiPy Nov 1st Registration Competition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Firstly, import modules: \n", + "- csv to upload the file. \n", + "- numpy for array manipulation.\n", + "- pandas for dataframe and plot creation.\n", + "\n", + "and allow viewing of the plots." + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import csv\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Module CSV is used to import the registration data as a list" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [], + "source": [ + "with open('registration_data.csv', newline='') as f:\n", + " reg_csv = csv.reader(f, delimiter=',', skipinitialspace=True)\n", + " \n", + " reg_list = [line for line in reg_csv]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inital list includes headings in first row. For just the data the first row is excluded." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[['Stephenson Institute', 'Email'],\n", + " ['Chemistry', 'Email'],\n", + " ['Biology', 'Poster/Flyer'],\n", + " ['Management School', 'Other'],\n", + " ['Other', 'Other']]" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "reg_labels = reg_list[0]\n", + "\n", + "reg_list = reg_list[1:]\n", + "reg_list[:5] #To view first five rows as example" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To provide more information, it would be nice to include the percentage of the HiPy methods attendees were exposed to that lead to the successful registration. \n", + "\n", + "A pandas dataframe is utilised from the reg_list and by using the in-built 'groupby' function the frequency for each successful exposure methods is counted." + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Email (53.2%) Other (19.1%) \\\n", + "Architecture 1.0 0.0 \n", + "Astrophysics 1.0 0.0 \n", + "Biology 12.0 2.0 \n", + "Chemistry 2.0 0.0 \n", + "Computer Science 1.0 0.0 \n", + "EEE 0.0 1.0 \n", + "Earth, Ocean, Ecology 1.0 0.0 \n", + "External 2.0 0.0 \n", + "Global Health 1.0 0.0 \n", + "Management School 0.0 2.0 \n", + "Mathematics 0.0 2.0 \n", + "Other 1.0 2.0 \n", + "Physics 2.0 0.0 \n", + "Stephenson Institute 1.0 0.0 \n", + "\n", + " Poster/Flyer (4.3%) UoL video screen (4.3%) \\\n", + "Architecture 0.0 0.0 \n", + "Astrophysics 0.0 0.0 \n", + "Biology 1.0 1.0 \n", + "Chemistry 0.0 0.0 \n", + "Computer Science 0.0 0.0 \n", + "EEE 0.0 0.0 \n", + "Earth, Ocean, Ecology 1.0 0.0 \n", + "External 0.0 0.0 \n", + "Global Health 0.0 0.0 \n", + "Management School 0.0 0.0 \n", + "Mathematics 0.0 1.0 \n", + "Other 0.0 0.0 \n", + "Physics 0.0 0.0 \n", + "Stephenson Institute 0.0 0.0 \n", + "\n", + " Word of mouth (19.1%) \n", + "Architecture 0.0 \n", + "Astrophysics 0.0 \n", + "Biology 1.0 \n", + "Chemistry 1.0 \n", + "Computer Science 1.0 \n", + "EEE 0.0 \n", + "Earth, Ocean, Ecology 0.0 \n", + "External 1.0 \n", + "Global Health 0.0 \n", + "Management School 1.0 \n", + "Mathematics 2.0 \n", + "Other 2.0 \n", + "Physics 0.0 \n", + "Stephenson Institute 0.0 " + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(pop_list, columns=found_list, index=dep_list)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A horizontal bar chat with stacked colours using Pandas is created and labels/title set." + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RF5fTn/irn4J4zjgIeF1ESmJFcXuwl0KBd4GvReSAvW/cG/hURIrY158Hdtvvi4jIOqwb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\nPAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph = df.plot.barh(stacked=True, title='HiPy Registrations - 1$^{st}$ Nov 2017', width=0.85)\n", + "graph.set_xlabel(\"Number of People\")\n", + "graph.set_ylabel(\"Department\")\n", + "graph.legend(title=\"Exposure Method\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "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": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}