First Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Python: Parkrun Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Python Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "\n",
    "# Register plot converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Parkrun Data and Clean It"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Parkrun csv data\n",
    "data = pd.read_csv(\"ParkrunData.csv\")\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data['Time'] = pd.to_datetime(data['Time'], format='%H:%M:%S').dt.time\n",
    "\n",
    "# Select my runs only\n",
    "dai = data.loc[data['Parkrunner'] == 'David RENSHAW']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise My Running Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.plot(dai['Date'], dai['Time'], 'bo')\n",
    "plt.title(\"My Parkrun Progress\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Time\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}