process_typing_progress_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdf0a8a5",
   "metadata": {},
   "source": [
    "## Process typing progress data\n",
    "\n",
    "The reason why we have asked you to keep track of typing data such as WPM and accuracy as part of excel or Google sheet is so that you can use your data itself as use case as part of exercises while learning programming.\n",
    "* You can learn use it for several scenarios. Here are few of them.\n",
    "  * Processing data in CSV and look for insights using Pandas\n",
    "  * Processing data from Excel\n",
    "  * Processing data from Google Sheets using Google API\n",
    "  * Writing data from the file into Database\n",
    "* We would like to give a demo using first scenario to make you understand the power of our platform as well as our program.\n",
    "  * Convert the Excel or Google Sheet as CSV\n",
    "  * Upload the file to our platform.\n",
    "  * Use Pandas which is already setup on our platform to read the data.\n",
    "  * Use Pandas Plotting capabilities to get insights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "961989da",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "351dd2a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "typing_data = pd.read_csv('Typing Speed Tracker.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "689887ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>WPM</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>23</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>25</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-06-17</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>29</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>19</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>26</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>24</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>28</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>27</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  WPM  Accuracy\n",
       "0  2021-06-15   23        90\n",
       "1  2021-06-16   25        91\n",
       "2  2021-06-17   24        95\n",
       "3  2021-06-18   29        91\n",
       "4  2021-06-19   19        98\n",
       "5  2021-06-20   26        89\n",
       "6  2021-06-21   24        91\n",
       "7  2021-06-22   28        92\n",
       "8  2021-06-23   27        93"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09cbfcd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typing_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee704696",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>WPM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-06-17</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  WPM\n",
       "0  2021-06-15   23\n",
       "1  2021-06-16   25\n",
       "2  2021-06-17   24\n",
       "3  2021-06-18   29\n",
       "4  2021-06-19   19\n",
       "5  2021-06-20   26\n",
       "6  2021-06-21   24\n",
       "7  2021-06-22   28\n",
       "8  2021-06-23   27"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typing_data[['Date', 'WPM']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "651e991b",
   "metadata": {},
   "source": [
    "* You can just pass the Date on x-axis and WPM on y-axix. By default it uses the range of values as limits and you can see how the graph looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fec7c1f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "typing_data.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "522c782d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "typing_data.plot('Date', 'WPM')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a932c5",
   "metadata": {},
   "source": [
    "* Here we are passing Date on x-axis, both the typing metrics on y-axis.\n",
    "* Also we are defining range of values on y-axis using `ylim`. `ylim` stands for limits on y-axis.\n",
    "* To make sure dates as part of x-axis labels, we use `rot`. `rot` stands for rotation and we are rotating by 75 degrees.\n",
    "* `plot` take several arguments, first 2 arguments are related to x-axis and y-axis. Rest, we can pass using keywords (example `ylim` and `rot`) depending up on our requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ad92bac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "typing_data.plot('Date', ['WPM', 'Accuracy'], ylim=(0, 150), rot=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7798383e",
   "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}