5. Advanced Excel Spreadsheets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Transforming Excel Analysis into pandas Data Models</h1>\n",
    "<h1>Advanced Excel Spreadsheets</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import openpyxl\n",
    "from openpyxl.drawing.image import Image\n",
    "from openpyxl.chart import ScatterChart, Reference, Series\n",
    "from openpyxl.utils.dataframe import dataframe_to_rows\n",
    "from openpyxl.styles import Font\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 21.6.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 8cd8c4969f6d4b5f0dbf5d55746efe085989b1e3\n",
      "\n",
      "pandas    : 1.1.3\n",
      "watermark : 2.1.0\n",
      "sklearn   : 0.0\n",
      "numpy     : 1.19.2\n",
      "openpyxl  : 3.0.5\n",
      "matplotlib: 3.3.2\n",
      "json      : 2.0.9\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "book = openpyxl.Workbook()\n",
    "sheet = book.active\n",
    "sheet.title = 'Formulas'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm that the sheet is empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A1:A1'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet.dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet['B1'] = 'x'\n",
    "sheet['C1'] = 'y'\n",
    "\n",
    "rows = np.arange(20).reshape(10, 2)\n",
    "\n",
    "for row in rows:\n",
    "    sheet.append(tuple([\"\", row[0], row[1]])) # Rows have to be lists or tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A1:C11'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet.dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to a simple formula for the totals at the bottom of the values we just added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet['B12'] = \"=SUM(B2:B11)\"\n",
    "sheet['C12'] = \"=SUM(C2:C11)\"\n",
    "sheet['A12'] = \"Totals:\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And finally we can save our new workbooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "book.save('data/Advanced.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open data/Advanced.xlsx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'=SUM(B2:B11)'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet['B12'].value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the function names that openpyxl knowns about"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frozenset({'NORMDIST', 'GETPIVOTDATA', 'NOT', 'INDEX', 'CUBESETCOUNT', 'ACCRINT', 'FREQUENCY', 'REPT', 'IMLOG2', 'IMDIV', 'MAXA', 'IMCONJUGATE', 'VARPA', 'SMALL', 'MATCH', 'ERF', 'ISERR', 'DATE', 'SIGN', 'IMAGINARY', 'VDB', 'COUPDAYSNC', 'TIME', 'HYPERLINK', 'QUOTIENT', 'COUNT', 'YIELD', 'TYPE', 'OR', 'VLOOKUP', 'MIDB', 'LOGNORMDIST', 'NETWORKDAYS.INTL', 'YEAR', 'EFFECT', 'COUNTIF', 'BETAINV', 'MINUTE', 'TRIM', 'BINOMDIST', 'REPLACEB', 'LOWER', 'AVERAGEIFS', 'GAMMAINV', 'WEEKDAY', 'XIRR', 'ISEVEN', 'COMBIN', 'TANH', 'STDEV STDEVA', 'DVARP', 'RIGHT', 'TODAY', 'SLN', 'PROPER', 'TRUE ADDRESS', 'BESSELK', 'PROB', 'CUBEMEMBER', 'BIN2OCT', 'CUBEVALUE', 'DPRODUCT', 'QUARTILE', 'EOMONTH', 'IMLN', 'ROUNDUP', 'PERMUT', 'OCT2BIN', 'NPV', 'RIGHTB', 'WEIBULL', 'SQRT', 'DMIN', 'IMEXP', 'HEX2DEC', 'WORKDAY ', 'T', 'ASINH', 'INTERCEPT', 'MINVERSE', 'POISSON', 'AVEDEV', 'TDIST', 'SQRTPI', 'ISNA', 'ACOS', 'UPPER', 'WEEKNUM', 'TINV', 'LOGEST', 'BESSELJ', 'SUMX2PY2', 'ISERROR', 'NA', 'HLOOKUP', 'ATAN', 'EVEN', 'CRITBINOM', 'TBILLEQ', 'ROW', 'TRIMMEAN', 'PI', 'ECMA.CEILING', 'RATE', 'PHONETIC', 'ODD', 'PPMT', 'DVAR', 'YIELDMAT', 'IFERROR', 'COUNTIFS', 'TBILLPRICE', 'DDB', 'COUPNCD', 'ODDFPRICE', 'COUNTBLANK', 'LINEST', 'SIN', 'BETADIST', 'CONVERT', 'MODE', 'ISNUMBER', 'IMCOS', 'IF', 'IMLOG10', 'ATANH', 'ISODD', 'SECOND', 'LEN', 'MIRR', 'EXACT', 'BESSELI', 'ERROR.TYPE', 'SKEW', 'AMORDEGRC', 'DSTDEV', 'IMPRODUCT', 'IMSQRT', 'VARA', 'ROWS', 'COLUMNS', 'FISHER', 'RSQ', 'ISO.CEILING', 'FINV', 'DAYS360', 'BIN2DEC', 'ACCRINTM', 'PRICEDISC', 'OCT2HEX', 'MAX', 'ASIN', 'DSTDEVP', 'GAMMADIST', 'MOD', 'NPER', 'PERCENTILE', 'COUPDAYBS', 'CUMPRINC', 'RTD', 'FALSE', 'ACOSH', 'CUBERANKEDMEMBER', 'CHIINV', 'LEFTB', 'GEOMEAN', 'LCM', 'FINDB', 'DURATION', 'WORKDAY.INTL', 'CORREL', 'COS', 'SUMXMY2', 'IPMT', 'TRANSPOSE', 'RANDBETWEEN', 'INDIRECT', 'PV', 'LOG', 'ZTEST', 'TEXT', 'COUPDAYS', 'SERIESSUM', 'STANDARDIZE', 'CONFIDENCE', 'CUBESET', 'DISC', 'OFFSET', 'CONCATENATE', 'BAHTTEXT', 'DOLLARDE', 'CHAR', 'GCD', 'SEARCHB', 'EXP', 'NOW', 'SUBTOTAL', 'YEARFRAC', 'DSUM', 'CUBEKPIMEMBER', 'DOLLAR', 'COVAR', 'POWER', 'RADIANS', 'SUMSQ', 'TRUNC', 'AVERAGE', 'DCOUNTA', 'EXPONDIST', 'FORECAST', 'FTEST', 'MULTINOMIAL', 'NORMSINV', 'REPLACE', 'MID', 'DEGREES', 'COUPNUM', 'MIN', 'AND', 'VAR', 'N', 'BIN2HEX', 'DEVSQ', 'SUMIF', 'FIXED', 'TIMEVALUE', 'DOLLARFR', 'IMSIN', 'IMPOWER', 'CUMIPMT', 'MDETERM', 'DAY', 'IMABS', 'NORMSDIST', 'YIELDDISC', 'INTRATE', 'LOG10', 'SUBSTITUTE', 'SUMIFS', 'DEC2BIN', 'COSH', 'TREND', 'FACT', 'MONTH', 'LEFT', 'NOMINAL', 'OCT2DEC', 'PRICEMAT', 'FISHERINV', 'CELL', 'PRICE', 'LOOKUP', 'TBILLYIELD', 'ROMAN', 'ISLOGICAL', 'AVERAGEIF', 'FDIST', 'FV', 'STDEVP', 'ODDLYIELD', 'AVERAGEA', 'COUNTA', 'IMREAL', 'TAN', 'CEILING', 'SUM', 'IMSUB', 'ROUND', 'PERCENTRANK', 'AMORLINC', 'HYPGEOMDIST', 'LENB', 'COMPLEX', 'CODE', 'SINH', 'HEX2OCT', 'LOGINV', 'DEC2OCT', 'ERFC', 'DATEVALUE', 'ISTEXT', 'ISNONTEXT', 'SUMPRODUCT', 'EDATE', 'FVSCHEDULE', 'INFO', 'GAMMALN', 'COLUMN', 'PRODUCT', 'RECEIVED', 'FACTDOUBLE', 'SLOPE', 'IMARGUMENT', 'VALUE', 'JIS', 'SEARCH', 'TTEST', 'MEDIAN', 'AREAS', 'NETWORKDAYS', 'VARP', 'FLOOR', 'STDEVPA STEYX', 'ISBLANK', 'HARMEAN', 'MMULT', 'BESSELY', 'FIND', 'GESTEP', 'GROWTH', 'LN', 'RANK', 'HOUR', 'NORMINV', 'RAND', 'NEGBINOMDIST', 'HEX2BIN', 'SUMX2MY2', 'CHITEST', 'DEC2HEX', 'ISPMT', 'DMAX', 'CHOOSE', 'ATAN2', 'ODDFYIELD', 'DGET', 'DCOUNT', 'CUBEMEMBERPROPERTY', 'XNPV', 'DELTA', 'ROUNDDOWN', 'DAVERAGE', 'IMSUM', 'DB', 'MROUND', 'MDURATION', 'COUPPCD', 'CLEAN', 'ISREF', 'ODDLPRICE', 'PMT', 'ABS', 'INT', 'LARGE', 'PEARSON', 'ASC', 'MINA', 'DATEDIF', 'KURT', 'SYD', 'IRR', 'CHIDIST'})\n"
     ]
    }
   ],
   "source": [
    "print(openpyxl.utils.FORMULAE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create a new worksheet where we will add a few images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet = book.create_sheet(title='Images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet['A1'] = 'Our logo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image('data/D4Sci_logo_full.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet.add_image(img, 'B1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "book.save('data/Advanced.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open data/Advanced.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try doing something slightly more useful. We'll read a data file from disk, perform a linear fit, generate a plot and add it to the workshet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/Anscombe1.dat', header=None, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 2)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet['A31'] = 'X'\n",
    "sheet['B31'] = 'Y'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x, y in data.values:\n",
    "    sheet.append((x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(data[[0]], data[[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2 = lm.predict(data[[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.50009091]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.00009091])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.plot.scatter(x=0, y=1, label='Data', s=300)\n",
    "ax.plot(data[0], y2, c='b', label='Fit')\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.legend()\n",
    "plt.savefig('data/Anscombe.png', dpi=72)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we can add it to the worksheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open data/Anscombe.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = Image('data/Anscombe.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet.add_image(img, 'D31')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "book.save('data/Advanced.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open data/Advanced.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet = book.create_sheet(title=\"Charts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1\n",
       "0   10.0   8.04\n",
       "1    8.0   6.95\n",
       "2   13.0   7.58\n",
       "3    9.0   8.81\n",
       "4   11.0   8.33\n",
       "5   14.0   9.96\n",
       "6    6.0   7.24\n",
       "7    4.0   4.26\n",
       "8   12.0  10.84\n",
       "9    7.0   4.82\n",
       "10   5.0   5.68"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[2] = y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.000818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>9.501273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "      <td>7.500909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.501091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.001364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.000636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.000455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.001182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>6.500727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "      <td>5.500545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1          2\n",
       "0   10.0   8.04   8.001000\n",
       "1    8.0   6.95   7.000818\n",
       "2   13.0   7.58   9.501273\n",
       "3    9.0   8.81   7.500909\n",
       "4   11.0   8.33   8.501091\n",
       "5   14.0   9.96  10.001364\n",
       "6    6.0   7.24   6.000636\n",
       "7    4.0   4.26   5.000455\n",
       "8   12.0  10.84   9.001182\n",
       "9    7.0   4.82   6.500727\n",
       "10   5.0   5.68   5.500545"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet['A1'] = 'X'\n",
    "sheet['B1'] = 'Y'\n",
    "sheet['C1'] = 'Fit'\n",
    "\n",
    "for x, y, z in data.values:\n",
    "    sheet.append((x, y, z))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create our chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart = ScatterChart()\n",
    "chart.title = \"Line Chart\"\n",
    "chart.y_axis.title = 'Y'\n",
    "chart.x_axis.title = 'X'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Reference objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X-values start in row 2 to skip the column title\n",
    "x_ref = Reference(sheet, min_col=1, min_row=2, max_row=12)\n",
    "\n",
    "# y-values start in row 1 to include the column title\n",
    "y_ref =  Reference(sheet, min_col=2, min_row=1, max_row=12)\n",
    "y2_ref = Reference(sheet, min_col=3, min_row=1, max_row=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Series objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "series_y  = Series(values=y_ref,  xvalues=x_ref, title_from_data=True)#, title='Y')\n",
    "series_y2 = Series(values=y2_ref, xvalues=x_ref, title_from_data=True)#, title='Fit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't want a line for the y Series, but we want to use squares as the symbols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "series_y.graphicalProperties.line.noFill = True\n",
    "series_y.marker.symbol = 'square'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart.series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart.series.append(series_y)\n",
    "chart.series.append(series_y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet.add_chart(chart, \"D1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "book.save('data/Advanced.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open data/Advanced.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>9.501273</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "      <td>7.500909</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.501091</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.001364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.000636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.000455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.001182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>6.500727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "      <td>5.500545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1          2\n",
       "0   10.0   8.04   8.001000\n",
       "1    8.0   6.95   7.000818\n",
       "2   13.0   7.58   9.501273\n",
       "3    9.0   8.81   7.500909\n",
       "4   11.0   8.33   8.501091\n",
       "5   14.0   9.96  10.001364\n",
       "6    6.0   7.24   6.000636\n",
       "7    4.0   4.26   5.000455\n",
       "8   12.0  10.84   9.001182\n",
       "9    7.0   4.82   6.500727\n",
       "10   5.0   5.68   5.500545"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.columns = ['X', 'Y', 'Fit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
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       "      <td>7.000818</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.58</td>\n",
       "      <td>9.501273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>8.81</td>\n",
       "      <td>7.500909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.501091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.0</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.001364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.000636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.000455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.001182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>6.500727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.68</td>\n",
       "      <td>5.500545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X      Y        Fit\n",
       "0   10.0   8.04   8.001000\n",
       "1    8.0   6.95   7.000818\n",
       "2   13.0   7.58   9.501273\n",
       "3    9.0   8.81   7.500909\n",
       "4   11.0   8.33   8.501091\n",
       "5   14.0   9.96  10.001364\n",
       "6    6.0   7.24   6.000636\n",
       "7    4.0   4.26   5.000455\n",
       "8   12.0  10.84   9.001182\n",
       "9    7.0   4.82   6.500727\n",
       "10   5.0   5.68   5.500545"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet = book.create_sheet('Pandas')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in dataframe_to_rows(data, header=True, index=True):\n",
    "    sheet.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "book.save('data/Advanced.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open data/Advanced.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And to extract the data back from a Worksheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'A1:D13'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet.dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = np.array([row for row in sheet.values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[None, 'X', 'Y', 'Fit'],\n",
       "       [None, None, None, None],\n",
       "       [0, 10.0, 8.04, 8.001000000000001],\n",
       "       [1, 8.0, 6.95, 7.000818181818183],\n",
       "       [2, 13.0, 7.58, 9.50127272727273],\n",
       "       [3, 9.0, 8.81, 7.500909090909093],\n",
       "       [4, 11.0, 8.33, 8.501090909090912],\n",
       "       [5, 14.0, 9.96, 10.001363636363639],\n",
       "       [6, 6.0, 7.24, 6.000636363636365],\n",
       "       [7, 4.0, 4.26, 5.000454545454547],\n",
       "       [8, 12.0, 10.84, 9.00118181818182],\n",
       "       [9, 7.0, 4.82, 6.500727272727274],\n",
       "       [10, 5.0, 5.68, 5.500545454545456]], dtype=object)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Fit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>8.04</td>\n",
       "      <td>8.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>6.95</td>\n",
       "      <td>7.00082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>7.58</td>\n",
       "      <td>9.50127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>8.81</td>\n",
       "      <td>7.50091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>8.33</td>\n",
       "      <td>8.50109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>9.96</td>\n",
       "      <td>10.0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.00064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.00045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.00118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>4.82</td>\n",
       "      <td>6.50073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>5.68</td>\n",
       "      <td>5.50055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X      Y      Fit\n",
       "0   10   8.04    8.001\n",
       "1    8   6.95  7.00082\n",
       "2   13   7.58  9.50127\n",
       "3    9   8.81  7.50091\n",
       "4   11   8.33  8.50109\n",
       "5   14   9.96  10.0014\n",
       "6    6   7.24  6.00064\n",
       "7    4   4.26  5.00045\n",
       "8   12  10.84  9.00118\n",
       "9    7   4.82  6.50073\n",
       "10   5   5.68  5.50055"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(new_data[2:], columns=new_data[0])\n",
    "df.set_index(df.columns[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Font formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "fontStyle = Font(size = \"20\", b=True) # Make it size 20 and Bold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet['B1'].font = fontStyle\n",
    "sheet['C1'].font = fontStyle\n",
    "sheet['D1'].font = fontStyle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "book.save('data/Advanced.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "!open data/Advanced.xlsx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
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   },
   "types_to_exclude": [
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   "window_display": false
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