{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optimus Example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This Notebook is a simple tutorial about DataFrameTransformer, DataFrameAnalyzer and Utilities modules.\n",
"\n",
"- DataFrameTransformer is a dedicated module to easily make dataframe transformations. \n",
"\n",
"- DataFrameProfiler is a dedicated module to run a basic profile of the dataframe.\n",
"\n",
"- DataFrameAnalyzer is a dedicated module to plot and see important features of a spark\n",
" Dataframe.\n",
"\n",
"- Utilities module contains tool classes that support use of DataFrameTransformer and DataFrameAnalyzer modules. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing Modules"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>Starting or getting SparkSession and SparkContext.</div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>Setting checkpoint folder (local). If you are in a cluster change it with set_check_point_folder(path,'hadoop').</div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Deleting previous folder if exists...\n",
"Creation of checkpoint directory...\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div style=\"margin:10px\">\n",
" <a href=\"https://github.com/ironmussa/Optimus\" target=\"_new\">\n",
" <img src=\"https://github.com/ironmussa/Optimus/raw/master/images/robotOptimus.png\" style=\"float:left;margin-right:10px;vertical-align:top;text-align:center\" height=\"50\" width=\"50\"/>\n",
" </a>\n",
" <span><b><h2>Optimus successfully imported. Have fun :).</h2></b></span>\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import optimus\n",
"import optimus as op"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instantiation of Utility class\n",
"The utility class is a tool class that includes functions to read csv files, setting checkpoint issues (to save or temporally save dataFrames)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Instance of Utilities class\n",
"tools = op.Utilities()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reading DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Reading dataframe in this case, local file \n",
"# system (hard drive of the pc) is used.\n",
"\n",
"df = tools.read_csv(path=\"foo.csv\", sep=',')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### General view of DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initially it is a good idea to see a general view of the DataFrame to be analyzed. \n",
"\n",
"In the following cell, a basic profile of the DataFrame is shown. This overview presents basic information about the DataFrame, like number of variable it has, how many are missing values and in which column, the types of each varaible, also some statistical information that describes the variable plus a frecuency plot. table that specifies the existing datatypes in each column dataFrame and other features. Also, for this particular case, the table of dataType is shown in order to visualize a sample of column content. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" }\n",
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"</style>\n",
"\n",
"<div class=\"container pandas-profiling\">\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Overview</h1>\n",
" </div>\n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-6 namecol\">\n",
" <p class=\"h4\">Dataset info</p>\n",
" <table class=\"stats\" style=\"margin-left: 1em;\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Number of variables</th>\n",
" <td>8 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Number of observations</th>\n",
" <td>19 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total Missing (%)</th>\n",
" <td>0.0% </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total size in memory</th>\n",
" <td>0.0 B </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Average record size in memory</th>\n",
" <td>0.0 B </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-6 namecol\">\n",
" <p class=\"h4\">Variables types</p>\n",
" <table class=\"stats\" style=\"margin-left: 1em;\">\n",
" <tbody>\n",
" <tr>\n",
" <th>Numeric</th>\n",
" <td>3 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Categorical</th>\n",
" <td>2 </td>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <td>0 </td>\n",
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" <tr>\n",
" <th>Text (Unique)</th>\n",
" <td>3 </td>\n",
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" <tr>\n",
" <th>Rejected</th>\n",
" <td>0 </td>\n",
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" </tbody>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
" <p class=\"h4\">Warnings</p>\n",
" <ul class=\"list-unstyled\"> </ul>\n",
" </div>\n",
"</div>\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Variables</h1>\n",
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" <p class=\"h4\">billingId<br/>\n",
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" <tr>\n",
" <th>Distinct count</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>100.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
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" <td>0.0%</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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"\n",
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" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>992</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
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"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram4045526885809405107\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAAR1JREFUeJzt28EJAjEQQFEVS7IIe/JsTxZhT/Eu8sGVdYO%2Bdw9MDp%2B5JPsxxtgBLx22HgBmdtx6gGeny%2B3tM/freYVJPrfkLkvMev9fYINAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUCY7j8I6/vWP5UlZvvbYoNAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAiEn3juPvPz7W/49/uvyQaBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoGwH2OMrYeAWdkgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEB5MlBRhvzVi7wAAAABJRU5ErkJggg%3D%3D\">\n",
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" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives4045526885809405107\">\n",
" <div class=\"col-sm-4\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>115.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>773.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>400.5</td>\n",
" </tr>\n",
" </table>\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>280.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.50395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>-1.0412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>225.05</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>-0.2137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>10564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>78511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>0.0 B</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
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vcd873vfU0FBAYEFAAAcwfGBlZ%2Bfr/Hjx%2BvJJ5/UuHHjFBLi/7KxQYMGacaMGTZMBwAA4M/xgfX6668rLi5OZ8%2Be9cbV%2B%2B%2B/rxEjRig0NFSSlJqaqtTUVDvHBAAA8HL8TxGeO3dOWVlZeuaZZ7xrubm5mjp1qj777DMbJwMAALg4xwfWv//7v%2BuGG27QnDlzvGtlZWX63ve%2Bp8LCQhsnAwAAuDjHB9Z7772npUuXKiEhwbsWHx%2BvxYsX6%2B2337ZxMgAAgItzfGC53W41Njb6rTc3N/OO7gAAwJEcH1jjxo3TmjVr9Mknn3jXPv30UxUWFmrs2LE2TgYAAHBxjv8pwiVLlmjOnDmaOHGiYmNjJUmNjY0aMWKEHnjgAZunAwAA8Of4wOrTp4927dqlN998U8eOHZPb7daQIUM0ZswYuVwuu8cDAADw4/jAkqTQ0FCNHTuWU4IAACAgOD6wPB6P1q9fr0OHDunChQt%2BL2x/5ZVXbJoMAADg4hwfWL/5zW9UVVWlyZMn6%2Bqrr7Z7HAAAgG/k%2BMB6%2B%2B23VVJSovT0dLtHAQAA6BLHv01DVFSU%2BvTpY/cYAAAAXeb4wJo6dapKSkrU0dFh9ygAAABd4vhThGfPntXevXv1pz/9Sddff73CwsJ8rv/d735n02QAAAAX5/jAkqQpU6bYPQIAAECXOT6wCgsL7R4BAADgijj%2BNViSVFdXp%2BLiYt1333363//9X%2B3bt0%2B1tbV2jwUAAHBRjg%2BsEydO6B//8R%2B1a9cu/c///I%2BamppUVlam6dOnq7Ky0u7xAAAA/Dg%2BsB5%2B%2BGHdcccd2r9/v6666ipJ0tq1a5WZmanHHnvM5ukAAAD8OT6wDh06pDlz5vj8Yme326358%2BerurraxskAAAAuzvGB1dnZqc7OTr/18%2BfPKzQ01IaJAAAALs/xgZWRkaFNmzb5RNbZs2dVVFSk0aNH2zgZAADAxTk%2BsJYuXaqqqiplZGSotbVV8%2BbN0/jx43Xy5EktWbLE7vEAAAD8OP59sPr166fdu3dr7969%2BvDDD9XZ2amf/vSnmjp1qmJiYuweDwAAwI/jA0uSIiMjlZ2dbfcYAAAAXeL4wJo1a9Zlr%2Bd3EQIAAKdxfGD179/f53J7e7tOnDihjz76SLNnz7ZpKgAAgEtzfGBd6ncRPvnkkzp9%2BvQV315bW5umTZum3/zmN7r11lsvesy8efP06quv%2Bqw99dRTGj9%2B/BXfHwAACD6OD6xLmTp1qu666y6tXr26y3%2BntbVV9913n44dO3bZ444fP66ioiKNGTPGu3bNNdd861kBAEBwCdjAOnz48BW90WhNTY3uu%2B8%2BWZZ12ePa2tp08uRJJSUlKSEh4buOCQAAgpDjA%2BtiL3I/d%2B6cjh49qp/97Gddvp2DBw/q1ltv1b/9279p5MiRlzyutrZWLpdL119//beaFwAAwPGBdd111/n8HkJJuuqqqzRjxgz90z/9U5dvp6sxVltbq5iYGC1evFgHDx7Utddeq0WLFun222%2B/orkBAEDwcnxgPfzwwz16f7W1tWppaVFGRoZyc3P18ssva968eSotLVVSUlKXbqOurk4ej8dnze2OUmJiYneMHDDc7u75xQGhoSE%2Bf8I%2B7IUzuN0h7AUCTnd9j7CL4wPr3Xff7fKxt9xyy3e%2Bv/nz52vmzJneF7UPGzZMH3zwgV544YUuB1ZpaamKi4t91hYsWKC8vLzvPF8gi4uL7tbbj42N7NbbR9exF/b66tcae4FA0d3fI3qa4wNr5syZ3lOEX32B%2BtfXXC6XPvzww%2B98fyEhIX4/MTho0CDV1NR0%2BTZycnKUmZnps%2BZ2R6mh4fx3ni%2BQddfjDw0NUWxspBobm9XR0fnNfwHdhr1whoaG8%2BwFAk53fY%2BwK9wcH1hPPfWU1qxZo1//%2BtcaNWqUwsLC9Je//EUPPfSQfvKTn%2BjOO%2B80en9Lly6Vy%2BXyef%2BtI0eOaOjQoV2%2BjcTERL/TgR7P52pvD%2B5/5Lr78Xd0dAb959gp2At7ffVzz14gUPS2/04df8KzsLBQy5cv18SJExUXF6fo6GiNHj1aDz30kLZt26b%2B/ft7P74tj8ejlpYWSVJmZqb27Nmj3bt368SJEyouLlZ5eblmzJhh6iEBAIBezvGBVVdXd9F4iomJUUNDg5H7yMjIUFlZmSRpwoQJWrFihTZu3KgpU6bo1VdfVUlJiQYMGGDkvgAAQO/n%2BFOEI0eO1Nq1a/XII48oJiZGknT27Fm/d1q/EkePHr3s5ezsbGVnZ3%2B7gQEAQNBzfGA9%2BOCDmjVrlsaNG6eBAwfKsiz99a9/VUJCgn73u9/ZPR4AAIAfxwfW4MGDVVZWpr179%2Br48eOSpJ///OeaPHmyIiP58WMAAOA8jg8s6YtftJydna2TJ096f4XNVVddZfNUAAAAF%2Bf4F7lblqXHHntMt9xyi6ZMmaLTp09ryZIlys/P14ULF%2BweDwAAwI/jA2vLli168cUXtWLFCoWFhUmS7rjjDu3fv9/v3dIBAACcwPGBVVpaquXLl2vatGned2%2B/8847tWbNGu3Zs8fm6QAAAPw5PrBOnjyp4cOH%2B60PGzbM7xcqAwAAOIHjA6t///76y1/%2B4rf%2B%2Buuve1/wDgAA4CSO/ynCX/7yl1q1apU8Ho8sy9Jbb72l0tJSbdmyRUuXLrV7PAAAAD%2BOD6zp06ervb1dGzduVEtLi5YvX674%2BHjdc889%2BulPf2r3eAAAAH4cH1h79%2B5VVlaWcnJy9Le//U2WZalPnz52jwUAAHBJjn8N1kMPPeR9MXt8fDxxBQAAHM/xgTVw4EB99NFHdo8BAADQZY4/RThs2DDdf//9Kikp0cCBAxUeHu5zfWFhoU2TAQAAXJzjA%2Bvjjz9WWlqaJPG%2BVwAAICA4MrAeffRRLVy4UFFRUdqyZYvd4wAAAFwRR74G67nnnlNzc7PPWm5ururq6myaCAAAoOscGViWZfmtvfvuu2ptbbVhGgAAgCvjyMACAAAIZAQWAACAYY4NLJfLZfcIAAAA34ojf4pQktasWePznlcXLlxQUVGRoqOjfY7jfbAAAIDTODKwbrnlFr/3vEpJSVFDQ4MaGhpsmgoAAKBrHBlYvPcVAAAIZI59DRYAAECgIrAAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMI7AAAAAMC7rAamtr05QpU/TOO%2B9c8pjq6mplZ2crOTlZ06dPV1VVVQ9OCAAAAl1QBVZra6vuvfdeHTt27JLHNDU1KTc3V%2Bnp6dq5c6dSUlI0d%2B5cNTU19eCkAAAgkAVNYNXU1Ohf/uVf9Mknn1z2uLKyMoWHh2vx4sUaPHiw8vPzFR0drX379vXQpAAAINAFTWAdPHhQt956q0pLSy97XGVlpdLS0uRyuSRJLpdLqampqqio6IkxAQBAL%2BC2e4Ce8rOf/axLx3k8Hg0ZMsRnrU%2BfPpc9rfh1dXV18ng8Pmtud5QSExO7fBu9kdvdPT0fGhri8yfsw144g9sdwl4g4HTX9wi7BE1gdVVzc7PCwsJ81sLCwtTW1tbl2ygtLVVxcbHP2oIFC5SXl2dkxkD148f%2Bn90jAEGBrzUEori4aLtHMIrA%2Bprw8HC/mGpra1NERESXbyMnJ0eZmZk%2Ba253lBoazhuZEQCA3qa7vkfaFW4E1tf069dP9fX1Pmv19fVXdHovMTHR73iP53O1t3camREAgN6mt32P7F0nPA1ITk7W4cOHZVmWJMmyLB06dEjJyck2TwYAAAIFgaUvXtje0tIiScrKylJjY6MKCgpUU1OjgoICNTc3a9KkSTZPCQAAAgWBJSkjI0NlZWWSpJiYGG3atEnl5eWaNm2aKisr9fTTTysqKsrmKQEAQKBwWV%2BeC0O38ng%2B75bbnbT%2BjW65XQAAetJ/33Nbt9xuQsLV3XK734RnsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwLmsBqbW3VsmXLlJ6eroyMDD377LOXPHbevHn6wQ9%2B4PNx4MCBHpwWAAAEMrfdA/SURx99VFVVVdq8ebNOnTqlJUuW6LrrrlNWVpbfscePH1dRUZHGjBnjXbvmmmt6clwAABDAgiKwmpqa9Pvf/17PPPOMRowYoREjRujYsWPaunWrX2C1tbXp5MmTSkpKUkJCgk0TAwCAQBYUpwiPHDmi9vZ2paSkeNfS0tJUWVmpzs5On2Nra2vlcrl0/fXX9/SYAACglwiKZ7A8Ho/i4uIUFhbmXevbt69aW1t19uxZxcfHe9dra2sVExOjxYsX6%2BDBg7r22mu1aNEi3X777V2%2Bv7q6Onk8Hp81tztKiYmJ3/3BAADQC7ndves5n6AIrObmZp%2B4kuS93NbW5rNeW1urlpYWZWRkKDc3Vy%2B//LLmzZun0tJSJSUlden%2BSktLVVxc7LO2YMEC5eXlfYdHAQBA7xUXF233CEYFRWCFh4f7hdSXlyMiInzW58%2Bfr5kzZ3pf1D5s2DB98MEHeuGFF7ocWDk5OcrMzPRZc7uj1NBw/ts%2BBAAAerXu%2Bh5pV7gFRWD169dPDQ0Nam9vl9v9xUP2eDyKiIhQbGysz7EhISF%2BPzE4aNAg1dTUdPn%2BEhMT/U4Hejyfq7298xJ/AwCA4Nbbvkf2rhOelzB8%2BHC53W5VVFR418rLy5WUlKSQEN9PwdKlS/XAAw/4rB05ckSDBg3qkVkBAEDgC4rAioyM1F133aWVK1fq/fff1/79%2B/Xss89q1qxZkr54NqulpUWSlJmZqT179mj37t06ceKEiouLVV5erhkzZtj5EAAAQAAJisCSpAceeEAjRozQ7NmztWrVKi1atEgTJkyQJGVkZKisrEySNGHCBK1YsUIbN27UlClT9Oqrr6qkpEQDBgywc3wAABBAXJZlWXYPEQw8ns%2B75XYnrX%2BjW24XAICe9N/33NYtt5uQcHW33O43CZpnsAAAAHoKgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGBY0ARWa2urli1bpvT0dGVkZOjZZ5%2B95LHV1dXKzs5WcnKypk%2Bfrqqqqh6cFAAABLqgCaxHH31UVVVV2rx5s1asWKHi4mLt27fP77impibl5uYqPT1dO3fuVEpKiubOnaumpiYbpgYAAIEoKAKrqalJv//975Wfn68RI0boxz/%2Bsf71X/9VW7du9Tu2rKxM4eHhWrx4sQYPHqz8/HxFR0dfNMYAAAAuJigC68iRI2pvb1dKSop3LS0tTZWVlers7PQ5trKyUmlpaXK5XJIkl8ul1NRUVVRU9OjMAAAgcLntHqAneDwexcXFKSwszLvWt29ftba26uzZs4qPj/c5dsiQIT5/v0%2BfPjp27FiX76%2Burk4ej8dnze2OUmJi4rd8BAAA9G5ud%2B96zicoAqu5udknriR5L7e1tXXp2K8fdzmlpaUqLi72WVu4cKEWLVp0JWN3yXsFWcZvM9DU1dWptLRUOTk5RKzN2AvnYC%2Bcg70ITkERWOHh4X6B9OXliIiILh379eMuJycnR5mZmT5rCQkJVzIyroDH41FxcbEyMzP5x8tm7IVzsBfOwV4Ep6AIrH79%2BqmhoUHt7e1yu794yB6PRxEREYqNjfU7tr6%2B3metvr4DzTBcAAAHWUlEQVT%2Bir4oEhMT%2BSICACCI9a4TnpcwfPhwud1unxeql5eXKykpSSEhvp%2BC5ORkHT58WJZlSZIsy9KhQ4eUnJzcozMDAIDAFRSBFRkZqbvuuksrV67U%2B%2B%2B/r/379%2BvZZ5/VrFmzJH3xbFZLS4skKSsrS42NjSooKFBNTY0KCgrU3NysSZMm2fkQAABAAAlduXLlSruH6AmjR49WdXW1Hn/8cb311lu6%2B%2B67NX36dElSamqqbrjhBg0fPlxhYWEaNWqU/uu//ktPPfWU2tvbtXbtWl133XU2PwJcTnR0tEaNGqXo6Gi7Rwl67IVzsBfOwV4EH5f15bkwAAAAGBEUpwgBAAB6EoEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFRztz5ozy8vI0atQojR07VoWFhWptbZUkffrpp/rFL36hkSNH6s4779Sf//xnn7/75ptvasqUKUpOTtasWbP06aef2vEQep3c3FwtXbrUe5l96HltbW1atWqVbrnlFv3whz/U2rVrvb8/lf3oWZ999pnmzp2r1NRUZWZm6re//a33OvYiuBFYcCzLspSXl6fm5mZt3bpV69at04EDB7R%2B/XpZlqUFCxaob9%2B%2B%2BsMf/qCpU6dq4cKFOnXqlCTp1KlTWrBggaZNm6YdO3YoPj5e8%2BfPF7%2B44Lt56aWX9Nprr3kvsw/2WLNmjd58803953/%2Bpx5//HG98MILKi0tZT9scM899ygqKko7d%2B7UsmXLtH79er388svsBSQLcKiamhpr6NChlsfj8a7t2bPHysjIsN58801r5MiR1vnz573XzZ4923riiScsy7Ks9evXWzNmzPBe19TUZKWkpFhvv/12zz2AXqahocEaN26cNX36dGvJkiWWZVnsgw0aGhqsG2%2B80XrnnXe8a5s2bbKWLl3KfvSws2fPWkOHDrWOHj3qXVu4cKG1atUq9gIWz2DBsRISElRSUqK%2Bffv6rJ87d06VlZW68cYbFRUV5V1PS0tTRUWFJKmyslLp6ene6yIjIzVixAjv9bhyjzzyiKZOnaohQ4Z419iHnldeXq6YmBiNGjXKu5abm6vCwkL2o4dFREQoMjJSO3fu1IULF1RbW6tDhw5p%2BPDh7AU4RQjnio2N1dixY72XOzs79fzzz2v06NHyeDxKTEz0Ob5Pnz46ffq0JH3j9bgyb731lt577z3Nnz/fZ5196Hmffvqp%2Bvfvr927dysrK0v/8A//oCeffFKdnZ3sRw8LDw/X8uXLVVpaquTkZE2aNEnjxo1TdnY2ewG57R4A6KqioiJVV1drx44d%2Bu1vf6uwsDCf68PCwtTW1iZJam5uvuz16LrW1latWLFCy5cvV0REhM913/R5Zh/Ma2pq0okTJ7R9%2B3YVFhbK4/Fo%2BfLlioyMZD9scPz4cY0fP15z5szRsWPHtHr1ao0ZM4a9AIGFwFBUVKTNmzdr3bp1Gjp0qMLDw3X27FmfY9ra2rwBEB4e7vcPVVtbm2JjY3ts5t6iuLhYN910k8%2BziV9iH3qe2%2B3WuXPn9Pjjj6t///6SvnjB9LZt2/TDH/6Q/ehBb731lnbs2KHXXntNERERSkpK0pkzZ7Rx40aNHj2avQhynCKE461evVrPPfecioqKNHHiRElSv379VF9f73NcfX299yn3S12fkJDQM0P3Ii%2B99JL279%2BvlJQUpaSkaM%2BePdqzZ49SUlLYBxskJCQoPDzcG1eS9Pd///f67LPP2I8eVlVVpRtuuMHnmd0bb7xRp06dYi9AYMHZiouLtX37dq1du1aTJ0/2ricnJ%2BuDDz5QS0uLd628vFzJycne68vLy73XNTc3q7q62ns9um7Lli3as2ePdu/erd27dyszM1OZmZnavXs3%2B2CD5ORktba26uOPP/au1dbWqn///uxHD0tMTNSJEyd8nomqra3VgAED2AsodOXKlSvtHgK4mOPHj%2Bvee%2B9Vbm6uJk6cqKamJu/HkCFDtHfvXlVUVGjw4MH6wx/%2BoJdeekkFBQW6%2BuqrNWDAAD322GMKDQ1VbGysCgsL1dHRofvvv18ul8vuhxZQYmNj9Xd/93fej9dff11hYWH653/%2BZ1133XXsQw%2BLi4tTVVWVXnrpJd1000368MMP9fDDD2vOnDmaNGkS%2B9GD%2Bvfvr23btunYsWMaMmSIDh8%2BrEceeURz587VhAkT2ItgZ/f7RACXsmnTJmvo0KEX/bAsy/rrX/9q/fznP7duuukma/LkydYbb7zh8/f/9Kc/WRMmTLBuvvlma/bs2dYnn3xix8PodZYsWeJ9HyzLYh/s0NjYaP3617%2B2Ro4caY0ZM8b6j//4D6uzs9OyLPajpx07dsz6xS9%2BYaWmplp33HGH9dxzz7EXsCzLslyWxdvGAgAAmMRrsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAwjsAAAAAz7/9A%2BN9BQ3pGXAAAAAElFTkSuQmCC\">\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">birth<br/>\n",
" <small>Categorical, Unique</small>\n",
" </p>\n",
" </div> <div class=\"col-md-3 collapse in\" id=\"minivalues2919009344056767353\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>First 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1990/07/11</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1899/01/01</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1990/07/09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
"<div class=\"col-md-6 collapse in\" id=\"minivalues2919009344056767353\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Last 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1954/07/10</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1994/01/04</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1920/04/22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#values2919009344056767353,#minivalues2919009344056767353\" aria-expanded=\"false\"\n",
" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"col-md-12 collapse\" id=\"values2919009344056767353\">\n",
" <p class=\"h4\">First 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1990/07/11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1899/01/01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1990/07/09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1999/02/15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1921/05/03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1956/11/30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1950/07/08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1930/08/12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1958/03/26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1923/03/12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1950/07/14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2000/03/22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1980/07/07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1970/07/13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1993/12/08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1997/06/27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1954/07/10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>1994/01/04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>1920/04/22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" <p class=\"h4\">Last 20 values</p>\n",
" \n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">dummyCol<br/>\n",
" <small>Categorical</small>\n",
" </p>\n",
" </div> <div class=\"col-md-3\">\n",
" <table class=\"stats \">\n",
" <tr class=\"\">\n",
" <th>Distinct count</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>68.4%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"</div>\n",
"<div class=\"col-md-6 collapse in\" id=\"minifreqtable-1260544103559101310\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>you</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:27%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 15.8%\">\n",
" 3\n",
" </div>\n",
" \n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <th>gonna</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:27%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 15.8%\">\n",
" 3\n",
" </div>\n",
" \n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <th>never</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:19%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 10.5%\">\n",
" \n",
" </div>\n",
" 2\n",
" </td>\n",
"</tr> <tr class=\"other\">\n",
" <th>Other values (10)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 57.9%\">\n",
" 11\n",
" </div>\n",
" \n",
" </td>\n",
"</tr> \n",
" </table>\n",
"</div> \n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-1260544103559101310, #minifreqtable-1260544103559101310\"\n",
" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"col-md-12 collapse extrapadding\" id=\"freqtable-1260544103559101310\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\"> </td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">you</td>\n",
" <td class=\"number\">3</td>\n",
" <td class=\"number\">15.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">gonna</td>\n",
" <td class=\"number\">3</td>\n",
" <td class=\"number\">15.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">never</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">10.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:67%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">#</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">10.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:67%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">never </td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">down</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">run </td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">desert</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">up</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">and</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">around</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">let</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">give</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:34%\"> </div>\n",
" </td>\n",
"</tr> \n",
" </table>\n",
"</div> \n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">firstName<br/>\n",
" <small>Categorical, Unique</small>\n",
" </p>\n",
" </div> <div class=\"col-md-3 collapse in\" id=\"minivalues6589813866731542545\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>First 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Luis</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Arthur</td>\n",
" </tr>\n",
" <tr>\n",
" <td>JAMES</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
"<div class=\"col-md-6 collapse in\" id=\"minivalues6589813866731542545\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Last 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>NiELS</td>\n",
" </tr>\n",
" <tr>\n",
" <td>JaMES</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Fred</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#values6589813866731542545,#minivalues6589813866731542545\" aria-expanded=\"false\"\n",
" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"col-md-12 collapse\" id=\"values6589813866731542545\">\n",
" <p class=\"h4\">First 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Luis</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Arthur</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>JAMES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>William</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Marie</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>((( Heinrich )))))</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>PAUL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>CaRL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Isaac</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Johannes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Galileo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>André</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Emmy%%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Albert</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>David</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Max!!!</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NiELS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>JaMES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Fred</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" <p class=\"h4\">Last 20 values</p>\n",
" \n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">id<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div> <div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>100.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram5730231854025664727\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAARBJREFUeJzt3MEJAjEQQFFXLMki7MmzPVmEPcW7yAcXJcv63j0wCXzmlmWMMQ7AW8fZA8CWnWYP8Op8vc8e4Wset8vHZ/Z0/zXWvNkv2SAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEDb3L9ae/PsfV3tgg0AQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIhGWMMWYPAVtlg0AQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUB4Ak95D/jqZxT/AAAAAElFTkSuQmCC\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives5730231854025664727,#minihistogram5730231854025664727\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives5730231854025664727\">\n",
" <div class=\"col-sm-4\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>14.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>18.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>9</td>\n",
" </tr>\n",
" </table>\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>5.6273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.56273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>-1.2067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>4.7368</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>31.667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>0.0 B</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-sm-8 histogram\">\n",
" <img 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AAAAJvO4gFVTU6O7775bO3fubLTPP/7xD91zzz2KiorS6NGj9d5777lsj4mJUd%2B%2BfV1%2BysvLm7t0AADgIexWF2Cm6upqpaSkKC8vr9E%2Bubm5mjVrllJTUxUXF6ePPvpIv/zlL/X6668rPDxcRUVFOnXqlLZv3y4/Pz/n8wICAlpiCgAAwAN4TMDKz89XSkqKDMO4YL%2BsrCwNGjRIkyZNkiRde%2B21%2Bvvf/663335b4eHhKigoUGhoqHr27NkSZQMAAA9k%2BSnCsWPH6k9/%2BpNOnTp1RePs2rVLAwcOVEZGxgX7jRkzRr/%2B9a/d2s%2B9fn5%2Bvq6//vorqgUAALRtlh/BGjRokFatWqW0tDTdcccdSkxM1ODBg2Wz2S5pnPvvv79J/Xr37u3yOC8vT5988ol%2B%2BtOfSpIKCgpUWVmpiRMn6sCBA%2BrXr58WLlxI6AIAAE1mecBKSUnRnDlz9M9//lNvvPGGZs%2BereDgYCUkJCghIaFZg83x48c1e/ZsDRgwQHfccYckqbCwUCdPntScOXMUFBSktWvXavLkyXrrrbcUFBTUpHGLi4vlcDhc2uz2AIWFhV1xzd7eXi7/Aq2R3e66/7JfAzj/c6G1szxgSZLNZtPgwYM1ePBgVVZWauPGjXrhhRe0Zs0aDRgwQA888IDuvPNOU1%2BzpKREU6ZMkWEYeu655%2BTldXZhX3zxRZ05c0aBgYGSpBUrViguLk47duzQ6NGjmzR2RkaG0tPTXdqSkpKUnJxsWv3Bwf6mjQW0tJCQwAbb2a%2BBtquxz4XW6qoIWNLZoz5//etf9de//lVff/21BgwYoDFjxujYsWP6zW9%2Bo88%2B%2B0yLFi0y5bWKioqcF7m/9NJL6tChg3Obj4%2BPfHx8nI99fX3Vo0cPFRUVNXn8cePGKT4%2B3qXNbg9QaemV3%2BrB29tLwcH%2BKiurVF1d/RWPB1jh/PcC%2BzUAM74jG2JVcLM8YG3ZskVbtmzRzp071aFDByUkJOi5557Tdddd5%2BzTtWtXLV261JSAVVFRoalTp8rLy0svvfSSQkNDndsMw9Dw4cM1c%2BZMJSYmOvsfPHhQvXr1avJrhIWFuZ0OdDhOqbbWvC%2BOurp6U8cDWlJj%2By77NdB2edp73/KAtWjRIt1%2B%2B%2B16/vnnddtttzlP1X1fr169NGHChMt%2BDYfDoXbt2snPz0%2BrV6/WoUOHtHHjRuc2SfLz81O7du00dOhQrVy5Ut27d1eHDh307LPPqkuXLoqLi7vs1wcAAG2L5QHrgw8%2BUEhIiE6cOOEMV3v37lX//v3l7e0tSRowYIAGDBhw2a8RGxurtLQ0JSYmatu2baqqqtLYsWNd%2BowZM0ZPPvmk5s6dK7vdrpSUFJ0%2BfVqDBg3SmjVrnLUAAABcjOUB6/Tp0xo/frzuuOMOpaamSpKmT5%2BuTp06ae3aterateslj/nvf/%2B70cfvvPPOBZ/r6%2Bur%2BfPna/78%2BZf8ugAAANJVcKPR3/3ud7r22ms1ZcoUZ9vWrVvVtWtXpaWlWVgZAADA5bE8YP3rX//S/PnzXS4279Chg1JTU/Xpp59aWBkAAMDlsTxg2e12lZWVubVXVlZe9O8KAgAAXI0sD1i33XabnnjiCR06dMjZdvjwYaWlpWnIkCEWVgYAAHB5LL/Ifd68eZoyZYpGjBih4OBgSVJZWZn69%2B%2BvBQsWWFwdAADApbM8YHXs2FF/%2Bctf9M9//lN5eXmy2%2B264YYb9MMf/vCS/%2BAzAADA1cDygCVJ3t7eGjJkCKcEAQCAR7A8YDkcDj3zzDPavXu3zpw543Zh%2B3vvvWdRZQAAAJfH8oD1yCOPaN%2B%2BfbrrrrvUrl07q8sBAAC4YpYHrE8//VTr1q1TTEyM1aUAAACYwvLbNAQEBKhjx45WlwEAAGAaywPWPffco3Xr1qmurs7qUgAAAExh%2BSnCEydOKCsrS//4xz/Us2dP%2Bfj4uGx/6aWXLKoMAADg8lgesCTp7rvvtroEAAAA01gesNLS0qwuAQAAwFSWX4MlScXFxUpPT1dKSoq%2B%2B%2B47vfPOOyosLLS6LAAAgMtiecA6ePCgRo8erb/85S/atm2bKioqtHXrVt17773KycmxujwAAIBLZnnAevLJJzVs2DBt375dP/jBDyRJTz31lOLj47VixQqLqwMAALh0lges3bt3a8qUKS5/2Nlut2vmzJnav3%2B/hZUBAABcHssDVn19verr693ay8vL5e3tbUFFAAAAV8bygBUbG6vVq1e7hKwTJ05o%2BfLlGjRokIWVAQAAXB7LA9b8%2BfO1b98%2BxcbGqrq6WjNmzNDtt9%2Bub7/9VvPmzbO6PAAAgEtm%2BX2wOnfurDfeeENZWVn66quvVF9fr/Hjx%2Buee%2B5RUFCQ1eUBAABcMssDliT5%2B/tr7NixVpcBAABgCssD1qRJky64nb9FCAAAWhvLA1b37t1dHtfW1urgwYP6%2Buuv9cADD1hUFQAAwOWzPGA19rcIn3/%2BeR07duySx6upqVFiYqIeeeQRDRw4sME%2B%2B/fv16OPPqqvv/5aN9xwgx577DHddNNNzu1ZWVl65pln5HA4FBsbq8cff1wdOnS45FoAAEDbZPlvETbmnnvu0dtvv31Jz6murtacOXOUl5fXaJ%2BKigpNnz5dMTEx2rx5s6KiovTQQw%2BpoqJCkrR3714tWrRIs2bNUkZGhsrKyrRgwYIrmgsAAGhbrtqA9fnnn1/SjUbz8/N133336dChQxfst3XrVvn6%2Bio1NVW9e/fWokWLFBgYqHfeeUeStGnTJo0aNUoJCQkKDw/XsmXL9P777%2Bvw4cNXNB8AANB2WH6KsKGL3E%2BfPq1///vfuv/%2B%2B5s8zq5duzRw4ED96le/0s0339xov5ycHEVHRzv/NI/NZtOAAQO0Z88eJSYmKicnR9OmTXP279q1q7p166acnBz17NnzEmYGAADaKssDVrdu3Vz%2BDqEk/eAHP9CECRP04x//uMnjNDWMORwO3XDDDS5tHTt2dJ5WLC4uVlhYmNv2y7keDAAAtE2WB6wnn3yyRV%2BvsrJSPj4%2BLm0%2BPj6qqamRJFVVVV1we1MUFxfL4XC4tNntAW7B7XJ4e3u5/Au0Rna76/7Lfg3g/M%2BF1s7ygPXZZ581ue8tt9xyxa/n6%2BvrFpZqamrk5%2Bd3we3%2B/v5Nfo2MjAylp6e7tCUlJSk5Ofkyq3YXHNz0eoCrzfAVH1pdAoCrTEhIoNUlmMrygDVx4kTnKULDMJzt57fZbDZ99dVXV/x6nTt3VklJiUtbSUmJ8%2BhSY9tDQ0Ob/Brjxo1TfHy8S5vdHqDS0vLLrPr/eHt7KTjYX2Vllaqrq7/4EwAAaAXM%2BI5siFXBzfKAtWrVKj3xxBOaO3eubr31Vvn4%2BOiLL77QkiVLNGbMGP3oRz8y9fUiIyO1du1aGYYhm80mwzC0e/du/eIXv3Buz87OVmJioiTp6NGjOnr0qCIjI5v8GmFhYW6nAx2OU6qtNS8Q1dXVmzoeAABW8rTvNMtPeKalpem3v/2tRowYoZCQEAUGBmrQoEFasmSJXn31VXXv3t35c7kcDoeqqqokSSNHjlRZWZmWLl2q/Px8LV26VJWVlRo1apQkafz48dqyZYsyMzOVm5ur1NRUDR06lN8gBAAATWZ5wCouLm4wPAUFBam0tNSU14iNjdXWrVud465evdp5lConJ0dr1qxRQECAJCkqKkpLlizR888/r/Hjx6t9%2B/aN3m0eAACgITbj%2Bxc%2BWWDKlCkKCAjQ//7v/yooKEiSdOLECaWkpMjX11cvvPCCleWZxuE4Zco4druXQkICVVpartraeo165mNTxgUAwEpvPzy4WcYNDW3XLONejOXXYP3mN7/RpEmTdNttt%2Bm6666TYRj65ptvFBoaqpdeesnq8gAAAC6Z5QGrd%2B/e2rp1q7KyslRQUCBJ%2BtnPfqa77rrrkm6NAAAAcLWwPGBJUvv27TV27Fh9%2B%2B23zovJf/CDH1hcFQAAwOWx/CJ3wzC0YsUK3XLLLbr77rt17NgxzZs3T4sWLdKZM2esLg8AAOCSWR6wNm7cqC1btujRRx91/omaYcOGafv27W53QwcAAGgNLA9YGRkZ%2Bu1vf6vExETn3dt/9KMf6YknntCbb75pcXUAAACXzvKA9e2336pfv35u7eHh4W5/MBkAAKA1sDxgde/eXV988YVb%2BwcffMDd0wEAQKtk%2BW8R/vznP9djjz0mh8MhwzD0ySefKCMjQxs3btT8%2BfOtLg8AAOCSWR6w7r33XtXW1uoPf/iDqqqq9Nvf/lYdOnTQww8/rPHjx1tdHgAAwCWzPGBlZWVp5MiRGjdunI4fPy7DMNSxY0erywIAALhsll%2BDtWTJEufF7B06dCBcAQCAVs/ygHXdddfp66%2B/troMAAAA01h%2BijA8PFy//vWvtW7dOl133XXy9fV12Z6WlmZRZQAAAJfH8oB14MABRUdHSxL3vQIAAB7BkoC1bNkyzZo1SwEBAdq4caMVJQAAADQbS67B%2BuMf/6jKykqXtunTp6u4uNiKcgAAAExlScAyDMOt7bPPPlN1dbUF1QAAAJjL8t8iBAAA8DQELAAAAJNZFrBsNptVLw0AANCsLLtNwxNPPOFyz6szZ85o%2BfLlCgwMdOnHfbAAAEBrY0nAuuWWW9zueRUVFaXS0lKVlpZaURIAAIBpLAlY3PsKAAB4Mi5yBwAAMBkBCwAAwGSW/y1Cs1RXV%2Buxxx7T3/72N/n5%2BenBBx/Ugw8%2B6NZv4sSJ2rVrl1t7YmKi84L6mJgYnTp1ymX77t273S7ABwAAaIjHBKxly5Zp37592rBhg44cOaJ58%2BapW7duGjlypEu/lStX6syZM87HOTk5evjhh3X//fdLkoqKinTq1Clt375dfn5%2Bzn4BAQEtMxEAANDqeUTAqqioUGZmptauXav%2B/furf//%2BysvL08svv%2BwWsK655hrn/%2Bvq6vT0009r6tSpioiIkCQVFBQoNDRUPXv2bNE5AAAAz%2BER12Dl5uaqtrZWUVFRzrbo6Gjl5OSovr6%2B0edt3rxZJ0%2Be1LRp05xt%2Bfn5uv7665u1XgAA4Nk8ImA5HA6FhITIx8fH2dapUydVV1frxIkTDT7HMAytW7dOkyZNcrm2qqCgQJWVlZo4caJiY2M1bdo0HThwoNnnAAAAPIdHnCKsrKx0CVeSnI9ramoafM7OnTt17Ngx3XfffS7thYWFOnnypObMmaOgoCCtXbtWkydP1ltvvaWgoKAm1VNcXOx2I1W7PUBhYWFNnVKjvL29XP4FAMAT2O2e9b3mEQHL19fXLUide/z9C9W/b9u2bbrttttcrsmSpBdffFFnzpxxHtVasWKF4uLitGPHDo0ePbpJ9WRkZCg9Pd2lLSkpScnJyU16flMEB/ubNhYAAFYLCfGs39T3iIDVuXNnlZaWqra2Vnb72Sk5HA75%2BfkpODi4wed8%2BOGHmjVrllu7j4%2BPy9EwX19f9ejRQ0VFRU2uZ9y4cYqPj3dps9sDVFpa3uQxGuPt7aXgYH%2BVlVWqrq7x68sAAGhNzPiObIhVwc0jAla/fv1kt9u1Z88excTESJKys7MVEREhLy/3Q47Hjx/X4cOHFR0d7dJuGIaGDx%2BumTNnKjExUdLZ31A8ePCgevXq1eR6wsLC3E4HOhynVFtrXiCqq6s3dTwAAKzkad9pHnHC09/fXwkJCVq8eLH27t2r7du3a/369Zo0aZKks0ezqqqqnP3z8vKcR6a%2Bz2azaejQoVq5cqV27typvLw8paamqkuXLoqLi2vROQEAgNbLIwKWJC1YsED9%2B/fXAw88oMcee0yzZ8/WnXfeKUmKjY3V1q1bnX2/%2B%2B47BQcHy2azuY0zd%2B5cjRgxQikpKRo7dqxqa2u1Zs0aeXt7t9hcAABA62YzDMOwuoi2wOE4dfFOTWC3eykkJFClpeWqra3XqGc%2BNmVcAACs9PbDg5tl3NDQds0y7sV4zBEsAACAqwUBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQeE7Cqq6u1cOFCxcTEKDY2VuvXr2%2B074wZM9QFXRnZAAAYX0lEQVS3b1%2BXnx07dji3Z2VladiwYYqMjFRSUpKOHz/eElMAAAAewm51AWZZtmyZ9u3bpw0bNujIkSOaN2%2BeunXrppEjR7r1LSgo0PLly/XDH/7Q2da%2BfXtJ0t69e7Vo0SI99thjCg8P19KlS7VgwQKtXr26xeYCAABaN48IWBUVFcrMzNTatWvVv39/9e/fX3l5eXr55ZfdAlZNTY2%2B/fZbRUREKDQ01G2sTZs2adSoUUpISJB0NrjdfvvtOnz4sHr27Nki8wEAAK2bR5wizM3NVW1traKiopxt0dHRysnJUX19vUvfwsJC2Wy2RsNSTk6OYmJinI%2B7du2qbt26KScnp3mKBwAAHscjApbD4VBISIh8fHycbZ06dVJ1dbVOnDjh0rewsFBBQUFKTU1VbGysfvKTn%2Bj99993bi8uLlZYWJjLczp27Khjx4417yQAAIDH8IhThJWVlS7hSpLzcU1NjUt7YWGhqqqqFBsbq%2BnTp%2Bvdd9/VjBkzlJGRoYiICFVVVTU41vnjXEhxcbEcDodLm90e4BbcLoe3t5fLvwAAeAK73bO%2B1zwiYPn6%2BroFoHOP/fz8XNpnzpypiRMnOi9qDw8P15dffqnXXntNERERjY7l7%2B/f5HoyMjKUnp7u0paUlKTk5OQmj3ExwcFNrwcAgKtdSEig1SWYyiMCVufOnVVaWqra2lrZ7Wen5HA45Ofnp%2BDgYJe%2BXl5eznB1Tq9evZSfn%2B8cq6SkxGV7SUlJgxfEN2bcuHGKj493abPbA1RaWt7kMRrj7e2l4GB/lZVVqq6u/uJPAACgFTDjO7IhVgU3jwhY/fr1k91u1549e5wXqGdnZysiIkJeXq6HHOfPny%2Bbzaa0tDRnW25urvr06SNJioyMVHZ2thITEyVJR48e1dGjRxUZGdnkesLCwtxOBzocp1Rba14gqqurN3U8AACs5GnfaR5xwtPf318JCQlavHix9u7dq%2B3bt2v9%2BvWaNGmSpLNHs6qqqiRJ8fHxevPNN/XGG2/o4MGDSk9PV3Z2tiZMmCBJGj9%2BvLZs2aLMzEzl5uYqNTVVQ4cO5RYNAACgyWyGYRhWF2GGyspKLV68WH/7298UFBSkn//855o8ebIkqW/fvkpLS3MelcrMzNS6det05MgR3XjjjVqwYIFuueUW51ibN2/Wc889p5MnT2rw4MF6/PHHFRISckX1ORynruj559jtXgoJCVRpablqa%2Bs16pmPTRkXAAArvf3w4GYZNzS0XbOMezEeE7CudgQsAAAa52kByyNOEQIAAFxNCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAm85iAVV1drYULFyomJkaxsbFav359o33/8Y9/6J577lFUVJRGjx6t9957z2V7TEyM%2Bvbt6/JTXl7e3FMAAAAewm51AWZZtmyZ9u3bpw0bNujIkSOaN2%2BeunXrppEjR7r0y83N1axZs5Samqq4uDh99NFH%2BuUvf6nXX39d4eHhKioq0qlTp7R9%2B3b5%2Bfk5nxcQENDSUwIAAK2URwSsiooKZWZmau3aterfv7/69%2B%2BvvLw8vfzyy24BKysrS4MGDdKkSZMkSddee63%2B/ve/6%2B2331Z4eLgKCgoUGhqqnj17WjEVAADgATwiYOXm5qq2tlZRUVHOtujoaK1atUr19fXy8vq/M6FjxozRmTNn3MY4deqUJCk/P1/XX3998xcNAAA8lkdcg%2BVwOBQSEiIfHx9nW6dOnVRdXa0TJ0649O3du7fCw8Odj/Py8vTJJ5/ohz/8oSSpoKBAlZWVmjhxomJjYzVt2jQdOHCgZSYCAAA8gkccwaqsrHQJV5Kcj2tqahp93vHjxzV79mwNGDBAd9xxhySpsLBQJ0%2Be1Jw5cxQUFKS1a9dq8uTJeuuttxQUFNSkeoqLi%2BVwOFza7PYAhYWFXcq0GuTt7eXyLwAAnsBu96zvNY8IWL6%2Bvm5B6tzj71%2Bo/n0lJSWaMmWKDMPQc8895zyN%2BOKLL%2BrMmTMKDAyUJK1YsUJxcXHasWOHRo8e3aR6MjIylJ6e7tKWlJSk5OTkS5rXhQQH%2B5s2FgAAVgsJCbS6BFN5RMDq3LmzSktLVVtbK7v97JQcDof8/PwUHBzs1r%2BoqMh5kftLL72kDh06OLf5%2BPi4HA3z9fVVjx49VFRU1OR6xo0bp/j4eJc2uz1ApaVXfqsHb28vBQf7q6ysUnV19Vc8HgAAVwMzviMbYlVw84iA1a9fP9ntdu3Zs0cxMTGSpOzsbEVERLhc4C6d/Y3DqVOnysvLSy%2B99JJCQ0Od2wzD0PDhwzVz5kwlJiY6%2Bx88eFC9evVqcj1hYWFupwMdjlOqrTUvENXV1Zs6HgAAVvK07zSPCFj%2B/v5KSEjQ4sWL9bvf/U7FxcVav3690tLSJJ09mtWuXTv5%2Bflp9erVOnTokDZu3OjcJp09ldiuXTsNHTpUK1euVPfu3dWhQwc9%2B%2Byz6tKli%2BLi4iybHwAAaF08ImBJ0oIFC7R48WI98MADCgoK0uzZs3XnnXdKkmJjY5WWlqbExERt27ZNVVVVGjt2rMvzx4wZoyeffFJz586V3W5XSkqKTp8%2BrUGDBmnNmjXy9va2YloAAKAVshmGYVhdRFvgcJwyZRy73UshIYEqLS1XbW29Rj3zsSnjAgBgpbcfHtws44aGtmuWcS/Gs34nEgAA4CpAwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkBCwAAACTEbAAAABMRsACAAAwGQELAADAZAQsAAAAkxGwAAAATEbAAgAAMBkBCwAAwGQELAAAAJMRsAAAAExGwAIAADAZAQsAAMBkHhOwqqurtXDhQsXExCg2Nlbr169vtO/%2B/fs1duxYRUZG6t5779W%2BfftctmdlZWnYsGGKjIxUUlKSjh8/3tzlAwAAD%2BIxAWvZsmXat2%2BfNmzYoEcffVTp6el655133PpVVFRo%2BvTpiomJ0ebNmxUVFaWHHnpIFRUVkqS9e/dq0aJFmjVrljIyMlRWVqYFCxa09HQAAEAr5hEBq6KiQpmZmVq0aJH69%2B%2Bv4cOHa%2BrUqXr55Zfd%2Bm7dulW%2Bvr5KTU1V7969tWjRIgUGBjrD2KZNmzRq1CglJCQoPDxcy5Yt0/vvv6/Dhw%2B39LQAAEAr5REBKzc3V7W1tYqKinK2RUdHKycnR/X19S59c3JyFB0dLZvNJkmy2WwaMGCA9uzZ49weExPj7N%2B1a1d169ZNOTk5LTATAADgCexWF2AGh8OhkJAQ%2Bfj4ONs6deqk6upqnThxQh06dHDpe8MNN7g8v2PHjsrLy5MkFRcXKywszG37sWPHmlxPcXGxHA6HS5vdHuA27uXw9vZy%2BRcAAE9gt3vW95pHBKzKykqXcCXJ%2BbimpqZJfc/1q6qquuD2psjIyFB6erpL26xZszR79uwmj9GY4uJibdiwTuPGjVNYWJj%2BtXTkFY95tSsuLlZGRoZzzm1BW5tzW5uv1Pbm3NbmKzHntjLnxnhEXPT19XULQOce%2B/n5NanvuX6Nbff3929yPePGjdPmzZtdfsaNG9fk51%2BIw%2BFQenq62xEyT8acPV9bm6/U9ubc1uYrMee2ziOOYHXu3FmlpaWqra2V3X52Sg6HQ35%2BfgoODnbrW1JS4tJWUlLiTNqNbQ8NDW1yPWFhYW0%2BuQMA0JZ5xBGsfv36yW63Oy9Ul6Ts7GxFRETIy8t1ipGRkfr8889lGIYkyTAM7d69W5GRkc7t2dnZzv5Hjx7V0aNHndsBAAAuxiMClr%2B/vxISErR48WLt3btX27dv1/r16zVp0iRJZ49mVVVVSZJGjhypsrIyLV26VPn5%2BVq6dKkqKys1atQoSdL48eO1ZcsWZWZmKjc3V6mpqRo6dKh69uxp2fwAAEDr4r148eLFVhdhhkGDBmn//v36/e9/r08%2B%2BUS/%2BMUvdO%2B990qSBgwYoGuvvVb9%2BvWTj4%2BPbr31Vr3yyitatWqVamtr9dRTT6lbt26Szt6WoUuXLkpPT9fLL7%2BsG2%2B8UUuXLr2ka7CaW2BgoG699VYFBgZaXUqLYc6er63NV2p7c25r85WYc1tmM86dKwMAAIApPOIUIQAAwNWEgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJiNgXYWqq6u1cOFCxcTEKDY2VuvXr2%2B07/79%2BzV27FhFRkbq3nvv1b59%2B1qwUvMUFRUpOTlZt956q4YMGaK0tDRVV1c32HfGjBnq27evy8%2BOHTtauOIr8%2B6777rNITk5ucG%2BnrDGmzdvdptv3759FR4e3mD/1r7GNTU1uvvuu7Vz505n2%2BHDhzV58mTdfPPN%2BtGPfqSPPvrogmNkZWVp2LBhioyMVFJSko4fP97cZV%2B2hua7Z88e/fSnP1VUVJRGjBihzMzMC44RExPjtubl5eXNXfpla2jOTzzxhNscNm3a1OgYrWmNJfc5z58/v8H39bk/U9eQ1rbOV8TAVWfJkiXG6NGjjX379hl/%2B9vfjKioKOPtt99261deXm4MHjzYePLJJ438/Hzj8ccfN/7nf/7HKC8vt6Dqy1dfX2/cd999xtSpU42vv/7a%2BOyzz4zhw4cbTz75ZIP9hw8fbmzZssUoLi52/lRXV7dw1VfmhRdeMB566CGXOZw8edKtn6escWVlpctcjxw5YgwfPtxYunRpg/1b8xpXVVUZSUlJRp8%2BfYxPP/3UMIyz%2B/jo0aONlJQUIz8/31i1apURGRlp/Oc//2lwjJycHOO///u/jb/85S/GV199ZUyYMMGYPn16S06jyRqab3FxsRETE2P8/ve/Nw4cOGBkZWUZERERxo4dOxoc49ixY0afPn2MQ4cOuax5fX19C86k6Rqas2EYxuTJk43Vq1e7zKGioqLBMVrTGhtGw3MuKytzmevnn39u3HTTTca7777b4BitbZ2vFAHrKlNeXm5ERES4vGmff/55Y8KECW59MzMzjfj4eOfOWV9fbwwfPtz485//3GL1miE/P9/o06eP4XA4nG1vvvmmERsb69a3urra6Nevn1FYWNiSJZouJSXF%2BP3vf3/Rfp6yxudbtWqVMWzYsAZDU2te47y8POPHP/6xMXr0aJcvon/%2B85/GzTff7BKMH3jgAeO5555rcJy5c%2Bca8%2BbNcz4%2BcuSI0bdvX%2BPQoUPNO4FL1Nh8X3nlFWPkyJEufR955BFjzpw5DY7z8ccfG4MHD272es3Q2JwNwzCGDBlifPjhh00ap7WssWFceM7f9%2BCDDxq//vWvGx2nNa2zGThFeJXJzc1VbW2toqKinG3R0dHKyclRfX29S9%2BcnBxFR0fLZrNJkmw2mwYMGKA9e/a0aM1XKjQ0VOvWrVOnTp1c2k%2BfPu3Wt7CwUDabTT179myp8ppFQUGBrrvuuov285Q1/r4TJ05o7dq1SklJkY%2BPj9v21rzGu3bt0sCBA5WRkeHSnpOTo//6r/9SQECAsy06OrrRdczJyVFMTIzzcdeuXdWtWzfl5OQ0T%2BGXqbH5njvNf76G3tOSlJ%2Bfr%2Buvv75ZajRbY3M%2Bffq0ioqKmvS%2BllrPGkuNz/n7PvnkE3322WeaM2dOo31a0zqbwW51AXDlcDgUEhLi8sXTqVMnVVdX68SJE%2BrQoYNL3xtuuMHl%2BR07dlReXl6L1WuG4OBgDRkyxPm4vr5emzZt0qBBg9z6FhYWKigoSKmpqdq1a5e6dOmi2bNnKy4uriVLviKGYejAgQP66KOPtHr1atXV1WnkyJFKTk52Cxyessbf9%2BqrryosLEwjR45scHtrXuP777%2B/wXaHw6GwsDCXto4dO%2BrYsWMN9i8uLr6k/lZpbL49evRQjx49nI%2B/%2B%2B47vfXWW5o9e3aD/QsKClRZWamJEyfqwIED6tevnxYuXHhVfhk3NueCggLZbDatWrVKH3zwga655hpNmTJFY8aMabB/a1ljqfE5f9%2BaNWs0ZswYde3atdE%2BrWmdzcARrKtMZWWl25fsucc1NTVN6nt%2Bv9Zm%2BfLl2r9/v371q1%2B5bSssLFRVVZViY2O1bt06xcXFacaMGfriiy8sqPTyHDlyxLl2zzzzjObNm6c333xTy5Ytc%2BvraWtsGIYyMzM1YcKERvt4whqf71LXsaqqymPWvaqqSrNnz1anTp00bty4BvsUFhbq5MmTmjFjhl544QX5%2Bflp8uTJjR7xuhqdO/Laq1cvrVmzRmPHjtUjjzyid999t8H%2BnrTGhw8f1qeffqqJEydesJ8nrPOl4AjWVcbX19ftDXbusZ%2BfX5P6nt%2BvNVm%2BfLk2bNigp59%2BWn369HHbPnPmTE2cOFHt27eXJIWHh%2BvLL7/Ua6%2B9poiIiJYu97J0795dO3fuVPv27WWz2dSvXz/V19dr7ty5WrBggby9vZ19PW2Nv/jiCxUVFemuu%2B5qtI8nrPH5fH19deLECZe2C61jY%2Bvu7%2B/fbDU2h/Lycs2cOVPffPONXnnllUbrf/HFF3XmzBkFBgZKklasWKG4uDjt2LFDo0ePbsmSL1tCQoJuv/12XXPNNZLO7rfffPONXn31VQ0fPtytv6essSRt27ZN/fr1czvafj5PWOdLwRGsq0znzp1VWlqq2tpaZ5vD4ZCfn5%2BCg4Pd%2BpaUlLi0lZSUuB12bi0ef/xx/fGPf9Ty5cs1YsSIBvt4eXk5v3jP6dWrl4qKilqiRNNcc801zuuqJKl3796qrq7WyZMnXfp52hp/%2BOGHiomJcVvD7/OUNf6%2BS13HxvqHhoY2W41mO336tH7%2B858rLy9PGzZsuOC1ST4%2BPs4vXels%2BOjRo0erWnObzeYMV%2BdcaL/1hDU%2B58MPP9Qdd9xx0X6esM6XgoB1lenXr5/sdrvLxa/Z2dmKiIiQl5frckVGRurzzz%2BXYRiSzp5%2B2b17tyIjI1u0ZjOkp6frT3/6k5566qkLHt2YP3%2B%2BFixY4NKWm5urXr16NXeJpvnwww81cOBAVVZWOtu%2B%2BuorXXPNNS7X2EmetcaStHfvXg0YMOCCfTxhjc8XGRmpL7/8UlVVVc627OzsRtcxMjJS2dnZzsdHjx7V0aNHW82619fXa9asWfr222%2B1ceNG3XjjjY32NQxDw4YN0%2BbNm51tFRUVOnjwYKta82effVaTJ092abvQftva1/gcwzD0xRdfXPR97SnrfCkIWFcZf39/JSQkaPHixdq7d6%2B2b9%2Bu9evXO2/c5nA4nB/SI0eOVFlZmZYuXar8/HwtXbpUlZWVGjVqlJVTuGQFBQV64YUXNG3aNEVHR8vhcDh/JNc5x8fH680339Qbb7yhgwcPKj09XdnZ2Re8pudqExUVJV9fX/3mN79RYWGh3n//fS1btkxTp06V5JlrfE5eXl6DpxE8bY3Pd%2Butt6pr165asGCB8vLytGbNGu3du1c/%2BclPJJ09NeRwOFRXVydJGj9%2BvLZs2aLMzEzl5uYqNTVVQ4cObTW/Wfn6669r586deuKJJxQcHOx8P587Tfr9%2BdpsNg0dOlQrV67Uzp07lZeXp9TUVHXp0qVV/GLDObfffrs%2B%2B%2Bwzvfjiizp06JBeeeUVvfHGG3rwwQcled4an/Of//xH5eXlDb6vPXGdL4llN4hAoyoqKozU1FTj5ptvNmJjY40//vGPzm19%2BvRxuQdSTk6OkZCQYERERBg/%2BclPjC%2B//NKCiq/M6tWrjT59%2BjT4Yxjuc37ttdeMO%2B%2B807jpppuMMWPGGLt27bKq9Mv29ddfG5MnTzZuvvlmY/DgwcbKlSud97ryxDU%2BJyIiwvjggw/c2j1xjc%2B/X9A333xj/OxnPzNuuukm46677jI%2B/vhj57ZPP/3U6NOnj3H48GFn25///GcjLi7OuPnmm42kpCTj%2BPHjLVr/pfr%2BfB988MEG38/n7ud3/nyrqqqMtLQ0Y/DgwUZkZKTx0EMPGUeOHLFsLk11/hq/%2B%2B67xujRo42IiAhj5MiRxrZt25zbPGGNDcN9znv27DH69OnT4D3tPGWdL5fNMP7/uQcAAACYglOEAAAAJiNgAQAAmIyABQAAYDICFgAAgMkIWAAAACYjYAEAAJiMgAUAAGAyAhYAAIDJCFgAAAAmI2ABAACYjIAFAABgMgIWAACAyQhYAAAAJvt/VU4MvWHAk00AAAAASUVORK5CYII%3D\">\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">lastName<br/>\n",
" <small>Categorical, Unique</small>\n",
" </p>\n",
" </div> <div class=\"col-md-3 collapse in\" id=\"minivalues-1203938114005516078\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>First 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Alvarez$$%!</td>\n",
" </tr>\n",
" <tr>\n",
" <td>M$$ax%%well</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Nöether$</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
"<div class=\"col-md-6 collapse in\" id=\"minivalues-1203938114005516078\"><table border=\"1\" class=\"dataframe example_values\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Last 3 values</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Hoy&&&le</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Ampère</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Ga%%%uss</td>\n",
" </tr>\n",
" </tbody>\n",
"</table></div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#values-1203938114005516078,#minivalues-1203938114005516078\" aria-expanded=\"false\"\n",
" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"col-md-12 collapse\" id=\"values-1203938114005516078\">\n",
" <p class=\"h4\">First 20 values</p>\n",
" <table border=\"1\" class=\"dataframe sample table table-hover\">\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alvarez$$%!</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>M$$ax%%well</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Nöether$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Planck!!!</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>COM%%%pton</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>KEPLER</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Einstein</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>H$$$ilbert</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Hertz</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Chadwick</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>CURIE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Gilbert###</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Newton</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>GALiLEI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Böhr//((%%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>dirac$</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Hoy&&&le</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Ampère</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Ga%%%uss</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" <p class=\"h4\">Last 20 values</p>\n",
" \n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">price<br/>\n",
" <small>Numeric</small>\n",
" </p>\n",
" </div> <div class=\"col-md-6\">\n",
" <div class=\"row\">\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
" <tr>\n",
" <th>Distinct count</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>42.1%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"\n",
" </div>\n",
" <div class=\"col-sm-6\">\n",
" <table class=\"stats \">\n",
"\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>6.0526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Zeros (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"<div class=\"col-md-3 collapse in\" id=\"minihistogram-1453917864483493867\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAAjdJREFUeJzt3LFKW2EYx%2BE3pYsXkBjEzVsorq4ujiregzjpVMSpOGUTvAYFV8k1SMHdxUWQeMaIo6ebIC1/WqHH0/o82wmE783w40tO8mXQtm1bwC99eu8BoM8%2Bv/cAdO/L1%2BkfP%2Bf7t/W/MEn/2UEgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCD7seRBnIvgddhAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgEHzYn7t34X/6Sf1bXstb9O3120EgEAgEAoFAIBAIBAKBQCAQCHr3PUhX99vfos%2Bz8XfYQSAQCAQCgUAgEAgEAoFAIBAIBm3btu89BCQPDw91dnZW29vbNRqNOl3bDkLvNU1TJycn1TRN52sLBAKBQCAQCARC7w2Hw9rd3a3hcNj52u5iQWAHgUAgEAgEAoFAIBAIBAKBQCAQCL30%2BPhYGxsbdXd3V1VVNzc3tbW1Vevr67W3t1dPT0%2BdzCEQeuf6%2Brp2dnbq9vb25bGDg4Pa39%2Bv6XRaKysrdXp62sksAqF3zs/P6%2Bjo6OXsx/39fc3n81pdXa2qqs3Nzbq8vOxklt79cRwcHx%2B/up7NZrW4uPhyPRqNajabdTKLHYTee35%2B/umxwWDQydoCoffG4/Gr04RN09R4PO5kbYHQe0tLS7WwsFBXV1dVVXVxcVFra2udrO0zCP%2BEyWRSh4eHNZ/Pa3l5uSaTSSfrOg8CgbdYEAgEAoFA8AO213WyLhfFOQAAAABJRU5ErkJggg%3D%3D\">\n",
"\n",
"</div>\n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-1453917864483493867,#minihistogram-1453917864483493867\"\n",
" aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"row collapse col-md-12\" id=\"descriptives-1453917864483493867\">\n",
" <div class=\"col-sm-4\">\n",
" <p class=\"h4\">Quantile statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Minimum</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5-th percentile</th>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q1</th>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Median</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q3</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95-th percentile</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maximum</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Range</th>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Interquartile range</th>\n",
" <td>5</td>\n",
" </tr>\n",
" </table>\n",
" <p class=\"h4\">Descriptive statistics</p>\n",
" <table class=\"stats indent\">\n",
" <tr>\n",
" <th>Standard deviation</th>\n",
" <td>2.9528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coef of variation</th>\n",
" <td>0.48786</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kurtosis</th>\n",
" <td>-1.4482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>6.0526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MAD</th>\n",
" <td>2.6814</td>\n",
" </tr>\n",
" <tr class=\"\">\n",
" <th>Skewness</th>\n",
" <td>-0.22564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sum</th>\n",
" <td>115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Variance</th>\n",
" <td>8.7193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Memory size</th>\n",
" <td>0.0 B</td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" <div class=\"col-sm-8 histogram\">\n",
" <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAIABJREFUeJzt3XtQ1XX%2Bx/EXnBMIGAXKcZJtpOtmZuaNtMRN1l3ZcqMyIytrdVvKMKYty%2BviBYoSvEyDYmYXs0bZza7a5lTbmk1m3suxTMU1HTWOG2QogcD5/bET8ztAifWBD%2Bf7fT5mHIaPni9vzoevPj3fwyEsEAgEBAAAAGPCbQ8AAADgNAQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYQQWAACAYV7bA7iF3/%2Bd7RFcIzw8TPHxMfrmm%2BOqrw/YHseV2AP72AP72AP7wsPD1KlTRzsf28pHBVpReHiYwsLCFB4eZnsU12IP7GMP7GMP7LN53xNYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYAAAAhhFYzaipqdHMmTPVv39/XXXVVZo7d64CAV6FFwAAtAw/KqcZeXl52rBhg5555hkdP35cf/3rX9W1a1fdeuuttkcDAAAhgEewGqmoqNDKlSuVm5uryy%2B/XAMHDtTYsWO1fft226MBAIAQwSNYjWzevFkdO3ZUcnJyw1pmZqbFiQAAQKghsBo5cOCAEhMT9dprr2nRokU6efKkbrrpJo0bN07h4S17wK%2BsrEx%2Bvz9ozeuNls/na42R0YjHEx70Fm2PPbCPPbCPPbDP5n1PYDVy4sQJ7d%2B/XytWrFB%2Bfr78fr9ycnIUFRWlsWPHtugYJSUlKioqClrLyspSdnZ2a4yMHxEbG2V7BNdjD%2Bxr73vQb%2Brbtkc4LZseTTvt27T3PUDrILAa8Xq9qqys1Jw5c5SYmChJOnTokJYvX97iwMrIyFBqamqj40arvPy48XnRlMcTrtjYKB07VqW6unrb47gSe2Afe9A6TufvcfbAvh/2wAYCq5GEhARFRkY2xJUknXfeeTp8%2BHCLj%2BHz%2BZpcDvT7v1NtLSdYW6qrq%2Bc%2Bt4w9sI89MOvn3JfsgTtxYbiRXr16qbq6Wvv27WtYKy0tDQouAACAn0JgNXL%2B%2Befrmmuu0eTJk/XFF19o3bp1Wrx4sUaNGmV7NAAAECK4RNiMwsJC5ebmatSoUYqKitLtt9%2Bu0aNH2x4LAACECAKrGWeeeaZmz55tewwAABCiuEQIAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIEFAABgGIHVjHfeeUe//vWvg35lZ2fbHgsAAIQIr%2B0B2qM9e/ZoyJAhys3NbViLjIy0OBEAAAglBFYz9u7dq4svvlgJCQm2RwEAACGIS4TN2Lt3r5KSkmyPAQAAQhSPYDUSCAS0b98%2Bffjhh3rqqadUV1entLQ0ZWdnKyIiokXHKCsrk9/vD1rzeqPl8/laY2Q04vGEB71F22MP7GMPWofX2/L7kz2wz%2BZ9T2A1cujQIVVVVSkiIkLz58/XwYMHlZeXp%2B%2B//17Tpk1r0TFKSkpUVFQUtJaVlcUT5dtYbGyU7RFcjz2wjz0wKy4u5rRvwx64U1ggEAjYHqK9qaio0FlnnaWwsDBJ0po1a/Twww9r69at8ng8p7w9j2DZ5fGEKzY2SseOVamurt72OK7EHtgXKnvwu8J1tkc4Le9MSGnxnw2VPXCyH/bABh7BasbZZ58d9P4FF1yg6upqffvtt4qPjz/l7X0%2BX5OY8vu/U20tJ1hbqqur5z63jD2wjz0w6%2Bfcl%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%2B/1Ba15vtHw%2Bn4kRcQoeT3jQW7Q99sA%2B9qB1eL0tvz/ZA/ts3vcEViNFRUW67LLLlJKS8rOPUVJSoqKioqC1rKwsZWdn/9LxcBpiY6N%2B0e37TX3b0CRtY9OjabZHaOKX7gF%2BOfbArLi4mNO%2BDXvgTgRWI6tXr9bRo0fVu3dvSVJNTY0kac2aNdq6dWuLjpGRkaHU1NSgNa83WuXlx80Oi2Z5POGKjY3SsWNVqqurtz1Om2lPX19u3YP2hD1oHadznrEH9v2wBzYQWI0sW7ZMtbW1De8XFhZKkiZMmNDiY/h8viaXA/3%2B71RbywnWlurq6l11n7fHz9Vte9AesQdm/Zz7kj1wJwKrkcTExKD3Y2L%2B93Bwt27dbIwDAABCEM%2B8AwAAMIxHsE7h8ccftz0CAAAIMTyCBQAAYBiBBQAAYBiBBQAAYBiBBQAAYJijAmvkyJFasWKFvvvuO9ujAAAAF3NUYA0YMECLFi3SoEGD9OCDD%2BrDDz9UIBCwPRYAAHAZRwXWQw89pPfff18LFy6Ux%2BPR/fffr2uuuUbz5s3Tvn37bI8HAABcwnGvgxUWFqarr75aV199taqqqrRs2TItXLhQixcvVp8%2BfXTXXXfp97//ve0xAQCAgzkusCSprKxMb7zxht544w19%2BeWX6tOnj2688UYdOXJE06ZN08aNGzV16lTbYwIAAIdyVGC9/vrrev3117VhwwbFx8frhhtu0JNPPqmkpKSGP3POOefo0UcfJbAAAECrcVRgTZ06VUOGDNGCBQs0ePBghYc3fYrZ%2BeefrzvuuMPCdAAAwC0cFVgffPCB4uLiVFFR0RBXn376qXr06CGPxyNJ6tOnj/r06WNzTAAA4HCO%2Bi7CyspKpaWl6emnn25Yy8zMVHp6ug4fPmxxMgAA4CaOCqzHHntM3bp105gxYxrW3nrrLZ1zzjnKz8%2B3OBkAAHATRwXWpk2bNGnSJCUkJDSsxcfH65FHHtHHH39scTIAAOAmjgosr9erY8eONVmvqqriFd0BAECbcVRgDR48WHl5efrqq68a1g4cOKD8/HylpKRYnAwAALiJo76LcOLEiRozZoyGDRum2NhYSdKxY8fUo0cPTZ482fJ0AADALRwVWJ06ddKrr76qjz76SLt375bX69WFF16ogQMHKiwszPZ4AADAJRwVWJLk8XiUkpLCJUEAAGCNowLL7/dr/vz52rJli06ePNnkie3vvfeepckAAICbOCqw/va3v2nHjh267rrrdOaZZ9oeBwAAuJSjAuvjjz/WkiVL1K9fP9ujAAAAF3PUyzRER0erU6dOtscAAAAu56jASk9P15IlS1RXV2d7FAAA4GKOukRYUVGhVatW6d///rfOPfdcRUREBP3%2BCy%2B8YGkyAADgJo4KLEkaPny47REAAIDLOSqw8vPzbY8AAADgrOdgSVJZWZmKior00EMP6b///a/efvttlZaW2h4LAAC4iKMCa//%2B/frjH/%2BoV199VWvWrNGJEyf01ltvacSIEdq%2Bfbvt8QAAgEs4KrAef/xxDR06VO%2B%2B%2B67OOOMMSdLcuXOVmpqqwsJCy9MBAAC3cFRgbdmyRWPGjAn6wc5er1f33Xefdu7caXEyAADgJo4KrPr6etXX1zdZP378uDwej4WJAACAGzkqsAYNGqSnnnoqKLIqKipUUFCgAQMGWJwMAAC4iaMCa9KkSdqxY4cGDRqk6upqjRs3TkOGDNHBgwc1ceJE2%2BMBAACXcNTrYHXp0kWvvfaaVq1apc8//1z19fUaNWqU0tPT1bFjR9vjAQAAl3BUYElSVFSURo4caXsMAADgYo4KrDvvvPMnf5%2BfRQgAANqCowIrMTEx6P3a2lrt379fX375pe666y5LUwEAALdxVGD92M8iXLBggY4cOdLi4%2Bzfv1%2BzZs3Sli1bdNZZZ%2BmOO%2B7Q3XffbWpMAADgcI76LsIfk56ern/%2B858t%2BrP19fXKzMxUXFycXn31Vc2cOVPFxcV68803W3lKAADgFK4IrK1bt7b4hUaPHj2q7t27a8aMGUpKStJvfvMbDRw4UJs3b27lKQEAgFM46hJhc09yr6ys1K5du3Tbbbe16Bg%2Bn0/z58%2BXJAUCAW3ZskUbN27U9OnTjc4KAACcy1GB1bVr16CfQyhJZ5xxhu644w5df/31p3281NRUHTp0SEOGDNGwYcNMjQkAABzOUYH1%2BOOPGz3ek08%2BqaNHj2rGjBnKz8/XtGnTWnS7srIy%2Bf3%2BoDWvN1o%2Bn8/ofGiexxMe9NYtvN728/m6dQ/aE/agdZzOecYe2GfzvndUYG3cuLHFf7Z///6n/DM9e/aUJFVXV2vChAl65JFHFBERccrblZSUqKioKGgtKytL2dnZLZ4Pv1xsbJTtEdpUXFyM7RGacNsetEfsgVk/5zxjD9zJUYE1evTohkuEgUCgYb3xWlhYmD7//PNmj3H06FFt27ZNQ4cObVi78MILdfLkSVVWVio%2BPv6Uc2RkZCg1NTVozeuNVnn58dP7hPCzeDzhio2N0rFjVaqrqz/1DRyiPX19uXUP2hP2oHWcznnGHtj3wx7Y4KjAWrRokfLy8vTwww8rOTlZERER%2BuyzzzRr1izdeOONuvbaa095jIMHD2r8%2BPFau3atunTpIknasWOH4uPjWxRX0v%2BeKN/4cqDf/51qaznB2lJdXb2r7vP2%2BLm6bQ/aI/bArJ9zX7IH7uSoC8P5%2BfnKycnRsGHDFBcXp5iYGA0YMECzZs3S8uXLlZiY2PDrx/Ts2VM9evTQlClTtGfPHq1du1YFBQW699572/AzAQAAocxRgVVWVtZsPHXs2FHl5eUtOobH49HChQsVFRWljIwMTZ06VaNHjz7lzzkEAAD4gaMuEV5xxRWaO3eunnjiCXXs2FGSVFFRoYKCAg0cOLDFx%2BnSpUuTJ6kDAAC0lKMCa9q0abrzzjs1ePBgJSUlKRAI6D//%2BY8SEhL0wgsv2B4PAAC4hKMC64ILLtBbb72lVatWae/evZKk22%2B/Xdddd52iovg2WQAA0DYcFViSdNZZZ2nkyJE6ePCgzj33XEn/ezV3AACAtuKoJ7kHAgEVFhaqf//%2BGj58uI4cOaKJEydq6tSpOnnypO3xAACASzgqsJYtW6bXX39d06dPb3jF9aFDh%2Brdd9/lSesAAKDNOCqwSkpKlJOTo5tuuqnh1duvvfZa5eXl6c0337Q8HQAAcAtHBdbBgwfVvXv3JuuXXHJJkx%2B%2BDAAA0FocFViJiYn67LPPmqx/8MEHDU94BwAAaG2O%2Bi7CP//5z5o5c6b8fr8CgYDWr1%2BvkpISLVu2TJMmTbI9HgAAcAlHBdaIESNUW1ur4uJiff/998rJyVF8fLweeOABjRo1yvZ4AADAJRwVWKtWrVJaWpoyMjL0zTffKBAIqFOnTrbHAgAALuOo52DNmjWr4cns8fHxxBUAALDCUYGVlJSkL7/80vYYAADA5Rx1ifCSSy7RhAkTtGTJEiUlJSkyMjLo9/Pz8y1NBgAA3MRRgbVv3z717dtXknjdKwAAYE3IB9bs2bM1fvx4RUdHa9myZbbHAQAACP3nYD333HOqqqoKWsvMzFRZWZmliQAAgNuFfGAFAoEmaxs3blR1dbWFaQAAABwQWAAAAO0NgQUAAGCYIwIrLCzM9ggAAAANQv67CCUpLy8v6DWvTp48qYKCAsXExAT9OV4HCwAAtIWQD6z%2B/fs3ec2r3r17q7y8XOXl5ZamAgAAbhbygcVrXwEAgPbGEc/BAgAAaE8ILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMILAAAAMMIrGZ8/fXXys7OVnJyslJSUpSfn6/q6mrbYwEAgBDhtT1AexMIBJSdna3Y2Fi99NJL%2BvbbbzVlyhSFh4dr4sSJtscDAAAhgEewGiktLdW2bduUn5%2Bviy66SP369VN2drZWrVplezQAABAiCKxGEhIStGTJEnXu3DlovbKy0tJEAAAg1HCJsJHY2FilpKQ0vF9fX68XX3xRAwYMaPExysrK5Pf7g9a83mj5fD5jc%2BLHeTzhQW/dwuttP5%2BvW/egPWEPWsfpnGfsgX0273sC6xQKCgq0c%2BdOvfzyyy2%2BTUlJiYqKioLWsrKylJ2dbXo8/ITY2CjbI7SpuLgY2yM08WN70G/q2208yS%2Bz6dE02yP8bG47D1rb7wrX2R7BsUL5PGsOgfUTCgoKtHTpUs2bN08XX3xxi2%2BXkZGh1NTUoDWvN1rl5cdNj4hmeDzhio2N0rFjVaqrq7c9TptpT19fTtuD9nTftpTT9gDO1xrn2Q/ngQ0E1o/Izc3V8uXLVVBQoGHDhp3WbX0%2BX5PLgX7/d6qt5S%2B5tlRXV%2B%2Bq%2B7w9fq5O2YNQ/hycsgdwPqd9nRJYzSgqKtKKFSs0d%2B5cpaU56yFLAADQ%2BgisRvbu3auFCxcqMzNTffv2DXqyekJCgsXJAABAqCCwGnnvvfdUV1en4uJiFRcXB/3erl27LE0FAABCCYHVSGZmpjIzM22PAQAAQhgvzgEAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgQUAAGAYgfUTampqNHz4cG3YsMH2KAAAIIQQWD%2BiurpaDz74oHbv3m17FAAAEGIIrGbs2bNHt9xyi7766ivbowAAgBBEYDXjk08%2B0ZVXXqmSkhLbowAAgBDktT1Ae3Tbbbf9otuXlZXJ7/cHrXm90fL5fL/ouGgZjyc86K1beL3t5/N12h60p/u2pZy2B3C%2B1jjPbH79E1itoKSkREVFRUFrWVlZys7ONv6x%2Bk192/gxEZp%2BV7jO9giOFRcXY3uEny02Nsr2CECLhPJ51hwCqxVkZGQoNTU1aM3rjVZ5%2BXFLEwH4JULx3PV4whUbG6Vjx6pUV1dvexzglFrjPPvhPLCBwGoFPp%2BvyeVAv/871dbylxwQikL53K2rqw/p%2BeEeTvs65eI8AACAYQQWAACAYQQWAACAYTwH6xR27dplewQAABBieAQLAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAILAADAMAKrGdXV1ZoyZYr69eunQYMG6dlnn7U9EgAACCFe2wO0R7Nnz9aOHTu0dOlSHTp0SBMnTlTXrl2VlpZmezQAABACCKxGTpw4oX/84x96%2Bumn1aNHD/Xo0UO7d%2B/WSy%2B9RGABAIAW4RJhI1988YVqa2vVu3fvhrW%2Bfftq%2B/btqq%2BvtzgZAAAIFTyC1Yjf71dcXJwiIiIa1jp37qzq6mpVVFQoPj7%2BlMcoKyuT3%2B8PWvN6o%2BXz%2BYzPC6D1eb2h939Rjyc86C3Q3rXGeWbz65/AaqSqqiooriQ1vF9TU9OiY5SUlKioqChobfz48br//vvNDPn/bHqUy5aNlZWVqaSkRBkZGUStJeyBfWVlZVq6dEm73wMn/x3GeWCfzfOA/9o0EhkZ2SSkfni/Q4cOLTpGRkaGXnnllaBfGRkZxmdF8/x%2Bv4qKipo8ioi2wx7Yxx7Yxx7YZ3MPeASrkS5duqi8vFy1tbXyev939/j9fnXo0EGxsbEtOobP5%2BN/KwAAuBiPYDXSvXt3eb1ebdu2rWFt8%2BbN6tmzp8LDubsAAMCpUQyNREVF6YYbbtCMGTP06aef6t1339Wzzz6rO%2B%2B80/ZoAAAgRHhmzJgxw/YQ7c2AAQO0c%2BdOzZkzR%2BvXr9e9996rESNG2B4LpyEmJkbJycmKiYmxPYprsQf2sQf2sQf22dqDsEAgEGjTjwgAAOBwXCIEAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMACAAAwjMCCo3z99dfKzs5WcnKyUlJSlJ%2Bfr%2BrqattjuVJmZqYmTZpkewxXqqmp0cyZM9W/f39dddVVmjt3rnhN6bZ1%2BPBh3XPPPerTp49SU1P1/PPP2x7JNWpqajR8%2BHBt2LChYe3AgQP605/%2BpCuuuELXXnutPvzww1afg8CCYwQCAWVnZ6uqqkovvfSS5s2bp/fff1/z58%2B3PZrrrF69WmvXrrU9hmvl5eXpo48%2B0jPPPKM5c%2Bbo73//u0pKSmyP5SoPPPCAoqOj9corr2jKlCmaP3%2B%2B3nnnHdtjOV51dbUefPBB7d69u2EtEAgoKytLnTt31sqVK5Wenq7x48fr0KFDrToLgQXHKC0t1bZt25Sfn6%2BLLrpI/fr1U3Z2tlatWmV7NFepqKjQ7Nmz1bNnT9ujuFJFRYVWrlyp3NxcXX755Ro4cKDGjh2r7du32x7NNb799ltt27ZN48aNU1JSkoYOHaqUlBStX7/e9miOtmfPHt1yyy366quvgtY//vhjHThwQLNmzdIFF1yge%2B65R1dccYVWrlzZqvMQWHCMhIQELVmyRJ07dw5ar6ystDSROz3xxBNKT0/XhRdeaHsUV9q8ebM6duyo5OTkhrXMzEzl5%2BdbnMpdOnTooKioKL3yyis6efKkSktLtWXLFnXv3t32aI72ySef6Morr2zyaO327dt16aWXKjo6umGtb9%2B%2B2rZtW6vOQ2DBMWJjY5WSktLwfn19vV588UUNGDDA4lTusn79em3atEn33Xef7VFc68CBA0pMTNRrr72mtLQ0/fa3v9WCBQtUX19vezTXiIyMVE5OjkpKStSrVy/94Q9/0ODBgzVy5EjboznabbfdpilTpigqKipo3e/3y%2BfzBa116tRJR44cadV5vK16dMCigoIC7dy5Uy%2B//LLtUVyhurpa06dPV05Ojjp06GB7HNc6ceKE9u/frxUrVig/P19%2Bv185OTmKiorS2LFjbY/nGnv37tWQIUM0ZswY7d69W7m5uRo4cKCuv/5626O5TlVVlSIiIoLWIiIiVFNT06ofl8CCIxUUFGjp0qWaN2%2BeLr74YtvjuEJRUZEuu%2ByyoEcR0fa8Xq8qKys1Z84cJSYmSpIOHTqk5cuXE1htZP369Xr55Ze1du1adejQQT179tTXX3%2Bt4uJiAsuCyMhIVVRUBK3V1NS0%2Bn8ECSw4Tm5urpYvX66CggINGzbM9jiusXr1ah09elS9e/eWpIb/Ha5Zs0Zbt261OZqrJCQkKDIysiGuJOm8887T4cOHLU7lLjt27FC3bt2C/gG/9NJLtWjRIotTuVeXLl20Z8%2BeoLWjR482uWxoGoEFRykqKtKKFSs0d%2B5cpaWl2R7HVZYtW6ba2tqG9wsLCyVJEyZMsDWSK/Xq1UvV1dXat2%2BfzjvvPEn/%2Bw7b/x9caF0%2Bn0/79%2B9XTU1Nw6Wp0tJS/epXv7I8mTv16tVLixcv1vfff98QvZs3b1bfvn1b9ePyJHc4xt69e7Vw4UL95S9/Ud%2B%2BfeX3%2Bxt%2BofUlJiaqW7duDb9iYmIUExOjbt262R7NVc4//3xdc801mjx5sr744gutW7dOixcv1qhRo2yP5hqpqak644wzNG3aNO3bt0//%2Bte/tGjRIo0ePdr2aK6UnJysc845R5MnT9bu3bu1ePFiffrpp7r55ptb9ePyCBYc47333lNdXZ2Ki4tVXFwc9Hu7du2yNBXQ9goLC5Wbm6tRo0YpKipKt99%2BO/%2B4t6HTYugFAAAAmUlEQVQzzzxTzz//vB599FHdfPPNio%2BP17hx45SRkWF7NFfyeDxauHChpk6dqptuukndunXTggUL1LVr11b9uGEBfn4CAACAUVwiBAAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMIzAAgAAMOz/AFvvh8Xs%2BGkeAAAAAElFTkSuQmCC\">\n",
" </div>\n",
"</div>\n",
"</div><div class=\"row variablerow\">\n",
" <div class=\"col-md-3 namecol\">\n",
" <p class=\"h4\">product<br/>\n",
" <small>Categorical</small>\n",
" </p>\n",
" </div> <div class=\"col-md-3\">\n",
" <table class=\"stats \">\n",
" <tr class=\"\">\n",
" <th>Distinct count</th>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unique (%)</th>\n",
" <td>68.4%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Missing (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (%)</th>\n",
" <td>0.0%</td>\n",
" </tr>\n",
" <tr class=\"ignore\">\n",
" <th>Infinite (n)</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </table>\n",
"</div>\n",
"<div class=\"col-md-6 collapse in\" id=\"minifreqtable-1676247420538665343\">\n",
" <table class=\"mini freq\">\n",
" <tr class=\"\">\n",
" <th>pizza</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:40%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 21.1%\">\n",
" 4\n",
" </div>\n",
" \n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <th>taco</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:30%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 15.8%\">\n",
" 3\n",
" </div>\n",
" \n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <th>pasta</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:20%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 10.5%\">\n",
" \n",
" </div>\n",
" 2\n",
" </td>\n",
"</tr> <tr class=\"other\">\n",
" <th>Other values (10)</th>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
" data-delay=500 title=\"Percentage: 52.6%\">\n",
" 10\n",
" </div>\n",
" \n",
" </td>\n",
"</tr> \n",
" </table>\n",
"</div> \n",
"<div class=\"col-md-12 text-right\">\n",
" <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-1676247420538665343, #minifreqtable-1676247420538665343\"\n",
" aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
" Toggle details\n",
" </a>\n",
"</div>\n",
"<div class=\"col-md-12 collapse extrapadding\" id=\"freqtable-1676247420538665343\">\n",
" <table class=\"freq table table-hover\">\n",
" <thead>\n",
" <tr>\n",
" <td class=\"fillremaining\">Value</td>\n",
" <td class=\"number\">Count</td>\n",
" <td class=\"number\">Frequency (%)</td>\n",
" <td style=\"min-width:200px\"> </td>\n",
" </tr>\n",
" </thead>\n",
" <tr class=\"\">\n",
" <td class=\"fillremaining\">pizza</td>\n",
" <td class=\"number\">4</td>\n",
" <td class=\"number\">21.1%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:100%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">taco</td>\n",
" <td class=\"number\">3</td>\n",
" <td class=\"number\">15.8%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:75%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">pasta</td>\n",
" <td class=\"number\">2</td>\n",
" <td class=\"number\">10.5%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:50%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">taaaccoo</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">piza</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">hamburguer</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">BEER</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">pizzza</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">arepa</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">Rice</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">110790</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">Cake</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> <tr class=\"\">\n",
" <td class=\"fillremaining\">null</td>\n",
" <td class=\"number\">1</td>\n",
" <td class=\"number\">5.3%</td>\n",
" <td>\n",
" <div class=\"bar\" style=\"width:25%\"> </div>\n",
" </td>\n",
"</tr> \n",
" </table>\n",
"</div> \n",
"</div>\n",
" <div class=\"row headerrow highlight\">\n",
" <h1>Sample</h1>\n",
" </div>\n",
" <div class=\"row variablerow\">\n",
" <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
" <table border=\"1\" class=\"dataframe sample\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>firstName</th>\n",
" <th>lastName</th>\n",
" <th>billingId</th>\n",
" <th>product</th>\n",
" <th>price</th>\n",
" <th>birth</th>\n",
" <th>dummyCol</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Luis</td>\n",
" <td>Alvarez$$%!</td>\n",
" <td>123</td>\n",
" <td>Cake</td>\n",
" <td>10</td>\n",
" <td>1980/07/07</td>\n",
" <td>never</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>André</td>\n",
" <td>Ampère</td>\n",
" <td>423</td>\n",
" <td>piza</td>\n",
" <td>8</td>\n",
" <td>1950/07/08</td>\n",
" <td>gonna</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>NiELS</td>\n",
" <td>Böhr//((%%</td>\n",
" <td>551</td>\n",
" <td>pizza</td>\n",
" <td>8</td>\n",
" <td>1990/07/09</td>\n",
" <td>give</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>PAUL</td>\n",
" <td>dirac$</td>\n",
" <td>521</td>\n",
" <td>pizza</td>\n",
" <td>8</td>\n",
" <td>1954/07/10</td>\n",
" <td>you</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Albert</td>\n",
" <td>Einstein</td>\n",
" <td>634</td>\n",
" <td>pizza</td>\n",
" <td>8</td>\n",
" <td>1990/07/11</td>\n",
" <td>up</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
" </div>\n",
"</div>\n",
"</div>"
],
"text/plain": [
"<spark_df_profiling_optimus.ProfileReport at 0x1128f3198>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Instance of profiler class\n",
"profiler = op.DataFrameProfiler(df)\n",
"profiler.profiler()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instantiation of analyzer class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But if you want more information for data exploration, Optimus has the DataFrameAnalizer which has several functions for analyzing your dataset. It presents a table that specifies the existing datatypes in each column dataFrame and other features. Also, for this particular case, the table of dataType is shown in order to visualize a sample of column content. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Instance of analyzer class\n",
"analyzer = op.DataFrameAnalyzer(df=df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"DataFrameAnalizer has a method called columnAnalize. This method can check all rows of\n",
"dataFrame and tries to parse each element of each row to determine if the corresponding \n",
"element is a string or a number. Besides, it can show 20 distinct values of each column\n",
"classified according the possible datatype value, i.e: a number can be a string, so this \n",
"function can recognize a number in a column of string dataType by trying to parse the string. \n",
"\n",
"Also the function can plot numerical or categorical histograms."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### General view of DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initially it is a good idea to see a general view of the DataFrame to be analyzed. \n",
"\n",
"In the following cell, the basic results of analyzing the DataFrame are made are shown. Basic results include a table that specifies the existing datatypes in each column dataFrame and other features. Also, for this particular case, the table of dataType is shown in order to visualize a sample of column content. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>id</td></tr><tr><td colspan=3 ><b> Column datatype: </b>int</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>0</td><td>0.00 %</td></tr><tr><td>Integer</td><td>19</td><td>100.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x112a237b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min value: 1\n",
"Max value: 19\n"
]
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{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x112a7aeb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 6.364997863769531\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>firstName</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>19</td><td>100.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
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"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 1.980332851409912\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>lastName</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>19</td><td>100.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
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"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x112a7a3c8>"
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"metadata": {},
"output_type": "display_data"
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"data": {
"text/plain": [
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"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 1.8731741905212402\n"
]
},
{
"data": {
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"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x112a50cc0>"
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"Min value: 111\n",
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"text/plain": [
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"metadata": {},
"output_type": "display_data"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 3.079150915145874\n"
]
},
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"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>product</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>18</td><td>94.74 %</td></tr><tr><td>Integer</td><td>1</td><td>5.26 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
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"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 1.797947883605957\n"
]
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"data": {
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"<table width=50%><tr><td colspan=3 ><b> Column name: </b>price</td></tr><tr><td colspan=3 ><b> Column datatype: </b>int</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>0</td><td>0.00 %</td></tr><tr><td>Integer</td><td>19</td><td>100.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
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"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 2.50827693939209\n"
]
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"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>birth</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>19</td><td>100.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
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"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 1.4246118068695068\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>dummyCol</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>19</td><td>100.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
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"text/plain": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 1.3449747562408447\n",
"Total execution time: 20.480989933013916\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><th colspan=3>General description</td></tr><tr><th colspan=1>Features</td><th colspan=2>Name or Quantity</td></tr><tr><th colspan=1>File Name</td><td colspan=2>file with no path</td></tr><tr><th colspan=1>Columns</td><td colspan=2>8</td></tr><tr><th colspan=1>Rows</td><td colspan=2>19</td></tr>"
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"text/plain": [
"<optimus.df_analyzer.GeneralDescripTable at 0x107b47240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analyzer_tables = analyzer.column_analyze(column_list=\"*\", print_type=True, plots=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results obtained by running the analyzer class, details the presence of special chars, \n",
"string columns with possible numbers on them and None and empty string values in columns."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also plot histograms for individual columns using the `plot_hist` function. This is an interesting feature because you are plotting from a Spark Dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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9PgfPoqIidenSRa1btw5EPQAAAAhRPt9qLyoqUnp6egBKAQAAQCjz6Yqnx+PR3r179dFH\nH2np0qWqrq7WkCFD5HK5FBMT0+D2JSUlKi0ttRYQnSCHw9HgtlFRkZbv4Y5+WNGPOvTCin4gVEVH\n+/eYZq5Y0Y86/uyBT8HzwIEDOnXqlGJiYrRw4UJ98803evLJJ3X69GnNnj27we3z8/OVl5dnWTZx\n4kSfPpyUlBTvS8khj35Y0Y869MKKfiDUJCcnBuR5mStW9MO/Ijwej8eXDY4ePaqWLVsqIiJCkvTu\nu+9q+vTp+uyzzxQVFVXvthd6xTMpKV7Hj59SdXWNLyWHJPphRT/q0Asr+oGmpvfTf/Nqvc9mDPTr\n6zJXrOhHnTO98AefP1zUqlUry+PLLrtMbrdbx44dU0pKSr3bOhyOn4TM0tITqqryfodWV9f4tH6o\nox9W9KMOvbCiHwg1gTqemStW9MO/fLppv3nzZvXv31+nTp2qXfbll1+qVatWDYZOAAAAhDefgmfv\n3r0VGxur2bNnq7i4WAUFBcrJydG4ceMCVR8AAABChE+32ps3b67ly5dr7ty5GjlypBITE3XnnXcS\nPAEAANAgn9/jefnll2vFihWBqAUAAAAhjF9OBQAAACMIngAAADCC4AkAAAAjCJ4AAAAwguAJAAAA\nIwieAAAAMILgCQAAACMIngAAADCC4AkAAAAjCJ4AAAAwguAJAAAAIwieAAAAMILgCQAAACMIngAA\nADCC4AkAAAAjCJ4AAAAwguAJAAAAIwieAAAAMILgCQAAACMIngAAADCC4AkAAAAjCJ4AAAAwguAJ\nAAAAIwieAAAAMILgCQAAACMIngAAADCC4AkAAAAjCJ4AAAAwguAJAAAAIwieAAAAMILgCQAAACMI\nngAAADCC4AkAAAAjou0uADDF7T6tTZs26osv/qmSkhJVVlYoLi5Oqalp6t49Q07nYMXGxtldJgAA\nIYsrnggLu3d/pTvuGKaVK19URUWFOnbspB49eqp9+3S53W6tXLlcmZnDVVi4x+5SAQAIWVzxRFiY\nNy9bTucNeuCBqeddZ+HCecrNnaulS1cYrAwAgPDBFU+Ehb17izR8+Mh617n11pEqKuKKJwAAgULw\nRFjo1KmzNmxYV+8669atVvv26WYKAgAgDHGrHWFh2rSZmj59sgoKNqlnz15KS2utZs2aqbKyUocP\nl2nnzh0qLy9XTs4Cu0sFACBkETwRFrp0+Zny89dq48Z3tWvXThUXF+r0abdiY2OUltZad911twYN\n+qUSEhLtLhUAgJBF8ETYiIuL09ChwzR06DC7SwEAICzxHk/gP9xut955Z4PdZQAAELIInsB/nDxZ\nrrlzH7e7DAAAQhbBE/iPlJRUbd681e4yAAAIWY0OnuPHj9fMmTP9WQtgixtuuE4HDnxrdxkAAIS8\nRn246O2331ZBQYGGDx/u73qAgKjvFnpFhVuLFj2nhIQESdKsWY+ZKgsAgLDi8xXPo0ePKicnRxkZ\nGYGoBwiII0e+1zvvbNDXX+89zxoeo/UAABCOfL7i+fTTT2vYsGEqKSkJRD1AQOTmPquNG9/VokXP\nqW/ffvrNb8YpJiZGkvTBB+9rwgSX2rW7xOYqAQAIbT5d8dyyZYs+/fRT/f73vw9UPUDADB58o156\n6XUdPlymsWPv1NatH9tdEgAAYcXrK55ut1uPPfaYHn30UcXFxTXqxUpKSlRaWmotIDpBDoejwW2j\noiIt38Md/bDyth8pKa30hz/M0aeffqKnn56rrl27y+OpUXR0pKKjQ6OXHBtW9AOhyt/nLOaKFf2o\n488eeB088/Ly1KNHDw0YMKDRL5afn6+8vDzLsokTJ8rlcnn9HElJ8Y1+/VBEP6zO9CN95tsNrvv/\n/vq2nn/+ee3alabU1CQlJ4fWn8vk2LCiHwg1gTpnMVes6Id/RXg8Hq8+VeF0OlVWVqaoqChJUkVF\nhSQpJiZGn332mVcvdqFXPJOS4nX8+ClVV9d49XqhjH5Ynd2P3k//rcFtPpsxMOB12YFjw4p+oKnx\n5vwl+f8cxlyxoh91zvTCH7y+4vnKK6+oqqqq9vG8efMkSdOmTfP6xRwOx09CZmnpCVVVeb9Dq6tr\nfFo/1NEPK1/6Eep949iwoh8INYE6npkrVvTDv7wOnu3atbM8Tkz89yX+Dh06+LciAAAAhCTeMQsA\nAAAjGvWXiyTpqaee8mcdAAAACHFc8QQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAA\nABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAE\nAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEE\nTwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAY\nQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAA\ngBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAET4Hz3379ul3v/udevfurYEDB2rZsmWBqAsAAAAh\nJtqXlWtqajR+/HhlZGRozZo12rdvnx588EG1adNGt9xyS6BqBAAAQAjw6YpnWVmZunbtqjlz5ig9\nPV3XXXedrrnmGm3bti1Q9QEAACBE+BQ8HQ6HFi5cqObNm8vj8Wjbtm3aunWr+vXrF6j6AAAAECJ8\nutX+Y06nUwcOHNCgQYN04403erVNSUmJSktLrQVEJ8jhcDS4bVRUpOV7uKMfVo3pR3R0aPaOY8OK\nfiBU+fscxlyxoh91/NmDRgfP5557TmVlZZozZ46ys7M1e/bsBrfJz89XXl6eZdnEiRPlcrm8ft2k\npHifaw1l9MPKl34kJycGsBL7cWxY0Q+EmkCdw5grVvTDvxodPDMyMiRJbrdb06ZN00MPPaSYmJh6\nt8nMzJTT6bQWEJ2gI0dONvh6UVGRSkqK1/Hjp1RdXdPYskMG/bBqTD+8Oe6aIo4NK/qBUOXvcxhz\nxYp+1DnTC3/wKXiWlZVp+/btGjx4cO2yzp07q7KyUuXl5UpJSal3e4fD8ZPb6qWlJ1RV5f0Ora6u\n8Wn9UEc/rHzpR6j3jWPDin4g1ATqeGauWNEP//Lppv0333yjSZMm6dChQ7XLdu7cqZSUlAZDJwAA\nAMKbT8EzIyND3bt316xZs1RYWKiCggLl5ubqvvvuC1R9AAAACBE+Bc+oqCgtWrRI8fHxyszM1COP\nPKIxY8Zo7NixgaoPAAAAIcLnDxe1adPmJ59MBwAAABrCL6cCAACAEQRPAAAAGEHwBAAAgBEETwAA\nABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAE\nAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEE\nTwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAY\nQfAEAACAEQRPAAAAGEHwBAAAgBHRdhcAAP7kdp/Wpk0b9cUX/1RJSYmqqirVokWikpKS1a1bDzmd\ngxUbG2d3mQAacPZcrqysUFxcnFJT09S9e0ZIzuVwGDNXPAGEjN27v9IddwzTypUvqqKiQh07dlJG\nRoY6deokt9utlSuXKzNzuAoL99hdKoB6nGsu9+jRU+3bp4fsXA6XMXPFE0DImDcvW07nDXrggam1\ny6KjI5WcnKgjR06qqqpGCxfOU27uXC1dusLGSgHU51xz+WyhNpfDZcxc8QQQMvbuLdLw4SPrXefW\nW0eqqKhpXzEAQl04zuVwGTPBE0DI6NSpszZsWFfvOuvWrVb79ulmCgLQKOE4l8NlzNxqBxAypk2b\nqenTJ6ugYJN69uyltLTWio2NUVSU9O23B7Vjx+cqLy9XTs4Cu0sFUI9zzeVmzZqpsrJShw+XaefO\nHSE3l8NlzARPACGjS5efKT9/rTZufFe7du1UcXGh3G63mjdPUKtWKbrrrrs1aNAvlZCQaHepAOpx\nrrl8+rRbsbExSktrHZJzOVzGTPAEEFLi4uI0dOgwDR06TNJPP1wEoGk4ey6Hg3AYM+/xBBBW3G63\n3nlng91lALhA4TiXQ2HMBE8AYeXkyXLNnfu43WUAuEDhOJdDYcwETwBhJSUlVZs3b7W7DAAXKBzn\nciiM2afgeejQIblcLvXr108DBgxQdna23G53oGoDAJ9UVlZq0aLnNGLEzbrhhus0a9Z07d1bbFnn\n++8P6xe/6GdThQC8ca65/PXXey3rhNpcDpcxex08PR6PXC6XTp06pddee00LFizQBx98oIULFway\nPgDw2pIlefrww7/p9793afr0h3XkyGH99re/1saNGy3reTwemyoE4I1zzeVx48boww//ZlkvlOZy\nuIzZ6+BZXFys7du3Kzs7W5dffrn69u0rl8ulDRua9ptcAYSODz7YqFmzHtXgwTfq+uuHaNGi5Rox\n4nZNnjxZ77//Xu16ERERNlYJoCHnmsu33nqbHn10pjZtqvsfyVCay+EyZq9/nVLr1q21bNkypaWl\nWZaXl5f7vSgAaIzTp0+rZctWtY8jIiLkck1RQkKsHnvsET3+eKQyMnraWCEAb5xrLk+aNFmRkZF6\n4onZioqKCrm5HC5j9jp4JiUlacCAAbWPa2pq9Oqrr+rqq6/2+sVKSkpUWlpqLSA6QQ6Ho8Fto6Ii\nLd/DHf2wakw/oqNDs3fhfGz06dNXixYt1B/+8LhatUqW9O8+TJ8+XcePl2vOnFkaO/Y3kkJ3/yN8\n+PsYDqZzx7nmsiS5XJNVUeE2MpdN9yMYxnw+/uxBo3+BfG5urnbt2qU333zT623y8/OVl5dnWTZx\n4kS5XC6vnyMpKd7rdcMB/bDypR/JyU37rz80JByOjfSZb1sXxF6rZttf0o1DBqvy/4yXx3GFvn7q\nZklSVtbjatOmtRYvXiwp9Pc/Ql+gjmE7zh2+zOU//vEJXXyxw9hcDlQ/gnnMgdSo4Jmbm6uVK1dq\nwYIF6tKli9fbZWZmyul0WguITtCRIycb3DYqKlJJSfE6fvyUqqv56yP0w6ox/fDmuGuKwvrYiG+p\nyoEPKOJEiTxxLSRJx4+fqu3HXXf9Vj//+XXavPnDkN3/CB/+PoaD6txxjrn84/GamMvG+xEEYz6f\nM73wB5+DZ1ZWll5//XXl5ubqxhtv9Glbh8Pxk9vqpaUnfPozdtXVNfzZux+hH1a+9CPU+xbOx4an\nRd155swPjDP9uPTSdI0enR62vUHoCNQxHEznjh/P5bNrMjWXTfcjGMYcSD4Fz7y8PK1atUrPPPOM\nhgwZEqiaAAAAEIK8Dp5FRUVatGiRxo8frz59+lg+JNS6deuAFAcAAIDQ4XXwfP/991VdXa3FixfX\nvrn1jN27d/u9MAAAAIQWr4Pn+PHjNX78+EDWAgAAgBBm/y/rAgAAQFggeAIAAMAIgicAAACMIHgC\nAADACIInAAAAjCB4AgAAwAiCJwAAAIwgeAIAAMAIgicAAACMIHgCAADACIInAAAAjCB4AgAAwAiC\nJwAAAIwgeAIAAMAIgicAAACMIHgCAADACIInAAAAjCB4AgAAwAiCJwAAAIwgeAIAAMAIgicAAACM\nIHgCAADACIInAAAAjCB4AgAAwAiCJwAAAIwgeAIAAMAIgicAAACMIHgCAADACIInAAAAjCB4AgAA\nwAiCJwAAAIwgeAIAAMAIgicAAACMIHgCAADAiGi7CwgWbvdpbdq0UV988U+VlJSosrJCcXFxSk1N\nU/fuGXI6Bys2Ns7uMv0qHMccjtjPAIBgwRVPSbt3f6U77himlStfVEVFhTp27KQePXqqfft0ud1u\nrVy5XJmZw1VYuMfuUv0mHMccjtjPAIBgwhVPSfPmZcvpvEEPPDD1vOssXDhPublztXTpCoOVBU44\njjkcsZ8BAMGEK56S9u4t0vDhI+td59ZbR6qoKHSuCoXjmMMR+xkAEEwInpI6deqsDRvW1bvOunWr\n1b59uplyNEINAAAOt0lEQVSCDAjHMYcj9jMAIJhwq13StGkzNX36ZBUUbFLPnr2UltZazZo1U2Vl\npQ4fLtPOnTtUXl6unJwFdpfqN+E45nDEfgYABBOCp6QuXX6m/Py12rjxXe3atVPFxYU6fdqt2NgY\npaW11l133a1Bg36phIREu0v1m3AcczhiPwMAggnB8z/i4uI0dOgwDR06zO5SjAnHMYcj9jMAIFjw\nHk8vud1uvfPOBrvLMCocxxyO2M8AAFMInl46ebJcc+c+bncZRoXjmMMR+xkAYArB00spKanavHmr\n3WUYFY5jDkfsZwCAKY0OnhUVFRo6dKg+/vhjf9YDAACAENWoDxe53W5NnTpVe/aExi+d3r79H16v\n26vXVQGsxJxwHHM4Yj8DAIKJz8GzsLBQU6dOlcfjCUQ9tnjmmaf19dd7JanecUVEROjDDz8xVVZA\nheOYwxH7GQAQTHwOnp988on69++vKVOmqFevXoGoybhly17RnDmP6ODBb7VkyQrFxsbaXVLAheOY\nwxH7GQAQTHwOnqNHj270i5WUlKi0tNRaQHSCHA5Hg9tGRUVavvtTdHScnnwyW+PG3a3ly5fI5Zri\n99fwtwvtR1Mcc30a04/o6ND8bN2Pe5GQEFr7uTECee4A7OTvc1iwzxXT5+xg6Eew/JzyZw+M/gL5\n/Px85eXlWZZNnDhRLpfL6+dISoq/oBrSZ7593n+LuPRWfbGrSI8lN52/4uJNP0JtzPXx5fhIDpEx\nnxFO+9kXZ46JCz13AMEmUOewYJ0rdp2z7exHqP2ckgwHz8zMTDmdTmsB0Qk6cuRkg9tGRUUqKSle\nx4+fUnV1TUDq8yS1kSepjVf12M1f/WhKY65PY/rR1Mfsi1DZz41x/PipgJ87ADv4ez6b+Dl7IUyf\nv4KhH8Fyzj7TC38wGjwdDsdPbquXlp5QVZX3O7S6usan9Rsj0M/vT/7qR1Mac3186UeojNkX4Tjm\nMz8wTJw7AJMCdTwH61yxqyY7+xGM++FCBcebBwAAABDyCJ4AAAAwguAJAAAAIy7oPZ67d+/2Vx0A\nAAAIcVzxBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB\n8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACA\nEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAA\nABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAE\nAACAEQRPAAAAGEHwBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPAAAAGEHwBAAAgBEE\nTwAAABjhc/B0u92aNWuW+vbtq2uvvVYvvvhiIOoCAABAiIn2dYOcnBzt3LlTK1eu1IEDBzRjxgy1\nbdtWQ4YMCUR9AAAACBE+Bc8ffvhBb7zxhv785z+re/fu6t69u/bs2aPXXnuN4AkAAIB6+XSr/auv\nvlJVVZV69+5du6xPnz76/PPPVVNT4/fiAAAAEDp8uuJZWlqq5ORkxcTE1C5LS0uT2+3W0aNHlZKS\nUu/2JSUlKi0ttRYQnSCHw9Hga0dFRVq+B1J0dPB/5srf/WgKY65PY/rR1MfcGOE4ZpPnDsAkf8/n\nYJ8rps9fwdCPYDln+7MHER6Px+PtymvXrtWzzz6rDz74oHbZ/v37NXjwYBUUFOiiiy6qd/vnn39e\neXl5lmWTJk3S/fff3+Brl5SUKD8/X5mZmV4F1VBHP6zoRx16YUU/AO8wV6zoRx1/9sKnCBsbG6uK\nigrLsjOP4+LiGtw+MzNTq1evtnxlZmZ69dqlpaXKy8v7yRXTcEU/rOhHHXphRT8A7zBXrOhHHX/2\nwqdb7W3atNGRI0dUVVWl6Ojo2mLi4uKUlJTU4PYOhyPs/68BAAAgXPl0xbNr166Kjo7W9u3ba5dt\n27ZNGRkZiowMjvchAAAAIDj5lBbj4+N16623as6cOdqxY4c2btyoF198UWPHjg1UfQAAAAgRUXPm\nzJnjywZXX321du3apfnz52vLli267777NHLkyACVZ5WYmKh+/fopMTHRyOsFO/phRT/q0Asr+gF4\nh7liRT/q+KsXPn2qHQAAAGgs3pgJAAAAIwieAAAAMILgCQAAACMIngAAADCC4AkAAAAjCJ4AAAAw\nguAJAAAAIwieAAAAMKJJBc+KigoNHTpUH3/8sd2l2OrQoUNyuVzq16+fBgwYoOzsbLndbrvLssW+\nffv0u9/9Tr1799bAgQO1bNkyu0sKGuPHj9fMmTPtLsNW7733nq644grLl8vlsrssIOgcPHhQ9957\nr6666io5nU699NJLdpdkm8OHD8vlcqlv3766/vrrtXr1artLssW5Mtf+/fv1m9/8Rr169dJNN92k\njz76yOfnjfZnkYHkdrs1depU7dmzx+5SbOXxeORyuZSUlKTXXntNx44d06xZsxQZGakZM2bYXZ5R\nNTU1Gj9+vDIyMrRmzRrt27dPDz74oNq0aaNbbrnF7vJs9fbbb6ugoEDDhw+3uxRbFRYWatCgQcrK\nyqpdFhsba2NFQHCaPHmy2rZtq9WrV6uwsFDTpk1Tu3btdP3119tdmlEej0cTJ05UTU2NXn75ZR06\ndEgzZsxQ8+bNdcMNN9hdnjHnylxnetOlSxe99dZb2rhxoyZNmqS//vWvatu2rdfP3SSueBYWFuqO\nO+7Qv/71L7tLsV1xcbG2b9+u7OxsXX755erbt69cLpc2bNhgd2nGlZWVqWvXrpozZ47S09N13XXX\n6ZprrtG2bdvsLs1WR48eVU5OjjIyMuwuxXZFRUXq0qWLWrduXfuVlJRkd1lAUDl27Ji2b9+uCRMm\nKD09XYMHD9aAAQO0ZcsWu0szbufOnfrss880f/58devWTYMGDdK4ceO0fPlyu0sz5nyZ6+9//7v2\n79+vJ554Qpdddpnuvfde9erVS2+99ZZPz98kgucnn3yi/v37Kz8/3+5SbNe6dWstW7ZMaWlpluXl\n5eU2VWQfh8OhhQsXqnnz5vJ4PNq2bZu2bt2qfv362V2arZ5++mkNGzZMnTt3trsU2xUVFSk9Pd3u\nMoCgFhcXp/j4eK1evVqVlZUqLi7WP/7xD3Xt2tXu0ozbv3+/UlJSdOmll9Yuu+KKK7Rz505VVlba\nWJk558tcn3/+ubp166aEhITaZX369NH27dt9ev4mcat99OjRdpcQNJKSkjRgwIDaxzU1NXr11Vd1\n9dVX21iV/ZxOpw4cOKBBgwbpxhtvtLsc22zZskWffvqp1q9frzlz5thdjq08Ho/27t2rjz76SEuX\nLlV1dbWGDBkil8ulmJgYu8sDgkZsbKweffRRZWVl6eWXX1Z1dbVGjBih22+/3e7SjEtLS9OJEyd0\n6tQpxcfHS5K+++47VVVV6cSJE0pJSbG5wsA7X+YqLS2Vw+GwLEtNTdV3333n0/M3iSueOL/c3Fzt\n2rVLU6ZMsbsUWz333HNasmSJvvzyS2VnZ9tdji3cbrcee+wxPfroo4qLi7O7HNsdOHBAp06dUkxM\njBYuXKgZM2Zo/fr1ysnJsbs0IOgUFRVp0KBBys/PV3Z2tv77v/9b69ats7ss46688ko5HA5lZWXp\nhx9+0L59+7RixQpJCpsrnudz5nz6YzExMaqoqPDpeZrEFU+cW25urlauXKkFCxaoS5cudpdjqzPv\nZ3S73Zo2bZoeeuihsLuqlZeXpx49eliuiIezdu3a6eOPP1bLli0VERGhrl27qqamRtOnT9fDDz+s\nqKgou0sEgsKWLVv05ptvqqCgQHFxccrIyNChQ4e0ePFi/epXv7K7PKNiY2O1cOFCTZ48WX369FFq\naqrGjRun7OxsNW/e3O7ybBUbG6ujR49allVUVPh8oYPg2URlZWXp9ddfV25ubtjeWi4rK9P27ds1\nePDg2mWdO3dWZWWlysvLw+KWyI+9/fbbKisrU+/evSWp9v9C3333XX322Wd2lmabVq1aWR5fdtll\ncrvdOnbsWNgdH8D57Ny5Ux06dLAEiG7dumnJkiU2VmWfnj17atOmTSotLVVycrL+53/+R8nJyUpM\nTLS7NFu1adNGhYWFlmVlZWU/uf3eEG61N0F5eXlatWqVnnnmGd188812l2Obb775RpMmTdKhQ4dq\nl+3cuVMpKSlhGSpeeeUVrV+/XmvXrtXatWvldDrldDq1du1au0uzxebNm9W/f3+dOnWqdtmXX36p\nVq1aheXxAZyPw+HQvn37LLdMi4uLdckll9hYlT2OHj2qUaNG6ciRI2rdurWio6P1t7/9Lew/tCr9\n+20IX3zxhU6fPl27bNu2bbryyit9eh6CZxNTVFSkRYsW6b/+67/Up08flZaW1n6Fm4yMDHXv3l2z\nZs1SYWGhCgoKlJubq/vuu8/u0mzRrl07dejQofYrMTFRiYmJ6tChg92l2aJ3796KjY3V7NmzVVxc\nrIKCAuXk5GjcuHF2lwYEFafTqWbNmmn27Nnau3evNm3apCVLlmjMmDF2l2Zcq1at9MMPPyg3N1f7\n9+/XG2+8obfeeovzhqR+/frp4osv1sMPP6w9e/boT3/6k3bs2KHbbrvNp+fhVnsT8/7776u6ulqL\nFy/W4sWLLf+2e/dum6qyR1RUlBYtWqSsrCxlZmYqPj5eY8aM0dixY+0uDUGgefPmWr58uebOnauR\nI0cqMTFRd955Jz9AgLO0aNFCL730kv74xz/qtttuU0pKiiZMmKDMzEy7S7PFggUL9Nhjj+mWW27R\nJZdcomeffVY9e/a0uyzbnfmZ+8gjj2jEiBHq0KGDXnjhBZ9+ebwkRXg8Hk+AagQAAABqcasdAAAA\nRhA8AQAAYATBEwAAAEYQPAEAAGAEwRMAAABGEDwBAABgBMETAAAARhA8AQAAYATBEwAAAEYQPAEA\nAGAEwRMAAABGEDwBAABgxP8HKZHDaxLgYs4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1075e8470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"analyzer.plot_hist(\"price\",\"numerical\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instantiation of DataFrameTransformer\n",
"DataFrameTransformer is a specialized class to make dataFrame transformations. Transformations are optimized as much as possible to internally used native spark \n",
"transformation functions."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Instance of transformer class \n",
"transformer = op.DataFrameTransformer(df)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---+---------+--------------------+---------+--------+-----+----------+--------+\n",
"| id|firstName| lastName|billingId| product|price| birth|dummyCol|\n",
"+---+---------+--------------------+---------+--------+-----+----------+--------+\n",
"| 1| Luis| Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n",
"| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n",
"| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n",
"| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n",
"| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n",
"| 6| Galileo| GALiLEI| 672| arepa| 5|1930/08/12| never|\n",
"| 7| CaRL| Ga%%%uss| 323| taco| 3|1970/07/13| gonna|\n",
"| 8| David| H$$$ilbert| 624|taaaccoo| 3|1950/07/14| let|\n",
"| 9| Johannes| KEPLER| 735| taco| 3|1920/04/22| you|\n",
"| 10| JaMES| M$$ax%%well| 875| taco| 3|1923/03/12| down|\n",
"+---+---------+--------------------+---------+--------+-----+----------+--------+\n",
"only showing top 10 rows\n",
"\n"
]
}
],
"source": [
"transformer.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Trimming blanck spaces at beginning and endings of cells dataFrames"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataFrame:\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n",
"| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n",
"| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n",
"| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n",
"| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"only showing top 5 rows\n",
"\n",
"Trimmed dataFrame:\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n",
"| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n",
"| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n",
"| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n",
"| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing of original dataFrame:\n",
"print('Original dataFrame:')\n",
"transformer.show(5)\n",
"\n",
"# Triming string blank spaces:\n",
"transformer.trim_col(\"*\")\n",
"\n",
"# Printing trimmed dataFrame:\n",
"print('Trimmed dataFrame:')\n",
"transformer.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Removing especial chars and accents:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataFrame:\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"| id|firstName| lastName|billingId|product|price| birth|dummyCol|\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"| 1| Luis|Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n",
"| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n",
"| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n",
"| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n",
"| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n",
"+---+---------+-----------+---------+-------+-----+----------+--------+\n",
"only showing top 5 rows\n",
"\n",
"Removing special chars and accents dataFrame:\n",
"+---+---------+--------+---------+-------+-----+--------+--------+\n",
"| id|firstName|lastName|billingId|product|price| birth|dummyCol|\n",
"+---+---------+--------+---------+-------+-----+--------+--------+\n",
"| 1| Luis| Alvarez| 123| Cake| 10|19800707| never|\n",
"| 2| Andre| Ampere| 423| piza| 8|19500708| gonna|\n",
"| 3| NiELS| Bohr| 551| pizza| 8|19900709| give|\n",
"| 4| PAUL| dirac| 521| pizza| 8|19540710| you|\n",
"| 5| Albert|Einstein| 634| pizza| 8|19900711| up|\n",
"+---+---------+--------+---------+-------+-----+--------+--------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing of original dataFrame:\n",
"print('Original dataFrame:')\n",
"transformer.show(5)\n",
"\n",
"# Remove special chars:\n",
"transformer.remove_special_chars(\"*\").clear_accents(\"*\")\n",
"\n",
"# This can also be done by passing a Regex if you want something more personalized\n",
"\n",
"#####################################################################\n",
"\n",
"#transformer.remove_special_chars_regex(\"*\",'[^\\w\\s]').clear_accents(\"*\")\n",
"\n",
"#####################################################################\n",
"\n",
"# Printing trimmed dataFrame:\n",
"print('Removing special chars and accents dataFrame:')\n",
"transformer.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Drop dummy column"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataFrame:\n",
"+---+---------+--------+---------+-------+-----+--------+--------+\n",
"| id|firstName|lastName|billingId|product|price| birth|dummyCol|\n",
"+---+---------+--------+---------+-------+-----+--------+--------+\n",
"| 1| Luis| Alvarez| 123| Cake| 10|19800707| never|\n",
"| 2| Andre| Ampere| 423| piza| 8|19500708| gonna|\n",
"| 3| NiELS| Bohr| 551| pizza| 8|19900709| give|\n",
"| 4| PAUL| dirac| 521| pizza| 8|19540710| you|\n",
"| 5| Albert|Einstein| 634| pizza| 8|19900711| up|\n",
"+---+---------+--------+---------+-------+-----+--------+--------+\n",
"only showing top 5 rows\n",
"\n",
"Dataframe without dummy column:\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| id|firstName|lastName|billingId|product|price| birth|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| 1| Luis| Alvarez| 123| Cake| 10|19800707|\n",
"| 2| Andre| Ampere| 423| piza| 8|19500708|\n",
"| 3| NiELS| Bohr| 551| pizza| 8|19900709|\n",
"| 4| PAUL| dirac| 521| pizza| 8|19540710|\n",
"| 5| Albert|Einstein| 634| pizza| 8|19900711|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing of original dataFrame:\n",
"print('Original dataFrame:')\n",
"transformer.show(5)\n",
"\n",
"# Droping a column:\n",
"transformer.drop_col(\"dummyCol\")\n",
"\n",
"# Printing trimmed dataFrame:\n",
"print('Dataframe without dummy column:')\n",
"transformer.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setting all letters to lowerCase"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataFrame:\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| id|firstName|lastName|billingId|product|price| birth|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| 1| Luis| Alvarez| 123| Cake| 10|19800707|\n",
"| 2| Andre| Ampere| 423| piza| 8|19500708|\n",
"| 3| NiELS| Bohr| 551| pizza| 8|19900709|\n",
"| 4| PAUL| dirac| 521| pizza| 8|19540710|\n",
"| 5| Albert|Einstein| 634| pizza| 8|19900711|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"only showing top 5 rows\n",
"\n",
"Setting all letters to lowerCase:\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| id|firstName|lastName|billingId|product|price| birth|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| 1| luis| alvarez| 123| cake| 10|19800707|\n",
"| 2| andre| ampere| 423| piza| 8|19500708|\n",
"| 3| niels| bohr| 551| pizza| 8|19900709|\n",
"| 4| paul| dirac| 521| pizza| 8|19540710|\n",
"| 5| albert|einstein| 634| pizza| 8|19900711|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing of original dataFrame:\n",
"print('Original dataFrame:')\n",
"transformer.show(5)\n",
"\n",
"print('Setting all letters to lowerCase:')\n",
"transformer.lower_case(\"*\")\n",
"transformer.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Date Transformation (Format of date is changed)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataFrame:\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| id|firstName|lastName|billingId|product|price| birth|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"| 1| luis| alvarez| 123| cake| 10|19800707|\n",
"| 2| andre| ampere| 423| piza| 8|19500708|\n",
"| 3| niels| bohr| 551| pizza| 8|19900709|\n",
"| 4| paul| dirac| 521| pizza| 8|19540710|\n",
"| 5| albert|einstein| 634| pizza| 8|19900711|\n",
"+---+---------+--------+---------+-------+-----+--------+\n",
"only showing top 5 rows\n",
"\n",
"Dataframe without dummy column:\n",
"+---+---------+--------+---------+-------+-----+----------+\n",
"| id|firstName|lastName|billingId|product|price| birth|\n",
"+---+---------+--------+---------+-------+-----+----------+\n",
"| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n",
"| 2| andre| ampere| 423| piza| 8|08-07-1950|\n",
"| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n",
"| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n",
"| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n",
"+---+---------+--------+---------+-------+-----+----------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing of original dataFrame:\n",
"print('Original dataFrame:')\n",
"transformer.show(5)\n",
"\n",
"# Priting the new date format:\n",
"print('Dataframe without dummy column:')\n",
"transformer.date_transform(\"birth\", \"yyyyMMdd\", \"dd-MM-YYYY\") \\\n",
" .show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Age calculation from birth date client"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataFrame:\n",
"+---+---------+--------+---------+-------+-----+----------+\n",
"| id|firstName|lastName|billingId|product|price| birth|\n",
"+---+---------+--------+---------+-------+-----+----------+\n",
"| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n",
"| 2| andre| ampere| 423| piza| 8|08-07-1950|\n",
"| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n",
"| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n",
"| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n",
"+---+---------+--------+---------+-------+-----+----------+\n",
"only showing top 5 rows\n",
"\n",
"Printing calculation of age born date client\n",
"+---+---------+--------+---------+-------+-----+----------+---------+\n",
"| id|firstName|lastName|billingId|product|price| birth|clientAge|\n",
"+---+---------+--------+---------+-------+-----+----------+---------+\n",
"| 1| luis| alvarez| 123| cake| 10|07-07-1980| 37.7823|\n",
"| 2| andre| ampere| 423| piza| 8|08-07-1950| 67.7769|\n",
"| 3| niels| bohr| 551| pizza| 8|09-07-1990| 27.7796|\n",
"| 4| paul| dirac| 521| pizza| 8|10-07-1954| 63.7903|\n",
"| 5| albert|einstein| 634| pizza| 8|11-07-1990| 27.7796|\n",
"+---+---------+--------+---------+-------+-----+----------+---------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing of original dataFrame:\n",
"print('Original dataFrame:')\n",
"transformer.show(5)\n",
"\n",
"print(\"Printing calculation of age born date client\")\n",
"transformer.age_calculate(\"birth\", \"dd-MM-YYYY\", \"clientAge\") \\\n",
" .show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Renaming columns:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataframe\n",
"+---+---------+--------+---------+-------+-----+----------+---------+\n",
"| id|firstName|lastName|billingId|product|price| birth|clientAge|\n",
"+---+---------+--------+---------+-------+-----+----------+---------+\n",
"| 1| luis| alvarez| 123| cake| 10|07-07-1980| 37.7823|\n",
"| 2| andre| ampere| 423| piza| 8|08-07-1950| 67.7769|\n",
"| 3| niels| bohr| 551| pizza| 8|09-07-1990| 27.7796|\n",
"| 4| paul| dirac| 521| pizza| 8|10-07-1954| 63.7903|\n",
"| 5| albert|einstein| 634| pizza| 8|11-07-1990| 27.7796|\n",
"+---+---------+--------+---------+-------+-----+----------+---------+\n",
"only showing top 5 rows\n",
"\n",
"Renaming some columns of dataFrame\n",
"+-------+---+---------+--------+---------+-------+-----+----------+\n",
"| age| id|firstName|lastName|billingId|product|price| birth|\n",
"+-------+---+---------+--------+---------+-------+-----+----------+\n",
"|37.7823| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n",
"|67.7769| 2| andre| ampere| 423| piza| 8|08-07-1950|\n",
"|27.7796| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n",
"|63.7903| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n",
"|27.7796| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n",
"+-------+---+---------+--------+---------+-------+-----+----------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing original dataframe:\n",
"print (\"Original dataframe\")\n",
"transformer.show(5)\n",
"print (\"Renaming some columns of dataFrame\")\n",
"transformer.rename_col(columns=[(\"clientAge\", \"age\")])\n",
"transformer.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Changing positions of columns dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataframe\n",
"+-------+---+---------+--------+---------+-------+-----+----------+\n",
"| age| id|firstName|lastName|billingId|product|price| birth|\n",
"+-------+---+---------+--------+---------+-------+-----+----------+\n",
"|37.7823| 1| luis| alvarez| 123| cake| 10|07-07-1980|\n",
"|67.7769| 2| andre| ampere| 423| piza| 8|08-07-1950|\n",
"|27.7796| 3| niels| bohr| 551| pizza| 8|09-07-1990|\n",
"|63.7903| 4| paul| dirac| 521| pizza| 8|10-07-1954|\n",
"|27.7796| 5| albert|einstein| 634| pizza| 8|11-07-1990|\n",
"+-------+---+---------+--------+---------+-------+-----+----------+\n",
"only showing top 5 rows\n",
"\n",
"age column moved\n",
"+---+---------+--------+-------+---------+-------+-----+----------+\n",
"| id|firstName|lastName| age|billingId|product|price| birth|\n",
"+---+---------+--------+-------+---------+-------+-----+----------+\n",
"| 1| luis| alvarez|37.7823| 123| cake| 10|07-07-1980|\n",
"| 2| andre| ampere|67.7769| 423| piza| 8|08-07-1950|\n",
"| 3| niels| bohr|27.7796| 551| pizza| 8|09-07-1990|\n",
"| 4| paul| dirac|63.7903| 521| pizza| 8|10-07-1954|\n",
"| 5| albert|einstein|27.7796| 634| pizza| 8|11-07-1990|\n",
"+---+---------+--------+-------+---------+-------+-----+----------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"# Printing original dataframe:\n",
"print (\"Original dataframe\")\n",
"transformer.show(5)\n",
"\n",
"# This action is to move column age, just after the lastName column\n",
"print (\"age column moved\")\n",
"transformer.move_col(\"age\", \"lastName\", \"after\")\n",
"transformer.show(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setting a custom transformation\n",
"The core of this function is base on the user define function provide from the lambda function provided in the 'func' argument. \n",
"\n",
"In this example, cells that are not greater than 20, are multiplied by 20, the rest of them stay intact."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original dataframe\n",
"+---+---------+--------+-------+---------+-------+-----+----------+\n",
"| id|firstName|lastName| age|billingId|product|price| birth|\n",
"+---+---------+--------+-------+---------+-------+-----+----------+\n",
"| 1| luis| alvarez|37.7823| 123| cake| 10|07-07-1980|\n",
"| 2| andre| ampere|67.7769| 423| piza| 8|08-07-1950|\n",
"| 3| niels| bohr|27.7796| 551| pizza| 8|09-07-1990|\n",
"| 4| paul| dirac|63.7903| 521| pizza| 8|10-07-1954|\n",
"| 5| albert|einstein|27.7796| 634| pizza| 8|11-07-1990|\n",
"+---+---------+--------+-------+---------+-------+-----+----------+\n",
"only showing top 5 rows\n",
"\n",
" Multiplying by 20 a number if value in cell is greater than 20:\n",
"+---+------------+--------+--------+---------+----------+-----+----------+\n",
"| id| firstName|lastName| age|billingId| product|price| birth|\n",
"+---+------------+--------+--------+---------+----------+-----+----------+\n",
"| 1| luis| alvarez| 37.7823| 123| cake| 200|07-07-1980|\n",
"| 2| andre| ampere| 67.7769| 423| piza| 160|08-07-1950|\n",
"| 3| niels| bohr| 27.7796| 551| pizza| 160|09-07-1990|\n",
"| 4| paul| dirac| 63.7903| 521| pizza| 160|10-07-1954|\n",
"| 5| albert|einstein| 27.7796| 634| pizza| 160|11-07-1990|\n",
"| 6| galileo| galilei| 87.7849| 672| arepa| 100|12-08-1930|\n",
"| 7| carl| gauss| 47.7876| 323| taco| 60|13-07-1970|\n",
"| 8| david| hilbert| 67.7769| 624| taaaccoo| 60|14-07-1950|\n",
"| 9| johannes| kepler| 97.7876| 735| taco| 60|22-04-1920|\n",
"| 10| james| maxwell| 94.7796| 875| taco| 60|12-03-1923|\n",
"| 11| isaac| newton| 18.7903| 992| pasta| 180|15-02-1999|\n",
"| 12| emmy| noether| 24.7903| 234| pasta| 180|08-12-1993|\n",
"| 13| max| planck| 23.7930| 111|hamburguer| 80|04-01-1994|\n",
"| 14| fred| hoyle| 20.7849| 553| pizzza| 160|27-06-1997|\n",
"| 15| heinrich | hertz| 61.7769| 116| pizza| 160|30-11-1956|\n",
"| 16| william| gilbert| 59.7849| 886| beer| 40|26-03-1958|\n",
"| 17| marie| curie| 17.7930| 912| rice| 20|22-03-2000|\n",
"| 18| arthur| compton|118.7769| 812| 110790| 100|01-01-1899|\n",
"| 19| james|chadwick| 96.7930| 467| null| 200|03-05-1921|\n",
"+---+------------+--------+--------+---------+----------+-----+----------+\n",
"\n"
]
}
],
"source": [
"# Printing original dataframe:\n",
"print (\"Original dataframe\")\n",
"transformer.show(5)\n",
"\n",
"print (' Multiplying by 20 a number if value in cell is greater than 20:')\n",
"# Replacing a number: \n",
"func = lambda cell: (cell * 20) if ((cell != None) and (cell < 20)) else cell\n",
"transformer.set_col(['price'], func, 'integer')\n",
"transformer.show(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After the transformation process detailed in the previous cells. It is a good idea to\n",
"analyze to see if the transformations have solved issued related with special characters, \n",
"presence of number in column where is to supposed only letters, etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analyzing columns after transformations"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>id</td></tr><tr><td colspan=3 ><b> Column datatype: </b>int</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>0</td><td>0.00 %</td></tr><tr><td>Integer</td><td>19</td><td>100.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x112a7a5c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min value: 1\n",
"Max value: 19\n"
]
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x112a40400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x112b5f550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 4.61762809753418\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>firstName</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>19</td><td>100.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x1084afc88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x112abada0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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NTU1VWlqaJKlDhw46c+aMxo0bp8OHD2vcuHG6ePGiOnbsWJgvCQAAAAso1FIa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\nKByUUoMmTpxoeoQil5aWpv3796tnz57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"text/plain": [
"<matplotlib.figure.Figure at 0x1128f3668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 2.2484610080718994\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>lastName</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>19</td><td>100.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x1127a06d8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x112f60da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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bpgYNGigyMlLx8fF/+5gjR44oPDxc69evz9OQAAAAsDdvdx8wceJEbd++XbNm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\nu3df9YjMzJkzDU5XMOx+CsaGDRtUv35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"text/plain": [
"<matplotlib.figure.Figure at 0x112d71278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 2.093127727508545\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>age</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>0</td><td>0.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>19</td><td>100.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x11306cb00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x11305f198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AwDhCKAAAAIwjhAIAAMA4QigAAACMI4QCAADAOEIoAAAAjCOEAgAAwDhCKAAAAIwjhAIA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\n9e++buLEidq8ebMWLlyo2bN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"text/plain": [
"<matplotlib.figure.Figure at 0x112f737f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 5.873319864273071\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>billingId</td></tr><tr><td colspan=3 ><b> Column datatype: </b>int</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>0</td><td>0.00 %</td></tr><tr><td>Integer</td><td>19</td><td>100.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x11373a0f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min value: 111\n",
"Max value: 992\n"
]
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x1137320f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AAABwAfPnp2jDhvUaODBZw4ePUm7uET3xxKNau3atx/0sy/LShJ5s+3S8ZVlK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\naeXKlR73udB+sHTpUhUVFal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"text/plain": [
"<matplotlib.figure.Figure at 0x11372d390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 4.413137912750244\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>product</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>18</td><td>94.74 %</td></tr><tr><td>Integer</td><td>1</td><td>5.26 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x11304d908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x112fc9ac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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AADAux7eVLCy3kwQAAED2cSQTAAAAxlEyAQAAYBwlEwAAAMZRMgEAAGAcJRMAAADGUTIB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\nkqQVK1YoODhYpUqV0vfffy9JqlOnjg4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"text/plain": [
"<matplotlib.figure.Figure at 0x11304dba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 1.9454371929168701\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>price</td></tr><tr><td colspan=3 ><b> Column datatype: </b>int</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>0</td><td>0.00 %</td></tr><tr><td>Integer</td><td>19</td><td>100.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x1084a3080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Min value: 20\n",
"Max value: 200\n"
]
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x112b03a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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tV7I8IeEp5ec7lZj4nO677wFJ/pUb9ubvxzO7Pq99NXelv4x92rRp2rNnj5Yv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\nI+rXr59++uknRUZGei5AJZW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"text/plain": [
"<matplotlib.figure.Figure at 0x1136e0be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 4.271012783050537\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><td colspan=3 ><b> Column name: </b>birth</td></tr><tr><td colspan=3 ><b> Column datatype: </b>string</td></tr><tr><th>Datatype</td><th>Quantity</td><th>Percentage</td></tr><tr><td>None</td><td>0</td><td>0.00 %</td></tr><tr><td>Empty str</td><td>0</td><td>0.00 %</td></tr><tr><td>String</td><td>19</td><td>100.00 %</td></tr><tr><td>Integer</td><td>0</td><td>0.00 %</td></tr><tr><td>Float</td><td>0</td><td>0.00 %</td></tr></table>"
],
"text/plain": [
"<optimus.df_analyzer.ColumnTables at 0x112b57e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<optimus.df_analyzer.DataTypeTable at 0x11308bf28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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KKQAAAIyjlAIAAMA4SikAAACMC7iU5ubm6tFHH1Xr1q2VkJCgmTNnnnbdr7/+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\nIAiCIAgCc2IoFQRBEARBEJj7HxPpcaak/USJAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1130a94a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"end of __analyze 2.1021029949188232\n",
"Total execution time: 27.68661904335022\n"
]
},
{
"data": {
"text/html": [
"<table width=50%><tr><th colspan=3>General description</td></tr><tr><th colspan=1>Features</td><th colspan=2>Name or Quantity</td></tr><tr><th colspan=1>File Name</td><td colspan=2>file with no path</td></tr><tr><th colspan=1>Columns</td><td colspan=2>8</td></tr><tr><th colspan=1>Rows</td><td colspan=2>19</td></tr>"
],
"text/plain": [
"<optimus.df_analyzer.GeneralDescripTable at 0x113761ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Setting the new dataFrame transformed into the analyzer class\n",
"analyzer.set_data_frame(transformer.get_data_frame)\n",
"analyzer_table = analyzer.column_analyze(\"*\", print_type=True, plots=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It can be seen from output of the analyzer object, that there are columns with numbers\n",
"even when ceratin column (for example) is supposed to be only of words or letters. \n",
"\n",
"In order to solve this problem, operationInType function of DataFrameTransformer class \n",
"can be used. \n",
"\n",
"operationInType function is useful to make operations in a certain element of one dataType. In this particular example, it can be seen in the last output cell (specifically in 'product' column' that are values that don't fit the rest of the data, the aren't strings but they are numbers or empty strings. operationInType can take care about them and clean the column dataFrame."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following example, operationInType of function of DataFrameTransformer class is run in order to converts all posible \n",
"parsables strings to integer into a null or none value. Notice how the 110790 value in product\n",
"column have been changed, but the rest of the column has remained intact."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Making transformation in the inferred dataType elements of a certains columns"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---+---------+--------+-------+---------+-----+----------+--------+\n",
"| id|firstName|lastName| age|billingId|price| birth| product|\n",
"+---+---------+--------+-------+---------+-----+----------+--------+\n",
"| 1| luis| alvarez|37.7823| 123| 200|07-07-1980| cake|\n",
"| 2| andre| ampere|67.7769| 423| 160|08-07-1950| piza|\n",
"| 3| niels| bohr|27.7796| 551| 160|09-07-1990| pizza|\n",
"| 4| paul| dirac|63.7903| 521| 160|10-07-1954| pizza|\n",
"| 5| albert|einstein|27.7796| 634| 160|11-07-1990| pizza|\n",
"| 6| galileo| galilei|87.7849| 672| 100|12-08-1930| arepa|\n",
"| 7| carl| gauss|47.7876| 323| 60|13-07-1970| taco|\n",
"| 8| david| hilbert|67.7769| 624| 60|14-07-1950|taaaccoo|\n",
"| 9| johannes| kepler|97.7876| 735| 60|22-04-1920| taco|\n",
"| 10| james| maxwell|94.7796| 875| 60|12-03-1923| taco|\n",
"+---+---------+--------+-------+---------+-----+----------+--------+\n",
"only showing top 10 rows\n",
"\n"
]
}
],
"source": [
"# This function makes changes or transformation in the column specified only in the cells\n",
"# that are recognized as the dataType specified. \n",
"transformer.operation_in_type([('product', 'integer', None)]).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes there a some values that are different but actually are the same. In the product\n",
"column for example, there are the following values: 'taaaccoo', 'piza'. It \n",
"can be inferred that the correct value is taco and piza and not the rest of them. This problem can\n",
"be solved with the lookup function of the DataFrameTransformer class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Replacing multiple string values to a single string"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---+------------+--------+--------+---------+-----+----------+----------+\n",
"| id| firstName|lastName| age|billingId|price| birth| product|\n",
"+---+------------+--------+--------+---------+-----+----------+----------+\n",
"| 1| luis| alvarez| 37.7823| 123| 200|07-07-1980| cake|\n",
"| 2| andre| ampere| 67.7769| 423| 160|08-07-1950| pizza|\n",
"| 3| niels| bohr| 27.7796| 551| 160|09-07-1990| pizza|\n",
"| 4| paul| dirac| 63.7903| 521| 160|10-07-1954| pizza|\n",
"| 5| albert|einstein| 27.7796| 634| 160|11-07-1990| pizza|\n",
"| 6| galileo| galilei| 87.7849| 672| 100|12-08-1930| arepa|\n",
"| 7| carl| gauss| 47.7876| 323| 60|13-07-1970| taco|\n",
"| 8| david| hilbert| 67.7769| 624| 60|14-07-1950| taco|\n",
"| 9| johannes| kepler| 97.7876| 735| 60|22-04-1920| taco|\n",
"| 10| james| maxwell| 94.7796| 875| 60|12-03-1923| taco|\n",
"| 11| isaac| newton| 18.7903| 992| 180|15-02-1999| pasta|\n",
"| 12| emmy| noether| 24.7903| 234| 180|08-12-1993| pasta|\n",
"| 13| max| planck| 23.7930| 111| 80|04-01-1994|hamburguer|\n",
"| 14| fred| hoyle| 20.7849| 553| 160|27-06-1997| pizza|\n",
"| 15| heinrich | hertz| 61.7769| 116| 160|30-11-1956| pizza|\n",
"| 16| william| gilbert| 59.7849| 886| 40|26-03-1958| beer|\n",
"| 17| marie| curie| 17.7930| 912| 20|22-03-2000| rice|\n",
"| 18| arthur| compton|118.7769| 812| 100|01-01-1899| null|\n",
"| 19| james|chadwick| 96.7930| 467| 200|03-05-1921| null|\n",
"+---+------------+--------+--------+---------+-----+----------+----------+\n",
"\n"
]
}
],
"source": [
"transformer.lookup('product', str_to_replace='taco', list_str=['taaaccoo']) \n",
"transformer.lookup('product', str_to_replace='pizza', list_str=['piza', 'pizzza']) \n",
"transformer.show(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As can be notice above, string specified in the list argument 'list_str' have been\n",
"replaced to 'str_to_replace' value. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chaining and lazy evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The past transformations were done step by step, but this can be achieved by chaining\n",
"all operations into one line of code, like the cell below. This way is much more efficient and scalable because it uses all optimization issues from the lazy evaluation approach."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All the transformation set before can be done into a single line of code thanks to the \n",
"chaining feature of the DataFrameTransformer class. This option is a optimal way to \n",
"make different transformations, because it uses as much as possible all advantages of\n",
"the lazy evaluation approach. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---+--------------------+--------------------+---------+----------+-----+----------+--------+\n",
"| id| firstName| lastName|billingId| product|price| birth|dummyCol|\n",
"+---+--------------------+--------------------+---------+----------+-----+----------+--------+\n",
"| 1| Luis| Alvarez$$%!| 123| Cake| 10|1980/07/07| never|\n",
"| 2| André| Ampère| 423| piza| 8|1950/07/08| gonna|\n",
"| 3| NiELS| Böhr//((%%| 551| pizza| 8|1990/07/09| give|\n",
"| 4| PAUL| dirac$| 521| pizza| 8|1954/07/10| you|\n",
"| 5| Albert| Einstein| 634| pizza| 8|1990/07/11| up|\n",
"| 6| Galileo| GALiLEI| 672| arepa| 5|1930/08/12| never|\n",
"| 7| CaRL| Ga%%%uss| 323| taco| 3|1970/07/13| gonna|\n",
"| 8| David| H$$$ilbert| 624| taaaccoo| 3|1950/07/14| let|\n",
"| 9| Johannes| KEPLER| 735| taco| 3|1920/04/22| you|\n",
"| 10| JaMES| M$$ax%%well| 875| taco| 3|1923/03/12| down|\n",
"| 11| Isaac| Newton| 992| pasta| 9|1999/02/15| never |\n",
"| 12| Emmy%%| Nöether$| 234| pasta| 9|1993/12/08| gonna|\n",
"| 13| Max!!!| Planck!!!| 111|hamburguer| 4|1994/01/04| run |\n",
"| 14| Fred| Hoy&&&le| 553| pizzza| 8|1997/06/27| around|\n",
"| 15|((( Heinrich )))))| Hertz| 116| pizza| 8|1956/11/30| and|\n",
"| 16| William| Gilbert###| 886| BEER| 2|1958/03/26| desert|\n",
"| 17| Marie| CURIE| 912| Rice| 1|2000/03/22| you|\n",
"| 18| Arthur| COM%%%pton| 812| 110790| 5|1899/01/01| #|\n",
"| 19| JAMES| Chadwick| 467| null| 10|1921/05/03| #|\n",
"+---+--------------------+--------------------+---------+----------+-----+----------+--------+\n",
"\n",
"+---+------------+--------+---------+-----+----------+---------+----------+\n",
"| id| firstName|lastName|billingId|price| birth|clientAge| product|\n",
"+---+------------+--------+---------+-----+----------+---------+----------+\n",
"| 1| luis| alvarez| 123| 10|07-07-1980| 37.7823| cake|\n",
"| 2| andre| ampere| 423| 8|08-07-1950| 67.7769| pizza|\n",
"| 3| niels| bohr| 551| 8|09-07-1990| 27.7796| pizza|\n",
"| 4| paul| dirac| 521| 8|10-07-1954| 63.7903| pizza|\n",
"| 5| albert|einstein| 634| 8|11-07-1990| 27.7796| pizza|\n",
"| 6| galileo| galilei| 672| 5|12-08-1930| 87.7849| arepa|\n",
"| 7| carl| gauss| 323| 3|13-07-1970| 47.7876| taco|\n",
"| 8| david| hilbert| 624| 3|14-07-1950| 67.7769| taco|\n",
"| 9| johannes| kepler| 735| 3|22-04-1920| 97.7876| taco|\n",
"| 10| james| maxwell| 875| 3|12-03-1923| 94.7796| taco|\n",
"| 11| isaac| newton| 992| 9|15-02-1999| 18.7903| pasta|\n",
"| 12| emmy| noether| 234| 9|08-12-1993| 24.7903| pasta|\n",
"| 13| max| planck| 111| 4|04-01-1994| 23.7930|hamburguer|\n",
"| 14| fred| hoyle| 553| 8|27-06-1997| 20.7849| pizza|\n",
"| 15| heinrich | hertz| 116| 8|30-11-1956| 61.7769| pizza|\n",
"| 16| william| gilbert| 886| 2|26-03-1958| 59.7849| beer|\n",
"| 17| marie| curie| 912| 1|22-03-2000| 17.7930| rice|\n",
"| 18| arthur| compton| 812| 5|01-01-1899| 118.7769| null|\n",
"| 19| james|chadwick| 467| 10|03-05-1921| 96.7930| null|\n",
"+---+------------+--------+---------+-----+----------+---------+----------+\n",
"\n"
]
}
],
"source": [
"# Instanciate DataFrameTransfomer\n",
"transformer = op.DataFrameTransformer(df)\n",
"# Get original dataFrame to show it.\n",
"transformer.show(20)\n",
"\n",
"# Chaining function transformations\n",
"transformer.trim_col(\"*\") \\\n",
" .remove_special_chars(\"*\") \\\n",
" .clear_accents(\"*\") \\\n",
" .lower_case(\"*\") \\\n",
" .drop_col(\"dummyCol\") \\\n",
" .date_transform(\"birth\", \"yyyyMMdd\", \"dd-MM-YYYY\") \\\n",
" .age_calculate(\"birth\", \"dd-MM-YYYY\", \"clientAge\") \\\n",
" .operation_in_type([('product', 'integer', None)]) \\\n",
" .lookup('product', str_to_replace='taco', list_str=['taaaccoo']) \\\n",
" .lookup('product', str_to_replace='pizza', list_str=['piza', 'pizzza']) \\\n",
" \n",
" \n",
"transformer.show(20)"
]
}
],
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