session-one/session-one-project-econ-solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Data Science Practice Session 1: Economics and Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Employees Performance Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Productivity Prediction of Garment Employees Data Set](https://archive.ics.uci.edu/ml/datasets/Productivity+Prediction+of+Garment+Employees) includes important attributes of the garment manufacturing process and the productivity of the employees which had been collected manually and also been validated by the industry experts.\n",
    "\n",
    "In this notebook, we will assume that this dataset includes data from just one company. The company's management is interested in extracting some specific information about their employee's performance. The tasks we are going to work on are:\n",
    "1. **Performance check** - get specified columns of a sample from the dataset\n",
    "2. **Performance ranking** - filter and sort the dataset following specified rules\n",
    "3. **Teams Ranking** - create a scoreboard for each team\n",
    "4. **Lottery** - take a random sample out of rows that satisfy given conditions\n",
    "\n",
    "If you are struggling with anything, check the **Tips** section at the end of the notebook. At the end of some tasks, you can find a number in parentheses that references the Tips section.\n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to import the libraries that we will be using - in our case, `pandas`. Import it as `pd`, so that we could refer to it easier in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to import the dataset. The dataset is available [here](https://archive.ics.uci.edu/ml/machine-learning-databases/00597/) (click on the `garments_worker_productivity.csv`, and it should download automatically). Save it as `all_data`. *(1)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.read_csv('garments_worker_productivity.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, check if you have correctly imported and saved `all_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>quarter</th>\n",
       "      <th>department</th>\n",
       "      <th>day</th>\n",
       "      <th>team</th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>smv</th>\n",
       "      <th>wip</th>\n",
       "      <th>over_time</th>\n",
       "      <th>incentive</th>\n",
       "      <th>idle_time</th>\n",
       "      <th>idle_men</th>\n",
       "      <th>no_of_style_change</th>\n",
       "      <th>no_of_workers</th>\n",
       "      <th>actual_productivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.80</td>\n",
       "      <td>26.16</td>\n",
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       "      <td>0.75</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.886500</td>\n",
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       "      <th>2</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>11</td>\n",
       "      <td>0.80</td>\n",
       "      <td>11.41</td>\n",
       "      <td>968.0</td>\n",
       "      <td>3660</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.800570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>0.80</td>\n",
       "      <td>25.90</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>1920</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.800382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>10</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.628333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.70</td>\n",
       "      <td>3.90</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>7</td>\n",
       "      <td>0.65</td>\n",
       "      <td>3.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.625625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>9</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.505889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>6</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.394722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1197 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   quarter  department        day  team  targeted_productivity  \\\n",
       "0      1/1/2015  Quarter1      sweing   Thursday     8                   0.80   \n",
       "1      1/1/2015  Quarter1  finishing    Thursday     1                   0.75   \n",
       "2      1/1/2015  Quarter1      sweing   Thursday    11                   0.80   \n",
       "3      1/1/2015  Quarter1      sweing   Thursday    12                   0.80   \n",
       "4      1/1/2015  Quarter1      sweing   Thursday     6                   0.80   \n",
       "...         ...       ...         ...        ...   ...                    ...   \n",
       "1192  3/11/2015  Quarter2   finishing  Wednesday    10                   0.75   \n",
       "1193  3/11/2015  Quarter2   finishing  Wednesday     8                   0.70   \n",
       "1194  3/11/2015  Quarter2   finishing  Wednesday     7                   0.65   \n",
       "1195  3/11/2015  Quarter2   finishing  Wednesday     9                   0.75   \n",
       "1196  3/11/2015  Quarter2   finishing  Wednesday     6                   0.70   \n",
       "\n",
       "        smv     wip  over_time  incentive  idle_time  idle_men  \\\n",
       "0     26.16  1108.0       7080         98        0.0         0   \n",
       "1      3.94     NaN        960          0        0.0         0   \n",
       "2     11.41   968.0       3660         50        0.0         0   \n",
       "3     11.41   968.0       3660         50        0.0         0   \n",
       "4     25.90  1170.0       1920         50        0.0         0   \n",
       "...     ...     ...        ...        ...        ...       ...   \n",
       "1192   2.90     NaN        960          0        0.0         0   \n",
       "1193   3.90     NaN        960          0        0.0         0   \n",
       "1194   3.90     NaN        960          0        0.0         0   \n",
       "1195   2.90     NaN       1800          0        0.0         0   \n",
       "1196   2.90     NaN        720          0        0.0         0   \n",
       "\n",
       "      no_of_style_change  no_of_workers  actual_productivity  \n",
       "0                      0           59.0             0.940725  \n",
       "1                      0            8.0             0.886500  \n",
       "2                      0           30.5             0.800570  \n",
       "3                      0           30.5             0.800570  \n",
       "4                      0           56.0             0.800382  \n",
       "...                  ...            ...                  ...  \n",
       "1192                   0            8.0             0.628333  \n",
       "1193                   0            8.0             0.625625  \n",
       "1194                   0            8.0             0.625625  \n",
       "1195                   0           15.0             0.505889  \n",
       "1196                   0            6.0             0.394722  \n",
       "\n",
       "[1197 rows x 15 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, to get a grasp of a dataset, check the number of rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1197, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any missing data could cause serious problems for the program. Inspect `all_data` to see the count of values in each column. Base on those pieces of information you can see if any values are missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>smv</th>\n",
       "      <th>wip</th>\n",
       "      <th>over_time</th>\n",
       "      <th>incentive</th>\n",
       "      <th>idle_time</th>\n",
       "      <th>idle_men</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1197.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>691.000000</td>\n",
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       "      <td>1197.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.426901</td>\n",
       "      <td>0.729632</td>\n",
       "      <td>15.062172</td>\n",
       "      <td>1190.465991</td>\n",
       "      <td>4567.460317</td>\n",
       "      <td>38.210526</td>\n",
       "      <td>0.730159</td>\n",
       "      <td>0.369256</td>\n",
       "      <td>0.150376</td>\n",
       "      <td>34.609858</td>\n",
       "      <td>0.735091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.463963</td>\n",
       "      <td>0.097891</td>\n",
       "      <td>10.943219</td>\n",
       "      <td>1837.455001</td>\n",
       "      <td>3348.823563</td>\n",
       "      <td>160.182643</td>\n",
       "      <td>12.709757</td>\n",
       "      <td>3.268987</td>\n",
       "      <td>0.427848</td>\n",
       "      <td>22.197687</td>\n",
       "      <td>0.174488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.233705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>3.940000</td>\n",
       "      <td>774.500000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.650307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>15.260000</td>\n",
       "      <td>1039.000000</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.773333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>24.260000</td>\n",
       "      <td>1252.500000</td>\n",
       "      <td>6960.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.850253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>54.560000</td>\n",
       "      <td>23122.000000</td>\n",
       "      <td>25920.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>1.120437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              team  targeted_productivity          smv           wip  \\\n",
       "count  1197.000000            1197.000000  1197.000000    691.000000   \n",
       "mean      6.426901               0.729632    15.062172   1190.465991   \n",
       "std       3.463963               0.097891    10.943219   1837.455001   \n",
       "min       1.000000               0.070000     2.900000      7.000000   \n",
       "25%       3.000000               0.700000     3.940000    774.500000   \n",
       "50%       6.000000               0.750000    15.260000   1039.000000   \n",
       "75%       9.000000               0.800000    24.260000   1252.500000   \n",
       "max      12.000000               0.800000    54.560000  23122.000000   \n",
       "\n",
       "          over_time    incentive    idle_time     idle_men  \\\n",
       "count   1197.000000  1197.000000  1197.000000  1197.000000   \n",
       "mean    4567.460317    38.210526     0.730159     0.369256   \n",
       "std     3348.823563   160.182643    12.709757     3.268987   \n",
       "min        0.000000     0.000000     0.000000     0.000000   \n",
       "25%     1440.000000     0.000000     0.000000     0.000000   \n",
       "50%     3960.000000     0.000000     0.000000     0.000000   \n",
       "75%     6960.000000    50.000000     0.000000     0.000000   \n",
       "max    25920.000000  3600.000000   300.000000    45.000000   \n",
       "\n",
       "       no_of_style_change  no_of_workers  actual_productivity  \n",
       "count         1197.000000    1197.000000          1197.000000  \n",
       "mean             0.150376      34.609858             0.735091  \n",
       "std              0.427848      22.197687             0.174488  \n",
       "min              0.000000       2.000000             0.233705  \n",
       "25%              0.000000       9.000000             0.650307  \n",
       "50%              0.000000      34.000000             0.773333  \n",
       "75%              0.000000      57.000000             0.850253  \n",
       "max              2.000000      89.000000             1.120437  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the row `count` tells us, some values are missing in the `wip` column - we need to keep that in mind."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin this part of the notebook by showing the sample of four rows of our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>quarter</th>\n",
       "      <th>department</th>\n",
       "      <th>day</th>\n",
       "      <th>team</th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>smv</th>\n",
       "      <th>wip</th>\n",
       "      <th>over_time</th>\n",
       "      <th>incentive</th>\n",
       "      <th>idle_time</th>\n",
       "      <th>idle_men</th>\n",
       "      <th>no_of_style_change</th>\n",
       "      <th>no_of_workers</th>\n",
       "      <th>actual_productivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>2/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>12</td>\n",
       "      <td>0.75</td>\n",
       "      <td>15.26</td>\n",
       "      <td>1276.0</td>\n",
       "      <td>1440</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.750451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>2/15/2015</td>\n",
       "      <td>Quarter3</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>5</td>\n",
       "      <td>0.80</td>\n",
       "      <td>30.10</td>\n",
       "      <td>679.0</td>\n",
       "      <td>7140</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.722569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>2/15/2015</td>\n",
       "      <td>Quarter3</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>4</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2400</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.287042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2/14/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>6</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.483333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date   quarter department       day  team  targeted_productivity  \\\n",
       "554   2/1/2015  Quarter1     sweing    Sunday    12                   0.75   \n",
       "772  2/15/2015  Quarter3     sweing    Sunday     5                   0.80   \n",
       "783  2/15/2015  Quarter3  finishing    Sunday     4                   0.75   \n",
       "764  2/14/2015  Quarter2  finishing  Saturday     6                   0.80   \n",
       "\n",
       "       smv     wip  over_time  incentive  idle_time  idle_men  \\\n",
       "554  15.26  1276.0       1440         45        0.0         0   \n",
       "772  30.10   679.0       7140          0        0.0         0   \n",
       "783   4.15     NaN       2400          0        0.0         0   \n",
       "764   2.90     NaN        960          0        0.0         0   \n",
       "\n",
       "     no_of_style_change  no_of_workers  actual_productivity  \n",
       "554                   0           35.0             0.750451  \n",
       "772                   0           59.5             0.722569  \n",
       "783                   0           20.0             0.287042  \n",
       "764                   0            8.0             0.483333  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.sample(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the sample includes a lot of different columns. To make working on the data easier, you can limit the displayed data using `loc[]`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Take for example `date`,`department`,`team`,`no_of_workers`,`targeted_productivity`,`actual_productivity`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>date</th>\n",
       "      <th>department</th>\n",
       "      <th>team</th>\n",
       "      <th>no_of_workers</th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>actual_productivity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>1/24/2015</td>\n",
       "      <td>finishing</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.651515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>2/28/2015</td>\n",
       "      <td>finishing</td>\n",
       "      <td>12</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.590617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>3/1/2015</td>\n",
       "      <td>sweing</td>\n",
       "      <td>2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.683551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>2/25/2015</td>\n",
       "      <td>finishing</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.845833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  department  team  no_of_workers  targeted_productivity  \\\n",
       "409  1/24/2015  finishing      4           12.0                   0.75   \n",
       "976  2/28/2015   finishing    12            9.0                   0.80   \n",
       "995   3/1/2015      sweing     2           58.0                   0.70   \n",
       "926  2/25/2015   finishing    10           10.0                   0.70   \n",
       "\n",
       "     actual_productivity  \n",
       "409             0.651515  \n",
       "976             0.590617  \n",
       "995             0.683551  \n",
       "926             0.845833  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.sample(4).loc[:,['date','department','team','no_of_workers','targeted_productivity','actual_productivity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case you find something concerning in the sample, you might want to save it for further analysis. Let's save it and call the variable `random_check`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_check = all_data.sample(4).loc[:,['date','department','team','no_of_workers','targeted_productivity','actual_productivity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check whether you have saved the data correctly by showing your variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <tbody>\n",
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       "      <th>879</th>\n",
       "      <td>2/22/2015</td>\n",
       "      <td>sweing</td>\n",
       "      <td>10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.456875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>1/8/2015</td>\n",
       "      <td>finishing</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.821354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>1/31/2015</td>\n",
       "      <td>finishing</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.971867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>2/17/2015</td>\n",
       "      <td>sweing</td>\n",
       "      <td>6</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.750621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  department  team  no_of_workers  targeted_productivity  \\\n",
       "879  2/22/2015      sweing    10           50.0                   0.70   \n",
       "135   1/8/2015  finishing      5            8.0                   0.70   \n",
       "525  1/31/2015  finishing      9            2.0                   0.75   \n",
       "809  2/17/2015      sweing     6           33.0                   0.75   \n",
       "\n",
       "     actual_productivity  \n",
       "879             0.456875  \n",
       "135             0.821354  \n",
       "525             0.971867  \n",
       "809             0.750621  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you think that some values in your sample are concerning (too high or too low), you can take the mean to check whether your observations should concern you. Let's say that the number of workers in your sample seems too low. Check if that's the case by taking the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.25"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_check.loc[:,'no_of_workers'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin once again by showing our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1/1/2015</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.886500</td>\n",
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       "      <th>2</th>\n",
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       "      <td>11</td>\n",
       "      <td>0.80</td>\n",
       "      <td>11.41</td>\n",
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       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.800570</td>\n",
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       "      <td>3660</td>\n",
       "      <td>50</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.800570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>6</td>\n",
       "      <td>0.80</td>\n",
       "      <td>25.90</td>\n",
       "      <td>1170.0</td>\n",
       "      <td>1920</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.800382</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>10</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.628333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.70</td>\n",
       "      <td>3.90</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>7</td>\n",
       "      <td>0.65</td>\n",
       "      <td>3.90</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.625625</td>\n",
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       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>9</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.505889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>6</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.90</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.394722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1197 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   quarter  department        day  team  targeted_productivity  \\\n",
       "0      1/1/2015  Quarter1      sweing   Thursday     8                   0.80   \n",
       "1      1/1/2015  Quarter1  finishing    Thursday     1                   0.75   \n",
       "2      1/1/2015  Quarter1      sweing   Thursday    11                   0.80   \n",
       "3      1/1/2015  Quarter1      sweing   Thursday    12                   0.80   \n",
       "4      1/1/2015  Quarter1      sweing   Thursday     6                   0.80   \n",
       "...         ...       ...         ...        ...   ...                    ...   \n",
       "1192  3/11/2015  Quarter2   finishing  Wednesday    10                   0.75   \n",
       "1193  3/11/2015  Quarter2   finishing  Wednesday     8                   0.70   \n",
       "1194  3/11/2015  Quarter2   finishing  Wednesday     7                   0.65   \n",
       "1195  3/11/2015  Quarter2   finishing  Wednesday     9                   0.75   \n",
       "1196  3/11/2015  Quarter2   finishing  Wednesday     6                   0.70   \n",
       "\n",
       "        smv     wip  over_time  incentive  idle_time  idle_men  \\\n",
       "0     26.16  1108.0       7080         98        0.0         0   \n",
       "1      3.94     NaN        960          0        0.0         0   \n",
       "2     11.41   968.0       3660         50        0.0         0   \n",
       "3     11.41   968.0       3660         50        0.0         0   \n",
       "4     25.90  1170.0       1920         50        0.0         0   \n",
       "...     ...     ...        ...        ...        ...       ...   \n",
       "1192   2.90     NaN        960          0        0.0         0   \n",
       "1193   3.90     NaN        960          0        0.0         0   \n",
       "1194   3.90     NaN        960          0        0.0         0   \n",
       "1195   2.90     NaN       1800          0        0.0         0   \n",
       "1196   2.90     NaN        720          0        0.0         0   \n",
       "\n",
       "      no_of_style_change  no_of_workers  actual_productivity  \n",
       "0                      0           59.0             0.940725  \n",
       "1                      0            8.0             0.886500  \n",
       "2                      0           30.5             0.800570  \n",
       "3                      0           30.5             0.800570  \n",
       "4                      0           56.0             0.800382  \n",
       "...                  ...            ...                  ...  \n",
       "1192                   0            8.0             0.628333  \n",
       "1193                   0            8.0             0.625625  \n",
       "1194                   0            8.0             0.625625  \n",
       "1195                   0           15.0             0.505889  \n",
       "1196                   0            6.0             0.394722  \n",
       "\n",
       "[1197 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you are interested only in some particular rows. To focus only on the data that you are interested in, you can use `loc[]`. (Show only the rows where `actual_productivity` is lower than `targeted_productivity`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>18.0</td>\n",
       "      <td>0.593056</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
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       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>6</td>\n",
       "      <td>0.70</td>\n",
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       "      <td>0.394722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   quarter  department        day  team  targeted_productivity  \\\n",
       "11     1/1/2015  Quarter1      sweing   Thursday    10                   0.75   \n",
       "12     1/1/2015  Quarter1      sweing   Thursday     5                   0.80   \n",
       "14     1/1/2015  Quarter1  finishing    Thursday     8                   0.75   \n",
       "15     1/1/2015  Quarter1  finishing    Thursday     4                   0.75   \n",
       "16     1/1/2015  Quarter1  finishing    Thursday     7                   0.80   \n",
       "...         ...       ...         ...        ...   ...                    ...   \n",
       "1192  3/11/2015  Quarter2   finishing  Wednesday    10                   0.75   \n",
       "1193  3/11/2015  Quarter2   finishing  Wednesday     8                   0.70   \n",
       "1194  3/11/2015  Quarter2   finishing  Wednesday     7                   0.65   \n",
       "1195  3/11/2015  Quarter2   finishing  Wednesday     9                   0.75   \n",
       "1196  3/11/2015  Quarter2   finishing  Wednesday     6                   0.70   \n",
       "\n",
       "        smv    wip  over_time  incentive  idle_time  idle_men  \\\n",
       "11    19.31  578.0       6480         45        0.0         0   \n",
       "12    11.41  668.0       3660         50        0.0         0   \n",
       "14     2.90    NaN        960          0        0.0         0   \n",
       "15     3.94    NaN       2160          0        0.0         0   \n",
       "16     2.90    NaN        960          0        0.0         0   \n",
       "...     ...    ...        ...        ...        ...       ...   \n",
       "1192   2.90    NaN        960          0        0.0         0   \n",
       "1193   3.90    NaN        960          0        0.0         0   \n",
       "1194   3.90    NaN        960          0        0.0         0   \n",
       "1195   2.90    NaN       1800          0        0.0         0   \n",
       "1196   2.90    NaN        720          0        0.0         0   \n",
       "\n",
       "      no_of_style_change  no_of_workers  actual_productivity  \n",
       "11                     0           54.0             0.712205  \n",
       "12                     0           30.5             0.707046  \n",
       "14                     0            8.0             0.676667  \n",
       "15                     0           18.0             0.593056  \n",
       "16                     0            8.0             0.540729  \n",
       "...                  ...            ...                  ...  \n",
       "1192                   0            8.0             0.628333  \n",
       "1193                   0            8.0             0.625625  \n",
       "1194                   0            8.0             0.625625  \n",
       "1195                   0           15.0             0.505889  \n",
       "1196                   0            6.0             0.394722  \n",
       "\n",
       "[322 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data['targeted_productivity']>all_data['actual_productivity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be working on those data later. To access them easier, save them as `below_target`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "below_target = all_data.loc[all_data['targeted_productivity']>all_data['actual_productivity']].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check if you have correctly saved the data filtered data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>5</td>\n",
       "      <td>0.80</td>\n",
       "      <td>11.41</td>\n",
       "      <td>668.0</td>\n",
       "      <td>3660</td>\n",
       "      <td>50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.707046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.676667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "      <td>0.75</td>\n",
       "      <td>3.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2160</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.593056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.540729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>10</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.628333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.70</td>\n",
       "      <td>3.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.625625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>7</td>\n",
       "      <td>0.65</td>\n",
       "      <td>3.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.625625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>9</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1800</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.505889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>3/11/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>6</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.394722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   quarter  department        day  team  targeted_productivity  \\\n",
       "11     1/1/2015  Quarter1      sweing   Thursday    10                   0.75   \n",
       "12     1/1/2015  Quarter1      sweing   Thursday     5                   0.80   \n",
       "14     1/1/2015  Quarter1  finishing    Thursday     8                   0.75   \n",
       "15     1/1/2015  Quarter1  finishing    Thursday     4                   0.75   \n",
       "16     1/1/2015  Quarter1  finishing    Thursday     7                   0.80   \n",
       "...         ...       ...         ...        ...   ...                    ...   \n",
       "1192  3/11/2015  Quarter2   finishing  Wednesday    10                   0.75   \n",
       "1193  3/11/2015  Quarter2   finishing  Wednesday     8                   0.70   \n",
       "1194  3/11/2015  Quarter2   finishing  Wednesday     7                   0.65   \n",
       "1195  3/11/2015  Quarter2   finishing  Wednesday     9                   0.75   \n",
       "1196  3/11/2015  Quarter2   finishing  Wednesday     6                   0.70   \n",
       "\n",
       "        smv    wip  over_time  incentive  idle_time  idle_men  \\\n",
       "11    19.31  578.0       6480         45        0.0         0   \n",
       "12    11.41  668.0       3660         50        0.0         0   \n",
       "14     2.90    NaN        960          0        0.0         0   \n",
       "15     3.94    NaN       2160          0        0.0         0   \n",
       "16     2.90    NaN        960          0        0.0         0   \n",
       "...     ...    ...        ...        ...        ...       ...   \n",
       "1192   2.90    NaN        960          0        0.0         0   \n",
       "1193   3.90    NaN        960          0        0.0         0   \n",
       "1194   3.90    NaN        960          0        0.0         0   \n",
       "1195   2.90    NaN       1800          0        0.0         0   \n",
       "1196   2.90    NaN        720          0        0.0         0   \n",
       "\n",
       "      no_of_style_change  no_of_workers  actual_productivity  \n",
       "11                     0           54.0             0.712205  \n",
       "12                     0           30.5             0.707046  \n",
       "14                     0            8.0             0.676667  \n",
       "15                     0           18.0             0.593056  \n",
       "16                     0            8.0             0.540729  \n",
       "...                  ...            ...                  ...  \n",
       "1192                   0            8.0             0.628333  \n",
       "1193                   0            8.0             0.625625  \n",
       "1194                   0            8.0             0.625625  \n",
       "1195                   0           15.0             0.505889  \n",
       "1196                   0            6.0             0.394722  \n",
       "\n",
       "[322 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "below_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to filter the data once again, you can do it using `loc[]` (keep only rows with positive `wip`). *(2)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "below_target = below_target.loc[all_data['wip']>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you got your data filtered, you can sort it by your chosen value. You can also combine it with `.head()` or `.tail()` to see only a specified number of 'best' or 'worst' rows. (sort data by `wip` and show 25 best rows) *(3)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>quarter</th>\n",
       "      <th>department</th>\n",
       "      <th>day</th>\n",
       "      <th>team</th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>smv</th>\n",
       "      <th>wip</th>\n",
       "      <th>over_time</th>\n",
       "      <th>incentive</th>\n",
       "      <th>idle_time</th>\n",
       "      <th>idle_men</th>\n",
       "      <th>no_of_style_change</th>\n",
       "      <th>no_of_workers</th>\n",
       "      <th>actual_productivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2/24/2015</td>\n",
       "      <td>Quarter4</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>10</td>\n",
       "      <td>0.70</td>\n",
       "      <td>21.25</td>\n",
       "      <td>1834.0</td>\n",
       "      <td>6360</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.471108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>2/22/2015</td>\n",
       "      <td>Quarter4</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>10</td>\n",
       "      <td>0.70</td>\n",
       "      <td>21.25</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.456875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>2/23/2015</td>\n",
       "      <td>Quarter4</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Monday</td>\n",
       "      <td>10</td>\n",
       "      <td>0.70</td>\n",
       "      <td>21.25</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.417917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>2/1/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.60</td>\n",
       "      <td>24.26</td>\n",
       "      <td>1196.0</td>\n",
       "      <td>6600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.466821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>2/2/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>sweing</td>\n",
       "      <td>Monday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.65</td>\n",
       "      <td>24.26</td>\n",
       "      <td>1435.0</td>\n",
       "      <td>6600</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.260979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date   quarter department      day  team  targeted_productivity  \\\n",
       "919  2/24/2015  Quarter4     sweing  Tuesday    10                   0.70   \n",
       "879  2/22/2015  Quarter4     sweing   Sunday    10                   0.70   \n",
       "898  2/23/2015  Quarter4     sweing   Monday    10                   0.70   \n",
       "558   2/1/2015  Quarter1     sweing   Sunday     8                   0.60   \n",
       "579   2/2/2015  Quarter1     sweing   Monday     8                   0.65   \n",
       "\n",
       "       smv     wip  over_time  incentive  idle_time  idle_men  \\\n",
       "919  21.25  1834.0       6360          0        0.0         0   \n",
       "879  21.25  1531.0       6000          0        0.0         0   \n",
       "898  21.25  1583.0       6000          0        0.0         0   \n",
       "558  24.26  1196.0       6600          0        0.0         0   \n",
       "579  24.26  1435.0       6600          0        0.0         0   \n",
       "\n",
       "     no_of_style_change  no_of_workers  actual_productivity  \n",
       "919                   1           53.0             0.471108  \n",
       "879                   1           50.0             0.456875  \n",
       "898                   1           50.0             0.417917  \n",
       "558                   0           55.0             0.466821  \n",
       "579                   0           55.0             0.260979  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "below_target.sort_values('wip', ascending=False).head(25).sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Teams Ranking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, based on the commands that you have done before, take `team`,`targeted_productivity`,`actual_productivity`,`over_time`,`wip` columns, and save it as `teams`. Make a copy, not a reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams = all_data.loc[:,['team','targeted_productivity','actual_productivity','over_time','wip']].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we remember, our dataset has some missing values in `wip` column. To prevent any errors, fill them with zeros. *(4)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams['wip'] = teams['wip'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use the command that you have used at the beginning to check if you have correctly filled missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>actual_productivity</th>\n",
       "      <th>over_time</th>\n",
       "      <th>wip</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1197.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "      <td>1197.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.426901</td>\n",
       "      <td>0.729632</td>\n",
       "      <td>0.735091</td>\n",
       "      <td>4567.460317</td>\n",
       "      <td>687.228070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.463963</td>\n",
       "      <td>0.097891</td>\n",
       "      <td>0.174488</td>\n",
       "      <td>3348.823563</td>\n",
       "      <td>1514.582341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.233705</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.650307</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.773333</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.850253</td>\n",
       "      <td>6960.000000</td>\n",
       "      <td>1083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.120437</td>\n",
       "      <td>25920.000000</td>\n",
       "      <td>23122.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              team  targeted_productivity  actual_productivity     over_time  \\\n",
       "count  1197.000000            1197.000000          1197.000000   1197.000000   \n",
       "mean      6.426901               0.729632             0.735091   4567.460317   \n",
       "std       3.463963               0.097891             0.174488   3348.823563   \n",
       "min       1.000000               0.070000             0.233705      0.000000   \n",
       "25%       3.000000               0.700000             0.650307   1440.000000   \n",
       "50%       6.000000               0.750000             0.773333   3960.000000   \n",
       "75%       9.000000               0.800000             0.850253   6960.000000   \n",
       "max      12.000000               0.800000             1.120437  25920.000000   \n",
       "\n",
       "                wip  \n",
       "count   1197.000000  \n",
       "mean     687.228070  \n",
       "std     1514.582341  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%      586.000000  \n",
       "75%     1083.000000  \n",
       "max    23122.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to combine values by chosen category (for example mean value of each column for each team), you can use `groupby`. Save the result of grouping as a new dataframe named `teams_performance`. *(5)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams_performance = teams.groupby('team').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if you correctly grouped data. Show `teams_performance`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>actual_productivity</th>\n",
       "      <th>over_time</th>\n",
       "      <th>wip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.746667</td>\n",
       "      <td>0.821054</td>\n",
       "      <td>4793.428571</td>\n",
       "      <td>858.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.739908</td>\n",
       "      <td>0.770855</td>\n",
       "      <td>4384.954128</td>\n",
       "      <td>693.559633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.742105</td>\n",
       "      <td>0.803880</td>\n",
       "      <td>5375.684211</td>\n",
       "      <td>860.410526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.717619</td>\n",
       "      <td>0.770035</td>\n",
       "      <td>5449.714286</td>\n",
       "      <td>684.780952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.673656</td>\n",
       "      <td>0.697981</td>\n",
       "      <td>5330.967742</td>\n",
       "      <td>482.548387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.731383</td>\n",
       "      <td>0.685385</td>\n",
       "      <td>3369.095745</td>\n",
       "      <td>587.840426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.714271</td>\n",
       "      <td>0.668006</td>\n",
       "      <td>4857.187500</td>\n",
       "      <td>572.635417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.708257</td>\n",
       "      <td>0.674148</td>\n",
       "      <td>4312.293578</td>\n",
       "      <td>505.733945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.758173</td>\n",
       "      <td>0.734462</td>\n",
       "      <td>4519.038462</td>\n",
       "      <td>715.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.738500</td>\n",
       "      <td>0.719736</td>\n",
       "      <td>4736.700000</td>\n",
       "      <td>871.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.703977</td>\n",
       "      <td>0.681985</td>\n",
       "      <td>4342.500000</td>\n",
       "      <td>649.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.774242</td>\n",
       "      <td>0.779055</td>\n",
       "      <td>3317.929293</td>\n",
       "      <td>750.858586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      targeted_productivity  actual_productivity    over_time         wip\n",
       "team                                                                     \n",
       "1                  0.746667             0.821054  4793.428571  858.238095\n",
       "2                  0.739908             0.770855  4384.954128  693.559633\n",
       "3                  0.742105             0.803880  5375.684211  860.410526\n",
       "4                  0.717619             0.770035  5449.714286  684.780952\n",
       "5                  0.673656             0.697981  5330.967742  482.548387\n",
       "6                  0.731383             0.685385  3369.095745  587.840426\n",
       "7                  0.714271             0.668006  4857.187500  572.635417\n",
       "8                  0.708257             0.674148  4312.293578  505.733945\n",
       "9                  0.758173             0.734462  4519.038462  715.923077\n",
       "10                 0.738500             0.719736  4736.700000  871.150000\n",
       "11                 0.703977             0.681985  4342.500000  649.090909\n",
       "12                 0.774242             0.779055  3317.929293  750.858586"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because actual productivity is a poor reflection of productivity, create a new column called `relative_productivity`, which is equal to `actual_productivity` divided by `targeted_productivity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "teams_performance['relative_productivity'] = teams_performance['actual_productivity']/teams_performance['targeted_productivity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a grasp of the relative productivity distribution, show minimum and maximum of relative productivity. *(6)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>actual_productivity</th>\n",
       "      <th>over_time</th>\n",
       "      <th>wip</th>\n",
       "      <th>relative_productivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.729063</td>\n",
       "      <td>0.733882</td>\n",
       "      <td>4565.791126</td>\n",
       "      <td>686.064163</td>\n",
       "      <td>1.006360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.027073</td>\n",
       "      <td>0.053645</td>\n",
       "      <td>695.892459</td>\n",
       "      <td>133.914372</td>\n",
       "      <td>0.058672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.673656</td>\n",
       "      <td>0.668006</td>\n",
       "      <td>3317.929293</td>\n",
       "      <td>482.548387</td>\n",
       "      <td>0.935227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.712767</td>\n",
       "      <td>0.684535</td>\n",
       "      <td>4334.948394</td>\n",
       "      <td>584.039173</td>\n",
       "      <td>0.964505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.734941</td>\n",
       "      <td>0.727099</td>\n",
       "      <td>4627.869231</td>\n",
       "      <td>689.170293</td>\n",
       "      <td>0.990404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.743246</td>\n",
       "      <td>0.772905</td>\n",
       "      <td>4975.632560</td>\n",
       "      <td>777.703463</td>\n",
       "      <td>1.049629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.774242</td>\n",
       "      <td>0.821054</td>\n",
       "      <td>5449.714286</td>\n",
       "      <td>871.150000</td>\n",
       "      <td>1.099626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       targeted_productivity  actual_productivity    over_time         wip  \\\n",
       "count              12.000000            12.000000    12.000000   12.000000   \n",
       "mean                0.729063             0.733882  4565.791126  686.064163   \n",
       "std                 0.027073             0.053645   695.892459  133.914372   \n",
       "min                 0.673656             0.668006  3317.929293  482.548387   \n",
       "25%                 0.712767             0.684535  4334.948394  584.039173   \n",
       "50%                 0.734941             0.727099  4627.869231  689.170293   \n",
       "75%                 0.743246             0.772905  4975.632560  777.703463   \n",
       "max                 0.774242             0.821054  5449.714286  871.150000   \n",
       "\n",
       "       relative_productivity  \n",
       "count              12.000000  \n",
       "mean                1.006360  \n",
       "std                 0.058672  \n",
       "min                 0.935227  \n",
       "25%                 0.964505  \n",
       "50%                 0.990404  \n",
       "75%                 1.049629  \n",
       "max                 1.099626  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teams_performance.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, to create a simple scoring system, follow the commands listed below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create `rel_min` and `rel_max` which are respectively minimum and maximum values of `relative_productivity` column. *(7)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "rel_min = teams_performance.describe().loc['min','relative_productivity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "rel_max = teams_performance.describe().loc['max','relative_productivity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do the same for `over_time` and `wip` columns (name them `time_min`,`time_max`, `wip_min` and `wip_max`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_min = teams_performance.describe().loc['min','over_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_max = teams_performance.describe().loc['max','over_time']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "wip_min = teams_performance.describe().loc['min','wip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "wip_max = teams_performance.describe().loc['max','wip']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new empty dataframe called `score_board` with the same indexes as `teams_performance`. *(8)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_board = pd.DataFrame(index=teams_performance.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create columns `productivity_points`, `overtime_points` and `wip_penalty` using given formula:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ points = \\frac{(value - min\\_value) \\cdot max\\_points}{max\\_value - min\\_value} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* min_value - minimum value of given column\n",
    "* max_value - maximum value of given column\n",
    "* max_points - points for a maximum score (100 for `productivity_points`, 50 for `overtime_points` and (-30) for `wip_penalty`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_board['performance_points'] = (teams_performance['relative_productivity'] - rel_min)*100/(rel_max-rel_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_board['overtime_points'] = (teams_performance['over_time'] - time_min)*50/(time_max-time_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_board['wip_penalty'] = (teams_performance['wip'] - wip_min)*-30/(wip_max-wip_min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now round all the numbers in `score_board` to intiger. *(9)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_board = score_board.round(decimals=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add up all scores in a new column called `total_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_board['total_score']=score_board['performance_points']+score_board['overtime_points']+score_board['wip_penalty']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the `score_board` to check if everything is alright."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>team</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>106.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>74.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>109.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>61.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-6.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      performance_points  overtime_points  wip_penalty  total_score\n",
       "team                                                               \n",
       "1                  100.0             35.0        -29.0        106.0\n",
       "2                   65.0             25.0        -16.0         74.0\n",
       "3                   90.0             48.0        -29.0        109.0\n",
       "4                   84.0             50.0        -16.0        118.0\n",
       "5                   61.0             47.0         -0.0        108.0\n",
       "6                    1.0              1.0         -8.0         -6.0\n",
       "7                    0.0             36.0         -7.0         29.0\n",
       "8                   10.0             23.0         -2.0         31.0\n",
       "9                   20.0             28.0        -18.0         30.0\n",
       "10                  24.0             33.0        -30.0         27.0\n",
       "11                  20.0             24.0        -13.0         31.0\n",
       "12                  43.0              0.0        -21.0         22.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_board"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have finished your scoring system, you can show the 3 worst teams ranked by `total_score`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>performance_points</th>\n",
       "      <th>overtime_points</th>\n",
       "      <th>wip_penalty</th>\n",
       "      <th>total_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      performance_points  overtime_points  wip_penalty  total_score\n",
       "team                                                               \n",
       "6                    1.0              1.0         -8.0         -6.0\n",
       "12                  43.0              0.0        -21.0         22.0\n",
       "10                  24.0             33.0        -30.0         27.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_board.sort_values('total_score').head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also easily show the 3 best teams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>performance_points</th>\n",
       "      <th>overtime_points</th>\n",
       "      <th>wip_penalty</th>\n",
       "      <th>total_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>61.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      performance_points  overtime_points  wip_penalty  total_score\n",
       "team                                                               \n",
       "4                   84.0             50.0        -16.0        118.0\n",
       "3                   90.0             48.0        -29.0        109.0\n",
       "5                   61.0             47.0         -0.0        108.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_board.sort_values('total_score', ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lottery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that the company is running the lottery for the rows with a productivity of more than 0.95. Filter the rows following this rule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>quarter</th>\n",
       "      <th>department</th>\n",
       "      <th>day</th>\n",
       "      <th>team</th>\n",
       "      <th>targeted_productivity</th>\n",
       "      <th>smv</th>\n",
       "      <th>wip</th>\n",
       "      <th>over_time</th>\n",
       "      <th>incentive</th>\n",
       "      <th>idle_time</th>\n",
       "      <th>idle_men</th>\n",
       "      <th>no_of_style_change</th>\n",
       "      <th>no_of_workers</th>\n",
       "      <th>actual_productivity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1/3/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>4</td>\n",
       "      <td>0.80</td>\n",
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       "      <td>6600</td>\n",
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       "      <td>20.0</td>\n",
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       "      <th>20</th>\n",
       "      <td>1/3/2015</td>\n",
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       "      <td>finishing</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>11</td>\n",
       "      <td>0.75</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>5640</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.987880</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1/3/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>9</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.956271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1/4/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>3</td>\n",
       "      <td>0.75</td>\n",
       "      <td>4.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1560</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.991389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1/5/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>3.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1920</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.961059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>3/3/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4200</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.999533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1068</th>\n",
       "      <td>3/5/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>8</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2640</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.980985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1069</th>\n",
       "      <td>3/5/2015</td>\n",
       "      <td>Quarter1</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2</td>\n",
       "      <td>0.60</td>\n",
       "      <td>3.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.950625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106</th>\n",
       "      <td>3/8/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>3</td>\n",
       "      <td>0.80</td>\n",
       "      <td>4.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.951944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>3/9/2015</td>\n",
       "      <td>Quarter2</td>\n",
       "      <td>finishing</td>\n",
       "      <td>Monday</td>\n",
       "      <td>11</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.90</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>960</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.960625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date   quarter  department       day  team  targeted_productivity  \\\n",
       "19    1/3/2015  Quarter1  finishing   Saturday     4                   0.80   \n",
       "20    1/3/2015  Quarter1  finishing   Saturday    11                   0.75   \n",
       "21    1/3/2015  Quarter1  finishing   Saturday     9                   0.80   \n",
       "40    1/4/2015  Quarter1  finishing     Sunday     3                   0.75   \n",
       "61    1/5/2015  Quarter1  finishing     Monday     1                   0.80   \n",
       "...        ...       ...         ...       ...   ...                    ...   \n",
       "1025  3/3/2015  Quarter1   finishing   Tuesday     7                   0.80   \n",
       "1068  3/5/2015  Quarter1   finishing  Thursday     8                   0.80   \n",
       "1069  3/5/2015  Quarter1   finishing  Thursday     2                   0.60   \n",
       "1106  3/8/2015  Quarter2   finishing    Sunday     3                   0.80   \n",
       "1128  3/9/2015  Quarter2   finishing    Monday    11                   0.80   \n",
       "\n",
       "       smv  wip  over_time  incentive  idle_time  idle_men  \\\n",
       "19    4.15  NaN       6600          0        0.0         0   \n",
       "20    2.90  NaN       5640          0        0.0         0   \n",
       "21    4.15  NaN        960          0        0.0         0   \n",
       "40    4.15  NaN       1560          0        0.0         0   \n",
       "61    3.94  NaN       1920          0        0.0         0   \n",
       "...    ...  ...        ...        ...        ...       ...   \n",
       "1025  4.60  NaN       4200          0        0.0         0   \n",
       "1068  4.60  NaN       2640          0        0.0         0   \n",
       "1069  3.90  NaN        960          0        0.0         0   \n",
       "1106  4.60  NaN       1440          0        0.0         0   \n",
       "1128  2.90  NaN          0        960        0.0         0   \n",
       "\n",
       "      no_of_style_change  no_of_workers  actual_productivity  \n",
       "19                     0           20.0             0.988025  \n",
       "20                     0           17.0             0.987880  \n",
       "21                     0            8.0             0.956271  \n",
       "40                     0            8.0             0.991389  \n",
       "61                     0            8.0             0.961059  \n",
       "...                  ...            ...                  ...  \n",
       "1025                   0           10.0             0.999533  \n",
       "1068                   0           22.0             0.980985  \n",
       "1069                   0            8.0             0.950625  \n",
       "1106                   0           12.0             0.951944  \n",
       "1128                   0            8.0             0.960625  \n",
       "\n",
       "[109 rows x 15 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data['actual_productivity']>0.95]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, the lottery is only for 3 rows, so you need to take them out of the dataset. Also, the lottery organisers are interested only in columns: `date`, `team` and `actual_productivity`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>2/4/2015</td>\n",
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       "      <th>478</th>\n",
       "      <td>1/28/2015</td>\n",
       "      <td>3</td>\n",
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      ],
      "text/plain": [
       "          date  team  actual_productivity\n",
       "599   2/4/2015     2             1.050281\n",
       "602   2/4/2015     2             0.966759\n",
       "478  1/28/2015     3             1.000230"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.loc[all_data['actual_productivity']>0.95].sample(3).loc[:,['date','team','actual_productivity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You also need to save the results so that you can easily send them over to lottery organisers (save them as `lottery_results.csv`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.loc[all_data['actual_productivity']>0.95].sample(3).loc[:,['date','team','actual_productivity']].to_csv(\"lottery_results.csv\",index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tips:\n",
    "* (1)   Make sure that our dataset is **in the same folder** as the notebook. The data are sepereated by **comma**. -> [help](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html)\n",
    "* (2) Use the format: *dataframe = dataframe.loc[condition]*\n",
    "* (3) -> [help](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html)\n",
    "* (4) Use the format: *dataframe['column_name'] = dataframe['column_name'].fillna()*. -> [help](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html)\n",
    "* (5) Use the format: *new_dataframe = dataframe.groupby().mean()*. -> [help](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html)\n",
    "* (6) Use `.describe()`\n",
    "* (7) Combine `.describe()` with `loc[]`\n",
    "* (8) -> [help](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)\n",
    "* (9) -> [help](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.round.html)"
   ]
  }
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