Data Cleaning Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "rising-format",
   "metadata": {},
   "source": [
    "# Before cleaning"
   ]
  },
  {
   "attachments": {
    "before.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "adaptive-mediterranean",
   "metadata": {},
   "source": [
    "![before.PNG](attachment:before.PNG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "whole-aspect",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "absolute-panic",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv(\"./data_cleaning_challenge.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "polished-drawing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row Type</th>\n",
       "      <th>Iter Number</th>\n",
       "      <th>Power1</th>\n",
       "      <th>Speed1</th>\n",
       "      <th>Speed2</th>\n",
       "      <th>Electricity</th>\n",
       "      <th>Effort</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Torque</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>first name: Person</td>\n",
       "      <td>last name: Human</td>\n",
       "      <td>date: end of time</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row Type</td>\n",
       "      <td>Iter Number</td>\n",
       "      <td>Power1</td>\n",
       "      <td>Speed1</td>\n",
       "      <td>Speed2</td>\n",
       "      <td>Electricity</td>\n",
       "      <td>Effort</td>\n",
       "      <td>Weight</td>\n",
       "      <td>Torque</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iter</td>\n",
       "      <td>1</td>\n",
       "      <td>360</td>\n",
       "      <td>108</td>\n",
       "      <td>863</td>\n",
       "      <td>599</td>\n",
       "      <td>680</td>\n",
       "      <td>442</td>\n",
       "      <td>982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iter</td>\n",
       "      <td>2</td>\n",
       "      <td>684</td>\n",
       "      <td>508</td>\n",
       "      <td>613</td>\n",
       "      <td>241</td>\n",
       "      <td>249</td>\n",
       "      <td>758</td>\n",
       "      <td>639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Row Type       Iter Number             Power1  Speed1  Speed2  \\\n",
       "0  first name: Person  last name: Human  date: end of time     NaN     NaN   \n",
       "1                 NaN               NaN                NaN     NaN     NaN   \n",
       "2            Row Type       Iter Number             Power1  Speed1  Speed2   \n",
       "3                Iter                 1                360     108     863   \n",
       "4                Iter                 2                684     508     613   \n",
       "\n",
       "   Electricity  Effort  Weight  Torque  Unnamed: 9 Unnamed: 10  \n",
       "0          NaN     NaN     NaN     NaN         NaN         NaN  \n",
       "1          NaN     NaN     NaN     NaN         NaN         NaN  \n",
       "2  Electricity  Effort  Weight  Torque         NaN         NaN  \n",
       "3          599     680     442     982         NaN         NaN  \n",
       "4          241     249     758     639         NaN         NaN  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "everyday-spread",
   "metadata": {},
   "outputs": [],
   "source": [
    "id=[]\n",
    "count=0\n",
    "\n",
    "for i in df_drop_nans[\"Row Type\"]:\n",
    "        if \"first name\" in i :\n",
    "            count=count+1\n",
    "        id.append(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "revised-weekly",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 52,
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "former-dakota",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'first name: Person'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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    "df1[\"Row Type\"][0]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "royal-configuration",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drop_extra= df1.drop(columns=['Unnamed: 9','Unnamed: 10'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "amber-vancouver",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <td>531</td>\n",
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       "                 Row Type       Iter Number             Power1  Speed1  \\\n",
       "0      first name: Person  last name: Human  date: end of time     NaN   \n",
       "1                     NaN               NaN                NaN     NaN   \n",
       "2                Row Type       Iter Number             Power1  Speed1   \n",
       "3                    Iter                 1                360     108   \n",
       "4                    Iter                 2                684     508   \n",
       "...                   ...               ...                ...     ...   \n",
       "76372                Iter                 6                879      73   \n",
       "76373             Average               979                641     531   \n",
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       "76375            Std.Dev.               221                112     717   \n",
       "76376               Total               429                  -       -   \n",
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       "1         NaN          NaN     NaN     NaN     NaN  \n",
       "2      Speed2  Electricity  Effort  Weight  Torque  \n",
       "3         863          599     680     442     982  \n",
       "4         613          241     249     758     639  \n",
       "...       ...          ...     ...     ...     ...  \n",
       "76372     977          680     500     395     863  \n",
       "76373     374          448     407     185     439  \n",
       "76374      53          982     217     963     502  \n",
       "76375     630          239     561     142     909  \n",
       "76376       -            -     847     583     407  \n",
       "\n",
       "[76377 rows x 9 columns]"
      ]
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     "execution_count": 34,
     "metadata": {},
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    "df_drop_extra"
   ]
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  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "informed-loading",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drop_nans= df_drop_extra[df_drop_extra[\"Row Type\"].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "binary-contemporary",
   "metadata": {},
   "outputs": [
    {
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       "      <td>439</td>\n",
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       "      <th>76375</th>\n",
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       "                 Row Type       Iter Number             Power1  Speed1  \\\n",
       "0      first name: Person  last name: Human  date: end of time     NaN   \n",
       "2                Row Type       Iter Number             Power1  Speed1   \n",
       "3                    Iter                 1                360     108   \n",
       "4                    Iter                 2                684     508   \n",
       "5                    Iter                 3                365     126   \n",
       "...                   ...               ...                ...     ...   \n",
       "76372                Iter                 6                879      73   \n",
       "76373             Average               979                641     531   \n",
       "76374             Maximum               783                172     941   \n",
       "76375            Std.Dev.               221                112     717   \n",
       "76376               Total               429                  -       -   \n",
       "\n",
       "       Speed2  Electricity  Effort  Weight  Torque  \n",
       "0         NaN          NaN     NaN     NaN     NaN  \n",
       "2      Speed2  Electricity  Effort  Weight  Torque  \n",
       "3         863          599     680     442     982  \n",
       "4         613          241     249     758     639  \n",
       "5         825          407     855     164      86  \n",
       "...       ...          ...     ...     ...     ...  \n",
       "76372     977          680     500     395     863  \n",
       "76373     374          448     407     185     439  \n",
       "76374      53          982     217     963     502  \n",
       "76375     630          239     561     142     909  \n",
       "76376       -            -     847     583     407  \n",
       "\n",
       "[58397 rows x 9 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_drop_nans"
   ]
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  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "raising-broad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        None\n",
       "2        None\n",
       "3        None\n",
       "4        None\n",
       "5        None\n",
       "         ... \n",
       "76372    None\n",
       "76373    None\n",
       "76374    None\n",
       "76375    None\n",
       "76376    None\n",
       "Name: Row Type, Length: 58397, dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "rotary-invalid",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-3c0cfd7ad44a>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_drop_nans['id']=id\n"
     ]
    }
   ],
   "source": [
    "df_drop_nans['id']=id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "positive-separation",
   "metadata": {},
   "outputs": [
    {
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       "      <td>first name: Person</td>\n",
       "      <td>last name: Human</td>\n",
       "      <td>date: end of time</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row Type</td>\n",
       "      <td>Iter Number</td>\n",
       "      <td>Power1</td>\n",
       "      <td>Speed1</td>\n",
       "      <td>Speed2</td>\n",
       "      <td>Electricity</td>\n",
       "      <td>Effort</td>\n",
       "      <td>Weight</td>\n",
       "      <td>Torque</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iter</td>\n",
       "      <td>1</td>\n",
       "      <td>360</td>\n",
       "      <td>108</td>\n",
       "      <td>863</td>\n",
       "      <td>599</td>\n",
       "      <td>680</td>\n",
       "      <td>442</td>\n",
       "      <td>982</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iter</td>\n",
       "      <td>2</td>\n",
       "      <td>684</td>\n",
       "      <td>508</td>\n",
       "      <td>613</td>\n",
       "      <td>241</td>\n",
       "      <td>249</td>\n",
       "      <td>758</td>\n",
       "      <td>639</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Iter</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>126</td>\n",
       "      <td>825</td>\n",
       "      <td>407</td>\n",
       "      <td>855</td>\n",
       "      <td>164</td>\n",
       "      <td>86</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76372</th>\n",
       "      <td>Iter</td>\n",
       "      <td>6</td>\n",
       "      <td>879</td>\n",
       "      <td>73</td>\n",
       "      <td>977</td>\n",
       "      <td>680</td>\n",
       "      <td>500</td>\n",
       "      <td>395</td>\n",
       "      <td>863</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76373</th>\n",
       "      <td>Average</td>\n",
       "      <td>979</td>\n",
       "      <td>641</td>\n",
       "      <td>531</td>\n",
       "      <td>374</td>\n",
       "      <td>448</td>\n",
       "      <td>407</td>\n",
       "      <td>185</td>\n",
       "      <td>439</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76374</th>\n",
       "      <td>Maximum</td>\n",
       "      <td>783</td>\n",
       "      <td>172</td>\n",
       "      <td>941</td>\n",
       "      <td>53</td>\n",
       "      <td>982</td>\n",
       "      <td>217</td>\n",
       "      <td>963</td>\n",
       "      <td>502</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76375</th>\n",
       "      <td>Std.Dev.</td>\n",
       "      <td>221</td>\n",
       "      <td>112</td>\n",
       "      <td>717</td>\n",
       "      <td>630</td>\n",
       "      <td>239</td>\n",
       "      <td>561</td>\n",
       "      <td>142</td>\n",
       "      <td>909</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76376</th>\n",
       "      <td>Total</td>\n",
       "      <td>429</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>847</td>\n",
       "      <td>583</td>\n",
       "      <td>407</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58397 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Row Type       Iter Number             Power1  Speed1  \\\n",
       "0      first name: Person  last name: Human  date: end of time     NaN   \n",
       "2                Row Type       Iter Number             Power1  Speed1   \n",
       "3                    Iter                 1                360     108   \n",
       "4                    Iter                 2                684     508   \n",
       "5                    Iter                 3                365     126   \n",
       "...                   ...               ...                ...     ...   \n",
       "76372                Iter                 6                879      73   \n",
       "76373             Average               979                641     531   \n",
       "76374             Maximum               783                172     941   \n",
       "76375            Std.Dev.               221                112     717   \n",
       "76376               Total               429                  -       -   \n",
       "\n",
       "       Speed2  Electricity  Effort  Weight  Torque    id  \n",
       "0         NaN          NaN     NaN     NaN     NaN     1  \n",
       "2      Speed2  Electricity  Effort  Weight  Torque     1  \n",
       "3         863          599     680     442     982     1  \n",
       "4         613          241     249     758     639     1  \n",
       "5         825          407     855     164      86     1  \n",
       "...       ...          ...     ...     ...     ...   ...  \n",
       "76372     977          680     500     395     863  5994  \n",
       "76373     374          448     407     185     439  5994  \n",
       "76374      53          982     217     963     502  5994  \n",
       "76375     630          239     561     142     909  5994  \n",
       "76376       -            -     847     583     407  5994  \n",
       "\n",
       "[58397 rows x 10 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "wooden-cheese",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df =df_drop_nans.loc[df_drop_nans[\"Row Type\"] != \"Row Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "processed-scheduling",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Average</td>\n",
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       "      <td>374</td>\n",
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       "      <th>76374</th>\n",
       "      <td>Maximum</td>\n",
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       "      <td>172</td>\n",
       "      <td>941</td>\n",
       "      <td>53</td>\n",
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       "      <td>5994</td>\n",
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       "      <th>76375</th>\n",
       "      <td>Std.Dev.</td>\n",
       "      <td>221</td>\n",
       "      <td>112</td>\n",
       "      <td>717</td>\n",
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       "      <td>239</td>\n",
       "      <td>561</td>\n",
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       "      <td>5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76376</th>\n",
       "      <td>Total</td>\n",
       "      <td>429</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>583</td>\n",
       "      <td>407</td>\n",
       "      <td>5994</td>\n",
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       "<p>52403 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Row Type       Iter Number             Power1 Speed1 Speed2  \\\n",
       "0      first name: Person  last name: Human  date: end of time    NaN    NaN   \n",
       "3                    Iter                 1                360    108    863   \n",
       "4                    Iter                 2                684    508    613   \n",
       "5                    Iter                 3                365    126    825   \n",
       "6                    Iter                 4                764    594    304   \n",
       "...                   ...               ...                ...    ...    ...   \n",
       "76372                Iter                 6                879     73    977   \n",
       "76373             Average               979                641    531    374   \n",
       "76374             Maximum               783                172    941     53   \n",
       "76375            Std.Dev.               221                112    717    630   \n",
       "76376               Total               429                  -      -      -   \n",
       "\n",
       "      Electricity Effort Weight Torque    id  \n",
       "0             NaN    NaN    NaN    NaN     1  \n",
       "3             599    680    442    982     1  \n",
       "4             241    249    758    639     1  \n",
       "5             407    855    164     86     1  \n",
       "6             718    278    674    774     1  \n",
       "...           ...    ...    ...    ...   ...  \n",
       "76372         680    500    395    863  5994  \n",
       "76373         448    407    185    439  5994  \n",
       "76374         982    217    963    502  5994  \n",
       "76375         239    561    142    909  5994  \n",
       "76376           -    847    583    407  5994  \n",
       "\n",
       "[52403 rows x 10 columns]"
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     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "automotive-security",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_df =filtered_df[filtered_df['Row Type'].apply(lambda x: \"first name\" in x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "metropolitan-short",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:4305: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n"
     ]
    }
   ],
   "source": [
    "name_df.drop(columns=['Speed1','Speed2','Electricity','Effort','Weight','Torque'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "antique-practice",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:4438: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().rename(\n"
     ]
    }
   ],
   "source": [
    "name_df.rename(columns={\"Row Type\":\"First_name\",\"Iter Number\":\"Last_name\",\"Power1\":\"Date\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "stylish-night",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "               First_name         Last_name               Date    id\n",
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       "14     first name: Person  last name: Human  date: end of time     2\n",
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       "42     first name: Person  last name: Human  date: end of time     4\n",
       "56     first name: Person  last name: Human  date: end of time     5\n",
       "...                   ...               ...                ...   ...\n",
       "76309  first name: Person  last name: Human  date: end of time  5990\n",
       "76322  first name: Person  last name: Human  date: end of time  5991\n",
       "76336  first name: Person  last name: Human  date: end of time  5992\n",
       "76350  first name: Person  last name: Human  date: end of time  5993\n",
       "76364  first name: Person  last name: Human  date: end of time  5994\n",
       "\n",
       "[5994 rows x 4 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "relative-grave",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-90-5dc4d15ec577>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  name_df['First_name']=name_df[\"First_name\"].apply(lambda x: x[11:].strip())\n",
      "<ipython-input-90-5dc4d15ec577>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  name_df['Last_name']=name_df[\"Last_name\"].apply(lambda x: x[11:].strip())\n",
      "<ipython-input-90-5dc4d15ec577>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  name_df['Date']=name_df[\"Date\"].apply(lambda x: x[6:].strip())\n"
     ]
    }
   ],
   "source": [
    "name_df['First_name']=name_df[\"First_name\"].apply(lambda x: x[11:].strip())\n",
    "name_df['Last_name']=name_df[\"Last_name\"].apply(lambda x: x[11:].strip())\n",
    "name_df['Date']=name_df[\"Date\"].apply(lambda x: x[6:].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "defensive-legislature",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>76336</th>\n",
       "      <td>Person</td>\n",
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       "      <td>5992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76350</th>\n",
       "      <td>Person</td>\n",
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       "      <th>76364</th>\n",
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       "      <td>5994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5994 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      First_name Last_name         Date    id\n",
       "0         Person     Human  end of time     1\n",
       "14        Person     Human  end of time     2\n",
       "28        Person     Human  end of time     3\n",
       "42        Person     Human  end of time     4\n",
       "56        Person     Human  end of time     5\n",
       "...          ...       ...          ...   ...\n",
       "76309     Person     Human  end of time  5990\n",
       "76322     Person     Human  end of time  5991\n",
       "76336     Person     Human  end of time  5992\n",
       "76350     Person     Human  end of time  5993\n",
       "76364     Person     Human  end of time  5994\n",
       "\n",
       "[5994 rows x 4 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "owned-competition",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_name_df=filtered_df[filtered_df['Row Type'].apply(lambda x: \"first name\" not in x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "turned-greensboro",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>76372</th>\n",
       "      <td>Iter</td>\n",
       "      <td>6</td>\n",
       "      <td>879</td>\n",
       "      <td>73</td>\n",
       "      <td>977</td>\n",
       "      <td>680</td>\n",
       "      <td>500</td>\n",
       "      <td>395</td>\n",
       "      <td>863</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76373</th>\n",
       "      <td>Average</td>\n",
       "      <td>979</td>\n",
       "      <td>641</td>\n",
       "      <td>531</td>\n",
       "      <td>374</td>\n",
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       "      <th>76374</th>\n",
       "      <td>Maximum</td>\n",
       "      <td>783</td>\n",
       "      <td>172</td>\n",
       "      <td>941</td>\n",
       "      <td>53</td>\n",
       "      <td>982</td>\n",
       "      <td>217</td>\n",
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       "      <td>502</td>\n",
       "      <td>5994</td>\n",
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       "    <tr>\n",
       "      <th>76375</th>\n",
       "      <td>Std.Dev.</td>\n",
       "      <td>221</td>\n",
       "      <td>112</td>\n",
       "      <td>717</td>\n",
       "      <td>630</td>\n",
       "      <td>239</td>\n",
       "      <td>561</td>\n",
       "      <td>142</td>\n",
       "      <td>909</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76376</th>\n",
       "      <td>Total</td>\n",
       "      <td>429</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>847</td>\n",
       "      <td>583</td>\n",
       "      <td>407</td>\n",
       "      <td>5994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46409 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Row Type Iter Number Power1 Speed1 Speed2 Electricity Effort Weight  \\\n",
       "3          Iter           1    360    108    863         599    680    442   \n",
       "4          Iter           2    684    508    613         241    249    758   \n",
       "5          Iter           3    365    126    825         407    855    164   \n",
       "6          Iter           4    764    594    304         718    278    674   \n",
       "7          Iter           5    487     97    593         206    779    800   \n",
       "...         ...         ...    ...    ...    ...         ...    ...    ...   \n",
       "76372      Iter           6    879     73    977         680    500    395   \n",
       "76373   Average         979    641    531    374         448    407    185   \n",
       "76374   Maximum         783    172    941     53         982    217    963   \n",
       "76375  Std.Dev.         221    112    717    630         239    561    142   \n",
       "76376     Total         429      -      -      -           -    847    583   \n",
       "\n",
       "      Torque    id  \n",
       "3        982     1  \n",
       "4        639     1  \n",
       "5         86     1  \n",
       "6        774     1  \n",
       "7        123     1  \n",
       "...      ...   ...  \n",
       "76372    863  5994  \n",
       "76373    439  5994  \n",
       "76374    502  5994  \n",
       "76375    909  5994  \n",
       "76376    407  5994  \n",
       "\n",
       "[46409 rows x 10 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_name_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "collect-lawyer",
   "metadata": {},
   "outputs": [],
   "source": [
    "result =pd.merge(name_df,no_name_df, on=\"id\", how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "congressional-blair",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Person</td>\n",
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       "      <td>188</td>\n",
       "      <td>358</td>\n",
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       "    <tr>\n",
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       "      <td>Person</td>\n",
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       "      <td>-</td>\n",
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       "      <td>788</td>\n",
       "      <td>160</td>\n",
       "      <td>262</td>\n",
       "      <td>415</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13894</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>1797</td>\n",
       "      <td>Total</td>\n",
       "      <td>39</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>737</td>\n",
       "      <td>8</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10876</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>1397</td>\n",
       "      <td>Iter</td>\n",
       "      <td>4</td>\n",
       "      <td>651</td>\n",
       "      <td>890</td>\n",
       "      <td>615</td>\n",
       "      <td>552</td>\n",
       "      <td>804</td>\n",
       "      <td>538</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39101</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>5104</td>\n",
       "      <td>Iter</td>\n",
       "      <td>4</td>\n",
       "      <td>913</td>\n",
       "      <td>133</td>\n",
       "      <td>57</td>\n",
       "      <td>752</td>\n",
       "      <td>381</td>\n",
       "      <td>180</td>\n",
       "      <td>816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8754</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>1115</td>\n",
       "      <td>Maximum</td>\n",
       "      <td>992</td>\n",
       "      <td>567</td>\n",
       "      <td>699</td>\n",
       "      <td>820</td>\n",
       "      <td>316</td>\n",
       "      <td>806</td>\n",
       "      <td>325</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13928</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>1800</td>\n",
       "      <td>Average</td>\n",
       "      <td>922</td>\n",
       "      <td>882</td>\n",
       "      <td>272</td>\n",
       "      <td>959</td>\n",
       "      <td>729</td>\n",
       "      <td>340</td>\n",
       "      <td>79</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18357</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>2370</td>\n",
       "      <td>Iter</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>688</td>\n",
       "      <td>129</td>\n",
       "      <td>192</td>\n",
       "      <td>141</td>\n",
       "      <td>440</td>\n",
       "      <td>249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30583</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>4008</td>\n",
       "      <td>Maximum</td>\n",
       "      <td>408</td>\n",
       "      <td>409</td>\n",
       "      <td>289</td>\n",
       "      <td>114</td>\n",
       "      <td>788</td>\n",
       "      <td>361</td>\n",
       "      <td>594</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>136</td>\n",
       "      <td>Average</td>\n",
       "      <td>638</td>\n",
       "      <td>817</td>\n",
       "      <td>130</td>\n",
       "      <td>635</td>\n",
       "      <td>189</td>\n",
       "      <td>455</td>\n",
       "      <td>422</td>\n",
       "      <td>829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25647</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>3371</td>\n",
       "      <td>Total</td>\n",
       "      <td>647</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>803</td>\n",
       "      <td>39</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36707</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>4841</td>\n",
       "      <td>Iter</td>\n",
       "      <td>1</td>\n",
       "      <td>258</td>\n",
       "      <td>435</td>\n",
       "      <td>436</td>\n",
       "      <td>910</td>\n",
       "      <td>142</td>\n",
       "      <td>486</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17846</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>2301</td>\n",
       "      <td>Total</td>\n",
       "      <td>570</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>194</td>\n",
       "      <td>888</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14765</th>\n",
       "      <td>Person</td>\n",
       "      <td>Human</td>\n",
       "      <td>end of time</td>\n",
       "      <td>1909</td>\n",
       "      <td>Iter</td>\n",
       "      <td>2</td>\n",
       "      <td>665</td>\n",
       "      <td>16</td>\n",
       "      <td>938</td>\n",
       "      <td>256</td>\n",
       "      <td>170</td>\n",
       "      <td>756</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      First_name Last_name         Date    id  Row Type Iter Number Power1  \\\n",
       "16026     Person     Human  end of time  2068      Iter           2    445   \n",
       "27321     Person     Human  end of time  3580   Average         616    605   \n",
       "20790     Person     Human  end of time  2704  Std.Dev.         802    377   \n",
       "37512     Person     Human  end of time  4942      Iter           3    886   \n",
       "38267     Person     Human  end of time  5018      Iter           6    458   \n",
       "15296     Person     Human  end of time  1973     Total         663      -   \n",
       "13168     Person     Human  end of time  1698   Maximum         433    101   \n",
       "16599     Person     Human  end of time  2145      Iter           1    914   \n",
       "13894     Person     Human  end of time  1797     Total          39      -   \n",
       "10876     Person     Human  end of time  1397      Iter           4    651   \n",
       "39101     Person     Human  end of time  5104      Iter           4    913   \n",
       "8754      Person     Human  end of time  1115   Maximum         992    567   \n",
       "13928     Person     Human  end of time  1800   Average         922    882   \n",
       "18357     Person     Human  end of time  2370      Iter           2    143   \n",
       "30583     Person     Human  end of time  4008   Maximum         408    409   \n",
       "1084      Person     Human  end of time   136   Average         638    817   \n",
       "25647     Person     Human  end of time  3371     Total         647      -   \n",
       "36707     Person     Human  end of time  4841      Iter           1    258   \n",
       "17846     Person     Human  end of time  2301     Total         570      -   \n",
       "14765     Person     Human  end of time  1909      Iter           2    665   \n",
       "\n",
       "      Speed1 Speed2 Electricity Effort Weight Torque  \n",
       "16026    350    864         217    388    623    441  \n",
       "27321    179    205         420    936    140    373  \n",
       "20790    958    698         706    319    356    840  \n",
       "37512    736    382         801    381    202    526  \n",
       "38267     69    526         358    188    358    386  \n",
       "15296      -      -           -    496    329     46  \n",
       "13168    510    317         273    618    565     60  \n",
       "16599     69    788         160    262    415    211  \n",
       "13894      -      -           -    737      8    508  \n",
       "10876    890    615         552    804    538    359  \n",
       "39101    133     57         752    381    180    816  \n",
       "8754     699    820         316    806    325    510  \n",
       "13928    272    959         729    340     79    313  \n",
       "18357    688    129         192    141    440    249  \n",
       "30583    289    114         788    361    594    543  \n",
       "1084     130    635         189    455    422    829  \n",
       "25647      -      -           -    803     39    580  \n",
       "36707    435    436         910    142    486    291  \n",
       "17846      -      -           -    194    888    899  \n",
       "14765     16    938         256    170    756    973  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "desirable-event",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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