concatinating databases New.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The task is to combine 4 databases into one file.\n",
    "The databases have common values in the key cells that need to remove.\n",
    "coded by Natalia Sashnikova.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = r'C:\\\\Users\\\\PycharmProjects\\\\all for work\\all eqs.xlsx'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#The 1st variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = pd.concat(list(pd.read_excel(table, sheet_name = \"Sheet{}\".format(i)) for i in range(1,5)), sort = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# instead of df1 = df1.fillna({'q-ty1':0,'q-ty2':0,'q-ty3':0,'q-ty4':0})\n",
    "\n",
    "l_q = ['q-ty1','q-ty2','q-ty3','q-ty4']\n",
    "d = {i:0 for i in l_q}\n",
    "df1 = df1.fillna(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# instead of \"df1['sum_m']= df1['q-ty1'].astype(int) + df1['q-ty2'].astype(int) + df1['q-ty3'].astype(int) + df1['q-ty4'].astype(int)\"\n",
    "\n",
    "df1['sum_m']= sum(list(df1[i] for i in l_q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>RIT</td>\n",
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       "      <th>code</th>\n",
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       "      <td>RIT</td>\n",
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       "      <td>RIT</td>\n",
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       "      <td>шт</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>20.0</td>\n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = {'model': 'first', 'name' : 'first', 'code' : 'first', \n",
    "     'customer' : 'first', 'prod' : 'first', 'item' : 'first', 'sum_m': 'sum', \n",
    "     'sheet1' : 'first','sheet2' : 'first', 'sheet3' : 'first', 'sheet4' : 'first', }\n",
    "\n",
    "df1['-'] = ['$' for i in range(len(df1))]\n",
    "df1['name_model'] = df1[['name', '-', 'model']].astype(str).sum(axis=1)\n",
    "df1 = df1.groupby('name_model', as_index=False).agg(f)\n",
    "\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The variants placed below just to see how the code was developed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 2d variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_dfn = []\n",
    "for i in range(1,5):\n",
    "\n",
    "    dfn = pd.read_excel(table, sheet_name=\"Sheet{}\".format(i))\n",
    "    list_dfn.append(dfn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>RIT</td>\n",
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       "      <td>OOO</td>\n",
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       "      <td>CS</td>\n",
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       "      <td>5</td>\n",
       "      <td>RIT</td>\n",
       "      <td>шт</td>\n",
       "      <td>CS</td>\n",
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       "      <th>5</th>\n",
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       "      <td>32</td>\n",
       "      <td>F</td>\n",
       "      <td>Klemm2</td>\n",
       "      <td>6</td>\n",
       "      <td>OOO</td>\n",
       "      <td>шт</td>\n",
       "      <td>HON</td>\n",
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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df2 = pd.concat(list_dfn, sort = True)\n",
    "df2['-'] = ['$' for i in range(len(df2))]\n",
    "df2['name_model'] = df2[['name', '-', 'model']].astype(str).sum(axis=1)\n",
    "df2 = df2.fillna({'q-ty1':0,'q-ty2':0,'q-ty3':0,'q-ty4':0})\n",
    "df2['sum_m']= df2['q-ty1'].astype(int) + df2['q-ty2'].astype(int) + df2['q-ty3'].astype(int) + df2['q-ty4'].astype(int)\n",
    "\n",
    "f = {'sum_m': 'sum', 'model': 'first', 'name' : 'first', 'code' : 'first', 'customer' : 'first', 'item' : 'first', 'prod' : 'first'}\n",
    "df2 = df2.groupby('name_model', as_index=False, axis=0, sort=False).agg(f)\n",
    "df2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The common lines for variants N3,4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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    "df_2"
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   "execution_count": 11,
   "metadata": {},
   "outputs": [
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    "df_3"
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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <td>4</td>\n",
       "      <td>Relay2</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>CS</td>\n",
       "      <td>RIT</td>\n",
       "      <td>2</td>\n",
       "      <td>шт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>UPS2</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
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       "      <td>шт</td>\n",
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       "      <td>Klemm1</td>\n",
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       "      <td>67</td>\n",
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       "      <td>5</td>\n",
       "      <td>шт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Klemm2</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>HON</td>\n",
       "      <td>OOO</td>\n",
       "      <td>6</td>\n",
       "      <td>шт</td>\n",
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       "      <th>sheet4</th>\n",
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       "      <th>1</th>\n",
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       "      <td>Relay2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>RIT</td>\n",
       "      <td>2</td>\n",
       "      <td>шт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>UPS2</td>\n",
       "      <td>D</td>\n",
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Klemm1</td>\n",
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       "      <td>67</td>\n",
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       "      <td>RIT</td>\n",
       "      <td>5</td>\n",
       "      <td>шт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Klemm2</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>HON</td>\n",
       "      <td>OOO</td>\n",
       "      <td>6</td>\n",
       "      <td>шт</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_4 = pd.read_excel(table, sheet_name = 'Sheet4') #, skipinitialspace = True)\n",
    "df_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 3d variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_1['-'] = ['$' for i in range(len(df_1))]\n",
    "df_1['name_model'] = df_1[['name', '-', 'model']].astype(str).sum(axis=1)\n",
    "df_2['-'] = ['$' for i in range(len(df_2))]\n",
    "df_2['name_model'] = df_2[['name', '-', 'model']].astype(str).sum(axis=1)\n",
    "df_3['-'] = ['$' for i in range(len(df_3))]\n",
    "df_3['name_model'] = df_3[['name', '-', 'model']].astype(str).sum(axis=1)\n",
    "df_4['-'] = ['$' for i in range(len(df_4))]\n",
    "df_4['name_model'] = df_4[['name', '-', 'model']].astype(str).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_2 = df_1.set_index('name_model').combine_first(df_2.set_index('name_model')).reset_index()\n",
    "df1_3 = df1_2.set_index('name_model').combine_first(df_3.set_index('name_model')).reset_index()\n",
    "df3= df1_3.set_index('name_model').combine_first(df_4.set_index('name_model')).reset_index()\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['name'] = df3['name_model'].str.split('$').str[0]\n",
    "df3 = df3.drop(['name_model','-'], axis=1)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df3.fillna({'q-ty1':0,'q-ty2':0,'q-ty3':0,'q-ty4':0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "      <td>4.0</td>\n",
       "      <td>20</td>\n",
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      "text/plain": [
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       "      <td>Klemm1</td>\n",
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       "      <td>C</td>\n",
       "      <td>UPS1</td>\n",
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       "      <th>5</th>\n",
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       "      <td>D</td>\n",
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       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['sum']= df3['q-ty1'].astype(int) + df3['q-ty2'].astype(int) + df3['q-ty3'].astype(int) + df3['q-ty4'].astype(int)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 4th variant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>E</td>\n",
       "      <td>67.0</td>\n",
       "      <td>CS</td>\n",
       "      <td>RIT</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>$</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>Klemm2</td>\n",
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       "      <td>шт</td>\n",
       "      <td>$</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 37 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1_2 = pd.merge(df_1, df_2, how = 'outer', on = 'name_model')\n",
    "df_1_3 = pd.merge(df_1_2, df_3, how = 'outer', on = 'name_model')\n",
    "df4 = pd.merge(df_1_3, df_4, how = 'outer', on = 'name_model')\n",
    "\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "<p>6 rows × 37 columns</p>\n",
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['name'] = df4['name_model'].str.split('$').str[0]\n",
    "df4 = df4.drop(['name_model','-'], axis=1)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "<p>6 rows × 37 columns</p>\n",
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       "      <td>$</td>\n",
       "      <td>Klemm1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Klemm2</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>HON</td>\n",
       "      <td>OOO</td>\n",
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       "      <td>шт</td>\n",
       "      <td>$</td>\n",
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       "      <td>$</td>\n",
       "      <td>Klemm2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 37 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df4 = df4.fillna({'q-ty1':0,'q-ty2':0,'q-ty3':0,'q-ty4':0})  doesn't work\n",
    "df4 = df4.fillna(value = 0)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>шт</td>\n",
       "      <td>$</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>UPS2</td>\n",
       "      <td>D</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CS</td>\n",
       "      <td>RIT</td>\n",
       "      <td>4.0</td>\n",
       "      <td>шт</td>\n",
       "      <td>$</td>\n",
       "      <td>UPS2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Klemm1</td>\n",
       "      <td>E</td>\n",
       "      <td>67</td>\n",
       "      <td>CS</td>\n",
       "      <td>RIT</td>\n",
       "      <td>5</td>\n",
       "      <td>шт</td>\n",
       "      <td>$</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Klemm1</td>\n",
       "      <td>E</td>\n",
       "      <td>67.0</td>\n",
       "      <td>CS</td>\n",
       "      <td>RIT</td>\n",
       "      <td>5.0</td>\n",
       "      <td>шт</td>\n",
       "      <td>$</td>\n",
       "      <td>Klemm1</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>Klemm2</td>\n",
       "      <td>F</td>\n",
       "      <td>8</td>\n",
       "      <td>HON</td>\n",
       "      <td>OOO</td>\n",
       "      <td>6</td>\n",
       "      <td>шт</td>\n",
       "      <td>$</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>Klemm2</td>\n",
       "      <td>F</td>\n",
       "      <td>8.0</td>\n",
       "      <td>HON</td>\n",
       "      <td>OOO</td>\n",
       "      <td>6.0</td>\n",
       "      <td>шт</td>\n",
       "      <td>$</td>\n",
       "      <td>Klemm2</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 38 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['sum'] = df4['q-ty1'].astype(int) + df4['q-ty2'].astype(int) + df4['q-ty3'].astype(int) + df4['q-ty4'].astype(int)\n",
    "df4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = pd.ExcelWriter('C:\\\\Users\\\\PycharmProjects\\\\all for work\\\\to compare files !!!!.xlsx', engine='xlsxwriter')\n",
    "df1.to_excel(writer, sheet_name='Sheet1')\n",
    "df2.to_excel(writer, sheet_name='Sheet2')\n",
    "df3.to_excel(writer, sheet_name='Sheet3')\n",
    "df4.to_excel(writer, sheet_name='Sheet4')\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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