insert missing rows.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The task is to insert missing data in database"
   ]
  },
  {
   "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": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A' : [1, 2, 3, 6, 7],\n",
    "                   'B' : [i for i in range(5)],\n",
    "                   'C' : [i for i in range(3,8)]\n",
    "                   })"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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   "source": [
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   "source": [
    "df.index = df['A']\n",
    "df.head(10)"
   ]
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    "df = df.reindex(np.arange(df.index.min(), df.index.max() + 1))\n",
    "df.head(10)"
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    "# there is the same result\n",
    "# to backpropagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.\n",
    "df1 = df.reindex(np.arange(df.index.min(), df.index.max() + 1), method='bfill')\n",
    "df1.head(100)\n"
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       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</div>"
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       "      <td>NaN</td>\n",
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       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# an array through the minimum index to 17 (16 inclusive)\n",
    "#df2 = df.reindex(np.arange(df.index.min(), 17), method='bfill')\n",
    "df2 = df1.reindex(np.arange(1, 17), fill_value=None)  # more correct way (assured) to fill cells with nan \n",
    "df2.head(100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing index names \n",
    "del(df2.index.name)  # or  df100.index.name = None or print(df100.rename_axis(None)\n",
    "df2.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#https://stackoverflow.com/questions/27012151/forward-fill-specific-columns-in-pandas-dataframe\n",
    "# filling NaN cells with values of top cells\n",
    "col = ['B', 'C']\n",
    "df2[col] = df2[col].fillna(method='ffill')\n",
    "df2.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
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       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "      <th>5</th>\n",
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       "      <td>SPARE</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>SPARE</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
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       "      <th>14</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing NaN with SPARE\n",
    "df2[\"A\"].fillna(\"SPARE\", inplace=True)\n",
    "df2.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <th>10</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <th>11</th>\n",
       "      <td>SPARE</td>\n",
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       "      <td>SPARE</td>\n",
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       "      <td>7.0</td>\n",
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       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <th>14</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <th>15</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SPARE</td>\n",
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       "      <td>7.0</td>\n",
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       "</table>\n",
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      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop(df2.index[[1,3]]) # 1 , 3 - порядковый номер индекса, а не значение or df2.drop(df2.index[1], df2.index[3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th>15</th>\n",
       "      <td>SPARE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.drop([1,3]) # 1 , 3 - значение индекса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 13,
     "metadata": {},
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    }
   ],
   "source": [
    "df2.drop('A',1) # удалить столбец А по оси axis = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a0  a  b\n0   0  1  3\n1  11  2  4\n"
     ]
    }
   ],
   "source": [
    "#  Some tips and exercises below \n",
    "df_0 = pd.DataFrame({'a0': [0, 11],'a': [1, 2],'b': [3, 4]})\n",
    "print (df_0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a0  b\na       \n1   0  3\n2  11  4\n   a0  a  b\n0   0  1  3\n1  11  2  4\n------ вытянуть данные ячейки --------\n4\n   a0  a  b\n0   0  1  3\n1  11  2  4\n4\n"
     ]
    }
   ],
   "source": [
    "# назначает вместо индексов столбец a\n",
    "df_1 = df_0.set_index('a')\n",
    "print(df_1)\n",
    "print(df_0)\n",
    "print(\"------ вытянуть данные ячейки --------\")\n",
    "print(df_0.set_index('a').loc[2,'b']) #  Индекс 2, столбец b в df2.set_index('a'), это df4\n",
    "print(df_0)\n",
    "print(df_0.loc[1,'b']) #  Индекс 1, столбец b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# while reindex change the indexes but keeps the values in column 'b' associated to the indexes in the original df\n",
    "df_0.reindex(df_0.a.values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a0</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# удалить столбец\n",
    "df_0.reindex(df_0.a.values).drop('a',1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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